From 24a01ecaa5ccffc7a22ad84d02406728888292c8 Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Wed, 13 Nov 2024 16:44:34 +0530 Subject: [PATCH 01/23] feat(memory-store): Tune options file (#832) Signed-off-by: Diwank Singh Tomer ---- > [!IMPORTANT] > Tune RocksDB options in `memory-store/options` to enhance performance by adjusting buffer sizes, job limits, and compression settings. > > - **DBOptions**: > - Increase `max_background_jobs` from 6 to 16. > - Increase `max_subcompactions` from 1 to 2. > - Increase `writable_file_max_buffer_size` from 1048576 to 5048576. > - Set `create_if_missing` to true. > - Increase `WAL_size_limit_MB` from 0 to 512. > - Enable `use_fsync`. > - **CFOptions "default"**: > - Increase `write_buffer_size` from 134217728 to 267108864. > - Increase `max_write_buffer_number` from 5 to 25. > - Increase `level0_file_num_compaction_trigger` from 4 to 16. > - Increase `max_compaction_bytes` from 1677721600 to 2677721600. > - Change `compression` from `kLZ4Compression` to `kNoCompression`. > - Increase `max_write_buffer_size_to_maintain` from 134217728 to 534217728. > - Increase `min_write_buffer_number_to_merge` from 1 to 4. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 1aab742b3d95f77df0ec1905d2fbca571f1b5272. It will automatically update as commits are pushed. --------- Signed-off-by: Diwank Singh Tomer Co-authored-by: Ahmad-mtos --- memory-store/options | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/memory-store/options b/memory-store/options index 1ce8d1599..8a8e9b474 100644 --- a/memory-store/options +++ b/memory-store/options @@ -12,13 +12,13 @@ compaction_readahead_size=2097152 strict_bytes_per_sync=false bytes_per_sync=1048576 - max_background_jobs=6 + max_background_jobs=16 avoid_flush_during_shutdown=false max_background_flushes=-1 delayed_write_rate=16777216 max_open_files=-1 - max_subcompactions=1 - writable_file_max_buffer_size=1048576 + max_subcompactions=2 + writable_file_max_buffer_size=5048576 wal_bytes_per_sync=0 max_background_compactions=-1 max_total_wal_size=0 @@ -47,7 +47,7 @@ track_and_verify_wals_in_manifest=false compaction_verify_record_count=true paranoid_checks=true - create_if_missing=false + create_if_missing=true max_write_batch_group_size_bytes=1048576 avoid_flush_during_recovery=false file_checksum_gen_factory=nullptr @@ -57,7 +57,7 @@ error_if_exists=false use_direct_io_for_flush_and_compaction=false create_missing_column_families=true - WAL_size_limit_MB=0 + WAL_size_limit_MB=512 use_direct_reads=false persist_stats_to_disk=false allow_2pc=true @@ -69,7 +69,7 @@ allow_mmap_reads=false allow_mmap_writes=false use_adaptive_mutex=false - use_fsync=false + use_fsync=true table_cache_numshardbits=6 dump_malloc_stats=false db_write_buffer_size=0 @@ -100,10 +100,10 @@ memtable_protection_bytes_per_key=0 target_file_size_multiplier=1 report_bg_io_stats=false - write_buffer_size=134217728 + write_buffer_size=267108864 memtable_huge_page_size=0 max_successive_merges=0 - max_write_buffer_number=5 + max_write_buffer_number=25 prefix_extractor=rocksdb.CappedPrefix.9 bottommost_compression_opts={checksum=false;max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;max_compressed_bytes_per_kb=896;parallel_threads=1;zstd_max_train_bytes=0;level=32767;use_zstd_dict_trainer=true;strategy=0;window_bits=-14;} paranoid_file_checks=false @@ -121,8 +121,8 @@ hard_pending_compaction_bytes_limit=274877906944 soft_pending_compaction_bytes_limit=68719476736 target_file_size_base=67108864 - level0_file_num_compaction_trigger=4 - max_compaction_bytes=1677721600 + level0_file_num_compaction_trigger=16 + max_compaction_bytes=2677721600 disable_auto_compactions=false check_flush_compaction_key_order=true min_blob_size=0 @@ -134,7 +134,7 @@ max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1 max_sequential_skip_in_iterations=8 prepopulate_blob_cache=kDisable - compression=kLZ4Compression + compression=kNoCompression compaction_options_universal={incremental=false;compression_size_percent=-1;allow_trivial_move=false;max_size_amplification_percent=200;max_merge_width=4294967295;stop_style=kCompactionStopStyleTotalSize;min_merge_width=2;size_ratio=1;} blob_garbage_collection_age_cutoff=0.250000 ttl=2592000 @@ -153,7 +153,7 @@ memtable_insert_with_hint_prefix_extractor=nullptr memtable_factory=SkipListFactory compaction_pri=kMinOverlappingRatio - max_write_buffer_size_to_maintain=134217728 + max_write_buffer_size_to_maintain=534217728 level_compaction_dynamic_file_size=true max_write_buffer_number_to_maintain=0 optimize_filters_for_hits=false @@ -162,7 +162,7 @@ inplace_update_support=false merge_operator=nullptr table_factory=BlockBasedTable - min_write_buffer_number_to_merge=1 + min_write_buffer_number_to_merge=4 compaction_filter=nullptr compaction_style=kCompactionStyleLevel bloom_locality=0 From 28a4702afbbf3b6ea6e961baa3f37f0405feb1aa Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Thu, 14 Nov 2024 08:38:33 +0300 Subject: [PATCH 02/23] fix(agents-api): Fix retrying the `resource busy` errors (#834) When a `retry`-decorated function finishes the maximum amount of retries and still throws an error, `tenacity` throws an error with type `RetryError`, so I added that to the list of retryable errors ---- > [!IMPORTANT] > Added `RetryError` to `RETRYABLE_ERROR_TYPES` in `tasks.py` to handle exhausted retry cases. > > - **Error Handling**: > - Added `RetryError` to `RETRYABLE_ERROR_TYPES` in `tasks.py` to handle cases where a `retry`-decorated function exhausts retries and throws this error. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for d20007767bd9cce0f0bcaa8c7c6312bf33efcfda. It will automatically update as commits are pushed. --- agents-api/agents_api/common/exceptions/tasks.py | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) diff --git a/agents-api/agents_api/common/exceptions/tasks.py b/agents-api/agents_api/common/exceptions/tasks.py index 78f3e967a..aec452f21 100644 --- a/agents-api/agents_api/common/exceptions/tasks.py +++ b/agents-api/agents_api/common/exceptions/tasks.py @@ -24,6 +24,7 @@ import pydantic import requests import temporalio.exceptions +from tenacity import RetryError # 🚫 The "No Second Chances" Club - errors that we won't retry # Because sometimes, no means no! @@ -130,6 +131,9 @@ # # Database/storage related (when the database needs a nap) asyncio.TimeoutError, + # + # Tenacity exceptions (retry when retrying goes wrong lol) + RetryError, ) # HTTP status codes that say "maybe try again later?" @@ -176,15 +180,3 @@ def is_retryable_error(error: BaseException) -> bool: # If we don't know this error, we play it safe and don't retry # (stranger danger!) return False - - # Check for specific HTTP errors that should be retried - if isinstance(error, fastapi.exceptions.HTTPException): - if error.status_code in RETRYABLE_HTTP_STATUS_CODES: - return True - - if isinstance(error, httpx.HTTPStatusError): - if error.response.status_code in RETRYABLE_HTTP_STATUS_CODES: - return True - - # If we don't know about the error, we should not retry - return False From e154e37ec9b561d6726dc6229cfa13dc152e1321 Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Fri, 15 Nov 2024 16:09:48 +0300 Subject: [PATCH 03/23] fix(agents-api): Convert assertion errors to 422 http errors (#837) > [!IMPORTANT] > Convert `AssertionError` to HTTP 422 error in `prepare_execution_input.py`. > > - **Error Handling**: > - Convert `AssertionError` to HTTP 422 error in `prepare_execution_input.py` using `rewrap_exceptions` decorator. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for b07f915f6df289abdbdaed200a3a2c128c288204. It will automatically update as commits are pushed. --- .../agents_api/models/execution/prepare_execution_input.py | 1 + 1 file changed, 1 insertion(+) diff --git a/agents-api/agents_api/models/execution/prepare_execution_input.py b/agents-api/agents_api/models/execution/prepare_execution_input.py index 513c44a16..80382d38d 100644 --- a/agents-api/agents_api/models/execution/prepare_execution_input.py +++ b/agents-api/agents_api/models/execution/prepare_execution_input.py @@ -31,6 +31,7 @@ QueryException: partialclass(HTTPException, status_code=400), ValidationError: partialclass(HTTPException, status_code=400), TypeError: partialclass(HTTPException, status_code=400), + AssertionError: lambda e: HTTPException(status_code=422, detail=str(e)), } ) @wrap_in_class( From 9bf691fd1b46c8ed01c34531b34aa759bf65e3ec Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Fri, 15 Nov 2024 16:17:26 +0300 Subject: [PATCH 04/23] fix(agents-api): Change wrapped assertion error status code to 400 (#838) > [!IMPORTANT] > Add handling for `AssertionError` in `prepare_execution_input.py`, mapping it to HTTP 429 error. > > - **Error Handling**: > - Add handling for `AssertionError` in `prepare_execution_input.py`, mapping it to `HTTPException` with status code 429. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 901bec8cefc7bc3790e3e64c1dae03d015f1c39d. It will automatically update as commits are pushed. --- .../agents_api/models/execution/prepare_execution_input.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents-api/agents_api/models/execution/prepare_execution_input.py b/agents-api/agents_api/models/execution/prepare_execution_input.py index 80382d38d..33d467465 100644 --- a/agents-api/agents_api/models/execution/prepare_execution_input.py +++ b/agents-api/agents_api/models/execution/prepare_execution_input.py @@ -31,7 +31,7 @@ QueryException: partialclass(HTTPException, status_code=400), ValidationError: partialclass(HTTPException, status_code=400), TypeError: partialclass(HTTPException, status_code=400), - AssertionError: lambda e: HTTPException(status_code=422, detail=str(e)), + AssertionError: lambda e: HTTPException(status_code=400, detail=str(e)), } ) @wrap_in_class( From 0555e913284fa382deb4fe87b4e902096a90c2da Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Sat, 16 Nov 2024 13:46:47 +0530 Subject: [PATCH 05/23] feat(.github): Add refactor template Signed-off-by: Diwank Singh Tomer --- .github/ISSUE_TEMPLATE/bug_report.yml | 10 +--- .github/ISSUE_TEMPLATE/docs_improvement.yml | 52 --------------------- .github/ISSUE_TEMPLATE/feature_request.yml | 27 ++--------- .github/ISSUE_TEMPLATE/refactor.yml | 17 +++++++ 4 files changed, 23 insertions(+), 83 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/docs_improvement.yml create mode 100644 .github/ISSUE_TEMPLATE/refactor.yml diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 3269cdd1f..882584ad3 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -3,22 +3,16 @@ description: "Submit a bug report to help us improve" title: "[Bug]: " labels: ["bug"] body: - - type: markdown - attributes: - value: We value your time and your efforts to submit this bug report is appreciated. 🙏 - - type: textarea id: description attributes: label: "📜 Description" - description: "A clear and concise description of what the bug is." - placeholder: "It bugs out when ..." + placeholder: "A clear and concise description of what the bug is." - type: textarea id: steps-to-reproduce attributes: label: "👟 Reproduction steps" - description: "How do you trigger this bug? Please walk us through it step by step." placeholder: "1. Go to '...' 2. Click on '....' 3. Scroll down to '....' @@ -27,7 +21,7 @@ body: - type: checkboxes id: no-duplicate-issues attributes: - label: "👀 Have you spent some time to check if this bug has been raised before?" + label: "👀 Have you searched previous issues to see if this has been raised before?" options: - label: "I checked and didn't find similar issue" required: true diff --git a/.github/ISSUE_TEMPLATE/docs_improvement.yml b/.github/ISSUE_TEMPLATE/docs_improvement.yml deleted file mode 100644 index 1654e3345..000000000 --- a/.github/ISSUE_TEMPLATE/docs_improvement.yml +++ /dev/null @@ -1,52 +0,0 @@ -name: "📝 Docs Improvement" -description: "Suggest improvements or additions to the documentation" -title: "[Docs]: " -labels: ["documentation"] -assignees: [] -body: - - type: markdown - attributes: - value: Thanks for taking the time to help improve our documentation! Please provide the following details - - - type: input - id: current_section - attributes: - label: "Current Section" - description: "Which section of the documentation needs improvement?" - placeholder: "e.g., Installation Guide" - - - type: textarea - id: improvement_suggestion - attributes: - label: "Improvement Suggestion" - description: "Describe the improvement or addition you are suggesting." - placeholder: "e.g., Add more details about setting up the environment." - - - type: textarea - id: additional_context - attributes: - label: "Additional Context" - description: "Any additional context or information that might be helpful." - placeholder: "e.g., Links to related documentation, screenshots, etc." - - type: checkboxes - id: no-duplicate-issues - attributes: - label: "👀 Have you spent some time to check if this issue has been raised before?" - options: - - label: "I checked and didn't find similar issue" - required: true - - type: dropdown - id: willing-to-submit-pr - attributes: - label: Are you willing to submit PR? - description: This is absolutely not required, but we are happy to guide you in the contribution process. - options: - - "Yes I am willing to submit a PR!" - - type: checkboxes - id: terms - attributes: - label: 🧑‍⚖️ Code of Conduct - description: By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/julep-ai/julep/blob/dev/.github/CODE_OF_CONDUCT.md) - options: - - label: I agree to follow this project's Code of Conduct - required: true \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml index 2fe63f09f..147471178 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.yml +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -3,46 +3,27 @@ description: "Submit a proposal for a new feature" title: "[Feature]: " labels: ["enhancement"] body: - - type: markdown - attributes: - value: We value your time and your efforts to submit this feature request is appreciated. 🙏 - type: textarea id: feature-description validations: required: true attributes: label: "🔖 Feature description" - description: "A clear and concise description of what the feature is." - placeholder: "You should add ..." + placeholder: "A clear and concise description of what the feature is." - type: textarea id: pitch attributes: label: "🎤 Why is this feature needed ?" - description: "Please explain why this feature should be implemented and how it would be used. Add examples, if applicable." - placeholder: "In my use-case, ..." + placeholder: "Please explain why this feature should be implemented and how it would be used. Add examples, if applicable." - type: textarea id: solution attributes: label: "✌️ How do you aim to achieve this?" - description: "A clear and concise description of what you want to happen." - placeholder: "I want this feature to, ..." - - type: textarea - id: alternative - attributes: - label: "🔄️ Additional Information" - description: "A clear and concise description of any alternative solutions or additional solutions you've considered." - placeholder: "I tried, ..." + placeholder: "A clear and concise description of what you want to happen." - type: checkboxes id: no-duplicate-issues attributes: - label: "👀 Have you spent some time to check if this feature request has been raised before?" + label: "👀 Have you searched issues and PRs to see if this feature request has been raised before?" options: - label: "I checked and didn't find similar issue" required: true - - type: dropdown - id: willing-to-submit-pr - attributes: - label: Are you willing to submit PR? - description: This is absolutely not required, but we are happy to guide you in the contribution process. - options: - - "Yes I am willing to submit a PR!" diff --git a/.github/ISSUE_TEMPLATE/refactor.yml b/.github/ISSUE_TEMPLATE/refactor.yml new file mode 100644 index 000000000..0334e9568 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/refactor.yml @@ -0,0 +1,17 @@ +name: "🛠️ RRefactor" +description: "Submit a bug report to help us improve" +title: "[Refactor]: " +labels: ["refactor"] +body: + - type: textarea + id: description + attributes: + label: "📜 Description" + placeholder: "A clear and concise description of what should be refactored." + + - type: textarea + id: relevant-files + attributes: + label: "👟 Relevant files" + placeholder: "1. `agents-api/Dockerfile` needs to change + 2. `integrations/**/*.py` files need to be reformatted" From 16a5a5181953d3a0771e55440f623374051c1e12 Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Sat, 16 Nov 2024 11:39:36 +0300 Subject: [PATCH 06/23] fix(integrations-service): Return errors for failed integration executions (#836) > [!IMPORTANT] > Enhances error handling in integration executions by introducing `ExecutionError` and updating functions to return it on exceptions. > > - **Behavior**: > - `execute_integration` in `execute_integration.py` raises an exception if `integration_service_response` contains an error. > - Integration functions in `brave.py`, `browserbase.py`, `email.py`, `llama_parse.py`, `remote_browser.py`, `spider.py`, `weather.py`, and `wikipedia.py` return `ExecutionError` on exceptions. > - **Models**: > - Adds `ExecutionError` model to `execution.py` to encapsulate error messages. > - **Functions**: > - Updates return types of integration functions to include `ExecutionError` in `brave.py`, `browserbase.py`, `email.py`, `llama_parse.py`, `remote_browser.py`, `spider.py`, `weather.py`, and `wikipedia.py`. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 968fe53e5276a5704b3967b628d3ae775f90e840. It will automatically update as commits are pushed. --------- Co-authored-by: HamadaSalhab --- .../activities/execute_integration.py | 7 ++++++- .../integrations/models/execution.py | 9 +++++++++ .../integrations/utils/execute_integration.py | 19 +++++++++++++------ .../utils/integrations/browserbase.py | 2 +- .../utils/integrations/llama_parse.py | 1 + .../utils/integrations/wikipedia.py | 4 +++- 6 files changed, 33 insertions(+), 9 deletions(-) diff --git a/agents-api/agents_api/activities/execute_integration.py b/agents-api/agents_api/activities/execute_integration.py index 55cc7192b..2badce0f9 100644 --- a/agents-api/agents_api/activities/execute_integration.py +++ b/agents-api/agents_api/activities/execute_integration.py @@ -42,13 +42,18 @@ async def execute_integration( if integration.provider == "dummy": return arguments - return await integrations.run_integration_service( + integration_service_response = await integrations.run_integration_service( provider=integration.provider, setup=setup, method=integration.method, arguments=arguments, ) + if "error" in integration_service_response: + raise Exception(integration_service_response["error"]) + + return integration_service_response + except BaseException as e: if activity.in_activity(): activity.logger.error(f"Error in execute_integration: {e}") diff --git a/integrations-service/integrations/models/execution.py b/integrations-service/integrations/models/execution.py index 1a109c25c..40ba3fdba 100644 --- a/integrations-service/integrations/models/execution.py +++ b/integrations-service/integrations/models/execution.py @@ -46,6 +46,14 @@ from .weather import WeatherGetOutput from .wikipedia import WikipediaSearchOutput + +class ExecutionError(BaseModel): + error: str + """ + The error message of the integration execution + """ + + ExecutionSetup = Union[ EmailSetup, SpiderSetup, @@ -90,6 +98,7 @@ BrowserbaseListSessionsOutput, RemoteBrowserOutput, LlamaParseFetchOutput, + ExecutionError, ] diff --git a/integrations-service/integrations/utils/execute_integration.py b/integrations-service/integrations/utils/execute_integration.py index 42104bd4a..f98f1eec8 100644 --- a/integrations-service/integrations/utils/execute_integration.py +++ b/integrations-service/integrations/utils/execute_integration.py @@ -5,7 +5,12 @@ from .. import providers as available_providers from ..models.base_models import BaseProvider, IdentifierName -from ..models.execution import ExecutionArguments, ExecutionResponse, ExecutionSetup +from ..models.execution import ( + ExecutionArguments, + ExecutionError, + ExecutionResponse, + ExecutionSetup, +) @beartype @@ -54,8 +59,10 @@ async def execute_integration( parsed_arguments = arguments_class(**arguments.model_dump()) else: parsed_arguments = arguments - - if setup_obj: - return await execution_function(setup=setup_obj, arguments=parsed_arguments) - else: - return await execution_function(arguments=parsed_arguments) + try: + if setup_obj: + return await execution_function(setup=setup_obj, arguments=parsed_arguments) + else: + return await execution_function(arguments=parsed_arguments) + except BaseException as e: + return ExecutionError(error=str(e)) diff --git a/integrations-service/integrations/utils/integrations/browserbase.py b/integrations-service/integrations/utils/integrations/browserbase.py index 36a3b892b..2cdd813ff 100644 --- a/integrations-service/integrations/utils/integrations/browserbase.py +++ b/integrations-service/integrations/utils/integrations/browserbase.py @@ -127,7 +127,6 @@ async def get_live_urls( setup: BrowserbaseSetup, arguments: BrowserbaseGetSessionLiveUrlsArguments ) -> BrowserbaseGetSessionLiveUrlsOutput: """Get the live URLs for a session.""" - client = get_browserbase_client(setup) urls: DebugConnectionURLs = client.get_debug_connection_urls(arguments.id) return BrowserbaseGetSessionLiveUrlsOutput(urls=urls) @@ -159,6 +158,7 @@ async def install_extension_from_github( setup: BrowserbaseSetup, arguments: BrowserbaseExtensionArguments ) -> BrowserbaseExtensionOutput: """Download and install an extension from GitHub to the user's Browserbase account.""" + github_url = f"https://github.com/{arguments.repository_name}/archive/refs/tags/{ arguments.ref}.zip" diff --git a/integrations-service/integrations/utils/integrations/llama_parse.py b/integrations-service/integrations/utils/integrations/llama_parse.py index cd74c6f28..1e60dee60 100644 --- a/integrations-service/integrations/utils/integrations/llama_parse.py +++ b/integrations-service/integrations/utils/integrations/llama_parse.py @@ -21,6 +21,7 @@ async def parse( """ Parse and extract content from files using LlamaParse. """ + assert isinstance(setup, LlamaParseSetup), "Invalid setup" assert isinstance(arguments, LlamaParseFetchArguments), "Invalid arguments" diff --git a/integrations-service/integrations/utils/integrations/wikipedia.py b/integrations-service/integrations/utils/integrations/wikipedia.py index 189043533..235d9512a 100644 --- a/integrations-service/integrations/utils/integrations/wikipedia.py +++ b/integrations-service/integrations/utils/integrations/wikipedia.py @@ -12,7 +12,9 @@ reraise=True, stop=stop_after_attempt(4), ) -async def search(arguments: WikipediaSearchArguments) -> WikipediaSearchOutput: +async def search( + arguments: WikipediaSearchArguments, +) -> WikipediaSearchOutput: """ Searches Wikipedia for a given query and returns formatted results. """ From 37879f9603a3cca31f692226b906d826cefe1f1a Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Sat, 16 Nov 2024 14:20:46 +0530 Subject: [PATCH 07/23] Update refactor.yml --- .github/ISSUE_TEMPLATE/refactor.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/refactor.yml b/.github/ISSUE_TEMPLATE/refactor.yml index 0334e9568..107c8fdd9 100644 --- a/.github/ISSUE_TEMPLATE/refactor.yml +++ b/.github/ISSUE_TEMPLATE/refactor.yml @@ -1,4 +1,4 @@ -name: "🛠️ RRefactor" +name: "🛠️ Refactor" description: "Submit a bug report to help us improve" title: "[Refactor]: " labels: ["refactor"] From add82c7f325038da6a3f996b7be16dc3655aff35 Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Sat, 16 Nov 2024 11:52:23 +0300 Subject: [PATCH 08/23] fix(agents-api): Enable retrying assertion errors for prepare execution input (#842) > [!IMPORTANT] > Change `AssertionError` handling in `prepare_execution_input.py` to return status code 429 with a retry header. > > - **Behavior**: > - Change `AssertionError` handling in `prepare_execution_input.py` to return status code 429 instead of 422. > - Add `x-should-retry: true` header to `AssertionError` responses, indicating retry capability. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 9719afe22909c14cf102992eef5c7f689c6f6086. It will automatically update as commits are pushed. --- .../agents_api/models/execution/prepare_execution_input.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/agents-api/agents_api/models/execution/prepare_execution_input.py b/agents-api/agents_api/models/execution/prepare_execution_input.py index 33d467465..5e841b9f2 100644 --- a/agents-api/agents_api/models/execution/prepare_execution_input.py +++ b/agents-api/agents_api/models/execution/prepare_execution_input.py @@ -31,7 +31,11 @@ QueryException: partialclass(HTTPException, status_code=400), ValidationError: partialclass(HTTPException, status_code=400), TypeError: partialclass(HTTPException, status_code=400), - AssertionError: lambda e: HTTPException(status_code=400, detail=str(e)), + AssertionError: lambda e: HTTPException( + status_code=429, + detail=str(e), + headers={"x-should-retry": "true"}, + ), } ) @wrap_in_class( From b3869e89034f79f6eb1ebc1a9f84aea893ce816f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Nov 2024 10:21:57 +0530 Subject: [PATCH 09/23] chore(deps): bump aiohttp from 3.10.10 to 3.10.11 in /agents-api in the pip group (#849) [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=aiohttp&package-manager=pip&previous-version=3.10.10&new-version=3.10.11)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- agents-api/poetry.lock | 186 ++++++++++++++++++++--------------------- 1 file changed, 93 insertions(+), 93 deletions(-) diff --git a/agents-api/poetry.lock b/agents-api/poetry.lock index e144ca0f7..1611b41af 100644 --- a/agents-api/poetry.lock +++ b/agents-api/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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feat(agents-api,typespec): Add file storage api (#847) - **feat(typespec,agents-api): Add files api models** - **feat(agents-api): Add files relation to cozo** - **feat(typespec,agents-api): Add file cozo queries** ---- > [!IMPORTANT] > Add file storage API to agents-api and typespec, including models, endpoints, and tests for file operations. > > - **Behavior**: > - Adds file storage API to `agents-api` and `typespec`. > - Supports creating, retrieving, and deleting files. > - **Models**: > - Adds `File` and `CreateFileRequest` models in `agents-api/autogen/Files.py` and `typespec/files/models.tsp`. > - **Endpoints**: > - Adds `FilesRoute` in `typespec/main.tsp` and `typespec/files/endpoints.tsp` for file operations. > - **Routers**: > - Adds `files_router` in `agents-api/routers/__init__.py` and `agents-api/routers/files` for handling file routes. > - **Database**: > - Creates `files` relation in `migrations/migrate_1731953383_create_files_relation.py`. > - **Tests**: > - Adds tests for file operations in `tests/test_files_queries.py` and `tests/test_files_routes.py`. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 271076bbb70d9877b675966669e8266e03ae32c1. It will automatically update as commits are pushed. --------- Signed-off-by: Diwank Singh Tomer Co-authored-by: creatorrr Co-authored-by: vedantsahai18 --- .../activities/task_steps/base_evaluate.py | 12 +- agents-api/agents_api/autogen/Files.py | 82 ++ agents-api/agents_api/autogen/Tools.py | 1 - .../agents_api/autogen/openapi_model.py | 1 + agents-api/agents_api/models/__init__.py | 19 +- .../agents_api/models/chat/gather_messages.py | 3 +- .../agents_api/models/files/__init__.py | 3 + .../agents_api/models/files/create_file.py | 121 +++ .../agents_api/models/files/delete_file.py | 97 +++ .../agents_api/models/files/get_file.py | 116 +++ agents-api/agents_api/routers/__init__.py | 3 + .../agents_api/routers/docs/list_docs.py | 6 +- .../agents_api/routers/files/__init__.py | 6 + .../agents_api/routers/files/create_file.py | 44 ++ .../agents_api/routers/files/delete_file.py | 30 + .../agents_api/routers/files/get_file.py | 31 + agents-api/agents_api/routers/files/router.py | 3 + agents-api/agents_api/web.py | 2 + ...igrate_1731953383_create_files_relation.py | 29 + agents-api/poetry.lock | 723 +++++++++--------- agents-api/tests/fixtures.py | 38 +- agents-api/tests/test_files_queries.py | 57 ++ agents-api/tests/test_files_routes.py | 88 +++ agents-api/tests/test_sessions.py | 2 +- agents-api/tests/utils.py | 52 ++ .../integrations/autogen/Files.py | 82 ++ .../integrations/autogen/Tools.py | 1 - integrations-service/tests/mocks/brave.py | 2 - .../tests/test_provider_execution.py | 6 - integrations-service/tests/test_providers.py | 1 - typespec/common/scalars.tsp | 13 +- typespec/files/endpoints.tsp | 14 + typespec/files/main.tsp | 8 + typespec/files/models.tsp | 41 + typespec/main.tsp | 4 + .../@typespec/openapi3/openapi-1.0.0.yaml | 115 +++ 36 files changed, 1457 insertions(+), 399 deletions(-) create mode 100644 agents-api/agents_api/autogen/Files.py create mode 100644 agents-api/agents_api/models/files/__init__.py create mode 100644 agents-api/agents_api/models/files/create_file.py create mode 100644 agents-api/agents_api/models/files/delete_file.py create mode 100644 agents-api/agents_api/models/files/get_file.py create mode 100644 agents-api/agents_api/routers/files/__init__.py create mode 100644 agents-api/agents_api/routers/files/create_file.py create mode 100644 agents-api/agents_api/routers/files/delete_file.py create mode 100644 agents-api/agents_api/routers/files/get_file.py create mode 100644 agents-api/agents_api/routers/files/router.py create mode 100644 agents-api/migrations/migrate_1731953383_create_files_relation.py create mode 100644 agents-api/tests/test_files_queries.py create mode 100644 agents-api/tests/test_files_routes.py create mode 100644 integrations-service/integrations/autogen/Files.py create mode 100644 typespec/files/endpoints.tsp create mode 100644 typespec/files/main.tsp create mode 100644 typespec/files/models.tsp diff --git a/agents-api/agents_api/activities/task_steps/base_evaluate.py b/agents-api/agents_api/activities/task_steps/base_evaluate.py index 2c56b315f..06dacb361 100644 --- a/agents-api/agents_api/activities/task_steps/base_evaluate.py +++ b/agents-api/agents_api/activities/task_steps/base_evaluate.py @@ -9,13 +9,13 @@ # Increase the max string length to 300000 simpleeval.MAX_STRING_LENGTH = 300000 -from simpleeval import NameNotDefined, SimpleEval -from temporalio import activity -from thefuzz import fuzz +from simpleeval import NameNotDefined, SimpleEval # noqa: E402 +from temporalio import activity # noqa: E402 +from thefuzz import fuzz # noqa: E402 -from ...common.storage_handler import auto_blob_store -from ...env import testing -from ..utils import get_evaluator +from ...common.storage_handler import auto_blob_store # noqa: E402 +from ...env import testing # noqa: E402 +from ..utils import get_evaluator # noqa: E402 class EvaluateError(Exception): diff --git a/agents-api/agents_api/autogen/Files.py b/agents-api/agents_api/autogen/Files.py new file mode 100644 index 000000000..b7640f8bc --- /dev/null +++ b/agents-api/agents_api/autogen/Files.py @@ -0,0 +1,82 @@ +# generated by datamodel-codegen: +# filename: openapi-1.0.0.yaml + +from __future__ import annotations + +from typing import Annotated +from uuid import UUID + +from pydantic import AwareDatetime, BaseModel, ConfigDict, Field + + +class CreateFileRequest(BaseModel): + """ + Payload for creating a file + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + name: Annotated[ + str, + Field( + max_length=120, + pattern="^[\\p{L}\\p{Nl}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]+[\\p{ID_Start}\\p{Mn}\\p{Mc}\\p{Nd}\\p{Pc}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]*$", + ), + ] + """ + Name of the file + """ + description: str = "" + """ + Description of the file + """ + mime_type: str | None = None + """ + MIME type of the file + """ + content: str + """ + Base64 encoded content of the file + """ + + +class File(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + id: Annotated[UUID, Field(json_schema_extra={"readOnly": True})] + created_at: Annotated[AwareDatetime, Field(json_schema_extra={"readOnly": True})] + """ + When this resource was created as UTC date-time + """ + name: Annotated[ + str, + Field( + max_length=120, + pattern="^[\\p{L}\\p{Nl}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]+[\\p{ID_Start}\\p{Mn}\\p{Mc}\\p{Nd}\\p{Pc}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]*$", + ), + ] + """ + Name of the file + """ + description: str = "" + """ + Description of the file + """ + mime_type: str | None = None + """ + MIME type of the file + """ + content: str + """ + Base64 encoded content of the file + """ + size: Annotated[int, Field(ge=1, json_schema_extra={"readOnly": True})] + """ + Size of the file in bytes + """ + hash: Annotated[str, Field(json_schema_extra={"readOnly": True})] + """ + Hash of the file + """ diff --git a/agents-api/agents_api/autogen/Tools.py b/agents-api/agents_api/autogen/Tools.py index 0b7e8c224..a48e2255e 100644 --- a/agents-api/agents_api/autogen/Tools.py +++ b/agents-api/agents_api/autogen/Tools.py @@ -12,7 +12,6 @@ BaseModel, ConfigDict, Field, - RootModel, StrictBool, ) diff --git a/agents-api/agents_api/autogen/openapi_model.py b/agents-api/agents_api/autogen/openapi_model.py index 3e44a71b2..751239386 100644 --- a/agents-api/agents_api/autogen/openapi_model.py +++ b/agents-api/agents_api/autogen/openapi_model.py @@ -21,6 +21,7 @@ from .Docs import * from .Entries import * from .Executions import * +from .Files import * from .Jobs import * from .Sessions import * from .Tasks import * diff --git a/agents-api/agents_api/models/__init__.py b/agents-api/agents_api/models/__init__.py index b1f918ee7..e59b5b01c 100644 --- a/agents-api/agents_api/models/__init__.py +++ b/agents-api/agents_api/models/__init__.py @@ -8,12 +8,13 @@ # ruff: noqa: F401, F403, F405 -import agents_api.models.agent as agent -import agents_api.models.developer as developer -import agents_api.models.docs as docs -import agents_api.models.entry as entry -import agents_api.models.execution as execution -import agents_api.models.session as session -import agents_api.models.task as task -import agents_api.models.tools as tools -import agents_api.models.user as user +from . import agent as agent +from . import developer as developer +from . import docs as docs +from . import entry as entry +from . import execution as execution +from . import files as files +from . import session as session +from . import task as task +from . import tools as tools +from . import user as user diff --git a/agents-api/agents_api/models/chat/gather_messages.py b/agents-api/agents_api/models/chat/gather_messages.py index 1a2216a0a..cd9feab29 100644 --- a/agents-api/agents_api/models/chat/gather_messages.py +++ b/agents-api/agents_api/models/chat/gather_messages.py @@ -12,7 +12,6 @@ from ...common.protocol.developers import Developer from ...common.protocol.sessions import ChatContext from ..docs import search_docs_by_embedding -from ..docs.search_docs_hybrid import search_docs_hybrid from ..entry.get_history import get_history from ..utils import ( partialclass, @@ -96,7 +95,7 @@ async def gather_messages( ) # Truncate on the right to take only the first `search_query_chars` characters - query_text = search_messages[-1]["content"].strip()[:search_query_chars] + search_messages[-1]["content"].strip()[:search_query_chars] # List all the applicable owners to search docs from active_agent_id = chat_context.get_active_agent().id diff --git a/agents-api/agents_api/models/files/__init__.py b/agents-api/agents_api/models/files/__init__.py new file mode 100644 index 000000000..444c0a6eb --- /dev/null +++ b/agents-api/agents_api/models/files/__init__.py @@ -0,0 +1,3 @@ +from .create_file import create_file as create_file +from .delete_file import delete_file as delete_file +from .get_file import get_file as get_file diff --git a/agents-api/agents_api/models/files/create_file.py b/agents-api/agents_api/models/files/create_file.py new file mode 100644 index 000000000..224597180 --- /dev/null +++ b/agents-api/agents_api/models/files/create_file.py @@ -0,0 +1,121 @@ +""" +This module contains the functionality for creating a new user in the CozoDB database. +It defines a query for inserting user data into the 'users' relation. +""" + +import base64 +import hashlib +from typing import Any, TypeVar +from uuid import UUID, uuid4 + +from beartype import beartype +from fastapi import HTTPException +from pycozo.client import QueryException +from pydantic import ValidationError + +from ...autogen.openapi_model import CreateFileRequest, File +from ...metrics.counters import increase_counter +from ..utils import ( + cozo_query, + partialclass, + rewrap_exceptions, + verify_developer_id_query, + wrap_in_class, +) + +ModelT = TypeVar("ModelT", bound=Any) +T = TypeVar("T") + + +@rewrap_exceptions( + { + lambda e: isinstance(e, QueryException) + and "asserted to return some results, but returned none" + in str(e): lambda *_: HTTPException( + detail="Developer not found. Please ensure the provided auth token (which refers to your developer_id) is valid and the developer has the necessary permissions to create an agent.", + status_code=403, + ), + QueryException: partialclass( + HTTPException, + status_code=400, + detail="A database query failed to return the expected results. This might occur if the requested resource doesn't exist or your query parameters are incorrect.", + ), + ValidationError: partialclass( + HTTPException, + status_code=400, + detail="Input validation failed. Please check the provided data for missing or incorrect fields, and ensure it matches the required format.", + ), + TypeError: partialclass( + HTTPException, + status_code=400, + detail="A type mismatch occurred. This likely means the data provided is of an incorrect type (e.g., string instead of integer). Please review the input and try again.", + ), + } +) +@wrap_in_class( + File, + one=True, + transform=lambda d: { + **d, + "id": d["file_id"], + "content": "DUMMY: NEED TO FETCH CONTENT FROM BLOB STORAGE", + }, + _kind="inserted", +) +@cozo_query +@increase_counter("create_file") +@beartype +def create_file( + *, + developer_id: UUID, + file_id: UUID | None = None, + data: CreateFileRequest, +) -> tuple[list[str], dict]: + """ + Constructs and executes a datalog query to create a new file in the CozoDB database. + + Parameters: + user_id (UUID): The unique identifier for the user. + developer_id (UUID): The unique identifier for the developer creating the file. + """ + + file_id = file_id or uuid4() + file_data = data.model_dump(exclude={"content"}) + + content_bytes = base64.b64decode(data.content) + size = len(content_bytes) + hash = hashlib.sha256(content_bytes).hexdigest() + + create_query = """ + # Then create the file + ?[file_id, developer_id, name, description, mime_type, size, hash] <- [ + [to_uuid($file_id), to_uuid($developer_id), $name, $description, $mime_type, $size, $hash] + ] + + :insert files { + developer_id, + file_id => + name, + description, + mime_type, + size, + hash, + } + :returning + """ + + queries = [ + verify_developer_id_query(developer_id), + create_query, + ] + + return ( + queries, + { + "file_id": str(file_id), + "developer_id": str(developer_id), + "size": size, + "hash": hash, + **file_data, + }, + ) diff --git a/agents-api/agents_api/models/files/delete_file.py b/agents-api/agents_api/models/files/delete_file.py new file mode 100644 index 000000000..053402e2f --- /dev/null +++ b/agents-api/agents_api/models/files/delete_file.py @@ -0,0 +1,97 @@ +""" +This module contains the implementation of the delete_user_query function, which is responsible for deleting an user and its related default settings from the CozoDB database. +""" + +from typing import Any, TypeVar +from uuid import UUID + +from beartype import beartype +from fastapi import HTTPException +from pycozo.client import QueryException +from pydantic import ValidationError + +from ...autogen.openapi_model import ResourceDeletedResponse +from ...common.utils.datetime import utcnow +from ..utils import ( + cozo_query, + partialclass, + rewrap_exceptions, + verify_developer_id_query, + verify_developer_owns_resource_query, + wrap_in_class, +) + +ModelT = TypeVar("ModelT", bound=Any) +T = TypeVar("T") + + +@rewrap_exceptions( + { + lambda e: isinstance(e, QueryException) + and "Developer does not exist" in str(e): lambda *_: HTTPException( + detail="The specified developer does not exist.", + status_code=403, + ), + lambda e: isinstance(e, QueryException) + and "Developer does not own resource" + in e.resp["display"]: lambda *_: HTTPException( + detail="The specified developer does not own the requested resource. Please verify the ownership or check if the developer ID is correct.", + status_code=404, + ), + QueryException: partialclass( + HTTPException, + status_code=400, + detail="A database query failed to return the expected results. This might occur if the requested resource doesn't exist or your query parameters are incorrect.", + ), + ValidationError: partialclass( + HTTPException, + status_code=400, + detail="Input validation failed. Please check the provided data for missing or incorrect fields, and ensure it matches the required format.", + ), + TypeError: partialclass( + HTTPException, + status_code=400, + detail="A type mismatch occurred. This likely means the data provided is of an incorrect type (e.g., string instead of integer). Please review the input and try again.", + ), + } +) +@wrap_in_class( + ResourceDeletedResponse, + one=True, + transform=lambda d: { + "id": UUID(d.pop("file_id")), + "deleted_at": utcnow(), + "jobs": [], + }, + _kind="deleted", +) +@cozo_query +@beartype +def delete_file(*, developer_id: UUID, file_id: UUID) -> tuple[list[str], dict]: + """ + Constructs and returns a datalog query for deleting an file from the database. + + Parameters: + developer_id (UUID): The UUID of the developer owning the file. + file_id (UUID): The UUID of the file to be deleted. + client (CozoClient, optional): An instance of the CozoClient to execute the query. + + Returns: + ResourceDeletedResponse: The response indicating the deletion of the user. + """ + + queries = [ + verify_developer_id_query(developer_id), + verify_developer_owns_resource_query(developer_id, "files", file_id=file_id), + """ + ?[file_id, developer_id] <- [[$file_id, $developer_id]] + + :delete files { + developer_id, + file_id + } + :returning + """, + ] + + return (queries, {"file_id": str(file_id), "developer_id": str(developer_id)}) diff --git a/agents-api/agents_api/models/files/get_file.py b/agents-api/agents_api/models/files/get_file.py new file mode 100644 index 000000000..f3b85c2f7 --- /dev/null +++ b/agents-api/agents_api/models/files/get_file.py @@ -0,0 +1,116 @@ +from typing import Any, TypeVar +from uuid import UUID + +from beartype import beartype +from fastapi import HTTPException +from pycozo.client import QueryException +from pydantic import ValidationError + +from ...autogen.openapi_model import File +from ..utils import ( + cozo_query, + partialclass, + rewrap_exceptions, + verify_developer_id_query, + verify_developer_owns_resource_query, + wrap_in_class, +) + +ModelT = TypeVar("ModelT", bound=Any) +T = TypeVar("T") + + +@rewrap_exceptions( + { + lambda e: isinstance(e, QueryException) + and "Developer does not exist" in str(e): lambda *_: HTTPException( + detail="The specified developer does not exist.", + status_code=403, + ), + lambda e: isinstance(e, QueryException) + and "Developer does not own resource" + in e.resp["display"]: lambda *_: HTTPException( + detail="The specified developer does not own the requested resource. Please verify the ownership or check if the developer ID is correct.", + status_code=404, + ), + QueryException: partialclass( + HTTPException, + status_code=400, + detail="A database query failed to return the expected results. This might occur if the requested resource doesn't exist or your query parameters are incorrect.", + ), + ValidationError: partialclass( + HTTPException, + status_code=400, + detail="Input validation failed. Please check the provided data for missing or incorrect fields, and ensure it matches the required format.", + ), + TypeError: partialclass( + HTTPException, + status_code=400, + detail="A type mismatch occurred. This likely means the data provided is of an incorrect type (e.g., string instead of integer). Please review the input and try again.", + ), + } +) +@wrap_in_class( + File, + one=True, + transform=lambda d: { + **d, + "content": "DUMMY: NEED TO FETCH CONTENT FROM BLOB STORAGE", + }, +) +@cozo_query +@beartype +def get_file( + *, + developer_id: UUID, + file_id: UUID, +) -> tuple[list[str], dict]: + """ + Retrieves a file by their unique identifier. + + + Parameters: + developer_id (UUID): The unique identifier of the developer associated with the file. + file_id (UUID): The unique identifier of the file to retrieve. + + Returns: + File: The retrieved file. + """ + + # Convert UUIDs to strings for query compatibility. + file_id = str(file_id) + developer_id = str(developer_id) + + get_query = """ + input[developer_id, file_id] <- [[to_uuid($developer_id), to_uuid($file_id)]] + + ?[ + id, + name, + description, + mime_type, + size, + hash, + created_at, + ] := input[developer_id, id], + *files { + file_id: id, + developer_id, + name, + description, + mime_type, + size, + hash, + created_at, + } + + :limit 1 + """ + + queries = [ + verify_developer_id_query(developer_id), + verify_developer_owns_resource_query(developer_id, "files", file_id=file_id), + get_query, + ] + + return (queries, {"developer_id": developer_id, "file_id": file_id}) diff --git a/agents-api/agents_api/routers/__init__.py b/agents-api/agents_api/routers/__init__.py index 2d10aad3e..4e2d7b881 100644 --- a/agents-api/agents_api/routers/__init__.py +++ b/agents-api/agents_api/routers/__init__.py @@ -6,6 +6,8 @@ - `sessions`: Manages routing for session-related operations. This includes creating, updating, and deleting sessions, as well as handling chat functionalities and history within sessions. It is essential for managing interactive sessions between agents and users. - `users`: Responsible for routing user-related operations. This encompasses user creation, update, deletion, and managing user documents. It ensures that user data can be properly managed and accessed as needed. - `jobs`: Deals with routing for job status inquiries. This allows users to check the status of asynchronous jobs, providing insights into the progress and outcomes of long-running operations. +- `files`: Deals with routing for file operations. This includes creating, reading, updating, and deleting files. It provides endpoints for managing files, including uploading, downloading, and deleting file content. +- `docs`: Deals with routing for documentation operations. This includes creating, reading, updating, and deleting documentation. It provides endpoints for managing documentation, including uploading, downloading, and deleting documentation content. Each sub-module defines its own set of API endpoints and is responsible for handling requests and responses related to its domain, ensuring a modular and organized approach to API development. """ @@ -17,6 +19,7 @@ from .agents import router as agents_router from .docs import router as docs_router +from .files import router as files_router from .internal import router as internal_router from .jobs import router as jobs_router from .sessions import router as sessions_router diff --git a/agents-api/agents_api/routers/docs/list_docs.py b/agents-api/agents_api/routers/docs/list_docs.py index 78827b20f..2f663a324 100644 --- a/agents-api/agents_api/routers/docs/list_docs.py +++ b/agents-api/agents_api/routers/docs/list_docs.py @@ -1,9 +1,7 @@ -import json -from typing import Annotated, Literal, Optional +from typing import Annotated, Literal from uuid import UUID -from fastapi import Depends, Query -from pydantic import BaseModel, BeforeValidator, ConfigDict +from fastapi import Depends from ...autogen.openapi_model import Doc, ListResponse from ...dependencies.developer_id import get_developer_id diff --git a/agents-api/agents_api/routers/files/__init__.py b/agents-api/agents_api/routers/files/__init__.py new file mode 100644 index 000000000..5e3d5a62c --- /dev/null +++ b/agents-api/agents_api/routers/files/__init__.py @@ -0,0 +1,6 @@ +# ruff: noqa: F401 + +from .create_file import create_file +from .delete_file import delete_file +from .get_file import get_file +from .router import router diff --git a/agents-api/agents_api/routers/files/create_file.py b/agents-api/agents_api/routers/files/create_file.py new file mode 100644 index 000000000..88870ae4e --- /dev/null +++ b/agents-api/agents_api/routers/files/create_file.py @@ -0,0 +1,44 @@ +import base64 +from typing import Annotated +from uuid import UUID + +from fastapi import Depends +from starlette.status import HTTP_201_CREATED + +from ...autogen.openapi_model import ( + CreateFileRequest, + File, + ResourceCreatedResponse, +) +from ...clients import s3 +from ...dependencies.developer_id import get_developer_id +from ...models.files.create_file import create_file as create_file_query +from .router import router + + +async def upload_file_content(file_id: UUID, content: str) -> None: + """Upload file content to blob storage using the file ID as the key""" + s3.setup() + key = str(file_id) + content_bytes = base64.b64decode(content) + s3.add_object(key, content_bytes) + + +@router.post("/files", status_code=HTTP_201_CREATED, tags=["files"]) +async def create_file( + x_developer_id: Annotated[UUID, Depends(get_developer_id)], + data: CreateFileRequest, +) -> ResourceCreatedResponse: + file: File = create_file_query( + developer_id=x_developer_id, + data=data, + ) + + # Upload the file content to blob storage using the file ID as the key + await upload_file_content(file.id, data.content) + + return ResourceCreatedResponse( + id=file.id, + created_at=file.created_at, + jobs=[], + ) diff --git a/agents-api/agents_api/routers/files/delete_file.py b/agents-api/agents_api/routers/files/delete_file.py new file mode 100644 index 000000000..d46f82bf5 --- /dev/null +++ b/agents-api/agents_api/routers/files/delete_file.py @@ -0,0 +1,30 @@ +from typing import Annotated +from uuid import UUID + +from fastapi import Depends +from starlette.status import HTTP_202_ACCEPTED + +from ...autogen.openapi_model import ResourceDeletedResponse +from ...clients import s3 +from ...dependencies.developer_id import get_developer_id +from ...models.files.delete_file import delete_file as delete_file_query +from .router import router + + +async def delete_file_content(file_id: UUID) -> None: + """Delete file content from blob storage using the file ID as the key""" + s3.setup() + key = str(file_id) + s3.delete_object(key) + + +@router.delete("/files/{file_id}", status_code=HTTP_202_ACCEPTED, tags=["files"]) +async def delete_file( + file_id: UUID, x_developer_id: Annotated[UUID, Depends(get_developer_id)] +) -> ResourceDeletedResponse: + resource_deleted = delete_file_query(developer_id=x_developer_id, file_id=file_id) + + # Delete the file content from blob storage + await delete_file_content(file_id) + + return resource_deleted diff --git a/agents-api/agents_api/routers/files/get_file.py b/agents-api/agents_api/routers/files/get_file.py new file mode 100644 index 000000000..104c4c847 --- /dev/null +++ b/agents-api/agents_api/routers/files/get_file.py @@ -0,0 +1,31 @@ +import base64 +from typing import Annotated +from uuid import UUID + +from fastapi import Depends + +from ...autogen.openapi_model import File +from ...clients import s3 +from ...dependencies.developer_id import get_developer_id +from ...models.files.get_file import get_file as get_file_query +from .router import router + + +async def fetch_file_content(file_id: UUID) -> str: + """Fetch file content from blob storage using the file ID as the key""" + s3.setup() + key = str(file_id) + content = s3.get_object(key) + return base64.b64encode(content).decode("utf-8") + + +@router.get("/files/{file_id}", tags=["files"]) +async def get_file( + file_id: UUID, x_developer_id: Annotated[UUID, Depends(get_developer_id)] +) -> File: + file = get_file_query(developer_id=x_developer_id, file_id=file_id) + + # Fetch the file content from blob storage + file.content = await fetch_file_content(file.id) + + return file diff --git a/agents-api/agents_api/routers/files/router.py b/agents-api/agents_api/routers/files/router.py new file mode 100644 index 000000000..5c3ec9311 --- /dev/null +++ b/agents-api/agents_api/routers/files/router.py @@ -0,0 +1,3 @@ +from fastapi import APIRouter + +router: APIRouter = APIRouter() diff --git a/agents-api/agents_api/web.py b/agents-api/agents_api/web.py index 1a927c452..a9f191441 100644 --- a/agents-api/agents_api/web.py +++ b/agents-api/agents_api/web.py @@ -27,6 +27,7 @@ from .routers import ( agents, docs, + files, internal, jobs, sessions, @@ -182,6 +183,7 @@ async def scalar_html(): app.include_router(sessions.router, dependencies=[Depends(get_api_key)]) app.include_router(users.router, dependencies=[Depends(get_api_key)]) app.include_router(jobs.router, dependencies=[Depends(get_api_key)]) +app.include_router(files.router, dependencies=[Depends(get_api_key)]) app.include_router(docs.router, dependencies=[Depends(get_api_key)]) app.include_router(tasks.router, dependencies=[Depends(get_api_key)]) app.include_router(internal.router) diff --git a/agents-api/migrations/migrate_1731953383_create_files_relation.py b/agents-api/migrations/migrate_1731953383_create_files_relation.py new file mode 100644 index 000000000..9cdc4f8fe --- /dev/null +++ b/agents-api/migrations/migrate_1731953383_create_files_relation.py @@ -0,0 +1,29 @@ +# /usr/bin/env python3 + +MIGRATION_ID = "create_files_relation" +CREATED_AT = 1731953383.258172 + +create_files_query = dict( + up=""" + :create files { + developer_id: Uuid, + file_id: Uuid, + => + name: String, + description: String default "", + mime_type: String? default null, + size: Int, + hash: String, + created_at: Float default now(), + } + """, + down="::remove files", +) + + +def up(client): + client.run(create_files_query["up"]) + + +def down(client): + client.run(create_files_query["down"]) diff --git a/agents-api/poetry.lock b/agents-api/poetry.lock index 1611b41af..668babc6f 100644 --- a/agents-api/poetry.lock +++ b/agents-api/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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agents_api.models.user.delete_user import delete_user from agents_api.web import app -from tests.utils import patch_embed_acompletion as patch_embed_acompletion_ctx +from tests.utils import ( + patch_embed_acompletion as patch_embed_acompletion_ctx, +) +from tests.utils import ( + patch_s3_client, +) EMBEDDING_SIZE: int = 1024 @@ -81,6 +89,28 @@ def test_developer_id(cozo_client=cozo_client): ) +@fixture(scope="global") +def test_file(client=cozo_client, developer_id=test_developer_id): + file = create_file( + developer_id=developer_id, + data=CreateFileRequest( + name="Hello", + description="World", + mime_type="text/plain", + content="eyJzYW1wbGUiOiAidGVzdCJ9", + ), + client=client, + ) + + yield file + + delete_file( + developer_id=developer_id, + file_id=file.id, + client=client, + ) + + @fixture(scope="global") def test_developer(cozo_client=cozo_client, developer_id=test_developer_id): return get_developer( @@ -414,3 +444,9 @@ def _make_request(method, url, **kwargs): return client.request(method, url, headers=headers, **kwargs) return _make_request + + +@fixture(scope="global") +def s3_client(): + with patch_s3_client() as s3_client: + yield s3_client diff --git a/agents-api/tests/test_files_queries.py b/agents-api/tests/test_files_queries.py new file mode 100644 index 000000000..712a083ca --- /dev/null +++ b/agents-api/tests/test_files_queries.py @@ -0,0 +1,57 @@ +# Tests for entry queries + + +from ward import test + +from agents_api.autogen.openapi_model import CreateFileRequest +from agents_api.models.files.create_file import create_file +from agents_api.models.files.delete_file import delete_file +from agents_api.models.files.get_file import get_file +from tests.fixtures import ( + cozo_client, + test_developer_id, + test_file, +) + + +@test("model: create file") +def _(client=cozo_client, developer_id=test_developer_id): + create_file( + developer_id=developer_id, + data=CreateFileRequest( + name="Hello", + description="World", + mime_type="text/plain", + content="eyJzYW1wbGUiOiAidGVzdCJ9", + ), + client=client, + ) + + +@test("model: get file") +def _(client=cozo_client, file=test_file, developer_id=test_developer_id): + get_file( + developer_id=developer_id, + file_id=file.id, + client=client, + ) + + +@test("model: delete file") +def _(client=cozo_client, developer_id=test_developer_id): + file = create_file( + developer_id=developer_id, + data=CreateFileRequest( + name="Hello", + description="World", + mime_type="text/plain", + content="eyJzYW1wbGUiOiAidGVzdCJ9", + ), + client=client, + ) + + delete_file( + developer_id=developer_id, + file_id=file.id, + client=client, + ) diff --git a/agents-api/tests/test_files_routes.py b/agents-api/tests/test_files_routes.py new file mode 100644 index 000000000..662612ff5 --- /dev/null +++ b/agents-api/tests/test_files_routes.py @@ -0,0 +1,88 @@ +import base64 +import hashlib + +from ward import test + +from tests.fixtures import make_request, s3_client + + +@test("route: create file") +async def _(make_request=make_request, s3_client=s3_client): + data = dict( + name="Test File", + description="This is a test file.", + mime_type="text/plain", + content="eyJzYW1wbGUiOiAidGVzdCJ9", + ) + + response = make_request( + method="POST", + url="/files", + json=data, + ) + + assert response.status_code == 201 + + +@test("route: delete file") +async def _(make_request=make_request, s3_client=s3_client): + data = dict( + name="Test File", + description="This is a test file.", + mime_type="text/plain", + content="eyJzYW1wbGUiOiAidGVzdCJ9", + ) + + response = make_request( + method="POST", + url="/files", + json=data, + ) + + file_id = response.json()["id"] + + response = make_request( + method="DELETE", + url=f"/files/{file_id}", + ) + + assert response.status_code == 202 + + response = make_request( + method="GET", + url=f"/files/{file_id}", + ) + + assert response.status_code == 404 + + +@test("route: get file") +async def _(make_request=make_request, s3_client=s3_client): + data = dict( + name="Test File", + description="This is a test file.", + mime_type="text/plain", + content="eyJzYW1wbGUiOiAidGVzdCJ9", + ) + + response = make_request( + method="POST", + url="/files", + json=data, + ) + + file_id = response.json()["id"] + content_bytes = base64.b64decode(data["content"]) + expected_hash = hashlib.sha256(content_bytes).hexdigest() + + response = make_request( + method="GET", + url=f"/files/{file_id}", + ) + + assert response.status_code == 200 + + result = response.json() + + # Decode base64 content and compute its SHA-256 hash + assert result["hash"] == expected_hash diff --git a/agents-api/tests/test_sessions.py b/agents-api/tests/test_sessions.py index 8050a40e6..b25a8a706 100644 --- a/agents-api/tests/test_sessions.py +++ b/agents-api/tests/test_sessions.py @@ -1,6 +1,6 @@ from ward import test -from tests.fixtures import client, make_request, test_session +from tests.fixtures import make_request @test("model: list sessions") diff --git a/agents-api/tests/utils.py b/agents-api/tests/utils.py index dc1007d13..4dfa69844 100644 --- a/agents-api/tests/utils.py +++ b/agents-api/tests/utils.py @@ -1,8 +1,10 @@ import asyncio +import io import logging from contextlib import asynccontextmanager, contextmanager from unittest.mock import patch +from botocore import exceptions from fastapi.testclient import TestClient from litellm.types.utils import ModelResponse from temporalio.testing import WorkflowEnvironment @@ -102,3 +104,53 @@ def patch_integration_service(output: dict = {"result": "ok"}): run_integration_service.return_value = output yield run_integration_service + + +@contextmanager +def patch_s3_client(): + class InMemoryS3Client: + def __init__(self): + self.store = {} + + def list_buckets(self): + return {"Buckets": [{"Name": bucket} for bucket in self.store.keys()]} + + def create_bucket(self, Bucket): + self.store[Bucket] = {} + + def head_object(self, Bucket, Key): + obj = self.store.get(Bucket, {}).get(Key) + + if obj is None: + raise exceptions.ClientError( + {"Error": {"Code": "404", "Message": "Not Found"}}, "HeadObject" + ) + + return obj + + def put_object(self, Bucket, Key, Body): + self.store[Bucket] = self.store.get(Bucket, {}) + self.store[Bucket][Key] = Body + + def get_object(self, Bucket, Key): + obj = self.store.get(Bucket, {}).get(Key) + + if obj is None: + raise exceptions.ClientError( + {"Error": {"Code": "404", "Message": "Not Found"}}, "GetObject" + ) + + file_io = io.BytesIO(obj) + + return {"Body": file_io} + + def delete_object(self, Bucket, Key): + self.store[Bucket] = self.store.get(Bucket, {}) + del self.store[Bucket][Key] + + in_memory_s3_client = InMemoryS3Client() + + with patch("agents_api.clients.s3.get_s3_client") as get_s3_client: + get_s3_client.return_value = in_memory_s3_client + + yield get_s3_client diff --git a/integrations-service/integrations/autogen/Files.py b/integrations-service/integrations/autogen/Files.py new file mode 100644 index 000000000..b7640f8bc --- /dev/null +++ b/integrations-service/integrations/autogen/Files.py @@ -0,0 +1,82 @@ +# generated by datamodel-codegen: +# filename: openapi-1.0.0.yaml + +from __future__ import annotations + +from typing import Annotated +from uuid import UUID + +from pydantic import AwareDatetime, BaseModel, ConfigDict, Field + + +class CreateFileRequest(BaseModel): + """ + Payload for creating a file + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + name: Annotated[ + str, + Field( + max_length=120, + pattern="^[\\p{L}\\p{Nl}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]+[\\p{ID_Start}\\p{Mn}\\p{Mc}\\p{Nd}\\p{Pc}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]*$", + ), + ] + """ + Name of the file + """ + description: str = "" + """ + Description of the file + """ + mime_type: str | None = None + """ + MIME type of the file + """ + content: str + """ + Base64 encoded content of the file + """ + + +class File(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + id: Annotated[UUID, Field(json_schema_extra={"readOnly": True})] + created_at: Annotated[AwareDatetime, Field(json_schema_extra={"readOnly": True})] + """ + When this resource was created as UTC date-time + """ + name: Annotated[ + str, + Field( + max_length=120, + pattern="^[\\p{L}\\p{Nl}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]+[\\p{ID_Start}\\p{Mn}\\p{Mc}\\p{Nd}\\p{Pc}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]*$", + ), + ] + """ + Name of the file + """ + description: str = "" + """ + Description of the file + """ + mime_type: str | None = None + """ + MIME type of the file + """ + content: str + """ + Base64 encoded content of the file + """ + size: Annotated[int, Field(ge=1, json_schema_extra={"readOnly": True})] + """ + Size of the file in bytes + """ + hash: Annotated[str, Field(json_schema_extra={"readOnly": True})] + """ + Hash of the file + """ diff --git a/integrations-service/integrations/autogen/Tools.py b/integrations-service/integrations/autogen/Tools.py index 0b7e8c224..a48e2255e 100644 --- a/integrations-service/integrations/autogen/Tools.py +++ b/integrations-service/integrations/autogen/Tools.py @@ -12,7 +12,6 @@ BaseModel, ConfigDict, Field, - RootModel, StrictBool, ) diff --git a/integrations-service/tests/mocks/brave.py b/integrations-service/tests/mocks/brave.py index 7c92cfe01..958925aed 100644 --- a/integrations-service/tests/mocks/brave.py +++ b/integrations-service/tests/mocks/brave.py @@ -1,7 +1,5 @@ """Mock implementation of Brave Search API client""" -from typing import Optional - class MockBraveSearchClient: def __init__(self, api_key: str): diff --git a/integrations-service/tests/test_provider_execution.py b/integrations-service/tests/test_provider_execution.py index 4b409104b..9b96ee51b 100644 --- a/integrations-service/tests/test_provider_execution.py +++ b/integrations-service/tests/test_provider_execution.py @@ -1,16 +1,10 @@ """Tests for provider execution using mocks""" import pytest -from pydantic import AnyUrl from integrations.autogen.Tools import ( - BraveSearchArguments, - EmailArguments, - SpiderFetchArguments, - WeatherGetArguments, WikipediaSearchArguments, ) -from integrations.providers import available_providers from integrations.utils.execute_integration import execute_integration diff --git a/integrations-service/tests/test_providers.py b/integrations-service/tests/test_providers.py index 8590169f4..181248944 100644 --- a/integrations-service/tests/test_providers.py +++ b/integrations-service/tests/test_providers.py @@ -1,4 +1,3 @@ -import pytest from integrations.models import BaseProvider, BaseProviderMethod, ProviderInfo diff --git a/typespec/common/scalars.tsp b/typespec/common/scalars.tsp index 95c92d0b8..c718f6289 100644 --- a/typespec/common/scalars.tsp +++ b/typespec/common/scalars.tsp @@ -18,6 +18,11 @@ alias concreteType = numeric | string | boolean | null; @pattern("^[\\p{L}\\p{Nl}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]+[\\p{ID_Start}\\p{Mn}\\p{Mc}\\p{Nd}\\p{Pc}\\p{Pattern_Syntax}\\p{Pattern_White_Space}]*$") scalar identifierSafeUnicode extends string; +/** Valid mime types */ +@maxLength(120) +@pattern("^(application|audio|font|example|image|message|model|multipart|text|video|x-(?:[0-9A-Za-z!#$%&'*+.^_`|~-]+))\\/([0-9A-Za-z!#$%&'*+.^_`|~-]+)$") +scalar mimeType extends string; + /** Valid python identifier names */ @pattern("^[^\\W0-9]\\w*$") @maxLength(40) @@ -48,7 +53,13 @@ alias json = "application/json"; alias eventStream = "text/event-stream"; /** Different possible sources that can produce new entries */ -alias entrySource = "api_request" | "api_response" | "tool_response" | "internal" | "summarizer" | "meta"; +alias entrySource = + | "api_request" + | "api_response" + | "tool_response" + | "internal" + | "summarizer" + | "meta"; /** A simple python expression compatible with SimpleEval. */ scalar PyExpression extends string; diff --git a/typespec/files/endpoints.tsp b/typespec/files/endpoints.tsp new file mode 100644 index 000000000..787f1b1c9 --- /dev/null +++ b/typespec/files/endpoints.tsp @@ -0,0 +1,14 @@ +import "../common"; +import "./models.tsp"; + +using Common; + +namespace Files; + +// +// FILE ENDPOINTS +// + +interface Endpoints + extends GetEndpoint, + CreateEndpoint {} diff --git a/typespec/files/main.tsp b/typespec/files/main.tsp new file mode 100644 index 000000000..f60022763 --- /dev/null +++ b/typespec/files/main.tsp @@ -0,0 +1,8 @@ +import "./endpoints.tsp"; +import "./models.tsp"; + +namespace Files; + +// +// FILES +// diff --git a/typespec/files/models.tsp b/typespec/files/models.tsp new file mode 100644 index 000000000..943b64241 --- /dev/null +++ b/typespec/files/models.tsp @@ -0,0 +1,41 @@ +import "../common"; + +using Common; + +namespace Files; + +// +// FILE MODELS +// + +model File { + ...HasId; + ...HasCreatedAt; + + /** Name of the file */ + name: identifierSafeUnicode; + + /** Description of the file */ + description: string = ""; + + /** MIME type of the file */ + mime_type: mimeType | null = null; + + /** Base64 encoded content of the file */ + content: string; + + /** Size of the file in bytes */ + @visibility("read") + @minValue(1) + size: uint64; + + /** Hash of the file */ + @visibility("read") + hash: string; +} + +/** Payload for creating a file */ +@withVisibility("create") +model CreateFileRequest { + ...File; +} diff --git a/typespec/main.tsp b/typespec/main.tsp index aefb8adfc..3ec3739dd 100644 --- a/typespec/main.tsp +++ b/typespec/main.tsp @@ -7,6 +7,7 @@ import "./chat"; import "./docs"; import "./entries"; import "./executions"; +import "./files"; import "./jobs"; import "./sessions"; import "./tasks"; @@ -118,4 +119,7 @@ namespace Api { @route("/jobs") interface JobRoute extends Jobs.Endpoints {} + + @route("/files") + interface FilesRoute extends Files.Endpoints {} } diff --git a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml index d83ce87f5..8b8afa84f 100644 --- a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml +++ b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml @@ -755,6 +755,42 @@ paths: text/event-stream: schema: type: string + /files: + post: + operationId: FilesRoute_create + description: Create a new File + parameters: [] + responses: + '201': + description: The request has succeeded and a new resource has been created as a result. + content: + application/json: + schema: + $ref: '#/components/schemas/Common.ResourceCreatedResponse' + requestBody: + required: true + content: + application/json: + schema: + $ref: '#/components/schemas/Files.CreateFileRequest' + /files/{id}: + get: + operationId: FilesRoute_get + description: Get a File by its id + parameters: + - name: id + in: path + required: true + description: ID of the resource + schema: + $ref: '#/components/schemas/Common.uuid' + responses: + '200': + description: The request has succeeded. + content: + application/json: + schema: + $ref: '#/components/schemas/Files.File' /jobs/{id}: get: operationId: JobRoute_get @@ -2454,6 +2490,11 @@ components: format: float minimum: -100 maximum: 100 + Common.mimeType: + type: string + maxLength: 120 + pattern: ^(application|audio|font|example|image|message|model|multipart|text|video|x-(?:[0-9A-Za-z!#$%&'*+.^_`|~-]+))\/([0-9A-Za-z!#$%&'*+.^_`|~-]+)$ + description: Valid mime types Common.offset: type: integer format: uint32 @@ -3186,6 +3227,80 @@ components: mapping: cancelled: '#/components/schemas/Executions.StopExecutionRequest' running: '#/components/schemas/Executions.ResumeExecutionRequest' + Files.CreateFileRequest: + type: object + required: + - name + - description + - mime_type + - content + properties: + name: + allOf: + - $ref: '#/components/schemas/Common.identifierSafeUnicode' + description: Name of the file + description: + type: string + description: Description of the file + default: '' + mime_type: + oneOf: + - $ref: '#/components/schemas/Common.mimeType' + nullable: true + description: MIME type of the file + default: null + content: + type: string + description: Base64 encoded content of the file + description: Payload for creating a file + Files.File: + type: object + required: + - id + - created_at + - name + - description + - mime_type + - content + - size + - hash + properties: + id: + allOf: + - $ref: '#/components/schemas/Common.uuid' + readOnly: true + created_at: + type: string + format: date-time + description: When this resource was created as UTC date-time + readOnly: true + name: + allOf: + - $ref: '#/components/schemas/Common.identifierSafeUnicode' + description: Name of the file + description: + type: string + description: Description of the file + default: '' + mime_type: + oneOf: + - $ref: '#/components/schemas/Common.mimeType' + nullable: true + description: MIME type of the file + default: null + content: + type: string + description: Base64 encoded content of the file + size: + type: integer + format: uint64 + minimum: 1 + description: Size of the file in bytes + readOnly: true + hash: + type: string + description: Hash of the file + readOnly: true Jobs.JobState: type: string enum: From 7fca69cbce88d669e2142cf3bc75fee359d0bdde Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Tue, 19 Nov 2024 13:22:39 +0300 Subject: [PATCH 11/23] chore(agents-api): Refactor running background tasks in system tools (#851) > [!IMPORTANT] > Refactor `execute_system` in `execute_system.py` to improve background task handling for 'create' and 'chat' operations on 'doc' subresource. > > - **Behavior**: > - Refactor `execute_system` in `execute_system.py` to handle background tasks by creating `BackgroundTasks` instance before handler call and awaiting it after. > - Applied to 'create' and 'chat' operations for 'doc' subresource. > - **Misc**: > - No changes to search operations or regular operations handling. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for d216f34f14585d1ed67ad15b242e20448c781177. It will automatically update as commits are pushed. --- agents-api/agents_api/activities/execute_system.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index 02623c3a6..355d98614 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -64,11 +64,14 @@ async def execute_system( # Handle special cases for doc operations if system.operation == "create" and system.subresource == "doc": arguments["x_developer_id"] = arguments.pop("developer_id") - return await handler( + bg_runner = BackgroundTasks() + res = await handler( data=CreateDocRequest(**arguments.pop("data")), - background_tasks=BackgroundTasks(), + background_tasks=bg_runner, **arguments, ) + await bg_runner() + return res # Handle search operations if system.operation == "search" and system.subresource == "doc": @@ -82,13 +85,16 @@ async def execute_system( session_id = arguments.pop("session_id") x_custom_api_key = arguments.pop("x_custom_api_key", None) chat_input = ChatInput(**arguments) - return await handler( + bg_runner = BackgroundTasks() + res = await handler( developer=developer, session_id=session_id, - background_tasks=BackgroundTasks(), + background_tasks=bg_runner, x_custom_api_key=x_custom_api_key, chat_input=chat_input, ) + await bg_runner() + return res # Handle regular operations if asyncio.iscoroutinefunction(handler): From c957c3399cc30fd34bd39a2c21b92bda830c3ce5 Mon Sep 17 00:00:00 2001 From: Vedant Sahai Date: Wed, 20 Nov 2024 05:24:56 -0500 Subject: [PATCH 12/23] F/ffmpeg integration: added new ffmpeg + cloudinary integrations (#853) > [!IMPORTANT] > Add FFmpeg and Cloudinary integrations, update models and configurations, and enhance media processing functionalities. > > - **Integrations**: > - Add FFmpeg and Cloudinary integrations in `integrations-service`. > - Update `providers.py` to include FFmpeg and Cloudinary. > - Add `cloudinary.py` and `ffmpeg.py` for handling operations. > - **Models**: > - Add new models for Cloudinary and FFmpeg in `Tools.py`. > - Update `Chat.py`, `Entries.py`, and `Tasks.py` with new content models. > - **Configurations**: > - Update `Dockerfile` to install FFmpeg. > - Add `GEMINI_API_KEY` to `.env.example` and `docker-compose.yml`. > - Update `gunicorn_conf.py` for logging and worker management. > - **Functions**: > - Modify `execute_integration.py` for robust error handling. > - Update `execute_system.py` to support session creation. > - Enhance `base_evaluate.py` with increased `MAX_STRING_LENGTH` and deep blob storage. > - **Misc**: > - Update `litellm-config.yaml` with new Gemini models. > - Add new TypeSpec files for Cloudinary and FFmpeg. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 0fe1cecce07046141ff00b10604175ca8f3ccde7. It will automatically update as commits are pushed. --------- Signed-off-by: Diwank Singh Tomer Co-authored-by: Ahmad-mtos Co-authored-by: Ahmad-mtos Co-authored-by: Vedantsahai18 Co-authored-by: HamadaSalhab Co-authored-by: HamadaSalhab Co-authored-by: Diwank Singh Tomer --- .env.example | 1 + .../activities/execute_integration.py | 5 +- .../agents_api/activities/execute_system.py | 7 + .../activities/task_steps/base_evaluate.py | 6 +- agents-api/agents_api/autogen/Chat.py | 118 ++- agents-api/agents_api/autogen/Entries.py | 63 +- agents-api/agents_api/autogen/Tasks.py | 66 +- agents-api/agents_api/autogen/Tools.py | 327 ++++++++ agents-api/agents_api/clients/temporal.py | 8 +- .../agents_api/common/protocol/tasks.py | 11 +- .../workflows/task_execution/helpers.py | 25 +- agents-api/gunicorn_conf.py | 6 +- integrations-service/Dockerfile | 19 +- integrations-service/gunicorn_conf.py | 30 +- .../integrations/autogen/Chat.py | 118 ++- .../integrations/autogen/Entries.py | 63 +- .../integrations/autogen/Tasks.py | 66 +- .../integrations/autogen/Tools.py | 327 ++++++++ .../integrations/models/__init__.py | 7 + .../integrations/models/cloudinary.py | 23 + .../integrations/models/execution.py | 15 + .../integrations/models/ffmpeg.py | 15 + .../integrations/providers.py | 56 +- .../utils/integrations/cloudinary.py | 131 +++ .../integrations/utils/integrations/ffmpeg.py | 145 ++++ .../utils/integrations/remote_browser.py | 53 +- integrations-service/poetry.lock | 20 +- integrations-service/pyproject.toml | 1 + llm-proxy/docker-compose.yml | 2 +- llm-proxy/litellm-config.yaml | 13 + typespec/entries/models.tsp | 22 +- typespec/tools/cloudinary.tsp | 122 +++ typespec/tools/ffmpeg.tsp | 56 ++ typespec/tools/main.tsp | 2 + typespec/tools/models.tsp | 4 + .../@typespec/openapi3/openapi-1.0.0.yaml | 771 ++++++++++++++++++ 36 files changed, 2659 insertions(+), 65 deletions(-) create mode 100644 integrations-service/integrations/models/cloudinary.py create mode 100644 integrations-service/integrations/models/ffmpeg.py create mode 100644 integrations-service/integrations/utils/integrations/cloudinary.py create mode 100644 integrations-service/integrations/utils/integrations/ffmpeg.py create mode 100644 typespec/tools/cloudinary.tsp create mode 100644 typespec/tools/ffmpeg.tsp diff --git a/.env.example b/.env.example index 96fa0abc1..ab4882297 100644 --- a/.env.example +++ b/.env.example @@ -34,6 +34,7 @@ LITELLM_REDIS_PASSWORD= # HUGGING_FACE_HUB_TOKEN= # ANTHROPIC_API_KEY= # GROQ_API_KEY= +# GEMINI_API_KEY= # CLOUDFLARE_API_KEY= # CLOUDFLARE_ACCOUNT_ID= # NVIDIA_NIM_API_KEY= diff --git a/agents-api/agents_api/activities/execute_integration.py b/agents-api/agents_api/activities/execute_integration.py index 2badce0f9..d8b36fffb 100644 --- a/agents-api/agents_api/activities/execute_integration.py +++ b/agents-api/agents_api/activities/execute_integration.py @@ -49,7 +49,10 @@ async def execute_integration( arguments=arguments, ) - if "error" in integration_service_response: + if ( + "error" in integration_service_response + and integration_service_response["error"] + ): raise Exception(integration_service_response["error"]) return integration_service_response diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index 355d98614..43a2db8e5 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -14,6 +14,7 @@ TextOnlyDocSearchRequest, VectorDocSearchRequest, ) +from ..autogen.Sessions import CreateSessionRequest from ..autogen.Tools import SystemDef from ..common.protocol.tasks import StepContext from ..common.storage_handler import auto_blob_store @@ -96,6 +97,12 @@ async def execute_system( await bg_runner() return res + if system.operation == "create" and system.resource == "session": + developer_id = arguments.pop("developer_id") + session_id = arguments.pop("session_id", None) + data = CreateSessionRequest(**arguments) + return handler(developer_id=developer_id, session_id=session_id, data=data) + # Handle regular operations if asyncio.iscoroutinefunction(handler): return await handler(**arguments) diff --git a/agents-api/agents_api/activities/task_steps/base_evaluate.py b/agents-api/agents_api/activities/task_steps/base_evaluate.py index 06dacb361..d87b961d3 100644 --- a/agents-api/agents_api/activities/task_steps/base_evaluate.py +++ b/agents-api/agents_api/activities/task_steps/base_evaluate.py @@ -6,8 +6,8 @@ from box import Box from openai import BaseModel -# Increase the max string length to 300000 -simpleeval.MAX_STRING_LENGTH = 300000 +# Increase the max string length to 2048000 +simpleeval.MAX_STRING_LENGTH = 2048000 from simpleeval import NameNotDefined, SimpleEval # noqa: E402 from temporalio import activity # noqa: E402 @@ -63,7 +63,7 @@ def _recursive_evaluate(expr, evaluator: SimpleEval): raise ValueError(f"Invalid expression: {expr}") -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def base_evaluate( exprs: Any, diff --git a/agents-api/agents_api/autogen/Chat.py b/agents-api/agents_api/autogen/Chat.py index d013ae8b7..042f9164d 100644 --- a/agents-api/agents_api/autogen/Chat.py +++ b/agents-api/agents_api/autogen/Chat.py @@ -165,7 +165,107 @@ class Content(BaseModel): """ +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + +class ContentItemModel3(Content): + pass + + +class ContentItemModel4(ContentItemModel): + pass + + +class ContentItemModel5(Content): + pass + + +class ContentItemModel6(ContentItemModel): + pass + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] | list[ContentItemModel] + + +class ContentModel1(Content): + pass + + +class ContentModel2(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + + +class ContentModel3(Content): + pass + + +class ContentModel4(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel3] | list[ContentItemModel4] + + +class ContentModel5(Content): + pass + + +class ContentModel6(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel5] | list[ContentItemModel6] + + +class ContentModel7(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -193,7 +293,8 @@ class Delta(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel1 | ContentModel7 | ContentModel2] | None, + Field(...), ] = None """ The content parts of the message @@ -258,7 +359,8 @@ class Message(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[Content | ContentModel7 | ContentModel] | None, + Field(...), ] = None """ The content parts of the message @@ -305,7 +407,8 @@ class MessageModel(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel3 | ContentModel7 | ContentModel4] | None, + Field(...), ] = None """ The content parts of the message @@ -405,6 +508,15 @@ class SingleChatOutput(BaseChatOutput): message: MessageModel +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + + class TokenLogProb(BaseTokenLogProb): model_config = ConfigDict( populate_by_name=True, diff --git a/agents-api/agents_api/autogen/Entries.py b/agents-api/agents_api/autogen/Entries.py index f001fc880..de37e77d8 100644 --- a/agents-api/agents_api/autogen/Entries.py +++ b/agents-api/agents_api/autogen/Entries.py @@ -28,7 +28,7 @@ class BaseEntry(BaseModel): """ name: str | None = None content: ( - list[Content | ContentModel] + list[Content | ContentModel3 | ContentModel] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -37,7 +37,7 @@ class BaseEntry(BaseModel): | str | ToolResponse | list[ - list[Content | ContentModel] + list[ContentModel1 | ContentModel3 | ContentModel2] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -95,7 +95,57 @@ class Content(BaseModel): """ +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] | list[ContentItemModel] + + +class ContentModel1(Content): + pass + + +class ContentModel2(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + + +class ContentModel3(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -168,3 +218,12 @@ class Relation(BaseModel): head: UUID relation: str tail: UUID + + +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str diff --git a/agents-api/agents_api/autogen/Tasks.py b/agents-api/agents_api/autogen/Tasks.py index e62e6d3c3..8e98caaab 100644 --- a/agents-api/agents_api/autogen/Tasks.py +++ b/agents-api/agents_api/autogen/Tasks.py @@ -9,6 +9,7 @@ from pydantic import AwareDatetime, BaseModel, ConfigDict, Field, StrictBool from .Chat import ChatSettings +from .Common import JinjaTemplate from .Tools import ( ChosenBash20241022, ChosenComputer20241022, @@ -85,6 +86,26 @@ class Content(BaseModel): """ +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -99,14 +120,40 @@ class ContentModel(BaseModel): """ -class ContentModel1(Content): +class ContentModel1(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] | list[ContentItemModel] + + +class ContentModel2(Content): pass -class ContentModel2(ContentModel): +class ContentModel3(ContentModel): pass +class ContentModel4(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + + class CreateTaskRequest(BaseModel): """ Payload for creating a task @@ -655,7 +702,8 @@ class PromptItem(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - list[str] | list[Content | ContentModel] | str | None, Field(...) + list[str] | list[Content | ContentModel | ContentModel1] | str | None, + Field(...), ] """ The content parts of the message @@ -861,6 +909,18 @@ class SleepStep(BaseModel): """ +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + """ + A valid jinja template. + """ + + class SwitchStep(BaseModel): model_config = ConfigDict( populate_by_name=True, diff --git a/agents-api/agents_api/autogen/Tools.py b/agents-api/agents_api/autogen/Tools.py index a48e2255e..cb548c0e3 100644 --- a/agents-api/agents_api/autogen/Tools.py +++ b/agents-api/agents_api/autogen/Tools.py @@ -12,6 +12,7 @@ BaseModel, ConfigDict, Field, + RootModel, StrictBool, ) @@ -198,6 +199,8 @@ class BaseIntegrationDef(BaseModel): "email", "remote_browser", "llama_parse", + "ffmpeg", + "cloudinary", ] """ The provider of the integration @@ -235,6 +238,8 @@ class BaseIntegrationDefUpdate(BaseModel): "email", "remote_browser", "llama_parse", + "ffmpeg", + "cloudinary", ] | None ) = None @@ -652,6 +657,154 @@ class ChosenTextEditor20241022(BaseModel): """ +class CloudinaryEditArguments(BaseModel): + """ + Arguments for Cloudinary media edit + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + public_id: str + """ + The file Public ID in Cloudinary + """ + transformation: list[dict[str, Any]] + """ + The transformation to apply to the file + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + + +class CloudinaryEditArgumentsUpdate(BaseModel): + """ + Arguments for Cloudinary media edit + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + public_id: str | None = None + """ + The file Public ID in Cloudinary + """ + transformation: list[dict[str, Any]] | None = None + """ + The transformation to apply to the file + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + + +class CloudinarySetup(BaseModel): + """ + Setup parameters for Cloudinary integration + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cloudinary_api_key: str + """ + The API key for Cloudinary + """ + cloudinary_api_secret: str + """ + The API secret for Cloudinary + """ + cloudinary_cloud_name: str + """ + The Cloud name for Cloudinary + """ + params: dict[str, Any] | None = None + """ + Additional parameters for the Cloudinary API + """ + + +class CloudinarySetupUpdate(BaseModel): + """ + Setup parameters for Cloudinary integration + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cloudinary_api_key: str | None = None + """ + The API key for Cloudinary + """ + cloudinary_api_secret: str | None = None + """ + The API secret for Cloudinary + """ + cloudinary_cloud_name: str | None = None + """ + The Cloud name for Cloudinary + """ + params: dict[str, Any] | None = None + """ + Additional parameters for the Cloudinary API + """ + + +class CloudinaryUploadArguments(BaseModel): + """ + Arguments for Cloudinary media upload + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + file: str + """ + The URL of the file upload + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + public_id: str | None = None + """ + Optional public ID for the uploaded file + """ + upload_params: dict[str, Any] | None = None + """ + Optional upload parameters + """ + + +class CloudinaryUploadArgumentsUpdate(BaseModel): + """ + Arguments for Cloudinary media upload + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + file: str | None = None + """ + The URL of the file upload + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + public_id: str | None = None + """ + Optional public ID for the uploaded file + """ + upload_params: dict[str, Any] | None = None + """ + Optional upload parameters + """ + + class Computer20241022Def(BaseModel): """ Anthropic new tools @@ -731,6 +884,9 @@ class CreateToolRequest(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDef | RemoteBrowserIntegrationDef | LlamaParseIntegrationDef + | FfmpegIntegrationDef + | CloudinaryUploadIntegrationDef + | CloudinaryEditIntegrationDef | None ) = None """ @@ -922,6 +1078,94 @@ class EmailSetupUpdate(BaseModel): """ +class FfmpegIntegrationDef(BaseIntegrationDef): + """ + Ffmpeg integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["ffmpeg"] = "ffmpeg" + """ + The provider must be "ffmpeg" + """ + method: str | None = None + """ + The specific method of the integration to call + """ + setup: Any | None = None + """ + The setup parameters for Ffmpeg + """ + arguments: FfmpegSearchArguments | None = None + """ + The arguments for Ffmpeg Search + """ + + +class FfmpegIntegrationDefUpdate(BaseIntegrationDefUpdate): + """ + Ffmpeg integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["ffmpeg"] = "ffmpeg" + """ + The provider must be "ffmpeg" + """ + method: str | None = None + """ + The specific method of the integration to call + """ + setup: Any | None = None + """ + The setup parameters for Ffmpeg + """ + arguments: FfmpegSearchArgumentsUpdate | None = None + """ + The arguments for Ffmpeg Search + """ + + +class FfmpegSearchArguments(BaseModel): + """ + Arguments for Ffmpeg CMD + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cmd: str + """ + The bash command string + """ + file: str | None = None + """ + The base64 string of the file + """ + + +class FfmpegSearchArgumentsUpdate(BaseModel): + """ + Arguments for Ffmpeg CMD + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cmd: str | None = None + """ + The bash command string + """ + file: str | None = None + """ + The base64 string of the file + """ + + class FunctionCallOption(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -1165,6 +1409,9 @@ class PatchToolRequest(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDefUpdate | RemoteBrowserIntegrationDefUpdate | LlamaParseIntegrationDefUpdate + | FfmpegIntegrationDefUpdate + | CloudinaryUploadIntegrationDefUpdate + | CloudinaryEditIntegrationDefUpdate | None ) = None """ @@ -1589,6 +1836,9 @@ class Tool(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDef | RemoteBrowserIntegrationDef | LlamaParseIntegrationDef + | FfmpegIntegrationDef + | CloudinaryUploadIntegrationDef + | CloudinaryEditIntegrationDef | None ) = None """ @@ -1679,6 +1929,9 @@ class UpdateToolRequest(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDef | RemoteBrowserIntegrationDef | LlamaParseIntegrationDef + | FfmpegIntegrationDef + | CloudinaryUploadIntegrationDef + | CloudinaryEditIntegrationDef | None ) = None """ @@ -1954,6 +2207,32 @@ class BaseBrowserbaseIntegrationDefUpdate(BaseIntegrationDefUpdate): arguments: Any | None = None +class BaseCloudinaryIntegrationDef(BaseIntegrationDef): + """ + Base Cloudinary integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["cloudinary"] = "cloudinary" + setup: CloudinarySetup | None = None + method: Literal["media_upload", "media_edit"] | None = None + + +class BaseCloudinaryIntegrationDefUpdate(BaseIntegrationDefUpdate): + """ + Base Cloudinary integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["cloudinary"] = "cloudinary" + setup: CloudinarySetupUpdate | None = None + method: Literal["media_upload", "media_edit"] | None = None + + class BrowserbaseCompleteSessionIntegrationDef(BaseBrowserbaseIntegrationDef): """ browserbase complete session integration definition @@ -2192,3 +2471,51 @@ class BrowserbaseListSessionsIntegrationDefUpdate(BaseBrowserbaseIntegrationDefU """ The arguments for the method """ + + +class CloudinaryEditIntegrationDef(BaseCloudinaryIntegrationDef): + """ + Cloudinary edit integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_edit"] = "media_edit" + arguments: CloudinaryEditArguments | None = None + + +class CloudinaryEditIntegrationDefUpdate(BaseCloudinaryIntegrationDefUpdate): + """ + Cloudinary edit integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_edit"] = "media_edit" + arguments: CloudinaryEditArgumentsUpdate | None = None + + +class CloudinaryUploadIntegrationDef(BaseCloudinaryIntegrationDef): + """ + Cloudinary upload integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_upload"] = "media_upload" + arguments: CloudinaryUploadArguments | None = None + + +class CloudinaryUploadIntegrationDefUpdate(BaseCloudinaryIntegrationDefUpdate): + """ + Cloudinary upload integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_upload"] = "media_upload" + arguments: CloudinaryUploadArgumentsUpdate | None = None diff --git a/agents-api/agents_api/clients/temporal.py b/agents-api/agents_api/clients/temporal.py index 5737bd97e..316e0d256 100644 --- a/agents-api/agents_api/clients/temporal.py +++ b/agents-api/agents_api/clients/temporal.py @@ -10,8 +10,10 @@ ) from ..autogen.openapi_model import TransitionTarget +from ..common.protocol.remote import RemoteList from ..common.protocol.tasks import ExecutionInput from ..common.retry_policies import DEFAULT_RETRY_POLICY +from ..common.storage_handler import store_in_blob_store_if_large from ..env import ( temporal_client_cert, temporal_namespace, @@ -58,7 +60,9 @@ async def run_task_execution_workflow( previous_inputs: list[dict] = previous_inputs or [] client = client or (await get_client()) + execution_id = execution_input.execution.id execution_id_key = SearchAttributeKey.for_keyword("CustomStringField") + execution_input.arguments = store_in_blob_store_if_large(execution_input.arguments) return await client.start_workflow( TaskExecutionWorkflow.run, @@ -69,9 +73,7 @@ async def run_task_execution_workflow( retry_policy=DEFAULT_RETRY_POLICY, search_attributes=TypedSearchAttributes( [ - SearchAttributePair( - execution_id_key, str(execution_input.execution.id) - ), + SearchAttributePair(execution_id_key, str(execution_id)), ] ), ) diff --git a/agents-api/agents_api/common/protocol/tasks.py b/agents-api/agents_api/common/protocol/tasks.py index 6a379b6a4..2d5dd8946 100644 --- a/agents-api/agents_api/common/protocol/tasks.py +++ b/agents-api/agents_api/common/protocol/tasks.py @@ -75,7 +75,14 @@ valid_transitions: dict[TransitionType, list[TransitionType]] = { # Start state "init": ["wait", "error", "step", "cancelled", "init_branch", "finish"], - "init_branch": ["wait", "error", "step", "cancelled", "finish_branch"], + "init_branch": [ + "wait", + "error", + "step", + "cancelled", + "init_branch", + "finish_branch", + ], # End states "finish": [], "error": [], @@ -137,7 +144,7 @@ class ExecutionInput(BaseModel): task: TaskSpecDef agent: Agent agent_tools: list[Tool | CreateToolRequest] - arguments: dict[str, Any] + arguments: dict[str, Any] | RemoteObject # Not used at the moment user: User | None = None diff --git a/agents-api/agents_api/workflows/task_execution/helpers.py b/agents-api/agents_api/workflows/task_execution/helpers.py index 7fbc2c008..6d47b3a1f 100644 --- a/agents-api/agents_api/workflows/task_execution/helpers.py +++ b/agents-api/agents_api/workflows/task_execution/helpers.py @@ -66,7 +66,10 @@ async def execute_switch_branch( case_wf_name = f"`{context.cursor.workflow}`[{context.cursor.step}].case" case_task = execution_input.task.model_copy() - case_task.workflows = [Workflow(name=case_wf_name, steps=[chosen_branch.then])] + case_task.workflows = [ + Workflow(name=case_wf_name, steps=[chosen_branch.then]), + *case_task.workflows, + ] case_execution_input = execution_input.model_copy() case_execution_input.task = case_task @@ -99,7 +102,10 @@ async def execute_if_else_branch( if_else_wf_name += ".then" if condition else ".else" if_else_task = execution_input.task.model_copy() - if_else_task.workflows = [Workflow(name=if_else_wf_name, steps=[chosen_branch])] + if_else_task.workflows = [ + Workflow(name=if_else_wf_name, steps=[chosen_branch]), + *if_else_task.workflows, + ] if_else_execution_input = execution_input.model_copy() if_else_execution_input.task = if_else_task @@ -132,7 +138,10 @@ async def execute_foreach_step( f"`{context.cursor.workflow}`[{context.cursor.step}].foreach[{i}]" ) foreach_task = execution_input.task.model_copy() - foreach_task.workflows = [Workflow(name=foreach_wf_name, steps=[do_step])] + foreach_task.workflows = [ + Workflow(name=foreach_wf_name, steps=[do_step]), + *foreach_task.workflows, + ] foreach_execution_input = execution_input.model_copy() foreach_execution_input.task = foreach_task @@ -170,7 +179,10 @@ async def execute_map_reduce_step( f"`{context.cursor.workflow}`[{context.cursor.step}].mapreduce[{i}]" ) map_reduce_task = execution_input.task.model_copy() - map_reduce_task.workflows = [Workflow(name=workflow_name, steps=[map_defn])] + map_reduce_task.workflows = [ + Workflow(name=workflow_name, steps=[map_defn]), + *map_reduce_task.workflows, + ] map_reduce_execution_input = execution_input.model_copy() map_reduce_execution_input.task = map_reduce_task @@ -234,7 +246,10 @@ async def execute_map_reduce_step_parallel( # Note: Added PAR: prefix to easily identify parallel batches in logs workflow_name = f"PAR:`{context.cursor.workflow}`[{context.cursor.step}].mapreduce[{i}][{j}]" map_reduce_task = execution_input.task.model_copy() - map_reduce_task.workflows = [Workflow(name=workflow_name, steps=[map_defn])] + map_reduce_task.workflows = [ + Workflow(name=workflow_name, steps=[map_defn]), + *map_reduce_task.workflows, + ] map_reduce_execution_input = execution_input.model_copy() map_reduce_execution_input.task = map_reduce_task diff --git a/agents-api/gunicorn_conf.py b/agents-api/gunicorn_conf.py index f50b39c3d..d7c197e6d 100644 --- a/agents-api/gunicorn_conf.py +++ b/agents-api/gunicorn_conf.py @@ -1,7 +1,11 @@ import multiprocessing +import os + +TESTING = os.getenv("TESTING", "false").lower() == "true" +DEBUG = os.getenv("DEBUG", "false").lower() == "true" # Gunicorn config variables -workers = multiprocessing.cpu_count() - 1 +workers = multiprocessing.cpu_count() - 1 if not (TESTING or DEBUG) else 1 worker_class = "uvicorn.workers.UvicornWorker" bind = "0.0.0.0:8080" keepalive = 120 diff --git a/integrations-service/Dockerfile b/integrations-service/Dockerfile index 25c0abb12..d9a88d876 100644 --- a/integrations-service/Dockerfile +++ b/integrations-service/Dockerfile @@ -5,6 +5,19 @@ FROM python:3.12-slim WORKDIR /app +# Install system dependencies and FFmpeg with all required libraries +RUN apt-get update && apt-get install -y \ + ffmpeg \ + libavcodec-extra \ + libavformat-dev \ + libavutil-dev \ + libswscale-dev \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Verify FFmpeg installation +RUN ffmpeg -version + # Install Poetry RUN pip install poetry @@ -18,5 +31,9 @@ RUN poetry config virtualenvs.create false \ # Copy project COPY . ./ -# Run the application +# Set proper signal handling +ENV PYTHONUNBUFFERED=1 +ENV GUNICORN_CMD_ARGS="--capture-output --enable-stdio-inheritance" + +# Run the application with proper signal handling ENTRYPOINT ["gunicorn", "integrations.web:app", "-c", "gunicorn_conf.py"] \ No newline at end of file diff --git a/integrations-service/gunicorn_conf.py b/integrations-service/gunicorn_conf.py index 30f5a2e4f..100b816dc 100644 --- a/integrations-service/gunicorn_conf.py +++ b/integrations-service/gunicorn_conf.py @@ -1,10 +1,38 @@ import multiprocessing +import os + +TESTING = os.getenv("TESTING", "false").lower() == "true" +DEBUG = os.getenv("DEBUG", "false").lower() == "true" # Gunicorn config variables -workers = multiprocessing.cpu_count() - 1 +workers = multiprocessing.cpu_count() - 1 if not (TESTING or DEBUG) else 1 worker_class = "uvicorn.workers.UvicornWorker" bind = "0.0.0.0:8000" keepalive = 120 timeout = 120 errorlog = "-" accesslog = "-" +loglevel = "info" +graceful_timeout = 30 +max_requests = 1000 +max_requests_jitter = 50 +preload_app = False + + +def when_ready(server): + """Run when server is ready to handle requests.""" + # Ensure proper permissions for any required directories + for directory in ["logs", "run"]: + path = os.path.join(os.getcwd(), directory) + if not os.path.exists(path): + os.makedirs(path, mode=0o755) + + +def on_starting(server): + """Run when server starts.""" + server.log.setup(server.app.cfg) + + +def worker_exit(server, worker): + """Clean up on worker exit.""" + server.log.info(f"Worker {worker.pid} exiting gracefully") diff --git a/integrations-service/integrations/autogen/Chat.py b/integrations-service/integrations/autogen/Chat.py index d013ae8b7..042f9164d 100644 --- a/integrations-service/integrations/autogen/Chat.py +++ b/integrations-service/integrations/autogen/Chat.py @@ -165,7 +165,107 @@ class Content(BaseModel): """ +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + +class ContentItemModel3(Content): + pass + + +class ContentItemModel4(ContentItemModel): + pass + + +class ContentItemModel5(Content): + pass + + +class ContentItemModel6(ContentItemModel): + pass + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] | list[ContentItemModel] + + +class ContentModel1(Content): + pass + + +class ContentModel2(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + + +class ContentModel3(Content): + pass + + +class ContentModel4(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel3] | list[ContentItemModel4] + + +class ContentModel5(Content): + pass + + +class ContentModel6(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel5] | list[ContentItemModel6] + + +class ContentModel7(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -193,7 +293,8 @@ class Delta(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel1 | ContentModel7 | ContentModel2] | None, + Field(...), ] = None """ The content parts of the message @@ -258,7 +359,8 @@ class Message(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[Content | ContentModel7 | ContentModel] | None, + Field(...), ] = None """ The content parts of the message @@ -305,7 +407,8 @@ class MessageModel(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - str | list[str] | list[Content | ContentModel] | None, Field(...) + str | list[str] | list[ContentModel3 | ContentModel7 | ContentModel4] | None, + Field(...), ] = None """ The content parts of the message @@ -405,6 +508,15 @@ class SingleChatOutput(BaseChatOutput): message: MessageModel +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + + class TokenLogProb(BaseTokenLogProb): model_config = ConfigDict( populate_by_name=True, diff --git a/integrations-service/integrations/autogen/Entries.py b/integrations-service/integrations/autogen/Entries.py index f001fc880..de37e77d8 100644 --- a/integrations-service/integrations/autogen/Entries.py +++ b/integrations-service/integrations/autogen/Entries.py @@ -28,7 +28,7 @@ class BaseEntry(BaseModel): """ name: str | None = None content: ( - list[Content | ContentModel] + list[Content | ContentModel3 | ContentModel] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -37,7 +37,7 @@ class BaseEntry(BaseModel): | str | ToolResponse | list[ - list[Content | ContentModel] + list[ContentModel1 | ContentModel3 | ContentModel2] | Tool | ChosenFunctionCall | ChosenComputer20241022 @@ -95,7 +95,57 @@ class Content(BaseModel): """ +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] | list[ContentItemModel] + + +class ContentModel1(Content): + pass + + +class ContentModel2(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + + +class ContentModel3(BaseModel): model_config = ConfigDict( populate_by_name=True, ) @@ -168,3 +218,12 @@ class Relation(BaseModel): head: UUID relation: str tail: UUID + + +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str diff --git a/integrations-service/integrations/autogen/Tasks.py b/integrations-service/integrations/autogen/Tasks.py index e62e6d3c3..8e98caaab 100644 --- a/integrations-service/integrations/autogen/Tasks.py +++ b/integrations-service/integrations/autogen/Tasks.py @@ -9,6 +9,7 @@ from pydantic import AwareDatetime, BaseModel, ConfigDict, Field, StrictBool from .Chat import ChatSettings +from .Common import JinjaTemplate from .Tools import ( ChosenBash20241022, ChosenComputer20241022, @@ -85,6 +86,26 @@ class Content(BaseModel): """ +class ContentItem(Content): + pass + + +class ContentItemModel(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["image"] = "image" + source: Source + + +class ContentItemModel1(Content): + pass + + +class ContentItemModel2(ContentItemModel): + pass + + class ContentModel(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -99,14 +120,40 @@ class ContentModel(BaseModel): """ -class ContentModel1(Content): +class ContentModel1(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItem] | list[ContentItemModel] + + +class ContentModel2(Content): pass -class ContentModel2(ContentModel): +class ContentModel3(ContentModel): pass +class ContentModel4(BaseModel): + """ + Anthropic image content part + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + tool_use_id: str + type: Literal["tool_result"] = "tool_result" + content: list[ContentItemModel1] | list[ContentItemModel2] + + class CreateTaskRequest(BaseModel): """ Payload for creating a task @@ -655,7 +702,8 @@ class PromptItem(BaseModel): """ tool_call_id: str | None = None content: Annotated[ - list[str] | list[Content | ContentModel] | str | None, Field(...) + list[str] | list[Content | ContentModel | ContentModel1] | str | None, + Field(...), ] """ The content parts of the message @@ -861,6 +909,18 @@ class SleepStep(BaseModel): """ +class Source(BaseModel): + model_config = ConfigDict( + populate_by_name=True, + ) + type: Literal["base64"] = "base64" + media_type: str + data: str + """ + A valid jinja template. + """ + + class SwitchStep(BaseModel): model_config = ConfigDict( populate_by_name=True, diff --git a/integrations-service/integrations/autogen/Tools.py b/integrations-service/integrations/autogen/Tools.py index a48e2255e..cb548c0e3 100644 --- a/integrations-service/integrations/autogen/Tools.py +++ b/integrations-service/integrations/autogen/Tools.py @@ -12,6 +12,7 @@ BaseModel, ConfigDict, Field, + RootModel, StrictBool, ) @@ -198,6 +199,8 @@ class BaseIntegrationDef(BaseModel): "email", "remote_browser", "llama_parse", + "ffmpeg", + "cloudinary", ] """ The provider of the integration @@ -235,6 +238,8 @@ class BaseIntegrationDefUpdate(BaseModel): "email", "remote_browser", "llama_parse", + "ffmpeg", + "cloudinary", ] | None ) = None @@ -652,6 +657,154 @@ class ChosenTextEditor20241022(BaseModel): """ +class CloudinaryEditArguments(BaseModel): + """ + Arguments for Cloudinary media edit + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + public_id: str + """ + The file Public ID in Cloudinary + """ + transformation: list[dict[str, Any]] + """ + The transformation to apply to the file + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + + +class CloudinaryEditArgumentsUpdate(BaseModel): + """ + Arguments for Cloudinary media edit + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + public_id: str | None = None + """ + The file Public ID in Cloudinary + """ + transformation: list[dict[str, Any]] | None = None + """ + The transformation to apply to the file + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + + +class CloudinarySetup(BaseModel): + """ + Setup parameters for Cloudinary integration + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cloudinary_api_key: str + """ + The API key for Cloudinary + """ + cloudinary_api_secret: str + """ + The API secret for Cloudinary + """ + cloudinary_cloud_name: str + """ + The Cloud name for Cloudinary + """ + params: dict[str, Any] | None = None + """ + Additional parameters for the Cloudinary API + """ + + +class CloudinarySetupUpdate(BaseModel): + """ + Setup parameters for Cloudinary integration + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cloudinary_api_key: str | None = None + """ + The API key for Cloudinary + """ + cloudinary_api_secret: str | None = None + """ + The API secret for Cloudinary + """ + cloudinary_cloud_name: str | None = None + """ + The Cloud name for Cloudinary + """ + params: dict[str, Any] | None = None + """ + Additional parameters for the Cloudinary API + """ + + +class CloudinaryUploadArguments(BaseModel): + """ + Arguments for Cloudinary media upload + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + file: str + """ + The URL of the file upload + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + public_id: str | None = None + """ + Optional public ID for the uploaded file + """ + upload_params: dict[str, Any] | None = None + """ + Optional upload parameters + """ + + +class CloudinaryUploadArgumentsUpdate(BaseModel): + """ + Arguments for Cloudinary media upload + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + file: str | None = None + """ + The URL of the file upload + """ + return_base64: StrictBool = False + """ + Return base64 encoded file + """ + public_id: str | None = None + """ + Optional public ID for the uploaded file + """ + upload_params: dict[str, Any] | None = None + """ + Optional upload parameters + """ + + class Computer20241022Def(BaseModel): """ Anthropic new tools @@ -731,6 +884,9 @@ class CreateToolRequest(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDef | RemoteBrowserIntegrationDef | LlamaParseIntegrationDef + | FfmpegIntegrationDef + | CloudinaryUploadIntegrationDef + | CloudinaryEditIntegrationDef | None ) = None """ @@ -922,6 +1078,94 @@ class EmailSetupUpdate(BaseModel): """ +class FfmpegIntegrationDef(BaseIntegrationDef): + """ + Ffmpeg integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["ffmpeg"] = "ffmpeg" + """ + The provider must be "ffmpeg" + """ + method: str | None = None + """ + The specific method of the integration to call + """ + setup: Any | None = None + """ + The setup parameters for Ffmpeg + """ + arguments: FfmpegSearchArguments | None = None + """ + The arguments for Ffmpeg Search + """ + + +class FfmpegIntegrationDefUpdate(BaseIntegrationDefUpdate): + """ + Ffmpeg integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["ffmpeg"] = "ffmpeg" + """ + The provider must be "ffmpeg" + """ + method: str | None = None + """ + The specific method of the integration to call + """ + setup: Any | None = None + """ + The setup parameters for Ffmpeg + """ + arguments: FfmpegSearchArgumentsUpdate | None = None + """ + The arguments for Ffmpeg Search + """ + + +class FfmpegSearchArguments(BaseModel): + """ + Arguments for Ffmpeg CMD + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cmd: str + """ + The bash command string + """ + file: str | None = None + """ + The base64 string of the file + """ + + +class FfmpegSearchArgumentsUpdate(BaseModel): + """ + Arguments for Ffmpeg CMD + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + cmd: str | None = None + """ + The bash command string + """ + file: str | None = None + """ + The base64 string of the file + """ + + class FunctionCallOption(BaseModel): model_config = ConfigDict( populate_by_name=True, @@ -1165,6 +1409,9 @@ class PatchToolRequest(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDefUpdate | RemoteBrowserIntegrationDefUpdate | LlamaParseIntegrationDefUpdate + | FfmpegIntegrationDefUpdate + | CloudinaryUploadIntegrationDefUpdate + | CloudinaryEditIntegrationDefUpdate | None ) = None """ @@ -1589,6 +1836,9 @@ class Tool(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDef | RemoteBrowserIntegrationDef | LlamaParseIntegrationDef + | FfmpegIntegrationDef + | CloudinaryUploadIntegrationDef + | CloudinaryEditIntegrationDef | None ) = None """ @@ -1679,6 +1929,9 @@ class UpdateToolRequest(BaseModel): | BrowserbaseGetSessionConnectUrlIntegrationDef | RemoteBrowserIntegrationDef | LlamaParseIntegrationDef + | FfmpegIntegrationDef + | CloudinaryUploadIntegrationDef + | CloudinaryEditIntegrationDef | None ) = None """ @@ -1954,6 +2207,32 @@ class BaseBrowserbaseIntegrationDefUpdate(BaseIntegrationDefUpdate): arguments: Any | None = None +class BaseCloudinaryIntegrationDef(BaseIntegrationDef): + """ + Base Cloudinary integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["cloudinary"] = "cloudinary" + setup: CloudinarySetup | None = None + method: Literal["media_upload", "media_edit"] | None = None + + +class BaseCloudinaryIntegrationDefUpdate(BaseIntegrationDefUpdate): + """ + Base Cloudinary integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + provider: Literal["cloudinary"] = "cloudinary" + setup: CloudinarySetupUpdate | None = None + method: Literal["media_upload", "media_edit"] | None = None + + class BrowserbaseCompleteSessionIntegrationDef(BaseBrowserbaseIntegrationDef): """ browserbase complete session integration definition @@ -2192,3 +2471,51 @@ class BrowserbaseListSessionsIntegrationDefUpdate(BaseBrowserbaseIntegrationDefU """ The arguments for the method """ + + +class CloudinaryEditIntegrationDef(BaseCloudinaryIntegrationDef): + """ + Cloudinary edit integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_edit"] = "media_edit" + arguments: CloudinaryEditArguments | None = None + + +class CloudinaryEditIntegrationDefUpdate(BaseCloudinaryIntegrationDefUpdate): + """ + Cloudinary edit integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_edit"] = "media_edit" + arguments: CloudinaryEditArgumentsUpdate | None = None + + +class CloudinaryUploadIntegrationDef(BaseCloudinaryIntegrationDef): + """ + Cloudinary upload integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_upload"] = "media_upload" + arguments: CloudinaryUploadArguments | None = None + + +class CloudinaryUploadIntegrationDefUpdate(BaseCloudinaryIntegrationDefUpdate): + """ + Cloudinary upload integration definition + """ + + model_config = ConfigDict( + populate_by_name=True, + ) + method: Literal["media_upload"] = "media_upload" + arguments: CloudinaryUploadArgumentsUpdate | None = None diff --git a/integrations-service/integrations/models/__init__.py b/integrations-service/integrations/models/__init__.py index fdfc24951..2879a6fd3 100644 --- a/integrations-service/integrations/models/__init__.py +++ b/integrations-service/integrations/models/__init__.py @@ -33,7 +33,14 @@ from .browserbase import ( BrowserbaseListSessionsOutput as BrowserbaseListSessionsOutput, ) +from .cloudinary import ( + CloudinaryEditOutput as CloudinaryEditOutput, +) +from .cloudinary import ( + CloudinaryUploadOutput as CloudinaryUploadOutput, +) from .email import EmailOutput as EmailOutput +from .ffmpeg import FfmpegSearchOutput as FfmpegSearchOutput from .llama_parse import LlamaParseFetchOutput as LlamaParseFetchOutput from .remote_browser import RemoteBrowserOutput as RemoteBrowserOutput from .spider import SpiderFetchOutput as SpiderFetchOutput diff --git a/integrations-service/integrations/models/cloudinary.py b/integrations-service/integrations/models/cloudinary.py new file mode 100644 index 000000000..4ad59f4bf --- /dev/null +++ b/integrations-service/integrations/models/cloudinary.py @@ -0,0 +1,23 @@ +from typing import Optional + +from pydantic import Field + +from .base_models import BaseOutput + + +class CloudinaryUploadOutput(BaseOutput): + url: str = Field(..., description="The URL of the uploaded file") + public_id: str = Field(..., description="The public ID of the uploaded file") + base64: Optional[str] = Field( + None, description="The base64 encoded file if return_base64 is true" + ) + meta_data: Optional[dict] = Field( + None, description="Additional metadata from the upload response" + ) + + +class CloudinaryEditOutput(BaseOutput): + transformed_url: str = Field(..., description="The transformed URL") + base64: Optional[str] = Field( + None, description="The base64 encoded file if return_base64 is true" + ) diff --git a/integrations-service/integrations/models/execution.py b/integrations-service/integrations/models/execution.py index 40ba3fdba..693d9fa2d 100644 --- a/integrations-service/integrations/models/execution.py +++ b/integrations-service/integrations/models/execution.py @@ -16,8 +16,12 @@ BrowserbaseGetSessionLiveUrlsArguments, BrowserbaseListSessionsArguments, BrowserbaseSetup, + CloudinaryEditArguments, + CloudinarySetup, + CloudinaryUploadArguments, EmailArguments, EmailSetup, + FfmpegSearchArguments, LlamaParseFetchArguments, LlamaParseSetup, RemoteBrowserArguments, @@ -39,7 +43,9 @@ BrowserbaseGetSessionOutput, BrowserbaseListSessionsOutput, ) +from .cloudinary import CloudinaryEditOutput, CloudinaryUploadOutput from .email import EmailOutput +from .ffmpeg import FfmpegSearchOutput from .llama_parse import LlamaParseFetchOutput from .remote_browser import RemoteBrowserOutput from .spider import SpiderFetchOutput @@ -54,6 +60,7 @@ class ExecutionError(BaseModel): """ +# Setup configurations ExecutionSetup = Union[ EmailSetup, SpiderSetup, @@ -62,8 +69,10 @@ class ExecutionError(BaseModel): BrowserbaseSetup, RemoteBrowserSetup, LlamaParseSetup, + CloudinarySetup, ] +# Argument configurations ExecutionArguments = Union[ SpiderFetchArguments, WeatherGetArguments, @@ -80,6 +89,9 @@ class ExecutionError(BaseModel): BrowserbaseListSessionsArguments, RemoteBrowserArguments, LlamaParseFetchArguments, + FfmpegSearchArguments, + CloudinaryUploadArguments, + CloudinaryEditArguments, ] ExecutionResponse = Union[ @@ -98,6 +110,9 @@ class ExecutionError(BaseModel): BrowserbaseListSessionsOutput, RemoteBrowserOutput, LlamaParseFetchOutput, + FfmpegSearchOutput, + CloudinaryEditOutput, + CloudinaryUploadOutput, ExecutionError, ] diff --git a/integrations-service/integrations/models/ffmpeg.py b/integrations-service/integrations/models/ffmpeg.py new file mode 100644 index 000000000..ad773228c --- /dev/null +++ b/integrations-service/integrations/models/ffmpeg.py @@ -0,0 +1,15 @@ +from typing import Optional + +from pydantic import Field + +from .base_models import BaseOutput + + +class FfmpegSearchOutput(BaseOutput): + fileoutput: Optional[str] = Field( + None, description="The output file from the Ffmpeg command" + ) + result: bool = Field(..., description="Whether the Ffmpeg command was successful") + mime_type: Optional[str] = Field( + None, description="The MIME type of the output file" + ) diff --git a/integrations-service/integrations/providers.py b/integrations-service/integrations/providers.py index b467641bb..0589e4449 100644 --- a/integrations-service/integrations/providers.py +++ b/integrations-service/integrations/providers.py @@ -1,5 +1,7 @@ from .autogen.Tools import ( + # Arguments imports BraveSearchArguments, + # Setup imports BraveSearchSetup, BrowserbaseCompleteSessionArguments, BrowserbaseCreateSessionArguments, @@ -7,11 +9,14 @@ BrowserbaseGetSessionArguments, BrowserbaseGetSessionConnectUrlArguments, BrowserbaseGetSessionLiveUrlsArguments, - # WikipediaSearchSetup, BrowserbaseListSessionsArguments, BrowserbaseSetup, + CloudinaryEditArguments, + CloudinarySetup, + CloudinaryUploadArguments, EmailArguments, EmailSetup, + FfmpegSearchArguments, LlamaParseFetchArguments, LlamaParseSetup, RemoteBrowserArguments, @@ -33,7 +38,10 @@ BrowserbaseGetSessionLiveUrlsOutput, BrowserbaseGetSessionOutput, BrowserbaseListSessionsOutput, + CloudinaryEditOutput, + CloudinaryUploadOutput, EmailOutput, + FfmpegSearchOutput, LlamaParseFetchOutput, ProviderInfo, RemoteBrowserOutput, @@ -227,6 +235,50 @@ ), ) +ffmpeg = BaseProvider( + provider="ffmpeg", + setup=None, + methods=[ + BaseProviderMethod( + method="bash_cmd", + description="Run FFmpeg bash command", + arguments=FfmpegSearchArguments, + output=FfmpegSearchOutput, + ), + ], + info=ProviderInfo( + url="https://ffmpeg.org/", + docs="https://ffmpeg.org/documentation.html", + icon="https://upload.wikimedia.org/wikipedia/commons/5/5f/FFmpeg_Logo_new.svg", + friendly_name="Ffmpeg", + ), +) + +cloudinary = BaseProvider( + provider="cloudinary", + setup=CloudinarySetup, + methods=[ + BaseProviderMethod( + method="media_edit", + description="Edit media in Cloudinary", + arguments=CloudinaryEditArguments, + output=CloudinaryEditOutput, + ), + BaseProviderMethod( + method="media_upload", + description="Upload media to Cloudinary", + arguments=CloudinaryUploadArguments, + output=CloudinaryUploadOutput, + ), + ], + info=ProviderInfo( + url="https://cloudinary.com/", + docs="https://cloudinary.com/documentation/python_quickstart", + icon="https://cloudinary.com/favicon.ico", + friendly_name="Cloudinary", + ), +) + available_providers: dict[str, BaseProvider] = { "wikipedia": wikipedia, "weather": weather, @@ -236,4 +288,6 @@ "browserbase": browserbase, "remote_browser": remote_browser, "llama_parse": llama_parse, + "ffmpeg": ffmpeg, + "cloudinary": cloudinary, } diff --git a/integrations-service/integrations/utils/integrations/cloudinary.py b/integrations-service/integrations/utils/integrations/cloudinary.py new file mode 100644 index 000000000..5562ab968 --- /dev/null +++ b/integrations-service/integrations/utils/integrations/cloudinary.py @@ -0,0 +1,131 @@ +import base64 + +import aiohttp +import cloudinary +import cloudinary.uploader +from beartype import beartype +from tenacity import retry, stop_after_attempt, wait_exponential + +from ...autogen.Tools import ( + CloudinaryEditArguments, + CloudinarySetup, + CloudinaryUploadArguments, +) +from ...models.cloudinary import CloudinaryEditOutput, CloudinaryUploadOutput + + +@beartype +@retry( + wait=wait_exponential(multiplier=1, min=4, max=10), + reraise=True, + stop=stop_after_attempt(4), +) +async def media_upload( + setup: CloudinarySetup, arguments: CloudinaryUploadArguments +) -> CloudinaryUploadOutput: + """ + Upload media to Cloudinary. + """ + assert isinstance(setup, CloudinarySetup), "Invalid setup" + assert isinstance(arguments, CloudinaryUploadArguments), "Invalid arguments" + + try: + # Configure Cloudinary with credentials + cloudinary.config( + cloud_name=setup.cloudinary_cloud_name, + api_key=setup.cloudinary_api_key, + api_secret=setup.cloudinary_api_secret, + **(setup.params or {}), + ) + + # Upload the file + upload_params = arguments.upload_params or {} + if arguments.public_id: + upload_params["public_id"] = arguments.public_id + + result = cloudinary.uploader.upload(arguments.file, **upload_params) + + meta_data = { + key: value + for key, value in result.items() + if key not in ["secure_url", "public_id"] + } + + if arguments.return_base64: + async with aiohttp.ClientSession() as session: + async with session.get(result["secure_url"]) as response: + if response.status == 200: + content = await response.read() + base64_encoded = base64.b64encode(content).decode("utf-8") + result["base64"] = base64_encoded + else: + raise RuntimeError( + f"Failed to download file from URL: {result['secure_url']}" + ) + return CloudinaryUploadOutput( + url=result["secure_url"], + public_id=result["public_id"], + meta_data=meta_data, + base64=result["base64"] if arguments.return_base64 else None, + ) + + except cloudinary.exceptions.Error as e: + raise RuntimeError(f"Cloudinary error occurred: {e}") + except Exception as e: + raise RuntimeError(f"An unexpected error occurred: {e}") + + +@beartype +@retry( + wait=wait_exponential(multiplier=1, min=4, max=10), + reraise=True, + stop=stop_after_attempt(4), +) +async def media_edit( + setup: CloudinarySetup, arguments: CloudinaryEditArguments +) -> CloudinaryEditOutput: + """ + Edit media in Cloudinary. + """ + assert isinstance(setup, CloudinarySetup), "Invalid setup" + assert isinstance(arguments, CloudinaryEditArguments), "Invalid arguments" + + try: + # Configure Cloudinary with credentials + cloudinary.config( + cloud_name=setup.cloudinary_cloud_name, + api_key=setup.cloudinary_api_key, + api_secret=setup.cloudinary_api_secret, + **(setup.params or {}), + ) + + # Generate transformed URL + transformed_url = cloudinary.utils.cloudinary_url( + arguments.public_id, transformation=arguments.transformation + ) + if not transformed_url or not transformed_url[0]: + return CloudinaryEditOutput( + transformed_url="The transformation failed", + base64=None, + ) + if arguments.return_base64: + async with aiohttp.ClientSession() as session: + async with session.get(transformed_url[0]) as response: + if response.status == 200: + content = await response.read() + base64_encoded = base64.b64encode(content).decode("utf-8") + transformed_url_base64 = base64_encoded + else: + raise RuntimeError( + f"Failed to download file from URL: {transformed_url[0]}" + ) + + return CloudinaryEditOutput( + transformed_url=transformed_url[0], + base64=transformed_url_base64 if arguments.return_base64 else None, + ) + + except cloudinary.exceptions.Error as e: + raise RuntimeError(f"Cloudinary error occurred: {e}") + except Exception as e: + raise RuntimeError(f"An unexpected error occurred: {e}") diff --git a/integrations-service/integrations/utils/integrations/ffmpeg.py b/integrations-service/integrations/utils/integrations/ffmpeg.py new file mode 100644 index 000000000..3921e9736 --- /dev/null +++ b/integrations-service/integrations/utils/integrations/ffmpeg.py @@ -0,0 +1,145 @@ +import asyncio +import base64 +import os +import shutil +import tempfile +from functools import lru_cache +from typing import Tuple + +from beartype import beartype +from tenacity import retry, stop_after_attempt, wait_exponential + +from ...autogen.Tools import FfmpegSearchArguments +from ...models import FfmpegSearchOutput + + +# Cache for format validation +@lru_cache(maxsize=128) +def _sync_validate_format(binary_prefix: bytes) -> Tuple[bool, str]: + """Cached synchronous implementation of format validation""" + signatures = { + # Video formats + b"\x66\x74\x79\x70\x69\x73\x6f\x6d": "video/mp4", # MP4 + b"\x66\x74\x79\x70\x4d\x53\x4e\x56": "video/mp4", # MP4 + b"\x00\x00\x00\x1c\x66\x74\x79\x70": "video/mp4", # MP4 + b"\x1a\x45\xdf\xa3": "video/webm", # WebM + b"\x00\x00\x01\x00": "video/avi", # AVI + b"\x30\x26\xb2\x75": "video/wmv", # WMV + # Audio formats + b"\x49\x44\x33": "audio/mpeg", # MP3 + b"\xff\xfb": "audio/mpeg", # MP3 + b"\x52\x49\x46\x46": "audio/wav", # WAV + b"\x4f\x67\x67\x53": "audio/ogg", # OGG + b"\x66\x4c\x61\x43": "audio/flac", # FLAC + # Image formats + b"\xff\xd8\xff": "image/jpeg", # JPEG + b"\x89\x50\x4e\x47": "image/png", # PNG + b"\x47\x49\x46": "image/gif", # GIF + b"\x49\x49\x2a\x00": "image/tiff", # TIFF + b"\x42\x4d": "image/bmp", # BMP + } + + for signature, mime_type in signatures.items(): + if binary_prefix.startswith(signature): + return True, mime_type + + return False, "application/octet-stream" + + +async def validate_format(binary_data: bytes) -> Tuple[bool, str]: + """Validate file format using file signatures""" + # Only check first 16 bytes for efficiency + binary_prefix = binary_data[:16] + return _sync_validate_format(binary_prefix) + + +@beartype +@retry( + wait=wait_exponential(multiplier=1, min=4, max=10), + reraise=True, + stop=stop_after_attempt(4), +) +async def bash_cmd(arguments: FfmpegSearchArguments) -> FfmpegSearchOutput: + """Execute a FFmpeg bash command using base64-encoded input data.""" + try: + assert isinstance(arguments, FfmpegSearchArguments), "Invalid arguments" + + # Decode base64 input + try: + input_data = base64.b64decode(arguments.file) + except: + return FfmpegSearchOutput( + fileoutput="Error: Invalid base64 input", result=False, mime_type=None + ) + + # Validate input format + is_valid, input_mime = await validate_format(input_data) + + if not is_valid: + return FfmpegSearchOutput( + fileoutput="Error: Unsupported input file format", + result=False, + mime_type=None, + ) + + # Create temporary directory + temp_dir = tempfile.mkdtemp() + + # Get the output filename from the last argument of the FFmpeg command + cmd_parts = arguments.cmd.split() + output_filename = cmd_parts[-1] # e.g., "output.mp4" + output_path = os.path.join(temp_dir, output_filename) + + # Modify FFmpeg command + for i, part in enumerate(cmd_parts): + if part == "-i" and i + 1 < len(cmd_parts): + cmd_parts[i + 1] = "pipe:0" + cmd_parts[-1] = output_path # Replace the last argument with full output path + cmd = " ".join(cmd_parts) + + # Execute FFmpeg + process = await asyncio.create_subprocess_shell( + cmd, + stdin=asyncio.subprocess.PIPE, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + + # Process FFmpeg output + stdout, stderr = await process.communicate(input=input_data) + success = process.returncode == 0 + + if success and os.path.exists(output_path): + # Read the output file + with open(output_path, "rb") as f: + output_data = f.read() + # Convert to base64 + output_base64 = base64.b64encode(output_data).decode("utf-8") + + _, output_mime = await validate_format(output_data) + + # Clean up + shutil.rmtree(temp_dir) + + return FfmpegSearchOutput( + fileoutput=output_base64, + result=True, + mime_type=output_mime, + ) + + # Clean up in case of failure + shutil.rmtree(temp_dir) + error_msg = stderr.decode() if stderr else "Unknown error occurred" + return FfmpegSearchOutput( + fileoutput=f"Error: FFmpeg processing failed - {error_msg}", + result=False, + mime_type=None, + ) + + except Exception as e: + # Clean up in case of exception + if "temp_dir" in locals(): + shutil.rmtree(temp_dir) + return FfmpegSearchOutput( + fileoutput=f"Error: {str(e)}", result=False, mime_type=None + ) diff --git a/integrations-service/integrations/utils/integrations/remote_browser.py b/integrations-service/integrations/utils/integrations/remote_browser.py index 11e3e24a3..21ecc3a59 100644 --- a/integrations-service/integrations/utils/integrations/remote_browser.py +++ b/integrations-service/integrations/utils/integrations/remote_browser.py @@ -104,26 +104,26 @@ async def _reset_mouse(self) -> None: window.$$julep$$_initialized = true; """) - @staticmethod - def _with_error_and_screenshot(f): - @wraps(f) - async def wrapper(self: "PlaywrightActions", *args, **kwargs): - try: - result: RemoteBrowserOutput = await f(self, *args, **kwargs) - await self._wait_for_load() + # @staticmethod + # def _with_error_and_screenshot(f): + # @wraps(f) + # async def wrapper(self: "PlaywrightActions", *args, **kwargs): + # try: + # result: RemoteBrowserOutput = await f(self, *args, **kwargs) + # await self._wait_for_load() - screenshot: RemoteBrowserOutput = await self.take_screenshot() + # screenshot: RemoteBrowserOutput = await self.take_screenshot() - return RemoteBrowserOutput( - output=result.output, - base64_image=screenshot.base64_image, - system=result.system or f.__name__, - ) + # return RemoteBrowserOutput( + # output=result.output, + # base64_image=screenshot.base64_image, + # system=result.system or f.__name__, + # ) - except Exception as e: - return RemoteBrowserOutput(error=str(e)) + # except Exception as e: + # return RemoteBrowserOutput(error=str(e)) - return wrapper + # return wrapper async def _get_screen_size(self) -> tuple[int, int]: """Get the current browser viewport size""" @@ -198,7 +198,6 @@ def _overlay_cursor(self, screenshot_bytes: bytes, x: int, y: int) -> bytes: # --- # Actions - @_with_error_and_screenshot async def navigate(self, url: str) -> RemoteBrowserOutput: """Navigate to a specific URL""" await self.page.goto(url) @@ -208,7 +207,6 @@ async def navigate(self, url: str) -> RemoteBrowserOutput: output=url, ) - @_with_error_and_screenshot async def refresh(self) -> RemoteBrowserOutput: """Refresh the current page""" await self.page.reload() @@ -218,7 +216,6 @@ async def refresh(self) -> RemoteBrowserOutput: output="Refreshed page", ) - @_with_error_and_screenshot async def cursor_position(self) -> RemoteBrowserOutput: """Get current mouse coordinates""" x, y = await self._get_mouse_coordinates() @@ -226,11 +223,20 @@ async def cursor_position(self) -> RemoteBrowserOutput: output=f"X={x}, Y={y}", ) - @_with_error_and_screenshot async def press_key(self, key_combination: str) -> RemoteBrowserOutput: """Press a key or key combination""" # Split combination into individual keys keys = key_combination.split("+") + keys = [ + "Enter" + if k == "Return" + else "Control" + if k == "ctrl" + else "PageDown" + if k == "Page_Down" + else k + for k in keys + ] # Press modifier keys first for key in keys[:-1]: @@ -247,7 +253,6 @@ async def press_key(self, key_combination: str) -> RemoteBrowserOutput: output=f"Pressed {key_combination}", ) - @_with_error_and_screenshot async def type_text(self, text: str) -> RemoteBrowserOutput: """Type a string of text""" await self.page.keyboard.type(text) @@ -256,7 +261,6 @@ async def type_text(self, text: str) -> RemoteBrowserOutput: output=f"Typed {text}", ) - @_with_error_and_screenshot async def mouse_move(self, coordinate: tuple[int, int]) -> RemoteBrowserOutput: """Move mouse to specified coordinates""" await self.mouse.move(*coordinate) @@ -265,7 +269,6 @@ async def mouse_move(self, coordinate: tuple[int, int]) -> RemoteBrowserOutput: output=f"Moved mouse to {coordinate}", ) - @_with_error_and_screenshot async def left_click(self) -> RemoteBrowserOutput: """Perform left mouse click""" x, y = await self._get_mouse_coordinates() @@ -275,7 +278,6 @@ async def left_click(self) -> RemoteBrowserOutput: output="Left clicked", ) - @_with_error_and_screenshot async def left_click_drag(self, coordinate: tuple[int, int]) -> RemoteBrowserOutput: """Click and drag to specified coordinates""" await self.mouse.down() @@ -286,7 +288,6 @@ async def left_click_drag(self, coordinate: tuple[int, int]) -> RemoteBrowserOut output=f"Left clicked and dragged to {coordinate}", ) - @_with_error_and_screenshot async def right_click(self) -> RemoteBrowserOutput: """Perform right mouse click""" x, y = await self._get_mouse_coordinates() @@ -296,7 +297,6 @@ async def right_click(self) -> RemoteBrowserOutput: output="Right clicked", ) - @_with_error_and_screenshot async def middle_click(self) -> RemoteBrowserOutput: """Perform middle mouse click""" x, y = await self._get_mouse_coordinates() @@ -306,7 +306,6 @@ async def middle_click(self) -> RemoteBrowserOutput: output="Middle clicked", ) - @_with_error_and_screenshot async def double_click(self) -> RemoteBrowserOutput: """Perform double click""" x, y = await self._get_mouse_coordinates() diff --git a/integrations-service/poetry.lock b/integrations-service/poetry.lock index c106b4b4f..cbf0da504 100644 --- a/integrations-service/poetry.lock +++ b/integrations-service/poetry.lock @@ -517,6 +517,24 @@ files = [ [package.dependencies] colorama = {version = "*", markers = "platform_system == \"Windows\""} +[[package]] +name = "cloudinary" +version = "1.41.0" +description = "Python and Django SDK for Cloudinary" +optional = false +python-versions = "*" +files = [ + {file = "cloudinary-1.41.0.tar.gz", hash = "sha256:e189739a796a7d2ad15c19971741d33a9300816b16c0282b4b14ccf1dd2948c0"}, +] + +[package.dependencies] +certifi = "*" +six = "*" +urllib3 = ">=1.26.5" + +[package.extras] +dev = ["tox"] + [[package]] name = "colorama" version = "0.4.6" @@ -4062,4 +4080,4 @@ propcache = ">=0.2.0" [metadata] lock-version = "2.0" python-versions = ">=3.12,<3.13" -content-hash = "256945c59e69f4d19642f34ff712bd8d585832a7fc32548cd9d14e8813fa595a" +content-hash = "f395084232b0cbfaeb8c1fe90c971dbad935d5ef479178b672adaaed6c85e769" diff --git a/integrations-service/pyproject.toml b/integrations-service/pyproject.toml index b8de2236a..571846b9e 100644 --- a/integrations-service/pyproject.toml +++ b/integrations-service/pyproject.toml @@ -30,6 +30,7 @@ httpx = "^0.27.2" pillow = "^11.0.0" llama-index = "^0.11.22" llama-parse = "^0.5.13" +cloudinary = "^1.41.0" [tool.poe.tasks] format = "ruff format" diff --git a/llm-proxy/docker-compose.yml b/llm-proxy/docker-compose.yml index 0b084c385..0b9800b02 100644 --- a/llm-proxy/docker-compose.yml +++ b/llm-proxy/docker-compose.yml @@ -13,7 +13,7 @@ x--litellm-base: &litellm-base - LITELLM_MASTER_KEY=${LITELLM_MASTER_KEY} - DATABASE_URL=${LITELLM_DATABASE_URL:-postgresql://${LITELLM_POSTGRES_USER:-llmproxy}:${LITELLM_POSTGRES_PASSWORD}@${LITELLM_POSTGRES_HOST:-litellm-db}:${LITELLM_POSTGRES_PORT:-5432}/${LITELLM_POSTGRES_DB:-litellm}?sslmode=${LITELLM_POSTGRES_SSLMODE:-prefer_ssl}} - REDIS_URL=${LITELLM_REDIS_URL:-${LITELLM_REDIS_PROTOCOL:-redis}://${LITELLM_REDIS_USER:-default}:${LITELLM_REDIS_PASSWORD:-${LITELLM_REDIS_PASSWORD}}@${LITELLM_REDIS_HOST:-litellm-redis}:${LITELLM_REDIS_PORT:-6379}} - + - GEMINI_API_KEY=${GEMINI_API_KEY} - OPENAI_API_KEY=${OPENAI_API_KEY} - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} - GROQ_API_KEY=${GROQ_API_KEY} diff --git a/llm-proxy/litellm-config.yaml b/llm-proxy/litellm-config.yaml index 3e0127036..8423b3bc2 100644 --- a/llm-proxy/litellm-config.yaml +++ b/llm-proxy/litellm-config.yaml @@ -6,6 +6,19 @@ model_list: # ------------------- # Gemini models + +- model_name: gemini-1.5-pro + litellm_params: + model: gemini/gemini-1.5-pro + api_key: os.environ/GEMINI_API_KEY + tags: ["paid"] + +- model_name: gemini-1.5-pro-latest + litellm_params: + model: gemini/gemini-1.5-pro-latest + api_key: os.environ/GEMINI_API_KEY + tags: ["paid"] + - model_name: gemini-1.5-pro litellm_params: model: vertex_ai_beta/gemini-1.5-pro diff --git a/typespec/entries/models.tsp b/typespec/entries/models.tsp index 61f47bce2..7f8c8b9fa 100644 --- a/typespec/entries/models.tsp +++ b/typespec/entries/models.tsp @@ -50,7 +50,25 @@ model ChatMLImageContentPart { type: "image_url" = "image_url"; } -alias ChatMLContentPart = ChatMLTextContentPart | ChatMLImageContentPart; +model ChatMLAnthropicImageSource { + type: "base64" = "base64"; + media_type: string; + data: T; +} + +model ChatMLAnthropicImageContentPart { + type: "image" = "image"; + source: ChatMLAnthropicImageSource; +} + +/** Anthropic image content part */ +model ChatMLAnthropicContentPart { + tool_use_id: string; + type: "tool_result" = "tool_result"; + content: ChatMLTextContentPart[] | ChatMLAnthropicImageContentPart[]; +} + +alias ChatMLContentPart = ChatMLTextContentPart | ChatMLImageContentPart | ChatMLAnthropicContentPart; model ChatMLMessage { /** The role of the message */ @@ -119,4 +137,4 @@ model History { session_id: Session.id; ...HasCreatedAt; -} +} \ No newline at end of file diff --git a/typespec/tools/cloudinary.tsp b/typespec/tools/cloudinary.tsp new file mode 100644 index 000000000..ec66962e1 --- /dev/null +++ b/typespec/tools/cloudinary.tsp @@ -0,0 +1,122 @@ +import "../common"; + +using Common; + +namespace Tools; + +/** Setup parameters for Cloudinary integration */ +model CloudinarySetup { + /** The API key for Cloudinary */ + cloudinary_api_key: string; + + /** The API secret for Cloudinary */ + cloudinary_api_secret: string; + + /** The Cloud name for Cloudinary */ + cloudinary_cloud_name: string; + + /** Additional parameters for the Cloudinary API */ + params?: Record; +} + +alias CloudinaryMethod = + | /** Upload media to Cloudinary */ + "media_upload" + | /** Edit media in Cloudinary */ + "media_edit"; + +/** Arguments for Cloudinary media upload */ +model CloudinaryUploadArguments { + /** The URL of the file upload */ + file: string; + + /** Return base64 encoded file */ + return_base64: boolean = false; + + /** Optional public ID for the uploaded file */ + public_id?: string; + + /** Optional upload parameters */ + upload_params?: Record; +} + +/** Arguments for Cloudinary media edit */ +model CloudinaryEditArguments { + /** The file Public ID in Cloudinary */ + public_id: string; + + /** The transformation to apply to the file */ + transformation: Array>; + + /** Return base64 encoded file */ + return_base64: boolean = false; +} + +/** Base Cloudinary integration definition */ +model BaseCloudinaryIntegrationDef extends BaseIntegrationDef { + provider: "cloudinary" = "cloudinary"; + setup?: CloudinarySetup; + method?: CloudinaryMethod; +} + +/** Cloudinary upload integration definition */ +model CloudinaryUploadIntegrationDef extends BaseCloudinaryIntegrationDef { + method: "media_upload" = "media_upload"; + arguments?: CloudinaryUploadArguments; +} + +/** Cloudinary edit integration definition */ +model CloudinaryEditIntegrationDef extends BaseCloudinaryIntegrationDef { + method: "media_edit" = "media_edit"; + arguments?: CloudinaryEditArguments; +} + +alias CloudinaryIntegrationDef = CloudinaryUploadIntegrationDef | CloudinaryEditIntegrationDef; + +/** Output for Cloudinary upload */ +model CloudinaryUploadOutput { + /** The URL of the uploaded file */ + url: string; + + /** The public ID of the uploaded file */ + public_id: string; + + /** The base64 encoded file */ + base64?: string; + + /** The metadata of the uploaded file */ + metadata: Record; +} + +/** Output for Cloudinary edit */ +model CloudinaryEditOutput { + /** The transformed URL from Cloudinary */ + transformed_url: string; + + /** The base64 encoded file */ + base64?: string; +} + +alias CloudinaryOutput = CloudinaryUploadOutput | CloudinaryEditOutput; + +/** Cloudinary Provider Card */ +model CloudinaryProviderCard extends BaseProviderCard { + provider: "cloudinary" = "cloudinary"; + setup: CloudinarySetup; + methods: ProviderMethod[] = #[ + #{ + method: "media_upload", + description: "Upload media to Cloudinary", + }, + #{ + method: "media_edit", + description: "Edit media in Cloudinary", + } + ]; + info: ProviderInfo = #{ + url: "https://cloudinary.com/", + docs: "https://cloudinary.com/documentation/python_quickstart", + icon: "https://cloudinary.com/favicon.ico", + friendly_name: "Cloudinary", + }; +} \ No newline at end of file diff --git a/typespec/tools/ffmpeg.tsp b/typespec/tools/ffmpeg.tsp new file mode 100644 index 000000000..a8f8baf03 --- /dev/null +++ b/typespec/tools/ffmpeg.tsp @@ -0,0 +1,56 @@ +import "../common"; + +using Common; + +namespace Tools; + +/** Arguments for Ffmpeg CMD */ +model FfmpegSearchArguments { + /** The bash command string */ + cmd: string; + + /** The base64 string of the file*/ + file?: string; + +} + +/** Ffmpeg integration definition */ +model FfmpegIntegrationDef extends BaseIntegrationDef { + /** The provider must be "ffmpeg" */ + provider: "ffmpeg" = "ffmpeg"; + + /** The specific method of the integration to call */ + method?: string; + + /** The setup parameters for Ffmpeg */ + setup?: null = null; + + /** The arguments for Ffmpeg Search */ + arguments?: FfmpegSearchArguments; +} + +/** Ffmpeg Provider Card */ +model FfmpegProviderCard extends BaseProviderCard { + provider: "ffmpeg" = "ffmpeg"; + setup: null = null; + methods: ProviderMethod[] = #[ + #{ + method: "bash_cmd", + description: "Run FFmpeg bash command", + } + ]; + info: ProviderInfo = #{ + url: "https://ffmpeg.org/", + docs: "https://ffmpeg.org/documentation.html", + icon: "https://upload.wikimedia.org/wikipedia/commons/5/5f/FFmpeg_Logo_new.svg", + friendly_name: "Ffmpeg", + }; +} + +/** Ffmpeg Search Output */ +model FfmpegSearchOutput { + /** The documents returned from the Ffmpeg search */ + file: string; + result: boolean; + mime_type: string; +} \ No newline at end of file diff --git a/typespec/tools/main.tsp b/typespec/tools/main.tsp index 6d09a3fb0..443329191 100644 --- a/typespec/tools/main.tsp +++ b/typespec/tools/main.tsp @@ -9,6 +9,8 @@ import "./wikipedia.tsp"; import "./browserbase"; import "./remote_browser.tsp"; import "./llama_parse.tsp"; +import "./ffmpeg.tsp"; +import "./cloudinary.tsp"; namespace Tools; diff --git a/typespec/tools/models.tsp b/typespec/tools/models.tsp index e7f5deb5e..c41154de0 100644 --- a/typespec/tools/models.tsp +++ b/typespec/tools/models.tsp @@ -24,6 +24,8 @@ alias integrationProvider = ( | "email" | "remote_browser" | "llama_parse" + | "ffmpeg" + | "cloudinary" ); enum ToolType { @@ -116,6 +118,8 @@ alias IntegrationDef = ( | BrowserbaseIntegrationDef | RemoteBrowserIntegrationDef | LlamaParseIntegrationDef + | FfmpegIntegrationDef + | CloudinaryIntegrationDef ); // diff --git a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml index 8b8afa84f..5c9a57c4c 100644 --- a/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml +++ b/typespec/tsp-output/@typespec/openapi3/openapi-1.0.0.yaml @@ -1872,6 +1872,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + type: string + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -1972,6 +2031,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + type: string + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -2203,6 +2321,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + type: string + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -2353,6 +2530,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + type: string + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -2850,6 +3086,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + type: string + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part - $ref: '#/components/schemas/Tools.Tool' - $ref: '#/components/schemas/Tools.ChosenFunctionCall' - $ref: '#/components/schemas/Tools.ChosenComputer20241022' @@ -2902,6 +3197,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + type: string + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + type: string + description: Anthropic image content part - $ref: '#/components/schemas/Tools.Tool' - $ref: '#/components/schemas/Tools.ChosenFunctionCall' - $ref: '#/components/schemas/Tools.ChosenComputer20241022' @@ -4899,6 +5253,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + $ref: '#/components/schemas/Common.JinjaTemplate' + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + $ref: '#/components/schemas/Common.JinjaTemplate' + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -5049,6 +5462,65 @@ components: - image_url description: The type (fixed to 'image_url') default: image_url + - type: object + required: + - tool_use_id + - type + - content + properties: + tool_use_id: + type: string + type: + type: string + enum: + - tool_result + default: tool_result + content: + anyOf: + - type: array + items: + type: object + required: + - text + - type + properties: + text: + $ref: '#/components/schemas/Common.JinjaTemplate' + type: + type: string + enum: + - text + description: The type (fixed to 'text') + default: text + - type: array + items: + type: object + required: + - type + - source + properties: + type: + type: string + enum: + - image + default: image + source: + type: object + required: + - type + - media_type + - data + properties: + type: + type: string + enum: + - base64 + default: base64 + media_type: + type: string + data: + $ref: '#/components/schemas/Common.JinjaTemplate' + description: Anthropic image content part nullable: true description: The content parts of the message name: @@ -6200,6 +6672,44 @@ components: type: string readOnly: true description: The response tool value generated by the model + Tools.BaseCloudinaryIntegrationDef: + type: object + required: + - provider + properties: + provider: + type: string + enum: + - cloudinary + default: cloudinary + setup: + $ref: '#/components/schemas/Tools.CloudinarySetup' + method: + type: string + enum: + - media_upload + - media_edit + allOf: + - $ref: '#/components/schemas/Tools.BaseIntegrationDef' + description: Base Cloudinary integration definition + Tools.BaseCloudinaryIntegrationDefUpdate: + type: object + properties: + provider: + type: string + enum: + - cloudinary + default: cloudinary + setup: + $ref: '#/components/schemas/Tools.CloudinarySetupUpdate' + method: + type: string + enum: + - media_upload + - media_edit + allOf: + - $ref: '#/components/schemas/Tools.BaseIntegrationDefUpdate' + description: Base Cloudinary integration definition Tools.BaseIntegrationDef: type: object required: @@ -6217,6 +6727,8 @@ components: - email - remote_browser - llama_parse + - ffmpeg + - cloudinary description: The provider of the integration method: type: string @@ -6236,6 +6748,8 @@ components: browserbase: '#/components/schemas/Tools.BaseBrowserbaseIntegrationDef' remote_browser: '#/components/schemas/Tools.RemoteBrowserIntegrationDef' llama_parse: '#/components/schemas/Tools.LlamaParseIntegrationDef' + ffmpeg: '#/components/schemas/Tools.FfmpegIntegrationDef' + cloudinary: '#/components/schemas/Tools.BaseCloudinaryIntegrationDef' description: Integration definition Tools.BaseIntegrationDefUpdate: type: object @@ -6252,6 +6766,8 @@ components: - email - remote_browser - llama_parse + - ffmpeg + - cloudinary description: The provider of the integration method: type: string @@ -6271,6 +6787,8 @@ components: browserbase: '#/components/schemas/Tools.BaseBrowserbaseIntegrationDefUpdate' remote_browser: '#/components/schemas/Tools.RemoteBrowserIntegrationDefUpdate' llama_parse: '#/components/schemas/Tools.LlamaParseIntegrationDefUpdate' + ffmpeg: '#/components/schemas/Tools.FfmpegIntegrationDefUpdate' + cloudinary: '#/components/schemas/Tools.BaseCloudinaryIntegrationDefUpdate' description: Integration definition Tools.Bash20241022Def: type: object @@ -6866,6 +7384,177 @@ components: type: integer format: uint16 description: The line range to view + Tools.CloudinaryEditArguments: + type: object + required: + - public_id + - transformation + - return_base64 + properties: + public_id: + type: string + description: The file Public ID in Cloudinary + transformation: + type: array + items: + type: object + additionalProperties: {} + description: The transformation to apply to the file + return_base64: + type: boolean + description: Return base64 encoded file + default: false + description: Arguments for Cloudinary media edit + Tools.CloudinaryEditArgumentsUpdate: + type: object + properties: + public_id: + type: string + description: The file Public ID in Cloudinary + transformation: + type: array + items: + type: object + additionalProperties: {} + description: The transformation to apply to the file + return_base64: + type: boolean + description: Return base64 encoded file + default: false + description: Arguments for Cloudinary media edit + Tools.CloudinaryEditIntegrationDef: + type: object + required: + - method + properties: + method: + type: string + enum: + - media_edit + default: media_edit + arguments: + $ref: '#/components/schemas/Tools.CloudinaryEditArguments' + allOf: + - $ref: '#/components/schemas/Tools.BaseCloudinaryIntegrationDef' + description: Cloudinary edit integration definition + Tools.CloudinaryEditIntegrationDefUpdate: + type: object + properties: + method: + type: string + enum: + - media_edit + default: media_edit + arguments: + $ref: '#/components/schemas/Tools.CloudinaryEditArgumentsUpdate' + allOf: + - $ref: '#/components/schemas/Tools.BaseCloudinaryIntegrationDefUpdate' + description: Cloudinary edit integration definition + Tools.CloudinarySetup: + type: object + required: + - cloudinary_api_key + - cloudinary_api_secret + - cloudinary_cloud_name + properties: + cloudinary_api_key: + type: string + description: The API key for Cloudinary + cloudinary_api_secret: + type: string + description: The API secret for Cloudinary + cloudinary_cloud_name: + type: string + description: The Cloud name for Cloudinary + params: + type: object + additionalProperties: {} + description: Additional parameters for the Cloudinary API + description: Setup parameters for Cloudinary integration + Tools.CloudinarySetupUpdate: + type: object + properties: + cloudinary_api_key: + type: string + description: The API key for Cloudinary + cloudinary_api_secret: + type: string + description: The API secret for Cloudinary + cloudinary_cloud_name: + type: string + description: The Cloud name for Cloudinary + params: + type: object + additionalProperties: {} + description: Additional parameters for the Cloudinary API + description: Setup parameters for Cloudinary integration + Tools.CloudinaryUploadArguments: + type: object + required: + - file + - return_base64 + properties: + file: + type: string + description: The URL of the file upload + return_base64: + type: boolean + description: Return base64 encoded file + default: false + public_id: + type: string + description: Optional public ID for the uploaded file + upload_params: + type: object + additionalProperties: {} + description: Optional upload parameters + description: Arguments for Cloudinary media upload + Tools.CloudinaryUploadArgumentsUpdate: + type: object + properties: + file: + type: string + description: The URL of the file upload + return_base64: + type: boolean + description: Return base64 encoded file + default: false + public_id: + type: string + description: Optional public ID for the uploaded file + upload_params: + type: object + additionalProperties: {} + description: Optional upload parameters + description: Arguments for Cloudinary media upload + Tools.CloudinaryUploadIntegrationDef: + type: object + required: + - method + properties: + method: + type: string + enum: + - media_upload + default: media_upload + arguments: + $ref: '#/components/schemas/Tools.CloudinaryUploadArguments' + allOf: + - $ref: '#/components/schemas/Tools.BaseCloudinaryIntegrationDef' + description: Cloudinary upload integration definition + Tools.CloudinaryUploadIntegrationDefUpdate: + type: object + properties: + method: + type: string + enum: + - media_upload + default: media_upload + arguments: + $ref: '#/components/schemas/Tools.CloudinaryUploadArgumentsUpdate' + allOf: + - $ref: '#/components/schemas/Tools.BaseCloudinaryIntegrationDefUpdate' + description: Cloudinary upload integration definition Tools.Computer20241022Action: type: string enum: @@ -6983,6 +7672,9 @@ components: - $ref: '#/components/schemas/Tools.BrowserbaseGetSessionConnectUrlIntegrationDef' - $ref: '#/components/schemas/Tools.RemoteBrowserIntegrationDef' - $ref: '#/components/schemas/Tools.LlamaParseIntegrationDef' + - $ref: '#/components/schemas/Tools.FfmpegIntegrationDef' + - $ref: '#/components/schemas/Tools.CloudinaryUploadIntegrationDef' + - $ref: '#/components/schemas/Tools.CloudinaryEditIntegrationDef' description: The integration to call system: allOf: @@ -7147,6 +7839,76 @@ components: type: string description: The password of the email server description: Setup parameters for Email integration + Tools.FfmpegIntegrationDef: + type: object + required: + - provider + properties: + provider: + type: string + enum: + - ffmpeg + description: The provider must be "ffmpeg" + default: ffmpeg + method: + type: string + description: The specific method of the integration to call + setup: + nullable: true + description: The setup parameters for Ffmpeg + default: null + arguments: + allOf: + - $ref: '#/components/schemas/Tools.FfmpegSearchArguments' + description: The arguments for Ffmpeg Search + allOf: + - $ref: '#/components/schemas/Tools.BaseIntegrationDef' + description: Ffmpeg integration definition + Tools.FfmpegIntegrationDefUpdate: + type: object + properties: + provider: + type: string + enum: + - ffmpeg + description: The provider must be "ffmpeg" + default: ffmpeg + method: + type: string + description: The specific method of the integration to call + setup: + nullable: true + description: The setup parameters for Ffmpeg + default: null + arguments: + allOf: + - $ref: '#/components/schemas/Tools.FfmpegSearchArgumentsUpdate' + description: The arguments for Ffmpeg Search + allOf: + - $ref: '#/components/schemas/Tools.BaseIntegrationDefUpdate' + description: Ffmpeg integration definition + Tools.FfmpegSearchArguments: + type: object + required: + - cmd + properties: + cmd: + type: string + description: The bash command string + file: + type: string + description: The base64 string of the file + description: Arguments for Ffmpeg CMD + Tools.FfmpegSearchArgumentsUpdate: + type: object + properties: + cmd: + type: string + description: The bash command string + file: + type: string + description: The base64 string of the file + description: Arguments for Ffmpeg CMD Tools.FunctionCallOption: type: object required: @@ -7345,6 +8107,9 @@ components: - $ref: '#/components/schemas/Tools.BrowserbaseGetSessionConnectUrlIntegrationDefUpdate' - $ref: '#/components/schemas/Tools.RemoteBrowserIntegrationDefUpdate' - $ref: '#/components/schemas/Tools.LlamaParseIntegrationDefUpdate' + - $ref: '#/components/schemas/Tools.FfmpegIntegrationDefUpdate' + - $ref: '#/components/schemas/Tools.CloudinaryUploadIntegrationDefUpdate' + - $ref: '#/components/schemas/Tools.CloudinaryEditIntegrationDefUpdate' description: The integration to call system: allOf: @@ -7749,6 +8514,9 @@ components: - $ref: '#/components/schemas/Tools.BrowserbaseGetSessionConnectUrlIntegrationDef' - $ref: '#/components/schemas/Tools.RemoteBrowserIntegrationDef' - $ref: '#/components/schemas/Tools.LlamaParseIntegrationDef' + - $ref: '#/components/schemas/Tools.FfmpegIntegrationDef' + - $ref: '#/components/schemas/Tools.CloudinaryUploadIntegrationDef' + - $ref: '#/components/schemas/Tools.CloudinaryEditIntegrationDef' description: The integration to call system: allOf: @@ -7841,6 +8609,9 @@ components: - $ref: '#/components/schemas/Tools.BrowserbaseGetSessionConnectUrlIntegrationDef' - $ref: '#/components/schemas/Tools.RemoteBrowserIntegrationDef' - $ref: '#/components/schemas/Tools.LlamaParseIntegrationDef' + - $ref: '#/components/schemas/Tools.FfmpegIntegrationDef' + - $ref: '#/components/schemas/Tools.CloudinaryUploadIntegrationDef' + - $ref: '#/components/schemas/Tools.CloudinaryEditIntegrationDef' description: The integration to call system: allOf: From 6ebe65ed4e32de33008f046399438f7f7fa68269 Mon Sep 17 00:00:00 2001 From: Vedant Sahai Date: Thu, 21 Nov 2024 02:25:59 -0500 Subject: [PATCH 13/23] feat(integration-service): added env file to load the API_KEYS for various integrations (#855) > [!IMPORTANT] > Add environment variable handling for API keys in integration services using `environs`. > > - **Environment Variables**: > - Add `env.py` to load API keys and other sensitive data using `environs`. > - Update `docker-compose.yml` to include new environment variables for integrations. > - **Integration Updates**: > - Modify `brave.py`, `browserbase.py`, `cloudinary.py`, `email.py`, `llama_parse.py`, `spider.py`, and `weather.py` to use environment variables for API keys. > - Replace hardcoded 'DEMO_API_KEY' and similar placeholders with actual environment variables. > - **Dependencies**: > - Add `environs` to `pyproject.toml` for environment variable management. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 65d5c0aa4aec07e469f26c155c2612f5dc457d77. It will automatically update as commits are pushed. --- integrations-service/docker-compose.yml | 15 +++++++++- integrations-service/integrations/env.py | 21 ++++++++++++++ .../integrations/utils/integrations/brave.py | 5 ++++ .../utils/integrations/browserbase.py | 12 ++++++++ .../utils/integrations/cloudinary.py | 29 +++++++++++++++---- .../integrations/utils/integrations/email.py | 4 +++ .../utils/integrations/llama_parse.py | 4 +++ .../integrations/utils/integrations/spider.py | 4 +++ .../utils/integrations/weather.py | 4 +++ integrations-service/poetry.lock | 22 +++++++++++++- integrations-service/pyproject.toml | 1 + 11 files changed, 113 insertions(+), 8 deletions(-) create mode 100644 integrations-service/integrations/env.py diff --git a/integrations-service/docker-compose.yml b/integrations-service/docker-compose.yml index 2496e7c10..51e8d078f 100644 --- a/integrations-service/docker-compose.yml +++ b/integrations-service/docker-compose.yml @@ -2,8 +2,21 @@ name: julep-integrations # Shared environment variables x--shared-environment: &shared-environment - OPENAI_API_KEY: ${OPENAI_API_KEY} INTEGRATIONS_SERVICE_PORT: ${INTEGRATIONS_SERVICE_PORT:-8000} + RAPID_API_KEY: ${RAPID_API_KEY} + RAPID_API_HOST: ${RAPID_API_HOST} + ARYSHARE_KEY: ${ARYSHARE_KEY} + ARYSHARE_PROFILE_ID: ${ARYSHARE_PROFILE_ID} + BROWSERBASE_API_KEY: ${BROWSERBASE_API_KEY} + BROWSERBASE_PROJECT_ID: ${BROWSERBASE_PROJECT_ID} + OPENWEATHER_API_KEY: ${OPENWEATHER_API_KEY} + SPIDER_API_KEY: ${SPIDER_API_KEY} + BRAVE_API_KEY: ${BRAVE_API_KEY} + LLAMA_API_KEY: ${LLAMA_API_KEY} + CLOUDINARY_API_KEY: ${CLOUDINARY_API_KEY} + CLOUDINARY_API_SECRET: ${CLOUDINARY_API_SECRET} + CLOUDINARY_CLOUD_NAME: ${CLOUDINARY_CLOUD_NAME} + MAILGUN_PASSWORD: ${MAILGUN_PASSWORD} services: integrations: diff --git a/integrations-service/integrations/env.py b/integrations-service/integrations/env.py new file mode 100644 index 000000000..5102d3a00 --- /dev/null +++ b/integrations-service/integrations/env.py @@ -0,0 +1,21 @@ +from environs import Env + +# Initialize the Env object for environment variable parsing. +env = Env() +env.read_env() # Read .env file, if it exists + +# Load environment variables +rapid_api_key = env.str("RAPID_API_KEY") +rapid_api_host = env.str("RAPID_API_HOST") +aryshare_key = env.str("ARYSHARE_KEY") +aryshare_profile_id = env.str("ARYSHARE_PROFILE_ID") +browserbase_api_key = env.str("BROWSERBASE_API_KEY") +browserbase_project_id = env.str("BROWSERBASE_PROJECT_ID") +openweather_api_key = env.str("OPENWEATHER_API_KEY") +spider_api_key = env.str("SPIDER_API_KEY") +brave_api_key = env.str("BRAVE_API_KEY") +llama_api_key = env.str("LLAMA_API_KEY") +cloudinary_api_key = env.str("CLOUDINARY_API_KEY") +cloudinary_api_secret = env.str("CLOUDINARY_API_SECRET") +cloudinary_cloud_name = env.str("CLOUDINARY_CLOUD_NAME") +mailgun_password = env.str("MAILGUN_PASSWORD") diff --git a/integrations-service/integrations/utils/integrations/brave.py b/integrations-service/integrations/utils/integrations/brave.py index 549379a82..7414e081a 100644 --- a/integrations-service/integrations/utils/integrations/brave.py +++ b/integrations-service/integrations/utils/integrations/brave.py @@ -5,6 +5,7 @@ from tenacity import retry, stop_after_attempt, wait_exponential from ...autogen.Tools import BraveSearchArguments, BraveSearchSetup +from ...env import brave_api_key # Import env to access environment variables from ...models import BraveSearchOutput, SearchResult @@ -24,6 +25,10 @@ async def search( assert isinstance(setup, BraveSearchSetup), "Invalid setup" assert isinstance(arguments, BraveSearchArguments), "Invalid arguments" + # Check if the setup.api_key is 'DEMO_API_KEY' and load from environment if true + if setup.api_key == "DEMO_API_KEY": + setup.api_key = brave_api_key + tool = BraveSearch.from_api_key(api_key=setup.api_key, search_kwargs={"count": 3}) result = tool.run(arguments.query) diff --git a/integrations-service/integrations/utils/integrations/browserbase.py b/integrations-service/integrations/utils/integrations/browserbase.py index 2cdd813ff..f9885e976 100644 --- a/integrations-service/integrations/utils/integrations/browserbase.py +++ b/integrations-service/integrations/utils/integrations/browserbase.py @@ -22,6 +22,10 @@ BrowserbaseListSessionsArguments, BrowserbaseSetup, ) +from ...env import ( # Import env to access environment variables + browserbase_api_key, + browserbase_project_id, +) from ...models import ( BrowserbaseCompleteSessionOutput, BrowserbaseCreateSessionOutput, @@ -34,6 +38,11 @@ def get_browserbase_client(setup: BrowserbaseSetup) -> Browserbase: + if setup.api_key == "DEMO_API_KEY": + setup.api_key = browserbase_api_key + if setup.project_id == "DEMO_PROJECT_ID": + setup.project_id = browserbase_project_id + return Browserbase( api_key=setup.api_key, project_id=setup.project_id, @@ -71,6 +80,9 @@ async def create_session( ) -> BrowserbaseCreateSessionOutput: client = get_browserbase_client(setup) + if arguments.project_id == "DEMO_PROJECT_ID": + arguments.project_id = setup.browserbase_project_id + options = CreateSessionOptions( projectId=arguments.project_id or setup.project_id, extensionId=arguments.extension_id, diff --git a/integrations-service/integrations/utils/integrations/cloudinary.py b/integrations-service/integrations/utils/integrations/cloudinary.py index 5562ab968..213efd405 100644 --- a/integrations-service/integrations/utils/integrations/cloudinary.py +++ b/integrations-service/integrations/utils/integrations/cloudinary.py @@ -11,6 +11,11 @@ CloudinarySetup, CloudinaryUploadArguments, ) +from ...env import ( # Import env to access environment variables + cloudinary_api_key, + cloudinary_api_secret, + cloudinary_cloud_name, +) from ...models.cloudinary import CloudinaryEditOutput, CloudinaryUploadOutput @@ -32,9 +37,15 @@ async def media_upload( try: # Configure Cloudinary with credentials cloudinary.config( - cloud_name=setup.cloudinary_cloud_name, - api_key=setup.cloudinary_api_key, - api_secret=setup.cloudinary_api_secret, + cloud_name=setup.cloudinary_cloud_name + if setup.cloudinary_cloud_name != "DEMO_CLOUD_NAME" + else cloudinary_cloud_name, + api_key=setup.cloudinary_api_key + if setup.cloudinary_api_key != "DEMO_API_KEY" + else cloudinary_api_key, + api_secret=setup.cloudinary_api_secret + if setup.cloudinary_api_secret != "DEMO_API_SECRET" + else cloudinary_api_secret, **(setup.params or {}), ) @@ -93,9 +104,15 @@ async def media_edit( try: # Configure Cloudinary with credentials cloudinary.config( - cloud_name=setup.cloudinary_cloud_name, - api_key=setup.cloudinary_api_key, - api_secret=setup.cloudinary_api_secret, + cloud_name=setup.cloudinary_cloud_name + if setup.cloudinary_cloud_name != "DEMO_CLOUD_NAME" + else cloudinary_cloud_name, + api_key=setup.cloudinary_api_key + if setup.cloudinary_api_key != "DEMO_API_KEY" + else cloudinary_api_key, + api_secret=setup.cloudinary_api_secret + if setup.cloudinary_api_secret != "DEMO_API_SECRET" + else cloudinary_api_secret, **(setup.params or {}), ) diff --git a/integrations-service/integrations/utils/integrations/email.py b/integrations-service/integrations/utils/integrations/email.py index c38030128..d7967128f 100644 --- a/integrations-service/integrations/utils/integrations/email.py +++ b/integrations-service/integrations/utils/integrations/email.py @@ -5,6 +5,7 @@ from tenacity import retry, stop_after_attempt, wait_exponential from ...autogen.Tools import EmailArguments, EmailSetup +from ...env import mailgun_password # Import env to access environment variables from ...models import EmailOutput @@ -25,6 +26,9 @@ async def send(setup: EmailSetup, arguments: EmailArguments) -> EmailOutput: message["From"] = arguments.from_ message["To"] = arguments.to + if setup.password == "DEMO_PASSWORD": + setup.password = mailgun_password + with SMTP(setup.host, setup.port) as server: server.login(setup.user, setup.password) server.send_message(message) diff --git a/integrations-service/integrations/utils/integrations/llama_parse.py b/integrations-service/integrations/utils/integrations/llama_parse.py index 1e60dee60..5d15debe5 100644 --- a/integrations-service/integrations/utils/integrations/llama_parse.py +++ b/integrations-service/integrations/utils/integrations/llama_parse.py @@ -6,6 +6,7 @@ from tenacity import retry, stop_after_attempt, wait_exponential from ...autogen.Tools import LlamaParseFetchArguments, LlamaParseSetup +from ...env import llama_api_key # Import env to access environment variables from ...models import LlamaParseFetchOutput @@ -25,6 +26,9 @@ async def parse( assert isinstance(setup, LlamaParseSetup), "Invalid setup" assert isinstance(arguments, LlamaParseFetchArguments), "Invalid arguments" + if setup.llamaparse_api_key == "DEMO_API_KEY": + setup.llamaparse_api_key = llama_api_key + parser = LlamaParse( api_key=setup.llamaparse_api_key, result_type=arguments.result_format, diff --git a/integrations-service/integrations/utils/integrations/spider.py b/integrations-service/integrations/utils/integrations/spider.py index a75fb90c9..baac7c5e6 100644 --- a/integrations-service/integrations/utils/integrations/spider.py +++ b/integrations-service/integrations/utils/integrations/spider.py @@ -3,6 +3,7 @@ from tenacity import retry, stop_after_attempt, wait_exponential from ...autogen.Tools import SpiderFetchArguments, SpiderSetup +from ...env import spider_api_key # Import env to access environment variables from ...models import SpiderFetchOutput @@ -27,6 +28,9 @@ async def crawl( if not url: raise ValueError("URL parameter is required for spider") + if setup.spider_api_key == "DEMO_API_KEY": + setup.spider_api_key = spider_api_key + spider_loader = SpiderLoader( api_key=setup.spider_api_key, url=str(url), diff --git a/integrations-service/integrations/utils/integrations/weather.py b/integrations-service/integrations/utils/integrations/weather.py index 33e36ec2b..19e6c659e 100644 --- a/integrations-service/integrations/utils/integrations/weather.py +++ b/integrations-service/integrations/utils/integrations/weather.py @@ -3,6 +3,7 @@ from tenacity import retry, stop_after_attempt, wait_exponential from ...autogen.Tools import WeatherGetArguments, WeatherSetup +from ...env import openweather_api_key # Import env to access environment variables from ...models import WeatherGetOutput @@ -23,6 +24,9 @@ async def get(setup: WeatherSetup, arguments: WeatherGetArguments) -> WeatherGet location = arguments.location openweathermap_api_key = setup.openweathermap_api_key + if openweathermap_api_key == "DEMO_API_KEY": + openweathermap_api_key = openweather_api_key + if not location: raise ValueError("Location parameter is required for weather data") diff --git a/integrations-service/poetry.lock b/integrations-service/poetry.lock index cbf0da504..2d7e2e309 100644 --- a/integrations-service/poetry.lock +++ b/integrations-service/poetry.lock @@ -756,6 +756,26 @@ files = [ dnspython = ">=2.0.0" idna = ">=2.0.0" +[[package]] +name = "environs" +version = "11.2.1" +description = "simplified environment variable parsing" +optional = false +python-versions = ">=3.8" +files = [ + {file = "environs-11.2.1-py3-none-any.whl", hash = "sha256:9d2080cf25807a26fc0d4301e2d7b62c64fbf547540f21e3a30cc02bc5fbe948"}, + {file = "environs-11.2.1.tar.gz", hash = "sha256:e068ae3174cef52ba4b95ead22e639056a02465f616e62323e04ae08e86a75a4"}, +] + +[package.dependencies] +marshmallow = ">=3.13.0" +python-dotenv = "*" + +[package.extras] +dev = ["environs[tests]", "pre-commit (>=3.5,<5.0)", "tox"] +django = ["dj-database-url", "dj-email-url", "django-cache-url"] +tests = ["environs[django]", "pytest"] + [[package]] name = "fastapi" version = "0.115.4" @@ -4080,4 +4100,4 @@ propcache = ">=0.2.0" [metadata] lock-version = "2.0" python-versions = ">=3.12,<3.13" -content-hash = "f395084232b0cbfaeb8c1fe90c971dbad935d5ef479178b672adaaed6c85e769" +content-hash = "d36621b8c1cc5ff844eff52bb9d622b69dc9d6d512977d6d1fe11b38f22ca4f5" diff --git a/integrations-service/pyproject.toml b/integrations-service/pyproject.toml index 571846b9e..427a59b45 100644 --- a/integrations-service/pyproject.toml +++ b/integrations-service/pyproject.toml @@ -31,6 +31,7 @@ pillow = "^11.0.0" llama-index = "^0.11.22" llama-parse = "^0.5.13" cloudinary = "^1.41.0" +environs = "^11.2.1" [tool.poe.tasks] format = "ruff format" From 8b7727fee93a3cb941df73d8d3408da8decff035 Mon Sep 17 00:00:00 2001 From: Vedant Sahai Date: Thu, 21 Nov 2024 02:58:20 -0500 Subject: [PATCH 14/23] fix: removed unecessary keys in integration docker (#856) > [!IMPORTANT] > Remove unnecessary environment variables from `docker-compose.yml` and `env.py`. > > - **Environment Variables**: > - Removed `RAPID_API_KEY`, `RAPID_API_HOST`, `ARYSHARE_KEY`, and `ARYSHARE_PROFILE_ID` from `docker-compose.yml` and `env.py` as they are no longer needed. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 73e3a8a01d14c4d536135a3656afc5f93c8bbe21. It will automatically update as commits are pushed. --- integrations-service/docker-compose.yml | 4 ---- integrations-service/integrations/env.py | 24 ++++++++++-------------- 2 files changed, 10 insertions(+), 18 deletions(-) diff --git a/integrations-service/docker-compose.yml b/integrations-service/docker-compose.yml index 51e8d078f..b8c19957c 100644 --- a/integrations-service/docker-compose.yml +++ b/integrations-service/docker-compose.yml @@ -3,10 +3,6 @@ name: julep-integrations # Shared environment variables x--shared-environment: &shared-environment INTEGRATIONS_SERVICE_PORT: ${INTEGRATIONS_SERVICE_PORT:-8000} - RAPID_API_KEY: ${RAPID_API_KEY} - RAPID_API_HOST: ${RAPID_API_HOST} - ARYSHARE_KEY: ${ARYSHARE_KEY} - ARYSHARE_PROFILE_ID: ${ARYSHARE_PROFILE_ID} BROWSERBASE_API_KEY: ${BROWSERBASE_API_KEY} BROWSERBASE_PROJECT_ID: ${BROWSERBASE_PROJECT_ID} OPENWEATHER_API_KEY: ${OPENWEATHER_API_KEY} diff --git a/integrations-service/integrations/env.py b/integrations-service/integrations/env.py index 5102d3a00..1b6d59395 100644 --- a/integrations-service/integrations/env.py +++ b/integrations-service/integrations/env.py @@ -5,17 +5,13 @@ env.read_env() # Read .env file, if it exists # Load environment variables -rapid_api_key = env.str("RAPID_API_KEY") -rapid_api_host = env.str("RAPID_API_HOST") -aryshare_key = env.str("ARYSHARE_KEY") -aryshare_profile_id = env.str("ARYSHARE_PROFILE_ID") -browserbase_api_key = env.str("BROWSERBASE_API_KEY") -browserbase_project_id = env.str("BROWSERBASE_PROJECT_ID") -openweather_api_key = env.str("OPENWEATHER_API_KEY") -spider_api_key = env.str("SPIDER_API_KEY") -brave_api_key = env.str("BRAVE_API_KEY") -llama_api_key = env.str("LLAMA_API_KEY") -cloudinary_api_key = env.str("CLOUDINARY_API_KEY") -cloudinary_api_secret = env.str("CLOUDINARY_API_SECRET") -cloudinary_cloud_name = env.str("CLOUDINARY_CLOUD_NAME") -mailgun_password = env.str("MAILGUN_PASSWORD") +browserbase_api_key = env.str("BROWSERBASE_API_KEY", default=None) +browserbase_project_id = env.str("BROWSERBASE_PROJECT_ID", default=None) +openweather_api_key = env.str("OPENWEATHER_API_KEY", default=None) +spider_api_key = env.str("SPIDER_API_KEY", default=None) +brave_api_key = env.str("BRAVE_API_KEY", default=None) +llama_api_key = env.str("LLAMA_API_KEY", default=None) +cloudinary_api_key = env.str("CLOUDINARY_API_KEY", default=None) +cloudinary_api_secret = env.str("CLOUDINARY_API_SECRET", default=None) +cloudinary_cloud_name = env.str("CLOUDINARY_CLOUD_NAME", default=None) +mailgun_password = env.str("MAILGUN_PASSWORD", default=None) From d5701061c8c342f6b9e73e1dd7a21d586f6fb848 Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Thu, 21 Nov 2024 19:54:49 +0530 Subject: [PATCH 15/23] hotfix: Increase httpx timeouts Signed-off-by: Diwank Singh Tomer --- agents-api/agents_api/activities/excecute_api_call.py | 2 +- agents-api/agents_api/clients/integrations.py | 2 +- .../integrations/utils/integrations/browserbase.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/agents-api/agents_api/activities/excecute_api_call.py b/agents-api/agents_api/activities/excecute_api_call.py index 6c60a5cc1..19549281a 100644 --- a/agents-api/agents_api/activities/excecute_api_call.py +++ b/agents-api/agents_api/activities/excecute_api_call.py @@ -27,7 +27,7 @@ async def execute_api_call( request_args: RequestArgs, ) -> Any: try: - async with httpx.AsyncClient(timeout=60.0) as client: + async with httpx.AsyncClient(timeout=600) as client: arg_url = request_args.pop("url", None) arg_headers = request_args.pop("headers", None) diff --git a/agents-api/agents_api/clients/integrations.py b/agents-api/agents_api/clients/integrations.py index 7dd27d5a8..cb66c293a 100644 --- a/agents-api/agents_api/clients/integrations.py +++ b/agents-api/agents_api/clients/integrations.py @@ -21,7 +21,7 @@ async def run_integration_service( setup = setup or None - async with AsyncClient(timeout=60) as client: + async with AsyncClient(timeout=600) as client: response = await client.post( url, json={"arguments": arguments, "setup": setup}, diff --git a/integrations-service/integrations/utils/integrations/browserbase.py b/integrations-service/integrations/utils/integrations/browserbase.py index f9885e976..15e670a89 100644 --- a/integrations-service/integrations/utils/integrations/browserbase.py +++ b/integrations-service/integrations/utils/integrations/browserbase.py @@ -174,7 +174,7 @@ async def install_extension_from_github( github_url = f"https://github.com/{arguments.repository_name}/archive/refs/tags/{ arguments.ref}.zip" - async with httpx.AsyncClient() as client: + async with httpx.AsyncClient(timeout=600) as client: # Download the extension zip response = await client.get(github_url, follow_redirects=True) response.raise_for_status() From 5f9408fafbc25da496b76bcf002b3c90fae7a402 Mon Sep 17 00:00:00 2001 From: Vedant Sahai Date: Thu, 21 Nov 2024 13:53:22 -0500 Subject: [PATCH 16/23] fix: broswerbase integration (#859) > [!IMPORTANT] > Fixes `create_session` in `browserbase.py` to correctly use `browserbase_project_id` for `DEMO_PROJECT_ID`. > > - **Behavior**: > - Fixes `create_session` in `browserbase.py` to correctly assign `project_id` from `browserbase_project_id` when `DEMO_PROJECT_ID` is used. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 979435237bb404865311273c502f76503ff49bdb. It will automatically update as commits are pushed. --- .../integrations/utils/integrations/browserbase.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/integrations-service/integrations/utils/integrations/browserbase.py b/integrations-service/integrations/utils/integrations/browserbase.py index 15e670a89..a3fd1f2f8 100644 --- a/integrations-service/integrations/utils/integrations/browserbase.py +++ b/integrations-service/integrations/utils/integrations/browserbase.py @@ -81,7 +81,7 @@ async def create_session( client = get_browserbase_client(setup) if arguments.project_id == "DEMO_PROJECT_ID": - arguments.project_id = setup.browserbase_project_id + arguments.project_id = browserbase_project_id options = CreateSessionOptions( projectId=arguments.project_id or setup.project_id, From 3485a323c97e264f13636d96e8694751a1bca685 Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Fri, 22 Nov 2024 03:31:27 +0300 Subject: [PATCH 17/23] feat(docs): Update cookbooks (#858) > [!IMPORTANT] > Standardized cookbook file names and removed outdated files for improved consistency and organization. > > - **File Renames**: > - `01-Website_Crawler_using_Spider.py` to `01-website-crawler.py`. > - `02-Sarcastic_News_Headline_Generator.py` to `02-sarcastic-news-headline-generator.py`. > - `04-TripPlanner_With_Weather_And_WikiInfo.py` to `03-trip-planning-assistant.py`. > - `17-Hook-Generator-Trending-Reels.py` to `04-hook-generator-trending-reels.py`. > - **File Deletions**: > - Removed 13 outdated files including `03-SmartResearcher_With_WebSearch.py`, `05-Basic_Agent_Creation_and_Interaction.py`, and `06-Designing_Multi-Step_Tasks.py`. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 27fe0e6f2fc8888fcaf86ef5d25ebfba49cb0ad7. It will automatically update as commits are pushed. --------- Co-authored-by: vedantsahai18 --- cookbooks/00-Devfest-Email-Assistant.ipynb | 168 +++ .../01-Website_Crawler_using_Spider.ipynb | 492 -------- cookbooks/01-website-crawler.ipynb | 557 +++++++++ ..._using_Spider.py => 01-website-crawler.py} | 0 ...02-Sarcastic_News_Headline_Generator.ipynb | 357 ------ ...02-sarcastic-news-headline-generator.ipynb | 562 +++++++++ ...> 02-sarcastic-news-headline-generator.py} | 0 .../03-SmartResearcher_With_WebSearch.ipynb | 395 ------ .../03-SmartResearcher_With_WebSearch.py | 110 -- cookbooks/03-trip-planning-assistant.ipynb | 1065 +++++++++++++++++ ...iInfo.py => 03-trip-planning-assistant.py} | 81 +- ...ripPlanner_With_Weather_And_WikiInfo.ipynb | 418 ------- ...=> 04-hook-generator-trending-reels.ipynb} | 42 +- ...py => 04-hook-generator-trending-reels.py} | 1 - ...05-Basic_Agent_Creation_and_Interaction.py | 70 -- ...deo-processing-with-natural-language.ipynb | 579 +++++++++ cookbooks/06-Designing_Multi-Step_Tasks.py | 139 --- cookbooks/06-browser-use.ipynb | 664 ++++++++++ .../07-Integrating_External_Tools_and_APIs.py | 128 -- cookbooks/08-Managing_Persistent_Sessions.py | 146 --- .../09-User_Management_and_Personalization.py | 196 --- .../10-Document_Management_and_Search.py | 184 --- cookbooks/11-Advanced_Chat_Interactions.py | 177 --- cookbooks/12-Monitoring_Task_Executions.py | 162 --- cookbooks/13-Error_Handling_and_Recovery.py | 165 --- ...utomated_Webinar_Scheduling_Workflow.ipynb | 306 ----- ...4-Automated_Webinar_Scheduling_Workflow.py | 244 ---- cookbooks/15-Personal_Finance_Tracker.ipynb | 208 ---- cookbooks/15-Personal_Finance_Tracker.py | 164 --- ...E_commerce_Order_Processing_Workflow.ipynb | 381 ------ ...16-E_commerce_Order_Processing_Workflow.py | 238 ---- 31 files changed, 3690 insertions(+), 4709 deletions(-) delete mode 100644 cookbooks/01-Website_Crawler_using_Spider.ipynb create mode 100644 cookbooks/01-website-crawler.ipynb rename cookbooks/{01-Website_Crawler_using_Spider.py => 01-website-crawler.py} (100%) delete mode 100644 cookbooks/02-Sarcastic_News_Headline_Generator.ipynb create mode 100644 cookbooks/02-sarcastic-news-headline-generator.ipynb rename cookbooks/{02-Sarcastic_News_Headline_Generator.py => 02-sarcastic-news-headline-generator.py} (100%) delete mode 100644 cookbooks/03-SmartResearcher_With_WebSearch.ipynb delete mode 100644 cookbooks/03-SmartResearcher_With_WebSearch.py create mode 100644 cookbooks/03-trip-planning-assistant.ipynb rename cookbooks/{04-TripPlanner_With_Weather_And_WikiInfo.py => 03-trip-planning-assistant.py} (51%) delete mode 100644 cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.ipynb rename cookbooks/{17-Hook-Generator-Trending-Reels.ipynb => 04-hook-generator-trending-reels.ipynb} (96%) rename cookbooks/{17-Hook-Generator-Trending-Reels.py => 04-hook-generator-trending-reels.py} (99%) delete mode 100644 cookbooks/05-Basic_Agent_Creation_and_Interaction.py create mode 100644 cookbooks/05-video-processing-with-natural-language.ipynb delete mode 100644 cookbooks/06-Designing_Multi-Step_Tasks.py create mode 100644 cookbooks/06-browser-use.ipynb delete mode 100644 cookbooks/07-Integrating_External_Tools_and_APIs.py delete mode 100644 cookbooks/08-Managing_Persistent_Sessions.py delete mode 100644 cookbooks/09-User_Management_and_Personalization.py delete mode 100644 cookbooks/10-Document_Management_and_Search.py delete mode 100644 cookbooks/11-Advanced_Chat_Interactions.py delete mode 100644 cookbooks/12-Monitoring_Task_Executions.py delete mode 100644 cookbooks/13-Error_Handling_and_Recovery.py delete mode 100644 cookbooks/14-Automated_Webinar_Scheduling_Workflow.ipynb delete mode 100644 cookbooks/14-Automated_Webinar_Scheduling_Workflow.py delete mode 100644 cookbooks/15-Personal_Finance_Tracker.ipynb delete mode 100644 cookbooks/15-Personal_Finance_Tracker.py delete mode 100644 cookbooks/16-E_commerce_Order_Processing_Workflow.ipynb delete mode 100644 cookbooks/16-E_commerce_Order_Processing_Workflow.py diff --git a/cookbooks/00-Devfest-Email-Assistant.ipynb b/cookbooks/00-Devfest-Email-Assistant.ipynb index 6182d7152..18cafc1e8 100644 --- a/cookbooks/00-Devfest-Email-Assistant.ipynb +++ b/cookbooks/00-Devfest-Email-Assistant.ipynb @@ -1,5 +1,101 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \"julep\"\n", + "
\n", + "\n", + "

\n", + "
\n", + " Explore Docs (wip)\n", + " ·\n", + " Discord\n", + " ·\n", + " 𝕏\n", + " ·\n", + " LinkedIn\n", + "

\n", + "\n", + "

\n", + " \"NPM\n", + "  \n", + " \"PyPI\n", + "  \n", + " \"Docker\n", + "  \n", + " \"GitHub\n", + "

\n", + "\n", + "## Task Definition: Email Assistant\n", + "\n", + "### Overview\n", + "\n", + "This task creates an email assistant that can receive emails, search through Julep documentation, and respond to user inquiries automatically. It combines email integration with documentation search capabilities to provide relevant and informed responses.\n", + "\n", + "### Task Tools:\n", + "\n", + "**send_email**: An `integration` type tool that handles email sending via Mailgun SMTP.\n", + "**search_docs**: A `system` type tool that searches through agent documentation.\n", + "\n", + "### Task Input:\n", + "\n", + "A dictionary containing:\n", + "- **from**: Sender's email address\n", + "- **to**: Recipient's email address\n", + "- **subject**: Email subject\n", + "- **body**: Email content\n", + "\n", + "### Task Output:\n", + "\n", + "An email response sent to the inquirer with:\n", + "- Generated subject line\n", + "- Generated response body based on documentation search results\n", + "\n", + "### Task Flow\n", + "\n", + "1. **Input**: Receive email details (from, to, subject, body)\n", + "2. **Query Generation**: Generate a search query based on the email content\n", + "3. **Documentation Search**: Search Julep documentation using the generated query\n", + "4. **Response Generation**: Create a response using the found documentation\n", + "5. **Email Sending**: Send the response back to the original sender via Mailgun\n", + "\n", + "```plaintext\n", + "+----------+ +-------------+ +------------+ +-----------+\n", + "| Email | | Query | | Doc | | Email |\n", + "| Input | --> | Generation | --> | Search | --> | Response |\n", + "| (Query) | | | | | | Output |\n", + "+----------+ +-------------+ +------------+ +-----------+\n", + " | | | |\n", + " | | | |\n", + " v v v v\n", + " \"How do I\" Create search Find relevant \"Here's how to\n", + " \"use Julep?\" keywords documentation get started...\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "### Additional Information\n", + "\n", + "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", + "\n", + "**Author:** Julep AI \n", + "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -29,6 +125,13 @@ "!pip install julep" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Julep Client with the API Key" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -45,6 +148,17 @@ "julep = Julep(api_key=api_key, environment=\"dev\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an \"agent\"\n", + "\n", + "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", + "\n", + "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -82,6 +196,19 @@ "agent.id" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Task\n", + "\n", + "Tasks in Julep are Github-Actions-style workflows that define long-running, multi-step actions.\n", + "\n", + "You can use them to conduct complex actions by defining them step-by-step.\n", + "\n", + "To learn more about tasks, please refer to the `Tasks` section in [Julep Concepts](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#tasks)." + ] + }, { "cell_type": "code", "execution_count": 11, @@ -180,6 +307,22 @@ "\"\"\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notes:\n", + "- The reason for using the quadruple curly braces `{{{{}}}}` for the jinja template is to avoid conflicts with the curly braces when using the `f` formatted strings in python. [More information here](https://stackoverflow.com/questions/64493332/jinja-templating-in-airflow-along-with-formatted-text)\n", + "- The `unwrap: True` in the prompt step is used to unwrap the output of the prompt step (to unwrap the `choices[0].message.content` from the output of the model).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a task" + ] + }, { "cell_type": "code", "execution_count": 12, @@ -212,6 +355,15 @@ "task.id" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an Execution\n", + "\n", + "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." + ] + }, { "cell_type": "code", "execution_count": 14, @@ -244,6 +396,22 @@ "julep.executions.get(execution.id)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking execution details and output\n", + "\n", + "There are multiple ways to get the execution details and the output:\n", + "\n", + "1. **Get Execution Details**: This method retrieves the details of the execution, including the output of the last transition that took place.\n", + "\n", + "2. **List Transitions**: This method lists all the task steps that have been executed up to this point in time, so the output of a successful execution will be the output of the last transition (first in the transition list as it is in reverse chronological order), which should have a type of `finish`.\n", + "\n", + "\n", + "Note: You need to wait for a few seconds for the execution to complete before you can get the final output, so feel free to run the following cells multiple times until you get the final output.\n" + ] + }, { "cell_type": "code", "execution_count": 16, diff --git a/cookbooks/01-Website_Crawler_using_Spider.ipynb b/cookbooks/01-Website_Crawler_using_Spider.ipynb deleted file mode 100644 index e813be743..000000000 --- a/cookbooks/01-Website_Crawler_using_Spider.ipynb +++ /dev/null @@ -1,492 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \"julep\"\n", - "
\n", - "\n", - "## Task Definition: Spider Crawler Integration\n", - "\n", - "### Overview\n", - "\n", - "This task is a simple task that leverages the spider `integration` tool, and combines it with a prompt step to crawl a website for a given URL, and then create a summary of the results.\n", - "\n", - "### Task Tools:\n", - "\n", - "**Spider Crawler**: An `integration` type tool that can crawl the web and extract data from a given URL.\n", - "\n", - "### Task Input:\n", - "\n", - "**url**: The URL of the website to crawl.\n", - "\n", - "### Task Output:\n", - "\n", - "**output**: A dictionary that contains a `documents` key which contains the extracted data from the given URL. Check the output below for a detailed output schema.\n", - "\n", - "### Task Flow\n", - "\n", - "1. **Input**: The user provides a URL to crawl.\n", - "\n", - "2. **Spider Tool Integration**: The `spider_crawler` tool is called to crawl the web and extract data from the given URL.\n", - "\n", - "3. **Prompt Step**: The prompt step is used to create a summary of the results from the spider tool.\n", - "\n", - "4. **Output**: The final output is the summary of the results from the spider tool." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implementation\n", - "\n", - "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "### Additional Information\n", - "\n", - "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", - "\n", - "**Author:** Julep AI \n", - "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Installing the Julep Client" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install julep -U --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "\n", - "# NOTE: these UUIDs are used in order not to use the `create_or_update` methods instead of\n", - "# the `create` methods for the sake of not creating new resources every time a cell is run.\n", - "AGENT_UUID = uuid.uuid4()\n", - "TASK_UUID = uuid.uuid4() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Julep Client with the API Key" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from julep import Client\n", - "import os\n", - "\n", - "api_key = os.getenv(\"JULEP_API_KEY\")\n", - "\n", - "# Create a Julep client\n", - "client = Client(api_key=api_key, environment=\"dev\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an \"agent\"\n", - "\n", - "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", - "\n", - "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Create agent\n", - "agent = client.agents.create_or_update(\n", - " agent_id=AGENT_UUID,\n", - " name=\"Spiderman\",\n", - " about=\"AI that can crawl the web and extract data\",\n", - " model=\"gpt-4o\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining a Task\n", - "\n", - "Tasks in Julep are Github-Actions-style workflows that define long-running, multi-step actions.\n", - "\n", - "You can use them to conduct complex actions by defining them step-by-step.\n", - "\n", - "To learn more about tasks, please refer to the `Tasks` section in [Julep Concepts](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#tasks)." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "import yaml\n", - "\n", - "spider_api_key = os.getenv(\"SPIDER_API_KEY\")\n", - "\n", - "# Define the task\n", - "task_def = yaml.safe_load(f\"\"\"\n", - "name: Crawling Task\n", - "\n", - "# Define the tools that the agent will use in this workflow\n", - "tools:\n", - "- name: spider_crawler\n", - " type: integration\n", - " integration:\n", - " provider: spider\n", - " setup:\n", - " spider_api_key: \"{spider_api_key}\"\n", - "\n", - "# Define the steps of the workflow\n", - "main:\n", - "# Define a tool call step that calls the spider_crawler tool with the url input\n", - "- tool: spider_crawler\n", - " arguments:\n", - " url: \"_['url']\" # You can also use 'inputs[0]['url']'\n", - " \n", - " \n", - "- prompt: |\n", - " You are {{{{agent.about}}}}\n", - " I have given you this url: {{{{inputs[0]['url']}}}}\n", - " And you have crawled that website. Here are the results you found:\n", - " {{{{_['documents']}}}}\n", - " I want you to create a short summary (no longer than 100 words) of the results you found while crawling that website.\n", - "\n", - " unwrap: True\n", - "\"\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notes:\n", - "- The reason for using the quadruple curly braces `{{{{}}}}` for the jinja template is to avoid conflicts with the curly braces when using the `f` formatted strings in python. [More information here](https://stackoverflow.com/questions/64493332/jinja-templating-in-airflow-along-with-formatted-text)\n", - "- The `unwrap: True` in the prompt step is used to unwrap the output of the prompt step (to unwrap the `choices[0].message.content` from the output of the model).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating/Updating a task" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# creating the task object\n", - "task = client.tasks.create_or_update(\n", - " task_id=TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **task_def\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an Execution\n", - "\n", - "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creates a execution worklow for the Task defined in the yaml file." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# creating an execution object\n", - "execution = client.executions.create(\n", - " task_id=TASK_UUID,\n", - " input={\n", - " \"url\": \"https://spider.cloud\"\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are multiple ways to get the execution details and the output:\n", - "\n", - "1. **Get Execution Details**: This method retrieves the details of the execution, including the output of the last transition that took place.\n", - "\n", - "2. **List Transitions**: This method lists all the task steps that have been executed up to this point in time, so the output of a successful execution will be the output of the last transition (first in the transition list as it is in reverse chronological order), which should have a type of `finish`.\n", - "\n", - "\n", - "Note: You need to wait for a few seconds for the execution to complete before you can get the final output, so feel free to run the following cells multiple times until you get the final output.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Spider.cloud is a cutting-edge web crawling service designed for AI applications, offering high-speed and cost-effective data collection. Built with a robust Rust engine, Spider supports seamless integration with AI tools and provides various response formats, including LLM-ready markdown. It features concurrent streaming, caching, smart mode with headless Chrome, and auto proxy rotations. Ideal for large-scale projects, Spider ensures compliance with robots.txt and offers a free trial. Trusted by tech leaders, it enables efficient data curation and transformation, with capabilities to handle extreme workloads and dynamic content rendering.\n" - ] - } - ], - "source": [ - "# Get execution details\n", - "execution = client.executions.get(execution.id)\n", - "# Print the output\n", - "print(execution.output)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transition type: init\n", - "Transition output: {'url': 'https://spider.cloud'}\n", - "--------------------------------------------------\n", - "Transition type: step\n", - "Transition output: {'documents': [{'id': None, 'metadata': {'description': 'Experience cutting-edge web crawling with unparalleled speeds, perfect for LLMs, Machine Learning, and Artificial Intelligence. The fastest and most efficient web scraper tailored for AI applications.', 'domain': 'spider.cloud', 'extracted_data': None, 'file_size': 9112, 'keywords': ['AI agent stack', 'AI 🕷️ Spider', 'AWS infrastructure reduced', 'Auto Proxy rotations', 'Comprehensive Data Curation', 'Concurrent Streaming Save time', 'Data Collecting Projects Today Jumpstart web crawling', 'FAQ Frequently asked questions', 'Fastest Web Crawler', 'Multiple response formats', 'Open Source Spider engine', 'Performance Tuned Spider', 'Seamless Integrations Seamlessly integrate Spider', 'Smart Mode Spider dynamically switches', 'Spider accurately crawls', 'Spider convert web data', 'Spider outputs HTML', 'Toggle Theme Sign InRegister', 'Transform Convert raw HTML', 'WilliamEspegren Web crawler built', 'achieve crawling thousands', 'affordable web scraping', 'and`XML`for API responses', 'caching repeated web page crawls', 'cost step caching', 'crazy resource management Aaaaaaand', 'custom browser scripting', 'data = json', 'data formats including LLM-ready markdown', 'ensure continuous maintenance', 'ensuring data curation perfectly aligned', 'finest data collecting solution', 'full elastic scaling concurrency', 'handle extreme workloads', 'iammerrick Rust based crawler Spider', 'insightful data solutions', 'json headers =', 'large scraping projects', 'large-scale data collection', 'latest AI models', 'leading tech businesses worldwide', 'leading web crawling tool designed', 'real-time web data', 'request Python JSONL Copy ``` import requests', 'requires JavaScript rendering', 'response = requests', 'robust Rust engine scales effortlessly', 'scrapes significantly faster', 'search engine results', 'traditional scraping services Spider API Request Modes', 'training AI models'], 'pathname': '/', 'resource_type': '.md', 'title': 'Spider: The Web Crawler for AI', 'url': '8475428e-4e0c-44de-967f-c14fb73cf490/spider.cloud/_cloud/12638123428881205758.md', 'user_id': '8475428e-4e0c-44de-967f-c14fb73cf490'}, 'page_content': 'Spider: The Web Crawler for AI\\n[Spider](https://spider.cloud/)\\n[Github1k](https://github.com/spider-rs/spider)\\n[API](https://spider.cloud/docs/api)\\n[Docs](https://spider.cloud/docs/overview)\\n[Pricing](https://spider.cloud/credits/new)\\nToggle Theme\\nSign InRegister\\nTo help you get started with Spider, we’ll give you $200 in credits when you spend $100.[Terms apply](https://spider.cloud/promotion-spider-credits)\\n# The Web Crawler for AI Agents and LLMs\\nSpider offers the finest data collecting solution. Engineered for speed and scalability, it allows you to elevate your AI projects.\\n[Get Started](https://spider.cloud/credits/new)View Preview\\n* Basic\\n* Streaming\\nExample request\\nPython\\nJSONL\\nCopy\\n```\\nimport requests, os, json\\nheaders = {\\n \\'Authorization\\': os.getenv(\"SPIDER_API_KEY\"),\\n \\'Content-Type\\': \\'application/jsonl\\',\\n}\\njson_data = {\"limit\":50,\"metadata\":True,\"url\":\"https://spider.cloud\"}\\nresponse = requests.post(\\'https://api.spider.cloud/crawl\\', \\n headers=headers, json=json_data, stream=True)\\nwith response as r:\\n r.raise_for_status()\\nfor chunk in r.iter_lines(\\n chunk_size=None, \\n decode_unicode=True\\n ):\\n data = json.loads(chunk)\\n print(data)\\n```\\n[Free Trial](https://spider.cloud/credits/new?free-trial=1)\\nExample Response\\n## Built with the need for**Speed**\\nExperience the power of**Spider**, built fully in**Rust**for next-generation scalability.\\n### 2.4secs\\nTo crawl over 20,000 pages\\n### 500-1000x\\nFaster than alternatives\\n### 500x\\nCheaper than traditional scraping services\\nSpider API Request Modes · Benchmarked tailwindcss.com ·06/16/2024\\n[See framework benchmarks](https://github.com/spider-rs/spider/blob/main/benches/BENCHMARKS.md)\\n### Seamless Integrations\\nSeamlessly integrate Spider with a wide range of platforms, ensuring data curation perfectly aligned with your requirements. Compatible with all major AI tools.\\n[LangChain integration](https://python.langchain.com/docs/integrations/document_loaders/spider)[LlamaIndex integration](https://docs.llamaindex.ai/en/stable/examples/data_connectors/WebPageDemo/#using-spider-reader)[CrewAI integration](https://docs.crewai.com/tools/SpiderTool/)[FlowWiseAI integration](https://docs.flowiseai.com/integrations/langchain/document-loaders/spider-web-scraper-crawler)[Composio integration](https://docs.composio.dev/introduction/foundations/components/list_local_tools#spider-crawler)[PhiData integration](https://docs.phidata.com/tools/spider)\\n### Concurrent Streaming\\nSave time and money without having to worry about bandwidth concerns by effectively streaming all the results concurrently. The latency cost that is saved becomes drastic as you crawl more websites.\\n### Warp Speed\\nPowered by the cutting-edge[Spider](https://github.com/spider-rs/spider)open-source project, our robust Rust engine scales effortlessly to handle extreme workloads. We ensure continuous maintenance and improvement for top-tier performance.\\n## Kickstart Your Data Collecting Projects Today\\nJumpstart web crawling with full elastic scaling concurrency, optimal formats, and AI scraping.\\n### Performance Tuned\\nSpider is written in Rust and runs in full concurrency to achieve crawling thousands of pages in secs.\\n### Multiple response formats\\nGet clean and formatted markdown, HTML, or text content for fine-tuning or training AI models.\\n### Caching\\nFurther boost speed by caching repeated web page crawls to minimize expenses while building.\\n### Smart Mode\\nSpider dynamically switches to Headless Chrome when it needs to quick.\\nBeta\\n### Scrape with AI\\nDo custom browser scripting and data extraction using the latest AI models with no cost step caching.\\n### The crawler for LLMs\\nDon\\'t let crawling and scraping be the highest latency in your LLM & AI agent stack.\\n### Scrape with no headaches\\n* Auto Proxy rotations\\n* Agent headers\\n* Anti-bot detections\\n* Headless chrome\\n* Markdown responses\\n### The Fastest Web Crawler\\n* Powered by[spider-rs](https://github.com/spider-rs/spider)\\n* 20,000 pages/seconds\\n* Unlimited concurrency\\n* Simple API\\n* 50,000 RPM\\n### Do more with AI\\n* Browser scripting\\n* Advanced extraction\\n* Data pipelines\\n* Ideal for LLMs and AI Agents\\n* Accurate labeling\\n## Achieve more with these new API features\\nOur API is set to stream so you can act in realtime.\\n### Search\\nGet access to search engine results from anywhere and easily crawl and transform pages to LLM-ready markdown.\\n[Explore Search](https://spider.cloud/docs/api#search)\\n### Transform\\nConvert raw HTML into markdown easily by using this API. Transform thousands of html pages in seconds.\\n[Explore Transform](https://spider.cloud/docs/api#transform)\\n## Join the community\\nBacked by a network of early advocates, contributors, and supporters.\\n[GitHub discussions\\n](https://github.com/orgs/spider-rs/discussions)\\n[Discord\\n](https://discord.spider.cloud)\\n[\\n@iammerrick\\nRust based crawler Spider is next level for crawling & scraping sites. So fast. Their cloud offering is also so easy to use. Good stuff. https://github.com/spider-rs/spider\\n](https://twitter.com/iammerrick/status/1787873425446572462)\\n[\\n@WilliamEspegren\\nWeb crawler built in rust, currently the nr1 performance in the world with crazy resource management Aaaaaaand they have a cloud offer, that’s wayyyy cheaper than any competitor Name a reason for me to use anything else? github.com/spider-rs/spid…\\n](https://twitter.com/WilliamEspegren/status/1789419820821184764)\\n[\\n@gasa\\n@gasathenaper is the best crawling tool i have used. I had a complicated project where i needed to paste url and get the website whole website data. Spider cloud does it in an instant\\n](https://x.com/gasathenaper/status/1810612492596383948)\\n[\\n@Ashpreet Bedi\\n@ashpreetbedi is THE best crawler out there, give it a try\\n](https://x.com/ashpreetbedi/status/1815512219003572315?s=46&t=37F5QP_8oKqOsNpHSo6VVw)\\n[\\n@Troyusrex\\nI found a new tool, Spider-rs, which scrapes significantly faster and handles more scenarios than the basic scraper I built did. Our use of Spider-rs and AWS infrastructure reduced the scraping time from four months to under a week.\\n](https://medium.com/@troyusrex/inside-my-virtual-college-advisor-a-deep-dive-into-rag-ai-and-agent-technology-84731b2928f7#1326)\\n[\\n@Dify.AI\\n🕷️ Spider @spider\\\\_rust can be used as a built-in tool in #Dify Workflow or as an LLM-callable tool in Agent. It allows fast and affordable web scraping and crawling when your AI applications need real-time web data for context.\\n](https://x.com/dify_ai/status/1818226971056243089)\\n## FAQ\\nFrequently asked questions about Spider.\\n### What is Spider?\\nSpider is a leading web crawling tool designed for speed and cost-effectiveness, supporting various data formats including LLM-ready markdown.\\n### Why is my website not crawling?\\nYour crawl may fail if it requires JavaScript rendering. Try setting your request to \\'chrome\\' to solve this issue.\\n### Can you crawl all pages?\\nYes, Spider accurately crawls all necessary content without needing a sitemap.\\n### What formats can Spider convert web data into?\\nSpider outputs HTML, raw, text, and various markdown formats. It supports`JSON`,`JSONL`,`CSV`, and`XML`for API responses.\\n### Is Spider suitable for large scraping projects?\\nAbsolutely, Spider is ideal for large-scale data collection and offers a cost-effective dashboard for data management.\\n### How can I try Spider?\\nPurchase credits for our cloud system or test the Open Source Spider engine to explore its capabilities.\\n### Does it respect robots.txt?\\nYes, compliance with robots.txt is default, but you can disable this if necessary.\\n### Unable to get dynamic content?\\nIf you are having trouble getting dynamic pages, try setting the request parameter to \"chrome\" or \"smart.\" You may also need to set `disable\\\\_intercept` to allow third-party or external scripts to run.\\n### Why is my crawl going slow?\\nIf you are experiencing a slow crawl, it is most likely due to the robots.txt file for the website. The robots.txt file may have a crawl delay set, and we respect the delay up to 60 seconds.\\n### Do you offer a Free Trial?\\nYes, you can try out the service before being charged for free at[checkout](https://spider.cloud/credits/new?free-trial=1).\\n## Comprehensive Data Curation for Everyone\\nTrusted by leading tech businesses worldwide to deliver accurate and insightful data solutions.\\n[](https://zapier.com/apps/spider/integrations)\\n### Next generation data for AI, scale to millions\\n[Start now](https://spider.cloud/credits/new)\\n### Company\\n* [About](https://spider.cloud/about)\\n* [Privacy](https://spider.cloud/privacy)\\n* [Terms](https://spider.cloud/eula)\\n* [FAQ](https://spider.cloud/faq)\\n### Resources\\n* [API](https://spider.cloud/docs/api)\\n* [Docs](https://spider.cloud/docs/overview)\\n* [Guides](https://spider.cloud/guides)\\n* [Spider.rs Docs](https://docs.rs/spider/latest/spider/)\\n### Services\\n* [Pricing](https://spider.cloud/credits/new)\\n* [Web Crawling and Scraping](https://spider.cloud/web-crawling-and-scraping)\\n[All systems normal.](https://spidercloud.statuspage.io/)\\n[GitHub](https://github.com/spider-rs/spider)\\n[Discord](https://discord.spider.cloud)\\n[Twitter](https://twitter.com/spider_rust)\\n', 'type': 'Document'}]}\n", - "--------------------------------------------------\n", - "Transition type: finish\n", - "Transition output: Spider.cloud is a cutting-edge web crawling service designed for AI applications, offering high-speed and cost-effective data collection. Built with a robust Rust engine, Spider supports seamless integration with AI tools and provides various response formats, including LLM-ready markdown. It features concurrent streaming, caching, smart mode with headless Chrome, and auto proxy rotations. Ideal for large-scale projects, Spider ensures compliance with robots.txt and offers a free trial. Trusted by tech leaders, it enables efficient data curation and transformation, with capabilities to handle extreme workloads and dynamic content rendering.\n", - "--------------------------------------------------\n" - ] - } - ], - "source": [ - "# Lists all the task steps that have been executed up to this point in time\n", - "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", - "\n", - "# Transitions are retreived in reverse chronological order\n", - "for transition in reversed(transitions):\n", - " print(\"Transition type: \", transition.type)\n", - " print(\"Transition output: \", transition.output)\n", - " print(\"-\"*50)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the same task with a different URL" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use the same code to run the same task, but with a different URL" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "execution = client.executions.create(\n", - " task_id=TASK_UUID,\n", - " input={\n", - " \"url\": \"https://www.harvard.edu/\"\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Harvard University's website highlights its commitment to excellence in teaching, research, and leadership development globally\n", - "It offers diverse academic programs, including undergraduate, graduate, and professional learning\n", - "The site features information about Harvard's various schools, libraries, museums, and research initiatives\n", - "It emphasizes Harvard's global impact, historical achievements, and contributions to fields like climate change, medicine, and biodiversity\n", - "The site also provides resources for campus visits, events, and news updates, showcasing Harvard's vibrant community and its role in advancing knowledge and societal well-being.\n" - ] - } - ], - "source": [ - "execution = client.executions.get(execution.id)\n", - "print(\"\\n\".join(execution.output.split(\". \")))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: you can get the output of the crawling step by accessing the corresponding transition's output from the transitions list.\n", - "\n", - "Example:" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'documents': [{'id': None,\n", - " 'metadata': {'description': 'Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders who make a difference globally.',\n", - " 'domain': 'www.harvard.edu',\n", - " 'extracted_data': None,\n", - " 'file_size': 19620,\n", - " 'keywords': ['Amazon rainforest immersion',\n", - " 'Ants Army ants',\n", - " 'Arnold Arboretum Horticultural Library',\n", - " 'Cabot Science Library',\n", - " 'Dumbarton Oaks Research Library',\n", - " 'Ernst Mayr Library',\n", - " 'Fine Arts Library',\n", - " 'Frances Loeb Library',\n", - " 'Griffin Graduate School',\n", - " 'Harvard Amazon Rainforest Immersion program',\n", - " 'Harvard Art Museums',\n", - " 'Harvard Business School',\n", - " 'Harvard Divinity School',\n", - " 'Harvard Divinity School Library',\n", - " 'Harvard Film Archive',\n", - " 'Harvard Forest research project explored naturalist Frank Morton Jones',\n", - " 'Harvard Graduate School',\n", - " 'Harvard Kennedy School',\n", - " 'Harvard Law School',\n", - " 'Harvard Law School Library',\n", - " 'Harvard Map Collection',\n", - " 'Harvard Medical School',\n", - " 'Harvard Radcliffe Institute',\n", - " 'Harvard Schools Academics Visit',\n", - " 'Harvard University Archives',\n", - " 'Harvard University Herbaria',\n", - " 'Harvard scientist Gary Ruvkun awarded medicine prize',\n", - " 'Historical Scientific Instruments',\n", - " 'Howler monkeys Monkeys don',\n", - " 'Loeb Music Library',\n", - " 'Meet Professor Gary Ruvkun',\n", - " 'Moose Harvard Forest',\n", - " 'Recent topics include',\n", - " 'Report Copyright Infringement',\n", - " 'Report Security Issue',\n", - " 'Trending News Stories News',\n", - " 'Warren Anatomical Museum',\n", - " 'Woodberry Poetry Room',\n", - " 'Worms Invasive worms',\n", - " 'altering soil composition',\n", - " 'boost mental health',\n", - " 'climate change mitigation',\n", - " 'collective foraging behavior',\n", - " 'die-hard Harvard buffs',\n", - " 'eastern North America',\n", - " 'eliminating ground cover',\n", - " 'endangered Chinese tree',\n", - " 'forest fires impact',\n", - " 'forests Climate change',\n", - " 'growing global population continue',\n", - " 'long-term ecological research site located',\n", - " 'mycorrhizae plays important roles',\n", - " 'offset carbon emissions',\n", - " 'people experience long COVID symptoms',\n", - " 'pitcher plant research',\n", - " 'rainforests promote biodiversity',\n", - " 'temperate woody plants',\n", - " 'woods Research shows'],\n", - " 'pathname': '/',\n", - " 'resource_type': '.md',\n", - " 'title': 'Harvard University',\n", - " 'url': '8475428e-4e0c-44de-967f-c14fb73cf490/www.harvard.edu/www_edu/12638123428881205758.md',\n", - " 'user_id': '8475428e-4e0c-44de-967f-c14fb73cf490'},\n", - " 'page_content': \"Harvard University\\n[Skip to main content](#main-content)\\n[Harvard University](https://www.harvard.edu/)\\nSearch\\n**SearchQuick Links**\\n1. [A to Z index](https://www.harvard.edu/a-to-z/)\\nMenu\\n1. Academics\\n\\nAcademics\\n**[Academics](https://www.harvard.edu/academics/)**Learning at Harvard can happen for every type of learner, at any phase of life.\\n\\n1. Degree programs\\n\\nAcademics\\n**Degree programs**Browse all of our undergraduate concentrations and graduate degrees.\\n\\n1. [**Undergraduate Degrees**](https://www.harvard.edu//programs/?degree_levels=undergraduate)\\n 2. [**Graduate Degrees**](https://www.harvard.edu//programs/?degree_levels=graduate)\\n 3. [**Other**](https://www.harvard.edu/academics/professional-and-lifelong-learning/)\\n\\n2. [Professional and Lifelong Learning](https://www.harvard.edu/academics/professional-and-lifelong-learning/)\\n 3. [Harvard Online](https://www.harvardonline.harvard.edu/)\\n 4. Harvard Schools\\n\\nAcademics\\nVisit each School for information on admissions and financial aid.[Explore more](/schools/)\\n\\n1. [**Harvard College**](https://college.harvard.edu/)\\n 2. [**Harvard Business School**](http://www.hbs.edu)\\n 3. [**Harvard Division of Continuing Education**](https://www.dce.harvard.edu/)\\n 4. [**Harvard Divinity School**](https://hds.harvard.edu/)\\n 5. [**Harvard Faculty of Arts and Sciences**](https://www.fas.harvard.edu/)\\n 6. [**Harvard Kenneth C. Griffin Graduate School of Arts and Sciences**](https://gsas.harvard.edu/)\\n 7. [**Harvard Graduate School of Design**](https://www.gsd.harvard.edu/)\\n 8. [**Harvard Graduate School of Education**](https://gse.harvard.edu/)\\n 9. [**Harvard John A. Paulson School of Engineering and Applied Sciences**](https://www.seas.harvard.edu/)\\n 10. [**Harvard Kennedy School**](https://www.hks.harvard.edu/)\\n 11. [**Harvard Law School**](https://hls.harvard.edu/)\\n 12. [**Harvard Medical School**](https://hms.harvard.edu/)\\n 13. [**Harvard Radcliffe Institute**](https://www.radcliffe.harvard.edu/)\\n 14. [**Harvard School of Dental Medicine**](https://hsdm.harvard.edu/)\\n 15. [**Harvard T.H. Chan School of Public Health**](https://www.hsph.harvard.edu/)\\n\\n2. Campus\\n\\nCampus\\n**[Harvard's Campus](https://www.harvard.edu/campus/)**Get tickets to our next game, hours and locations for our libraries and museums, and information about your next career move.\\n\\n1. Libraries\\n\\nCampus\\n[Explore our libraries](https://www.harvard.edu/campus/libraries/)\\n\\n1. [**Arnold Arboretum Horticultural Library**](https://arboretum.harvard.edu/research/library/)\\n 2. [**Baker Library and Special Collections**](https://www.library.hbs.edu/)\\n 3. [**Biblioteca Berenson**](http://itatti.harvard.edu/berenson-library)\\n 4. [**Botany Libraries**](https://huh.harvard.edu/libraries)\\n 5. [**Cabot Science Library**](https://library.harvard.edu/libraries/cabot)\\n 6. [**Countway Library**](https://countway.harvard.edu/)\\n 7. [**Dumbarton Oaks Research Library**](https://www.doaks.org/research/library-archives)\\n 8. [**Ernst Mayr Library**](https://library.mcz.harvard.edu/)\\n 9. [**Fine Arts Library**](https://library.harvard.edu/libraries/fine-arts)\\n 10. [**Frances Loeb Library**](https://www.gsd.harvard.edu/frances-loeb-library/)\\n 11. [**Fung Library**](https://library.harvard.edu/libraries/fung)\\n 12. [**Gutman Library**](https://gse.harvard.edu/community/library)\\n 13. [**Harvard Divinity School Library**](https://library.hds.harvard.edu/)\\n 14. [**Harvard Film Archive**](https://harvardfilmarchive.org/)\\n 15. [**Harvard Law School Library**](https://hls.harvard.edu/library/)\\n 16. [**Harvard Map Collection**](https://library.harvard.edu/libraries/harvard-map-collection)\\n 17. [**Harvard University Archives**](https://library.harvard.edu/libraries/harvard-university-archives)\\n 18. [**Harvard-Yenching Library**](https://library.harvard.edu/libraries/yenching)\\n 19. [**HKS Library and Knowledge Services**](https://www.hks.harvard.edu/research-insights/library-knowledge-services)\\n 20. [**Houghton Library**](https://library.harvard.edu/libraries/houghton)\\n 21. [**Lamont Library**](https://library.harvard.edu/libraries/lamont)\\n 22. [**Loeb Music Library**](https://library.harvard.edu/libraries/loeb-music)\\n 23. [**Robbins Library of Philosophy**](https://library.harvard.edu/libraries/robbins-philosophy)\\n 24. [**Schlesinger Library on the History of Women in America**](https://www.radcliffe.harvard.edu/schlesinger-library)\\n 25. [**Tozzer Library**](https://library.harvard.edu/libraries/tozzer)\\n 26. [**Widener Library**](https://library.harvard.edu/libraries/widener)\\n 27. [**Woodberry Poetry Room**](https://library.harvard.edu/libraries/poetryroom)\\n\\n2. Museums\\n\\nCampus\\n[Explore our museums](https://www.harvard.edu/campus/museums/)\\n\\n1. [**The Arnold Arboretum**](http://www.arboretum.harvard.edu/)\\n 2. [**Carpenter Center for the Visual Arts**](http://ccva.fas.harvard.edu/)\\n 3. [**Collection of Historical Scientific Instruments**](http://chsi.harvard.edu/)\\n 4. [**Graduate School of Design Exhibitions**](http://www.gsd.harvard.edu/exhibitions/)\\n 5. [**Harvard Art Museums**](http://www.harvardartmuseums.org/)\\n 6. [**Harvard Forest**](https://harvardforest.fas.harvard.edu/)\\n 7. [**Harvard Museum of Natural History**](http://www.hmnh.harvard.edu/)\\n 8. [**The Harvard Museum of the Ancient Near East**](https://hmane.harvard.edu/)\\n 9. [**Harvard Museums of Science and Culture**](http://hmsc.harvard.edu/)\\n 10. [**Harvard University Herbaria**](http://www.huh.harvard.edu/)\\n 11. [**Mineralogical and Geological Museum**](http://mgmh.fas.harvard.edu/)\\n 12. [**Museum of Comparative Zoology**](http://www.mcz.harvard.edu/)\\n 13. [**The Peabody Museum of Archaeology and Ethnology**](http://www.peabody.harvard.edu/)\\n 14. [**Warren Anatomical Museum**](https://countway.harvard.edu/center-history-medicine/collections-research-access/warren-anatomical-museum-collection)\\n\\n3. [Athletics](https://www.harvard.edu/campus/athletics/)\\n 4. [Work at Harvard](https://www.harvard.edu/campus/work-at-harvard/)\\n 5. [Events](https://news.harvard.edu/gazette/harvard-events/)\\n 6. [Commencement](http://commencement.harvard.edu/)\\n\\n3. In Focus\\n\\nIn Focus\\n**Explore and understand the world with Harvard**In Focus is a curated examination of Harvard's research, scholarly work, and community. Recent topics include:\\n\\n1. Forests\\n\\nIn Focus\\n**Forests**From sprawling jungles to city parks, forests are complex ecosystems that support the livelihoods of flora, fauna, humans, and the planet.[Take a hike](https://www.harvard.edu/in-focus/forests/)\\n\\n1. **What's inside?**\\n 1. [\\n **Urban forests can help cool cities**](https://www.gsd.harvard.edu/2023/10/the-forest-for-the-trees-and-the-birds-and-the-people-and-the-planet/)\\n 2. [\\n **Forests can help offset carbon emissions**](https://news.harvard.edu/gazette/story/2022/11/new-report-shows-forests-have-big-role-to-play-in-climate-change-fight/)\\n\\n2. Harvard in the World\\n\\nIn Focus\\n**Harvard in the World**Harvard faculty, students, and alumni compose a global network that is exploring\\xa0health, law, government, education, business, and more around the world.[Explore the globe](https://www.harvard.edu/in-focus/harvard-in-the-world/)\\n\\n1. **What's inside**\\n 1. [\\n **Journey to the Amazon**](https://news.harvard.edu/gazette/story/2024/10/journey-to-a-key-front-in-climate-change-fight/)\\n 2. [\\n **Learn about Worldwide Week at Harvard**](https://worldwide.harvard.edu/worldwide-week-harvard-2024)\\n\\n3. Nobels at Harvard\\n\\nIn Focus\\n**Nobels at Harvard**Explore Harvard's history with the Nobel Prize and learn about winners past and present.[View the feature](https://www.harvard.edu/in-focus/nobels-at-harvard/)\\n\\n1. **What's inside**\\n 1. [\\n **Meet Professor Gary Ruvkun, a 2024 winner**](https://news.harvard.edu/gazette/story/2024/10/harvard-scientist-awarded-nobel/)\\n 2. [\\n **Meet Harvard's Nobel Laureates**](https://www.harvard.edu/about/history/nobel-laureates/)\\n\\n4. [\\n Explore the In Focus archives](https://www.harvard.edu/in-focus/)\\n\\n4. Visit\\n\\nVisit\\n**[Visit Harvard](https://www.harvard.edu/visit/)**Ideas and assistance for your trip to our campus.\\n\\n1. [Tours](https://www.harvard.edu/visit/tours/)\\n 2. [Maps and directions](https://www.harvard.edu/visit/maps-directions/)\\n 3. [Tour Providers](https://www.harvard.edu/visit/tour-providers/)\\n\\n5. About\\n\\nAbout\\n**[About Harvard](https://www.harvard.edu/about/)**Learn how Harvard is structured, explore our long history, and discover our extended community.\\n\\n1. History of Harvard\\n\\nAbout\\n**History of Harvard**Harvard is perhaps best-known because of its enduring history of innovation in education. But even die-hard Harvard buffs are not likely to know all of these Harvard firsts and historical snippets.[Learn more](https://www.harvard.edu/about/history/)\\n\\n1. [**History timeline**](https://www.harvard.edu/about/history/timeline/)\\n 2. [**Nobel Laureates**](https://www.harvard.edu/about/history/nobel-laureates/)\\n 3. [**Honorary Degrees**](https://www.harvard.edu/about/history/honorary-degrees/)\\n 4. [**Harvard shields**](https://www.harvard.edu/about/history/shields/)\\n\\n2. Leadership and governance\\n\\nAbout\\n[Learn about our Leadership](https://www.harvard.edu/about/leadership-and-governance/)\\n\\n1. [**President**](https://www.harvard.edu//president/)\\n 2. [**Officers and Deans**](https://www.harvard.edu/about/leadership-and-governance/officers-and-deans/)\\n 3. [**Harvard Corporation**](https://www.harvard.edu/about/leadership-and-governance/harvard-corporation/)\\n 4. [**Board of Overseers**](https://www.harvard.edu/about/leadership-and-governance/board-of-overseers/)\\n\\n3. [University Professorships](https://www.harvard.edu/about/university-professorships/)\\n 4. [Diversity and Inclusion](https://www.harvard.edu/about/diversity-and-inclusion/)\\n 5. [Endowment](https://www.harvard.edu/about/endowment/)\\n 6. [Harvard in the Community](https://www.harvard.edu/about/harvard-in-the-community/)\\n 7. [Harvard in the World](https://www.harvard.edu/about/harvard-in-the-world/)\\n\\n6. News\\n\\nNews\\n**[The Harvard Gazette](https://news.harvard.edu/gazette/)**Official news from Harvard University about science, medicine, art, campus life, University issues, and broader national and global concerns.\\n\\n1. Trending News Stories\\n\\nNews\\n[Read more news](http://news.harvard.edu)\\n\\n1. [**Unearthed papyrus contains lost scenes from Euripides’ plays**Alums help identify, decipher ‘one of the most significant new finds in Greek literature in this century’\\n ](https://news.harvard.edu/gazette/story/2024/10/unearthed-papyrus-contains-lost-scenes-from-euripides-plays/)\\n2. [**What’s next after a Nobel? It’s a surprise.**Harvard scientist Gary Ruvkun awarded medicine prize for microRNA insights. ‘My ignorance is bliss,’ he says.\\n ](https://news.harvard.edu/gazette/story/2024/10/harvard-scientist-awarded-nobel/)\\n3. [**Getting to the bottom of long COVID**A reservoir of virus in the body may explain why some people experience long COVID symptoms\\n ](https://news.harvard.edu/gazette/story/2024/10/getting-to-the-bottom-of-long-covid/)\\n\\n2. [Sign up for the Daily Gazette](https://www.pages01.net/harvard/gazette?email=)\\n\\n**NavigationQuick Links**\\n1. [A to Z index](https://www.harvard.edu/a-to-z/)\\n2. [Find a person](https://connections.harvard.edu/)\\n3. [Events](https://news.harvard.edu/gazette/harvard-events/)\\n4. [Media Relations](/media-relations)\\n5. [Alumni](https://alumni.harvard.edu/)\\n6. [Give Now](https://alumni.harvard.edu/giving/givenow)\\n7. [Emergency](https://www.harvard.edu/emergency/)\\n[Harvard University](https://www.harvard.edu/)\\nClose\\n# Harvard University\\n## Forests\\nFrom sprawling jungles to city parks, forests are complex ecosystems that support the livelihoods of flora, fauna, humans, and the planet.\\n## Our forests\\nHarvard maintains and stewards two forests where we house a living collection of plants and expand our forest knowledge through research.\\n### Harvard Forest\\nA 4000-acre laboratory, classroom, and long-term ecological research site located in central Massachusetts.\\nLearn more about Harvard Forest\\n[Learn more about Harvard Forest](https://harvardforest.fas.harvard.edu/)\\n### Arnold Arboretum\\nA 281-acre preserve of temperate woody plants from eastern North America and eastern Asia, located in the Boston neighborhood of Jamaica Plain.\\nLearn more about the Arboretum\\n[Learn more about the Arboretum](https://arboretum.harvard.edu/)\\n## How forests help us\\nForest play a major role in[the planet’s overall wellbeing](https://repository.gheli.harvard.edu/repository/12580/), from sequestering carbon and creating oxygen to fostering a diversity of life.\\n### [Forests help offset carbon emissions](https://news.harvard.edu/gazette/story/2022/11/new-report-shows-forests-have-big-role-to-play-in-climate-change-fight/)\\n[Learn more about forests’ role in climate change mitigation](https://news.harvard.edu/gazette/story/2020/08/new-englands-trees-capturing-more-carbon-says-25-year-study/)\\n### [Rainforests are an engine of diverse life](https://news.harvard.edu/gazette/story/2018/07/study-says-rainforests-gave-birth-to-worlds-most-varied-tropical-region/)\\n[Learn more about how rainforests promote biodiversity](https://arboretum.harvard.edu/stories/resolving-the-enigma-of-rainforest-biodiversity/)\\n### [Urban forests can help cool cities](https://www.hsph.harvard.edu/news/features/linear-urban-forest-project-aims-to-mitigate-heat-improve-health-in-cities/)\\n[Learn more about the benefits of urban forests](https://www.gsd.harvard.edu/2023/10/the-forest-for-the-trees-and-the-birds-and-the-people-and-the-planet/)\\n## Forest flora\\n[Explore more plant life](https://arboretum.harvard.edu/plants/)\\n### Paperbark Maple\\nThe Arboretum has been active in the conservation of this endangered Chinese tree for more than a century.\\nLearn more about the tree\\n[Learn more about the tree](https://arboretum.harvard.edu/plant-bios/paperbark-maple/)\\n### Pitcher plants\\nA Harvard Forest research project explored naturalist Frank Morton Jones’ pitcher plant research.\\nLearn more about carnivorous plants\\n[Learn more about carnivorous plants](https://harvardforest.fas.harvard.edu/ellison/current-research/Frank-Morton-Jones)\\n### Sapria himalayana\\nThese unusual plants have no roots, stems, or leaves of their own, living only as a collection of cells until they produce some of the largest flowers in the world.\\nLearn more about this strange, stinky flower\\n[Learn more about this strange, stinky flower](https://news.harvard.edu/gazette/story/2021/01/harvard-researchers-sequence-sapria-genome/)\\nInside Harvard’s Amazon rainforest immersion\\n[Click to Play Video](https://youtu.be/yTOEaON95DA)\\n## Forests around the world\\nHarvard researchers are exploring and supporting forests all over the world, including plant expeditions in[Japan](https://arboretum.harvard.edu/stories/science-and-spirit-in-the-forests-of-central-honshu/)and the[Pacific Northwest](https://arboretum.harvard.edu/expeditions/oregon-and-washington-expedition-owe/), Amazon conservation in[Brazil](https://hir.harvard.edu/the-waiapi-tribe-the-protectors-of-the-amazon-rainforest/)and[Peru](https://revista.drclas.harvard.edu/saving-latin-america-forests/), and a deep dive into the forests right in our[New England](https://hmnh.harvard.edu/new-england-forests)backyard.\\n[Learn about the Harvard Amazon Rainforest Immersion program](https://news.harvard.edu/gazette/story/2024/10/journey-to-a-key-front-in-climate-change-fight/)\\n## How we can help forests\\nClimate change and a growing global population continue to be detrimental to the world’s forests, but researchers are working to understand and mitigate those threats.\\n### Invasion\\n[How global shipping may bring pests that threaten our forests](https://harvardforest.fas.harvard.edu/news/old-growth-forests-elders-under-threat)\\n### Preservation\\n[How deforestation can affect the surviving forests](https://environment.harvard.edu/news/deforestation-damages-even-rainforests-survive-it)\\n### Conflagration\\n[How forest fires impact the world](https://news.harvard.edu/gazette/story/2023/06/in-the-thick-of-canadian-wildfires/)\\n## Forest fauna\\n[Explore more wildlife](https://arboretum.harvard.edu/visit/wildlife-at-the-arboretum/)\\n### Mink\\nA member of the weasel family, minks have been seen at the Arboretum for several years, typically near wetlands.\\nLearn more about the Arboretum’s newest residents\\n[Learn more about the Arboretum’s newest residents](https://arboretum.harvard.edu/stories/fishers-weasels-are-latest-residents-of-bostons-arboretum/)\\n### Howler monkeys\\nMonkeys don’t just consume fruits, but also make forest by dispersing the seeds of trees they consume.\\nLearn more about monkeys’ relationship to the forests they inhabit\\n[Learn more about monkeys’ relationship to the forests they inhabit](https://revista.drclas.harvard.edu/how-monkeys-create-tropical-rainforests/)\\n### Moose\\nHarvard Forest’s wildlife cameras have spotted moose and deer using their trees.\\nExplore more animals from Harvard Forest\\n[Explore more animals from Harvard Forest](https://harvardforest.fas.harvard.edu/browsing-wildlife-photos)\\n## []()\\n### Walking in the woods\\nResearch shows that hiking and “forest baths” have physical and mental benefits.\\n[Learn how the woods can help boost mental health](https://magazine.hms.harvard.edu/articles/walk-woods-may-boost-mental-health)\\n[Learn more about “forest bathing”](https://arboretum.harvard.edu/stories/walk-into-a-forest-breathe-deeply-repeat-as-needed/)\\n## Underground dwellers\\n### Mycorrhizae\\nA connection produced between a plant and fungi that live in the plant’s roots, mycorrhizae plays important roles in plant nutrition and soil biology and chemistry.\\nLearn more about this symbiotic relationship\\n[Learn more about this symbiotic relationship](https://arboretum.harvard.edu/stories/food-poison-and-espionage-mycorrhizal-networks-in-action/)\\n### Worms\\nInvasive worms are eliminating ground cover and altering soil composition, threatening some plant communities in New England.\\nLearn more about worms\\n[Learn more about worms](https://arboretum.harvard.edu/stories/native-plants-and-the-crazy-snake-worm/)\\n### Ants\\nArmy ants’ mass raids are considered the pinnacle of collective foraging behavior in the animal kingdom, but scientists are only just learning how they evolved to create them.\\nLearn how ant raids have evolved\\n[Learn how ant raids have evolved](https://oeb.harvard.edu/news/how-army-ants-iconic-mass-raids-evolved)\\nYou may also like\\n## Related In Focus topics\\n* [Arboretum Anniversary](https://www.harvard.edu/in-focus/arboretum-anniversary/)\\n* [Animal Magnetism](https://www.harvard.edu/in-focus/animal-magnetism/)\\n* [Earth Science](https://www.harvard.edu/in-focus/earth-science/)\\n## Security & Brand\\n1. [Report Copyright Infringement](https://www.harvard.edu/copyright-issue/)\\n2. [Report Security Issue](https://www.harvard.edu/security-issue/)\\n3. [Trademark Notice](https://trademark.harvard.edu/pages/trademark-notice)\\n## Website\\n1. [Accessibility](https://accessibility.harvard.edu/)\\n2. [Digital Accessibility](https://accessibility.huit.harvard.edu/digital-accessibility-policy)\\n3. [Privacy statement](https://www.harvard.edu/privacy-statement/)\\n## Get In Touch\\n1. [Contact Harvard](/contact-harvard)\\n2. [Maps & Directions](/maps-directions)\\n3. [Jobs](http://hr.harvard.edu/jobs)\\nCopyright © 2024 The President and Fellows of Harvard College\\n[](https://www.harvard.edu/)\\n* [Instagram](https://www.instagram.com/harvard/)\\n* [TikTok](https://www.tiktok.com/@harvard)\\n* [LinkedIn](https://www.linkedin.com/school/harvard-university)\\n* [Facebook](https://www.facebook.com/Harvard/)\\n* [YouTube](https://www.youtube.com/harvard)\\n\",\n", - " 'type': 'Document'}]}" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", - "\n", - "transitions[1].output" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ai", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cookbooks/01-website-crawler.ipynb b/cookbooks/01-website-crawler.ipynb new file mode 100644 index 000000000..30deb0720 --- /dev/null +++ b/cookbooks/01-website-crawler.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \"julep\"\n", + "
\n", + "\n", + "

\n", + "
\n", + " Explore Docs (wip)\n", + " ·\n", + " Discord\n", + " ·\n", + " 𝕏\n", + " ·\n", + " LinkedIn\n", + "

\n", + "\n", + "

\n", + " \"NPM\n", + "  \n", + " \"PyPI\n", + "  \n", + " \"Docker\n", + "  \n", + " \"GitHub\n", + "

\n", + "\n", + "## Task Definition: Spider Crawler Integration\n", + "\n", + "### Overview\n", + "\n", + "This task is a simple task that leverages the spider `integration` tool, and combines it with a prompt step to crawl a website for a given URL, and then create a summary of the results.\n", + "\n", + "### Task Tools:\n", + "\n", + "**Spider Crawler**: An `integration` type tool that can crawl the web and extract data from a given URL.\n", + "\n", + "### Task Input:\n", + "\n", + "**url**: The URL of the website to crawl.\n", + "\n", + "### Task Output:\n", + "\n", + "**output**: A dictionary that contains a `documents` key which contains the extracted data from the given URL. Check the output below for a detailed output schema.\n", + "\n", + "### Task Flow\n", + "\n", + "1. **Input**: The user provides a URL to crawl.\n", + "\n", + "2. **Spider Tool Integration**: The `spider_crawler` tool is called to crawl the web and extract data from the given URL.\n", + "\n", + "3. **Prompt Step**: The prompt step is used to create a summary of the results from the spider tool.\n", + "\n", + "4. **Output**: The final output is the summary of the results from the spider tool.\n", + "\n", + "```plaintext\n", + "+----------+ +-------------+ +------------+ +-----------+\n", + "| User | | Spider | | Prompt | | Output |\n", + "| Input | --> | Crawler | --> | Step | --> | Step |\n", + "| (URL) | | | | | | Output |\n", + "+----------+ +-------------+ +------------+ +-----------+\n", + " | | | |\n", + " | | | |\n", + " v v v v\n", + " \"https://spider.cloud\" Extract data Create summary \"Here are the\n", + " from URL of results results from the\n", + " spider tool\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "### Additional Information\n", + "\n", + "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", + "\n", + "**Author:** Julep AI \n", + "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Installing the Julep Client" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade julep --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "\n", + "# NOTE: these UUIDs are used in order not to use the `create_or_update` methods instead of\n", + "# the `create` methods for the sake of not creating new resources every time a cell is run.\n", + "AGENT_UUID = uuid.uuid4()\n", + "TASK_UUID = uuid.uuid4()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Julep Client with the API Key" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from julep import Client\n", + "import os\n", + "\n", + "api_key = os.getenv(\"JULEP_API_KEY\")\n", + "\n", + "# Create a Julep client\n", + "client = Client(api_key=api_key, environment=\"dev\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an \"agent\"\n", + "\n", + "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", + "\n", + "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Create agent\n", + "agent = client.agents.create_or_update(\n", + " agent_id=AGENT_UUID,\n", + " name=\"Spiderman\",\n", + " about=\"AI that can crawl the web and extract data\",\n", + " model=\"gpt-4o\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Task\n", + "\n", + "Tasks in Julep are Github-Actions-style workflows that define long-running, multi-step actions.\n", + "\n", + "You can use them to conduct complex actions by defining them step-by-step.\n", + "\n", + "To learn more about tasks, please refer to the `Tasks` section in [Julep Concepts](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#tasks)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import yaml\n", + "\n", + "spider_api_key = os.getenv(\"SPIDER_API_KEY\")\n", + "\n", + "# Define the task\n", + "task_def = yaml.safe_load(f\"\"\"\n", + "name: Crawling Task\n", + "\n", + "# Define the tools that the agent will use in this workflow\n", + "tools:\n", + "- name: spider_crawler\n", + " type: integration\n", + " integration:\n", + " provider: spider\n", + " setup:\n", + " spider_api_key: \"{spider_api_key}\"\n", + "\n", + "# Define the steps of the workflow\n", + "main:\n", + "# Define a tool call step that calls the spider_crawler tool with the url input\n", + "- tool: spider_crawler\n", + " arguments:\n", + " url: \"_['url']\" # You can also use 'inputs[0]['url']'\n", + " \n", + " \n", + "- prompt: |\n", + " You are {{{{agent.about}}}}\n", + " I have given you this url: {{{{inputs[0]['url']}}}}\n", + " And you have crawled that website. Here are the results you found:\n", + " {{{{_['documents']}}}}\n", + " I want you to create a short summary (no longer than 100 words) of the results you found while crawling that website.\n", + "\n", + " unwrap: True\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notes:\n", + "- The reason for using the quadruple curly braces `{{{{}}}}` for the jinja template is to avoid conflicts with the curly braces when using the `f` formatted strings in python. [More information here](https://stackoverflow.com/questions/64493332/jinja-templating-in-airflow-along-with-formatted-text)\n", + "- The `unwrap: True` in the prompt step is used to unwrap the output of the prompt step (to unwrap the `choices[0].message.content` from the output of the model).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating/Updating a task" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# creating the task object\n", + "task = client.tasks.create_or_update(\n", + " task_id=TASK_UUID,\n", + " agent_id=AGENT_UUID,\n", + " **task_def\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# creating an execution object\n", + "execution = client.executions.create(\n", + " task_id=TASK_UUID,\n", + " input={\n", + " \"url\": \"https://spider.cloud\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking execution details and output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are multiple ways to get the execution details and the output:\n", + "\n", + "1. **Get Execution Details**: This method retrieves the details of the execution, including the output of the last transition that took place.\n", + "\n", + "2. **List Transitions**: This method lists all the task steps that have been executed up to this point in time, so the output of a successful execution will be the output of the last transition (first in the transition list as it is in reverse chronological order), which should have a type of `finish`.\n", + "\n", + "\n", + "Note: You need to wait for a few seconds for the execution to complete before you can get the final output, so feel free to run the following cells multiple times until you get the final output.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spider.cloud is a leading web crawling tool designed for AI applications, offering high-speed, scalable, and cost-effective data collection solutions. Built in Rust, it can crawl over 20,000 pages in seconds, making it significantly faster and cheaper than traditional scrapers. Spider supports various data formats, including LLM-ready markdown, and integrates seamlessly with major AI tools. It offers features like auto proxy rotations, custom browser scripting, and caching to enhance performance. Users can start with $200 in credits and explore features through a free trial. Spider is trusted by tech businesses worldwide for insightful data solutions.\n" + ] + } + ], + "source": [ + "# Get execution details\n", + "execution = client.executions.get(execution.id)\n", + "# Print the output\n", + "print(execution.output)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transition type: init\n", + "Transition output: {'url': 'https://spider.cloud'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'documents': [{'id': None, 'metadata': {'description': 'Experience cutting-edge web crawling with unparalleled speeds, perfect for LLMs, Machine Learning, and Artificial Intelligence. The fastest and most efficient web scraper tailored for AI applications.', 'domain': 'spider.cloud', 'extracted_data': None, 'file_size': 10031, 'keywords': ['AI agent stack', 'AWS infrastructure reduced', 'Auto Proxy rotations', 'Comprehensive Data Curation', 'Concurrent Streaming Save time', 'Data Collecting Projects Today Jumpstart web crawling', 'FAQ Frequently asked questions', 'Fastest Web Crawler', 'Latest sports news', 'Multiple response formats', 'Open Source Spider engine', 'Performance Tuned Spider', 'Seamless Integrations Seamlessly integrate Spider', 'Smart Mode Spider dynamically switches', 'Spider accurately crawls', 'Spider convert web data', 'Spider outputs HTML', 'Transform Convert raw HTML', 'WilliamEspegren Web crawler built', 'achieve crawling thousands', 'affordable web scraping', 'and`XML`for API responses', 'caching repeated web page crawls', 'cost step caching', 'crazy resource management Aaaaaaand', 'custom browser scripting', 'data = json', 'data formats including LLM-ready markdown', 'ensure continuous maintenance', 'ensuring data curation perfectly aligned', 'finest data collecting solution', 'full elastic scaling concurrency', 'handle extreme workloads', 'iammerrick Rust based crawler Spider', 'insightful data solutions', 'json headers =', 'large scraping projects', 'large-scale data collection', 'leading tech businesses worldwide', 'leading web crawling tool designed', 'real-time web data', 'request Python JSONL Copy ``` `import requests', 'requires JavaScript rendering', 'response = requests', 'robust Rust engine scales effortlessly', 'scrapes significantly faster', 'search engine results', 'traditional scraping services Spider API Request Modes', 'training AI models', 'user interface segment showing'], 'pathname': '/', 'resource_type': '.md', 'title': 'Spider: The Web Crawler for AI', 'url': '8475428e-4e0c-44de-967f-c14fb73cf490/spider.cloud/_cloud/12638123428881205758.md', 'user_id': '8475428e-4e0c-44de-967f-c14fb73cf490'}, 'page_content': \"To help you get started with Spider, we’ll give you $200 in credits when you spend $100.[Terms apply](https://spider.cloud/promotion-spider-credits)\\n# The Web Crawler for AI Agents and LLMs\\nSpider offers the finest data collecting solution. Engineered for speed and scalability, it\\nallows you to elevate your AI projects.\\n[Get Started](https://spider.cloud/credits/new)View Preview\\n* Basic\\n* Streaming\\nExample request\\nPython\\nJSONL\\nCopy\\n```\\n`import requests, os, json\\nheaders = {\\n''Authorization'': f''Bearer {os.getenv(""SPIDER\\\\_API\\\\_KEY"")}'',\\n''Content-Type'': ''application/jsonl'',\\n}\\njson\\\\_data = {""limit"":50,""metadata"":True,""url"":""https://spider.cloud""}\\nresponse = requests.post(''https://api.spider.cloud/crawl'', headers=headers, json=json\\\\_data, stream=True)\\nwith response as r:\\nr.raise\\\\_for\\\\_status()\\nfor chunk in r.iter\\\\_lines(\\nchunk\\\\_size=None, decode\\\\_unicode=True\\n):\\ndata = json.loads(chunk)\\nprint(data)`\\n```\\n[Free Trial](https://spider.cloud/credits/new?free-trial=1)\\nExample Response\\n## Built with the need for**Speed**\\nExperience the power of**Spider**, built fully in**Rust**for\\nnext-generation scalability.\\n### 2secs\\nCapable of crawling over 20k pages in batch mode\\n### 500-1000x\\nFaster than alternatives\\n### 500x\\nCheaper than traditional scraping services\\nSpider API Request Modes · Benchmarked tailwindcss.com ·06/16/2024\\n[See framework benchmarks](https://github.com/spider-rs/spider/blob/main/benches/BENCHMARKS.md)\\n### Seamless Integrations\\nSeamlessly integrate Spider with a wide range of platforms, ensuring data curation\\nperfectly aligned with your requirements. Compatible with all major AI tools.\\n[LangChain integration](https://python.langchain.com/docs/integrations/document_loaders/spider)[LlamaIndex integration](https://docs.llamaindex.ai/en/stable/examples/data_connectors/WebPageDemo/#using-spider-reader)[CrewAI integration](https://docs.crewai.com/tools/SpiderTool/)[FlowWiseAI integration](https://docs.flowiseai.com/integrations/langchain/document-loaders/spider-web-scraper-crawler)[Composio integration](https://docs.composio.dev/introduction/foundations/components/list_local_tools#spider-crawler)[PhiData integration](https://docs.phidata.com/tools/spider)\\n### Concurrent Streaming\\nSave time and money without having to worry about bandwidth concerns by effectively\\nstreaming all the results concurrently. The latency cost that is saved becomes drastic as\\nyou crawl more websites.\\n### Warp Speed\\nPowered by the cutting-edge[Spider](https://github.com/spider-rs/spider)open-source project, our robust Rust engine scales effortlessly to handle extreme\\nworkloads. We ensure continuous maintenance and improvement for top-tier performance.\\n## Kickstart Your Data Collecting Projects Today\\nJumpstart web crawling with full elastic scaling concurrency, optimal formats, and AI scraping.\\n### Performance Tuned\\nSpider is written in Rust and runs in full concurrency to achieve crawling thousands of\\npages in secs.\\n### Multiple response formats\\nGet clean and formatted markdown, HTML, or text content for fine-tuning or training AI\\nmodels.\\n### Caching\\nFurther boost speed by caching repeated web page crawls to minimize expenses while\\nbuilding.\\n### Smart Mode\\nSpider dynamically switches to Headless Chrome when it needs to quick.\\nBeta\\n### Scrape with AI\\nDo custom browser scripting and data extraction using the latest AI models with no cost\\nstep caching.\\n### The crawler for LLMs\\nDon't let crawling and scraping be the highest latency in your LLM & AI agent stack.\\n### Scrape with no headaches\\n* Auto Proxy rotations\\n* Agent headers\\n* Anti-bot detections\\n* Headless chrome\\n* Markdown responses\\n### The Fastest Web Crawler\\n* Powered by[spider-rs](https://github.com/spider-rs/spider)\\n* 100,000 pages/seconds\\n* Unlimited concurrency\\n* Simple API\\n* 50,000 RPM\\n### Do more with AI\\n* Browser scripting\\n* Advanced extraction\\n* Data pipelines\\n* Ideal for LLMs and AI Agents\\n* Accurate labeling\\n## Achieve more with these new API features\\nOur API is set to stream so you can act in realtime.\\n![A user interface with a search bar containing the text "Latest sports news," a green "Submit" button, and two icon buttons to display searching and extracting with the service.](https://spider.cloud/img/search_feature.webp)\\n### Search\\nGet access to search engine results from anywhere and easily crawl and transform pages to\\nLLM-ready markdown.\\n[Explore Search](https://spider.cloud/docs/api#search)\\n![A user interface segment showing three icons representing different stages of data transformation.](https://spider.cloud/img/transform_feature_example.webp)\\n### Transform\\nConvert raw HTML into markdown easily by using this API. Transform thousands of html pages\\nin seconds.\\n[Explore Transform](https://spider.cloud/docs/api#transform)\\n## Join the community\\nBacked by a network of early advocates, contributors, and supporters.\\n[GitHub discussions\\n](https://github.com/orgs/spider-rs/discussions)[Discord\\n](https://discord.spider.cloud)\\n[\\n![iammerrick's avatar](https://spider.cloud/img/external/iammerrick_twitter.webp)\\n@iammerrick\\nRust based crawler Spider is next level for crawling & scraping sites. So fast.\\nTheir cloud offering is also so easy to use. Good stuff. https://github.com/spider-rs/spider\\n](https://twitter.com/iammerrick/status/1787873425446572462)\\n[\\n![WilliamEspegren's avatar](https://spider.cloud/img/external/william_twitter.webp)\\n@WilliamEspegren\\nWeb crawler built in rust, currently the nr1 performance in the world with crazy resource management Aaaaaaand they have a cloud offer, that’s wayyyy cheaper than any competitor\\nName a reason for me to use anything else?\\ngithub.com/spider-rs/spid…\\n](https://twitter.com/WilliamEspegren/status/1789419820821184764)\\n[\\n![gasa's avatar](https://spider.cloud/img/external/gaza_twitter.webp)\\n@gasa\\n@gasathenaper\\nis the best crawling tool i have used. I had a complicated project where i needed to paste url and get the website whole website data. Spider does it in an instant\\n](https://x.com/gasathenaper/status/1810612492596383948)\\n[\\n![Ashpreet Bedi's avatar](https://spider.cloud/img/external/ashpreet_bedi.webp)\\n@Ashpreet Bedi\\n@ashpreetbedi\\nis THE best crawler out there, give it a try\\n](https://x.com/ashpreetbedi/status/1815512219003572315?s=46&t=37F5QP_8oKqOsNpHSo6VVw)\\n[\\n![Troyusrex's avatar](https://spider.cloud/img/external/troy_twitter.webp)\\n@Troyusrex\\nI found a new tool, Spider-rs, which scrapes significantly faster and handles more scenarios than the basic scraper I built did. Our use of Spider-rs and AWS infrastructure reduced the scraping time from four months to under a week.\\n](https://medium.com/@troyusrex/inside-my-virtual-college-advisor-a-deep-dive-into-rag-ai-and-agent-technology-84731b2928f7#1326)\\n[\\n![Dify.AI's avatar](https://spider.cloud/img/external/difyai.webp)\\n@Dify.AI\\n🕷️Spider @spider\\\\_rust\\ncan be used as a built-in tool in #Dify Workflow or as an LLM-callable tool in Agent. It allows fast and affordable web scraping and crawling when your AI applications need real-time web data for context.\\n](https://x.com/dify_ai/status/1818226971056243089)\\n## FAQ\\nFrequently asked questions about Spider.\\n### What is Spider?\\nSpider is a leading web crawling tool designed for speed and cost-effectiveness, supporting various data formats including LLM-ready markdown.\\n### Why is my website not crawling?\\nYour crawl may fail if it requires JavaScript rendering. Try setting your request to 'chrome' to solve this issue.\\n### Can you crawl all pages?\\nYes, Spider accurately crawls all necessary content without needing a sitemap.\\n### What formats can Spider convert web data into?\\nSpider outputs HTML, raw, text, and various markdown formats. It supports`JSON`,`JSONL`,`CSV`, and`XML`for API responses.\\n### Is Spider suitable for large scraping projects?\\nAbsolutely, Spider is ideal for large-scale data collection and offers a cost-effective dashboard for data management.\\n### How can I try Spider?\\nPurchase credits for our cloud system or test the Open Source Spider engine to explore its capabilities.\\n### Does it respect robots.txt?\\nYes, compliance with robots.txt is default, but you can disable this if necessary.\\n### Unable to get dynamic content?\\nIf you are having trouble getting dynamic pages, try setting the request parameter to ""chrome"" or ""smart."" You may also need to set `disable\\\\_intercept` to allow third-party or external scripts to run.\\n### Why is my crawl going slow?\\nIf you are experiencing a slow crawl, it is most likely due to the robots.txt file for the website. The robots.txt file may have a crawl delay set, and we respect the delay up to 60 seconds.\\n### Do you offer a Free Trial?\\nYes, you can try out the service before being charged for free at[checkout](https://spider.cloud/credits/new?free-trial=1).\\n## Comprehensive Data Curation for Everyone\\nTrusted by leading tech businesses worldwide to deliver accurate and insightful data solutions.\\n[Zapier](https://zapier.com/apps/spider/integrations)\\n### Next generation data for AI, scale to millions\\n[Start now](https://spider.cloud/credits/new)\\n### Company\\n* [About](https://spider.cloud/about)\\n* [Privacy](https://spider.cloud/privacy)\\n* [Terms](https://spider.cloud/eula)\\n* [FAQ](https://spider.cloud/faq)\\n### Resources\\n* [API](https://spider.cloud/docs/api)\\n* [Docs](https://spider.cloud/docs/overview)\\n* [Guides](https://spider.cloud/guides)\\n* [Spider.rs Docs](https://docs.rs/spider/latest/spider/)\\n### Services\\n* [Pricing](https://spider.cloud/credits/new)\\n* [Web Crawling and Scraping](https://spider.cloud/web-crawling-and-scraping)\\n[All systems normal.](https://spidercloud.statuspage.io/)\\n[\\nGitHub\\n](https://github.com/spider-rs/spider)[\\nDiscord\\n](https://discord.spider.cloud)[\\nTwitter\\n](https://twitter.com/spider_rust)\", 'type': 'Document'}]}\n", + "--------------------------------------------------\n", + "Transition type: finish\n", + "Transition output: Spider.cloud is a leading web crawling tool designed for AI applications, offering high-speed, scalable, and cost-effective data collection solutions. Built in Rust, it can crawl over 20,000 pages in seconds, making it significantly faster and cheaper than traditional scrapers. Spider supports various data formats, including LLM-ready markdown, and integrates seamlessly with major AI tools. It offers features like auto proxy rotations, custom browser scripting, and caching to enhance performance. Users can start with $200 in credits and explore features through a free trial. Spider is trusted by tech businesses worldwide for insightful data solutions.\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "# Lists all the task steps that have been executed up to this point in time\n", + "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + "\n", + "# Transitions are retreived in reverse chronological order\n", + "for transition in reversed(transitions):\n", + " print(\"Transition type: \", transition.type)\n", + " print(\"Transition output: \", transition.output)\n", + " print(\"-\"*50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the same task with a different URL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the same code to run the same task, but with a different URL" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "execution = client.executions.create(\n", + " task_id=TASK_UUID,\n", + " input={\n", + " \"url\": \"https://www.harvard.edu/\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Harvard University's website emphasizes its commitment to excellence in teaching, learning, and research\n", + "It highlights initiatives related to food, including nutrition, sustainability, and healthful eating\n", + "The site features experts like Christina Warinner and Leah Penniman, and initiatives like the Harvard Food Systems Initiative and Food Literacy Project\n", + "It explores topics such as junk food cravings, vegan diets, and the impact of avocados on heart disease\n", + "The site also showcases Harvard's efforts in sustainable food practices, food donation programs, and educational resources like free online cooking courses\n", + "Additionally, it highlights the contributions of chefs within the Harvard community.\n" + ] + } + ], + "source": [ + "execution = client.executions.get(execution.id)\n", + "print(\"\\n\".join(execution.output.split(\". \")))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: you can get the output of the crawling step by accessing the corresponding transition's output from the transitions list.\n", + "\n", + "Example:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'documents': [{'id': None,\n", + " 'metadata': {'description': 'Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders who make a difference globally.',\n", + " 'domain': 'www.harvard.edu',\n", + " 'extracted_data': None,\n", + " 'file_size': 15310,\n", + " 'keywords': ['Business School podcast',\n", + " 'Christina Warinner Christina co-authored',\n", + " 'Dental Medicine shares advice',\n", + " 'Dining Services Learnings Report Learn',\n", + " 'Dining Services team',\n", + " 'Education alum started Bite Sized Education',\n", + " 'Expand Image Joanne Chang Stephanie Mitchell',\n", + " 'Expand Image Julia Child Paul Child Julia Child',\n", + " 'Expand Image Ludger Wessels Chef Wessels',\n", + " 'Expand Image Nick DiGiovanni Kris Snibbe',\n", + " 'Expand Image Nisha Vora Photo',\n", + " 'Flour Bakery owner Joanne Chang',\n", + " 'Food Donation Program',\n", + " 'Food Food nourishes',\n", + " 'Food Law',\n", + " 'Food Literacy Project',\n", + " 'Food Literacy Project hosts',\n", + " 'Free cooking courses Learn',\n", + " 'Graduate School',\n", + " 'Harvard Chan School',\n", + " 'Harvard Chan School found',\n", + " 'Harvard College first-year student explains',\n", + " 'Harvard College student',\n", + " 'Harvard College student stories',\n", + " 'Harvard College student story',\n", + " 'Harvard Divinity School explores',\n", + " 'Harvard Food Systems Initiative',\n", + " 'Harvard Food Systems Initiative Led',\n", + " 'Harvard Square food scene',\n", + " 'Harvard Staff Photographer',\n", + " 'Harvard University Dining Services',\n", + " 'Harvard University Dining Services launched',\n", + " 'Harvard alum',\n", + " 'Harvard alum Marcos Barrozo worked',\n", + " 'Healthful Food Standards',\n", + " 'Healthy Eating Plate',\n", + " 'Historian Joyce Chaplin explored',\n", + " 'Ingestible insights Harvard researchers',\n", + " 'James Beard Leadership Award winner',\n", + " 'Joanne Chang wears',\n", + " 'Julia Child pours hot syrup',\n", + " 'Leah Gose worked',\n", + " 'Leah Penniman Leah',\n", + " 'Luke MacQueen spent years creating',\n", + " 'Michael Marquand Law School grad Nisha Vora left',\n", + " 'Prevention Research Center',\n", + " 'Sustainable Food Harvard',\n", + " 'Sustainable Food Systems Graduate Certificate',\n", + " 'Teaching science concepts',\n", + " 'accelerating food shortages',\n", + " 'ancient humans adapted',\n", + " 'biggest problems facing farmers',\n", + " 'chef Ludger Wessels',\n", + " 'co-founded Soul Fire Farm',\n", + " 'converting food waste',\n", + " 'crave junk food',\n", + " 'crave junk food Harvard Medical School',\n", + " 'cultivate healthier eating habits',\n", + " 'engage secondary students',\n", + " 'exploring medical technology',\n", + " 'food Graduate School alum John Ahrens cultivated',\n", + " 'food Research shows',\n", + " 'food connoisseur shares',\n", + " 'food insecurity solutions',\n", + " 'free online courses',\n", + " 'gas fermentation-derived chocolate',\n", + " 'heart disease risk',\n", + " 'inspire elevated thinking',\n", + " 'lab-grown fish fillets',\n", + " 'leading authority providing science-based guidance',\n", + " 'local nonprofit Food',\n", + " 'peach industry fight brown rot',\n", + " 'postmenopausal weight loss',\n", + " 'reduce food waste',\n", + " 'reduce greenhouse gas emissions',\n", + " 'regenerative farming practices',\n", + " 'seasonal rainfall patterns',\n", + " 'selling meat-free meals',\n", + " 'shape future food systems leaders',\n", + " 'six-time American Culinary Federation competition award winner',\n", + " 'specialties include regional German',\n", + " 'vegetarian restaurant Clover'],\n", + " 'pathname': '/',\n", + " 'resource_type': '.md',\n", + " 'title': 'Harvard University',\n", + " 'url': '8475428e-4e0c-44de-967f-c14fb73cf490/www.harvard.edu/www_edu/12638123428881205758.md',\n", + " 'user_id': '8475428e-4e0c-44de-967f-c14fb73cf490'},\n", + " 'page_content': '[Skip to main content](#main-content)\\n[Harvard University](https://www.harvard.edu/)\\nSearch\\nMenu\\n[Harvard University](https://www.harvard.edu/)\\nClose\\n# Harvard University\\n## Food\\nFood nourishes us, inspires us, and brings us together. The Harvard community is exploring nutrition, sustainability, and the science behind the things we eat.\\n## [Tips for your teeth](https://hsdm.harvard.edu/news/maintaining-your-oral-health-during-holiday-season)\\nThe Harvard School of Dental Medicine shares advice for keeping your teeth healthy between holiday meals.\\n![]()\\n## Experts in the edible\\n### Christina Warinner\\nChristina co-authored a study showing that ancient humans adapted to eating starch-rich foods as far back as 100,000 years ago. These foods likely helped pave the way for the expansion of the human brain.\\nLearn more about the study\\n[Learn more about the study](https://news.harvard.edu/gazette/story/2021/05/study-explains-early-humans-ate-starch-and-why-it-matters/)\\n### Ayr Muir\\nAs the founder of the vegetarian restaurant Clover, Ayr is selling meat-free meals and reducing our impact on climate change one sandwich at a time.\\nExplore the Business School podcast\\n[Explore the Business School podcast](https://www.alumni.hbs.edu/stories/Pages/story-bulletin.aspx?num=7302)\\n### Leah Penniman\\nLeah, a James Beard Leadership Award winner, co-founded Soul Fire Farm to train people in regenerative farming practices that are now the go-to methods of sustainable and organic agriculture.\\nLearn more about her farm\\n[Learn more about her farm](https://hds.harvard.edu/news/2019/09/18/leah-penniman-fight-food-justice)\\n![](https://www.harvard.edu/wp-content/themes/core/assets/img/theme/shims/16x9.png)\\nTeaching science concepts through cooking\\n[Click to Play Video](https://www.youtube.com/watch?v=cOJYELbyYGg)\\n### Kate Strangfeld\\nThe Graduate School of Education alum started Bite Sized Education to engage secondary students in science. Her goal is to empower students to “think like a scientist” through food and cooking.\\n## Ingestible insights\\nHarvard researchers are exploring the effects that certain foods and types of eating have on our health.\\n[Learn more about what you should eat](https://nutritionsource.hsph.harvard.edu/what-should-you-eat/)\\n![Two cartoon heads, one with a soda inside it and one with a broccoli](https://www.harvard.edu/wp-content/uploads/2024/11/this_or_that-junk-food-craving-wondering2.png?w=736&h=491&crop=1)### Why we crave junk food\\nHarvard Medical School’s Uma Naidoo, the author of the books “This Is Your Brain on Food” and “Calm Your Mind with Food,” explains why we crave junk food and how to cultivate healthier eating habits.\\n[Why we crave junk food](https://news.harvard.edu/gazette/story/2024/09/why-do-we-crave-junk-food-diet-psychology/)\\n### Chocolate\\nand its effect on postmenopausal weight loss\\n![A bar of chocolate](https://www.harvard.edu/wp-content/uploads/2024/11/chocolate-1277002_1280.jpg?w=375&h=281&crop=1)[Chocolate](https://news.harvard.edu/gazette/story/2021/06/starting-the-day-off-with-chocolate-may-have-unexpected-benefits/)\\n### Ultra-processed food\\nand its influence on obesity and disease\\n![Nuggets and fries](https://www.harvard.edu/wp-content/uploads/2024/11/chicken-nuggets-246180_1280.jpg?w=375&h=281&crop=1)[Ultra-processed food](https://news.harvard.edu/gazette/story/2023/12/why-are-americans-so-sick-researchers-point-to-middle-grocery-aisles/)\\n### Vegan diet\\nand Alzheimer’s improvements\\n![A colorful salad](https://www.harvard.edu/wp-content/uploads/2024/11/food-1075228_1280.jpg?w=375&h=281&crop=1)[Vegan diet](https://news.harvard.edu/gazette/story/2024/07/alzheimers-study-finds-diet-lifestyle-changes-yield-improvements/)\\n### Avocados\\nand their effect on heart disease risk\\n![avocados](https://www.harvard.edu/wp-content/uploads/2024/11/avocado-8498520_1280.jpg?w=375&h=281&crop=1)[Avocados](https://news.harvard.edu/gazette/story/2022/04/an-avocado-a-week-may-lower-heart-disease-risk/)\\n### Late-night eating\\nand its role in depression and mood\\n![A person looking into a fridge at night](https://www.harvard.edu/wp-content/uploads/2024/11/AdobeStock_254026165.jpeg?w=375&h=281&crop=1)[Late-night eating](https://hms.harvard.edu/news/daytime-eating-mental-health)\\nHome cooking\\n## Chefs in our community\\n[Explore some of the amazing dishes from chefs all over Harvard](https://www.harvard.edu/in-focus/food/recipes/)\\nMeet the chefs\\n![Joanne Chang wears an apron in a kitchen](https://www.harvard.edu/wp-content/uploads/2024/11/020320_Chang_Joanne_09b.jpg?w=731)\\nExpand Image\\nJoanne Chang\\nStephanie Mitchell/Harvard Staff Photographer\\n"Pastry Love," a cookbook by Harvard alum and Flour Bakery owner Joanne Chang, honors some of her decadent and delicious favorites.\\n[Explore one of Joanne's recipes](https://www.harvard.edu/in-focus/food/recipes#joanne)\\n![A student wearing an apron stands in a kitchen with a cutting board of purple cabbage in front of him](https://www.harvard.edu/wp-content/uploads/2022/10/062629_Chef_006-e1675280860455.jpg?w=1200)\\nExpand Image\\nNick DiGiovanni\\nKris Snibbe/Harvard Staff Photographer\\nWhile at Harvard College, Nick DiGiovanni, who competed on MasterChef, created his own concentration in food and climate.\\n[Explore one of Nick's recipe](https://www.harvard.edu/in-focus/food/recipes#nick)\\n![Julia Child pours hot syrup over a Croquembouche in a kitchen](https://www.harvard.edu/wp-content/uploads/2022/10/Screen-Shot-2022-10-25-at-8.49.09-AM.png?w=634)\\nExpand Image\\nJulia Child\\nPaul Child\\nJulia Child’s television series “The French Chef,” which first aired in 1963, launched a revolution in cooking and eating in the United States. Her papers are part of the collection at the Schlesinger Library.\\n[Explore one of Julia's recipes](https://www.harvard.edu/in-focus/food/recipes#julia)\\n![A woman stands in a kitchen](https://www.harvard.edu/wp-content/uploads/2022/10/Nisha_Portrait_2500-copy-e1675280779442.jpg?w=1500)\\nExpand Image\\nNisha Vora\\nPhoto by Michael Marquand\\nLaw School grad Nisha Vora left the legal life for vegan cooking.\\n[Explore one of Nisha's recipes](https://www.harvard.edu/in-focus/food/recipes#nisha)\\n![Headshot of chef Ludger Wessels against a brick wall](https://www.harvard.edu/wp-content/uploads/2022/10/LudgerWessels-e1675281023551.jpg?w=1600)\\nExpand Image\\nLudger Wessels\\nChef Wessels, a six-time American Culinary Federation competition award winner, is executive chef at Harvard University Dining Services. His specialties include regional German and French cuisine.\\n[Explore one of Ludger's recipes](https://www.harvard.edu/in-focus/food/recipes#ludger)\\n## Further into food\\n### The Nutrition Source\\nA leading authority providing science-based guidance on food and nutrition. Explore articles, recipes, and tools.\\nExplore the resources\\n[Explore the resources](https://nutritionsource.hsph.harvard.edu/)\\n### Free cooking courses\\nLearn about fermentation or explore the chemistry and physics of cooking in these free online courses.\\nSign up for a free cooking course\\n[Sign up for a free cooking course](https://pll.harvard.edu/catalog?keywords=cooking)\\n### The sacredness of food\\nThe Harvard Divinity School explores the many ways that food overlaps with religions around the world.\\nLearn more from the Divinity School\\n[Learn more from the Divinity School](https://news-archive.hds.harvard.edu/news/2016/11/22/sacredness-food)\\n## Sustenance solutions\\nThe Harvard community is exploring solutions for the biggest problems facing farmers, consumers, and the planet.\\n### The need for food\\nReserachers at Harvard Chan School found an[increase in food insufficiency](https://www.hsph.harvard.edu/news/press-releases/)following the decrease in SNAP benefits; Graduate School of Design[students looked at food insecurity solutions in Mississippi](https://news.harvard.edu/gazette/story/newsplus/designing-food-security-in-rural-mississippi/), ranked as America’s hungriest state; Harvard alum[Leah Gose worked to improve how communities respond to hunger](https://gsas.harvard.edu/news/hungry-change), starting with her hometown of Atlanta; and Historian Joyce Chaplin explored[the long history of who gets to eat](https://hmsc.harvard.edu/2020/07/01/food-and-status-a-discussion-with-historian-joyce-chaplin/).\\n### The risks for food\\nResearch shows that changes to seasonal rainfall patterns are[accelerating food shortages](https://salatainstitute.harvard.edu/interview-unpredictable-rains-erode-food-security-gains/)across the globe; Harvard alum Marcos Barrozo worked to[lower emissions from Brazil’s cattle industry](https://gsas.harvard.edu/news/beef-climate-change); the Wyss Institute is exploring medical technology to[help the peach industry fight brown rot](https://wyss.harvard.edu/media-post/arbor-armor-could-fcmbl-save-the-peach-industry/?q=arbor); and the Schools of Engineering and Public Health are teaming up to[reduce food waste and foodborne illness](https://seas.harvard.edu/news/2022/06/food-packaging-system-reduces-health-risks-and-saves-food)in a novel way.\\n### The future of food\\nGraduate School alum John Ahrens cultivated[lab-grown fish fillets](https://gsas.harvard.edu/news/reel-hope-ocean-conservation)to address the global demand for seafood; Wyss Institute’s Luke MacQueen spent years creating[an animal-free meat that tastes and feels like the real thing](https://wyss.harvard.edu/news/from-montreal-to-a-menu-near-you/); the founders of Circe created the[world’s first gas fermentation-derived chocolate](https://otd.harvard.edu/news/circe-bioscience-licenses-technology-to-decarbonize-industry-with-microbes-developed-at-wyss-institute-at-harvard-university/)in response to the worldwide shortage of cocoa; and David Weitz’s lab is exploring[converting food waste into sugar alternatives](https://salatainstitute.harvard.edu/salata-institute-funds-five-new-climate-research-projects/).\\n## Food at Harvard\\n![](https://www.harvard.edu/wp-content/uploads/2024/10/082922_Global_158-1-1168x934-1.jpg?w=500&h=500&crop=1)\\n### Healthful and Sustainable Food\\nHarvard's Office for Sustainability pledges to reduce greenhouse gas emissions from food by 25% by 2030 as part of the Coolfood Pledge.\\n[Learn more about Harvard's sustainable and healthful food](https://sustainable.harvard.edu/our-plan/how-we-operate/food/)\\n![](https://www.harvard.edu/wp-content/uploads/2024/10/Family-Meals-before-and-after-768x639b.jpg?w=500&h=500&crop=1)\\n### Food Donation Program\\nIn 2014, Harvard University Dining Services launched an effort to address chronic hunger among its neighbors in Cambridge and Boston by partnering with the local nonprofit Food for Free to donate nearly 2,000 nutritious meals each week to families in need.\\n[Learn more about the program](https://dining.harvard.edu/about-huds/sustainability/food-donation-program)\\n![](https://www.harvard.edu/wp-content/uploads/2024/10/Annenberg_0.jpeg?w=500&h=500&crop=1)\\n### Finding home through food\\nA Harvard College first-year student explains what the food is like at Harvard, and how to cure food-homesickness.\\n[Learn more from Harvard College student stories](https://college.harvard.edu/student-life/student-stories/finding-home-through-food)\\n![](https://www.harvard.edu/wp-content/uploads/2024/10/MG_5399-1-1024x683-1.jpg?w=500&h=500&crop=1)\\n### Harvard Food Systems Initiative\\nLed by Harvard University Dining Services in collaboration with Harvard Faculty and practitioners in the field, Harvard Food Systems Initiative is an educational program to inspire elevated thinking and change to shape future food systems leaders for a more sustainable future.\\n[Learn more about the initiative](https://hfsi.harvard.edu/)\\n![](https://www.harvard.edu/wp-content/uploads/2024/10/img-2670_1-1.jpg?w=500&h=500&crop=1)\\n### The Harvard Square food scene\\nA Harvard College student and food connoisseur shares the best places to grab a bite in the square.\\n[Learn more from this Harvard College student story](https://college.harvard.edu/student-life/student-stories/harvard-square-food-scene)\\n![](https://www.harvard.edu/wp-content/uploads/2024/10/061824_Farmers_Market_Opening_052.jpg?w=500&h=500&crop=1)\\n### Food Literacy Project\\nThe Food Literacy Project hosts a fellowship program for students to help cultivate an understanding of food from the ground up. Education focuses on sustainability, nutrition, food preparation and community.\\n[Learn more about the program](https://dining.harvard.edu/food-literacy-project)\\n![](https://www.harvard.edu/wp-content/uploads/2024/11/LEARNINGS-REPORT-PRINT-AUG30LR_24_25.jpg?w=624&h=624&crop=1)\\n### Dining Services Learnings Report\\nLearn how Harvard's Dining Services team has created places and experiences that are formative to how Harvard lives on in the memories of our students.\\n[Explore the report](https://dining.harvard.edu/about-huds/learnings-reports)\\nExplore the food and nutrition organizations, exhibits, and collections at Harvard.\\n[Food Law and Policy Clinic](https://hls.harvard.edu/clinics/in-house-clinics/food-law-and-policy-clinic/)\\n[The Nutrition Source](https://www.hsph.harvard.edu/nutritionsource/)\\n[Harvard University Dining Services](https://dining.harvard.edu/)\\n[Harvard Food Systems Initiative (HFSI)](https://hfsi.harvard.edu/)\\n[Healthy Eating Plate](https://www.hsph.harvard.edu/nutritionsource/healthy-eating-plate/)\\n[Historic Cookbooks at Schlesinger Library\\n](https://guides.library.harvard.edu/schlesinger/historic_cookbooks)\\n[Food Literacy Project](https://dining.harvard.edu/food-literacy-project/flp-programs-events)\\n[Harvard Farmers’ Market](https://dining.harvard.edu/farmers-market)\\n[Sustainable Food Systems Graduate Certificate](https://extension.harvard.edu/academics/programs/sustainable-food-systems-graduate-certificate/)\\n[Prevention Research Center on Nutrition and Physical Activity at the Harvard Chan School (HPRC)](https://www.hsph.harvard.edu/prc/)\\n[Sustainable and Healthful Food Standards](https://green.harvard.edu/topics/food)\\nYOU MAY ALSO LIKE\\n## Related In Focus topics\\n* [Sleep](https://www.harvard.edu/in-focus/sleep/)\\n* [Healthy living](https://www.harvard.edu/in-focus/healthy-living/)\\n* [Mindfulness & Meditation](https://www.harvard.edu/in-focus/mindfulness-meditation/)\\n## Security & Brand\\n## Website\\n## Get In Touch\\nCopyright ©2024 The President and Fellows of Harvard College\\n[![Harvard University](https://www.harvard.edu/wp-content/themes/core/assets/img/theme/branding-assets/footer-logo.svg)](https://www.harvard.edu/)\\n* [Instagram![Instagram](https://www.harvard.edu/wp-content/uploads/2023/11/Instagram-1.png)](https://www.instagram.com/harvard/)\\n* [TikTok![TikTok](https://www.harvard.edu/wp-content/uploads/2023/11/tiktok-1.png)](https://www.tiktok.com/@harvard)\\n* [LinkedIn![LinkedIn](https://www.harvard.edu/wp-content/uploads/2023/11/Linkedin-1.png)](https://www.linkedin.com/school/harvard-university)\\n* [Facebook![Facebook](https://www.harvard.edu/wp-content/uploads/2023/11/FB3.png)](https://www.facebook.com/Harvard/)\\n* [YouTube![YouTube](https://www.harvard.edu/wp-content/uploads/2023/11/youtube-1.png)](https://www.youtube.com/harvard)',\n", + " 'type': 'Document'}]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + "\n", + "transitions[1].output" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cookbooks/01-Website_Crawler_using_Spider.py b/cookbooks/01-website-crawler.py similarity index 100% rename from cookbooks/01-Website_Crawler_using_Spider.py rename to cookbooks/01-website-crawler.py diff --git a/cookbooks/02-Sarcastic_News_Headline_Generator.ipynb b/cookbooks/02-Sarcastic_News_Headline_Generator.ipynb deleted file mode 100644 index fb7957a5f..000000000 --- a/cookbooks/02-Sarcastic_News_Headline_Generator.ipynb +++ /dev/null @@ -1,357 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \"julep\"\n", - "
\n", - "\n", - "## Task Definition: Sarcastic News Headline Generator using Brave Search Integration\n", - "\n", - "### Overview\n", - "\n", - "This task generates a sarcastic news headline on a user-provided topic. It utilizes web search to gather context and information about the topic, then uses this data to create a headline with a sarcastic tone. \n", - "\n", - "### Task Flow\n", - "\n", - "1. **Input**: \n", - " - User provides a topic for the sarcastic news headline (e.g., \"technology\" or \"politics\").\n", - "\n", - "2. **Web Search using Bravesearch Integration**: \n", - " - Conducts a search to find information about the topic, focusing on humorous or quirky content.\n", - " - Gathers relevant context and amusing details for headline generation.\n", - "\n", - "3. **Headline Creation**: \n", - " - Generates a sarcastic news headline based on the topic and gathered information.\n", - " - Combines factual or common perceptions with humor and sarcasm.\n", - " \n", - "4. **Output**: \n", - " - Produces a sarcastic, witty headline on the given topic.\n", - " - Can be used for entertainment or as a creative writing prompt.\n", - "\n", - "### Key Features\n", - "\n", - "- Leverages web search to enhance contextual relevance of sarcastic content.\n", - "- Combines real-world information with humorous twists for engaging headlines.\n", - "- Adaptable to various topics, allowing for diverse and creative outputs.\n", - "\n", - "```plaintext\n", - "+----------+ +------------+ +------------+ +-----------+\n", - "| User | | Brave | | Agent | | Sarcastic |\n", - "| Input | --> | Search | --> | Processing | --> | Headline |\n", - "| (Topic) | | | | | | Output |\n", - "+----------+ +------------+ +------------+ +-----------+\n", - " | | | |\n", - " | | | |\n", - " v v v v\n", - " \"politics\" Find funny Generate witty \"Area Man Still\n", - " content headline Believes in Democracy\"\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implementation\n", - "\n", - "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "### Additional Information\n", - "\n", - "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", - "\n", - "**Author:** Julep AI \n", - "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Installing the Julep Client" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install julep -U --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE:\n", - "\n", - "- UUIDs are generated for both the agent and task to uniquely identify them within the system.\n", - "- Once created, these UUIDs should remain unchanged for simplicity.\n", - "- Altering a UUID will result in the system treating it as a new agent or task.\n", - "- If a UUID is changed, the original agent or task will continue to exist in the system alongside the new one." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Global UUID is generated for agent and task\n", - "import uuid\n", - "\n", - "AGENT_UUID = uuid.uuid4()\n", - "TASK_UUID = uuid.uuid4() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Julep Client with the API Key" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from julep import Client\n", - "\n", - "api_key = \"\" # Your API key here\n", - "\n", - "# Create a client\n", - "client = Client(api_key=api_key, environment=\"dev\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an \"agent\"\n", - "\n", - "\n", - "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", - "\n", - "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the agent\n", - "name = \"Jarvis\"\n", - "about = \"The original AI conscious the Iron Man.\"\n", - "default_settings = {\n", - " \"temperature\": 0.7,\n", - " \"top_p\": 1,\n", - " \"min_p\": 0.01,\n", - " \"presence_penalty\": 0,\n", - " \"frequency_penalty\": 0,\n", - " \"length_penalty\": 1.0,\n", - " \"max_tokens\": 150,\n", - "}\n", - "\n", - "\n", - "# Create the agent\n", - "agent = client.agents.create_or_update(\n", - " agent_id=AGENT_UUID,\n", - " name=name,\n", - " about=about,\n", - " model=\"gpt-4o\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining a Task\n", - "\n", - "Tasks in Julep are Github Actions style workflows that define long-running, multi-step actions. \n", - "You can use them to conduct complex actions by defining them step-by-step. They have access to all Julep integrations.\n", - "\n", - "To learn more about tasks, visit [Julep documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#task)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More on how to define a task can be found [here](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# defining the task definition\n", - "task_def = yaml.safe_load(\"\"\"\n", - "name: Sarcasm Headline Generator\n", - "\n", - "tools:\n", - "- name: brave_search\n", - " type: integration\n", - " integration:\n", - " provider: brave\n", - " setup:\n", - " api_key: \"YOU_API_KEY\"\n", - "\n", - "main:\n", - "- tool: brave_search\n", - " arguments:\n", - " query: \"_.topic + ' funny'\"\n", - "\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " write a sarcastic news headline on the topic of {{inputs[0].topic}}.\n", - " Here's some more info on this: {{_}}\n", - " unwrap: true\n", - "\n", - "\"\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating/Updating a task." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# creating the task object\n", - "task = client.tasks.create_or_update(\n", - " task_id=TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **task_def\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an Execution\n", - "\n", - "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creates a execution worklow for the Task defined in the yaml file." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# creating an execution object\n", - "execution = client.executions.create(\n", - " task_id=TASK_UUID,\n", - " input={\n", - " \"topic\": \"elon musk\"\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# getting the execution details\n", - "execution = client.executions.get(execution.id)\n", - "#printing the output\n", - "execution.output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Retrieves and lists all the steps of a defined task that have been executed up to that point in time. Unlike streaming, this function does not continuously monitor the execution; it only provides a snapshot of the steps completed so far without displaying real-time updates as the task progresses." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.executions.transitions.list(execution_id=execution.id).items" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Continuously monitor and stream the steps of a defined task. It retrieves and displays the transitions or execution steps of the task identified by execution.id in real-time, showing each step sequentially until the task is either completed or an error causes it to terminate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.executions.transitions.stream(execution_id=execution.id)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ai", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cookbooks/02-sarcastic-news-headline-generator.ipynb b/cookbooks/02-sarcastic-news-headline-generator.ipynb new file mode 100644 index 000000000..f8f11183c --- /dev/null +++ b/cookbooks/02-sarcastic-news-headline-generator.ipynb @@ -0,0 +1,562 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \"julep\"\n", + "
\n", + "\n", + "

\n", + "
\n", + " Explore Docs (wip)\n", + " ·\n", + " Discord\n", + " ·\n", + " 𝕏\n", + " ·\n", + " LinkedIn\n", + "

\n", + "\n", + "

\n", + " \"NPM\n", + "  \n", + " \"PyPI\n", + "  \n", + " \"Docker\n", + "  \n", + " \"GitHub\n", + "

\n", + "\n", + "## Task Definition: Sarcastic News Headline Generator using Brave Search Integration\n", + "\n", + "### Overview\n", + "\n", + "This task generates a sarcastic news headline on a user-provided topic. It utilizes web search to gather context and information about the topic, then uses this data to create a headline with a sarcastic tone. \n", + "\n", + "### Task Flow\n", + "\n", + "1. **Input**: \n", + " - User provides a topic for the sarcastic news headline (e.g., \"technology\" or \"politics\").\n", + "\n", + "2. **Web Search using Bravesearch Integration**: \n", + " - Conducts a search to find information about the topic, focusing on humorous or quirky content.\n", + " - Gathers relevant context and amusing details for headline generation.\n", + "\n", + "3. **Headline Creation**: \n", + " - Generates a sarcastic news headline based on the topic and gathered information.\n", + " - Combines factual or common perceptions with humor and sarcasm.\n", + " \n", + "4. **Output**: \n", + " - Produces a sarcastic, witty headline on the given topic.\n", + " - Can be used for entertainment or as a creative writing prompt.\n", + "\n", + "### Key Features\n", + "\n", + "- Leverages web search to enhance contextual relevance of sarcastic content.\n", + "- Combines real-world information with humorous twists for engaging headlines.\n", + "- Adaptable to various topics, allowing for diverse and creative outputs.\n", + "\n", + "```plaintext\n", + "+----------+ +------------+ +------------+ +-----------+\n", + "| User | | Brave | | Agent | | Sarcastic |\n", + "| Input | --> | Search | --> | Processing | --> | Headline |\n", + "| (Topic) | | | | | | Output |\n", + "+----------+ +------------+ +------------+ +-----------+\n", + " | | | |\n", + " | | | |\n", + " v v v v\n", + " \"politics\" Find funny Generate witty \"Area Man Still\n", + " content headline Believes in Democracy\"\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "### Additional Information\n", + "\n", + "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", + "\n", + "**Author:** Julep AI \n", + "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Installing the Julep Client" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade julep --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### NOTE:\n", + "\n", + "- UUIDs are generated for both the agent and task to uniquely identify them within the system.\n", + "- Once created, these UUIDs should remain unchanged for simplicity.\n", + "- Altering a UUID will result in the system treating it as a new agent or task.\n", + "- If a UUID is changed, the original agent or task will continue to exist in the system alongside the new one." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Global UUID is generated for agent and task\n", + "import uuid\n", + "\n", + "AGENT_UUID = uuid.uuid4()\n", + "TASK_UUID = uuid.uuid4() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Julep Client with the API Key" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from julep import Client\n", + "import os\n", + "\n", + "api_key = os.getenv(\"JULEP_API_KEY\")\n", + "\n", + "# Create a client\n", + "client = Client(api_key=api_key, environment=\"dev\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an \"agent\"\n", + "\n", + "\n", + "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", + "\n", + "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the agent\n", + "name = \"Chad\"\n", + "about = \"Sarcastic news headline reporter.\"\n", + "default_settings = {\n", + " \"temperature\": 0.7,\n", + " \"top_p\": 1,\n", + " \"min_p\": 0.01,\n", + " \"presence_penalty\": 0,\n", + " \"frequency_penalty\": 0,\n", + " \"length_penalty\": 1.0,\n", + " \"max_tokens\": 150,\n", + "}\n", + "\n", + "\n", + "# Create the agent\n", + "agent = client.agents.create_or_update(\n", + " agent_id=AGENT_UUID,\n", + " name=name,\n", + " about=about,\n", + " model=\"claude-3.5-sonnet\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Task\n", + "\n", + "Tasks in Julep are Github Actions style workflows that define long-running, multi-step actions.\n", + "\n", + "You can use them to conduct complex actions by defining them step-by-step.\n", + "\n", + "To learn more about tasks, please refer to the `Tasks` section in [Julep Concepts](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#tasks)." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "import yaml\n", + "\n", + "brave_api_key = os.getenv(\"BRAVE_API_KEY\")\n", + "\n", + "# Define the task\n", + "task_def = yaml.safe_load(f\"\"\"\n", + "name: Sarcasm Headline Generator\n", + "\n", + "tools:\n", + "- name: brave_search\n", + " type: integration\n", + " integration:\n", + " provider: brave\n", + " setup:\n", + " api_key: \"{brave_api_key}\"\n", + "\n", + "main:\n", + "- tool: brave_search\n", + " arguments:\n", + " query: \"_.topic + ' funny news'\"\n", + "\n", + "- evaluate:\n", + " search_results: |-\n", + " [\n", + " {{\n", + " 'snippet': r['snippet'],\n", + " 'title': r['title']\n", + " }}\n", + " for r in _['result']\n", + " ]\n", + " \n", + "- prompt:\n", + " - role: system\n", + " content: >-\n", + " You are {{{{agent.about}}}}.\n", + " The user will send you a topic and search results for that topic.\n", + " Your goal is to write a sarcastic news headlines based on that topic and search results.\n", + " - role: user\n", + " content: >-\n", + " My topic is: {{{{inputs[0].topic}}}}.\n", + " Here are the search results: {{{{_}}}}\n", + " unwrap: true\n", + "\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notes:\n", + "- The reason for using the quadruple curly braces `{{{{}}}}` for the jinja template is to avoid conflicts with the curly braces when using the `f` formatted strings in python. [More information here](https://stackoverflow.com/questions/64493332/jinja-templating-in-airflow-along-with-formatted-text)\n", + "- The `unwrap: True` in the prompt step is used to unwrap the output of the prompt step (to unwrap the `choices[0].message.content` from the output of the model).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating/Updating a task." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# creating the task object\n", + "task = client.tasks.create_or_update(\n", + " task_id=TASK_UUID,\n", + " agent_id=AGENT_UUID,\n", + " **task_def\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "# creating an execution object\n", + "execution = client.executions.create(\n", + " task_id=TASK_UUID,\n", + " input={\n", + " \"topic\": \"Elon Musk\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking execution details and output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are multiple ways to get the execution details and the output:\n", + "\n", + "1. **Get Execution Details**: This method retrieves the details of the execution, including the output of the last transition that took place.\n", + "\n", + "2. **List Transitions**: This method lists all the task steps that have been executed up to this point in time, so the output of a successful execution will be the output of the last transition (first in the transition list as it is in reverse chronological order), which should have a type of `finish`.\n", + "\n", + "\n", + "Note: You need to wait for a few seconds for the execution to complete before you can get the final output, so feel free to run the following cells multiple times until you get the final output.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on these search results about Elon Musk's attempts at humor, I'll write some sarcastic headlines:\n", + "\n", + "1. \"Breaking: World's Richest Man Still Can't Buy a Sense of Humor, Studies Show\"\n", + "\n", + "2. \"Shocking: Billionaire's Joke Algorithm Malfunctions, Produces Only Cringe\"\n", + "\n", + "3. \"Tech Titan Discovers Comedy Is Harder Than Rocket Science; Mars Mission Suddenly Looks Easier\"\n", + "\n", + "4. \"EXCLUSIVE: Musk's Twitter Bio Updated to 'Chief Unfunny Officer' After Latest Failed Attempt at Humor\"\n", + "\n", + "5. \"Report: Man Worth $230 Billion Still Can't Afford Professional Comedy Writer\"\n", + "\n", + "6. \"Scientists Confirm: Musk's Jokes Have Lower Success Rate Than Tesla Cybertruck Windows\"\n", + "\n", + "7. \"Breaking: Social Media Platform Owner Becomes First Person to Ratio Himself With His Own Bad Jokes\"\n", + "\n", + "8. \"UPDATE: AI Refuses to Generate Fake Laughs for Billionaire's Twitter Jokes, Cites Ethical Concerns\"\n", + "\n", + "These headlines are crafted to playfully mock the recent news about Musk's attempts at humor and social media posts, particularly focusing on the apparent consensus that his jokes aren't landing as well as his rockets!\n" + ] + } + ], + "source": [ + "# Get execution details\n", + "execution = client.executions.get(execution.id)\n", + "# Print the output\n", + "print(execution.output)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transition type: init\n", + "Transition output: {'topic': 'Elon Musk'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'result': [{'link': 'https://www.rollingstone.com/culture/culture-lists/elon-musks-unfunniest-jokes-2023-1234929823/', 'snippet': 'Somehow, it didn’t get funnier the more he used it. If Zuck my 👅 really wants a lesson in why there are weight categories in fighting so badly, I could just head over to his house next week and teach him a lesson he won’t soon forget— Elon Musk (@elonmusk) August 11, 2023https://p...', 'title': 'Elon Musk’s 9 Unfunniest Jokes of 2023'}, {'link': 'https://www.beaumontenterprise.com/culture/article/elon-musk-funny-19742906.php', 'snippet': 'Your Beaumont local news source plus the latest in entertainment, sports and Jefferson County Texas news.', 'title': \"Elon Musk's unfunny acronyms are getting exhausting\"}, {'link': 'https://www.youtube.com/watch?v=EbxBv6mjRvY', 'snippet': 'A genius in business, technology and humor!Sometimes Elon Musk will go the extra mile to get a laugh. Here’s a collection of his funniest moments.', 'title': 'Elon Musk Funniest Moments - YouTube'}]}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'search_results': [{'snippet': 'Somehow, it didn’t get funnier the more he used it. If Zuck my 👅 really wants a lesson in why there are weight categories in fighting so badly, I could just head over to his house next week and teach him a lesson he won’t soon forget— Elon Musk (@elonmusk) August 11, 2023https://p...', 'title': 'Elon Musk’s 9 Unfunniest Jokes of 2023'}, {'snippet': 'Your Beaumont local news source plus the latest in entertainment, sports and Jefferson County Texas news.', 'title': \"Elon Musk's unfunny acronyms are getting exhausting\"}, {'snippet': 'A genius in business, technology and humor!Sometimes Elon Musk will go the extra mile to get a laugh. Here’s a collection of his funniest moments.', 'title': 'Elon Musk Funniest Moments - YouTube'}]}\n", + "--------------------------------------------------\n", + "Transition type: finish\n", + "Transition output: Based on these search results about Elon Musk's attempts at humor, I'll write some sarcastic headlines:\n", + "\n", + "1. \"Breaking: World's Richest Man Still Can't Buy a Sense of Humor, Studies Show\"\n", + "\n", + "2. \"Shocking: Billionaire's Joke Algorithm Malfunctions, Produces Only Cringe\"\n", + "\n", + "3. \"Tech Titan Discovers Comedy Is Harder Than Rocket Science; Mars Mission Suddenly Looks Easier\"\n", + "\n", + "4. \"EXCLUSIVE: Musk's Twitter Bio Updated to 'Chief Unfunny Officer' After Latest Failed Attempt at Humor\"\n", + "\n", + "5. \"Report: Man Worth $230 Billion Still Can't Afford Professional Comedy Writer\"\n", + "\n", + "6. \"Scientists Confirm: Musk's Jokes Have Lower Success Rate Than Tesla Cybertruck Windows\"\n", + "\n", + "7. \"Breaking: Social Media Platform Owner Becomes First Person to Ratio Himself With His Own Bad Jokes\"\n", + "\n", + "8. \"UPDATE: AI Refuses to Generate Fake Laughs for Billionaire's Twitter Jokes, Cites Ethical Concerns\"\n", + "\n", + "These headlines are crafted to playfully mock the recent news about Musk's attempts at humor and social media posts, particularly focusing on the apparent consensus that his jokes aren't landing as well as his rockets!\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "# Lists all the task steps that have been executed up to this point in time\n", + "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + "\n", + "# Transitions are retreived in reverse chronological order\n", + "for transition in reversed(transitions):\n", + " print(\"Transition type: \", transition.type)\n", + " print(\"Transition output: \", transition.output)\n", + " print(\"-\"*50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the same task with a different topic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the same code to run the same task, but with a different topic" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "execution = client.executions.create(\n", + " task_id=TASK_UUID,\n", + " input={\n", + " \"topic\": \"Tottenham Hotspur\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on the search results about Tottenham Hotspur, here are some sarcastic headlines:\n", + "\n", + "1. \"BREAKING: Tottenham Somehow Manages to Draw a Match They Haven't Even Played Yet\"\n", + "\n", + "2. \"SHOCKING: Spurs Fan Found Who Still Has Hope for This Season, Doctors Baffled\"\n", + "\n", + "3. \"EXCLUSIVE: Tottenham's Trophy Cabinet Files Complaint for Workplace Neglect, Cites 'Years of Emptiness'\"\n", + "\n", + "4. \"REPORT: Tottenham Players Perfect the Art of 'Almost Winning' - Declare It Their New Club Strategy\"\n", + "\n", + "5. \"URGENT: Scientists Confirm Supporting Tottenham May Be Leading Cause of Premature Gray Hair\"\n", + "\n", + "6. \"DEVELOPMENT: Spurs Consider Renaming Stadium to 'The Almost Champions Arena' to Better Reflect Club's Legacy\"\n", + "\n", + "7. \"INVESTIGATION: Local Man Claims He's Seen Tottenham Win Trophy - Experts Say It Was Just a Mirage\"\n", + "\n", + "8. \"STUDY: Tottenham Fans Develop Superhuman Ability to Handle Disappointment, Medical Community Amazed\"\n", + "\n", + "9. \"REVEALED: Club's 'To Do' List Found - 'Win Something' Written 362 Times in Increasingly Desperate Handwriting\"\n", + "\n", + "10. \"WEATHER ALERT: Forecast Predicts Flying Pigs Before Tottenham's Next Trophy\"\n", + "\n", + "These headlines playfully poke fun at Tottenham's notorious reputation for coming close but not quite achieving major trophies, while drawing from their current status as a prominent Premier League club with a dedicated fanbase.\n" + ] + } + ], + "source": [ + "execution = client.executions.get(execution.id)\n", + "print(execution.output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: you can get the output of the search step by accessing the corresponding transition's output from the transitions list.\n", + "\n", + "Example:" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'search_results': [{'snippet': 'The latest Tottenham news, transfers, fixtures and more. Including Live blogs, pictures, video, podcasts, polls and indepth analysis from our dedicated Spurs writers.',\n", + " 'title': 'Tottenham Hotspur FC - latest news, pictures, video comment - Football.london'},\n", + " {'snippet': 'Latest Spurs news, transfer rumours, team news, fixtures and more from the Tottenham Hotspur Stadium. Breaking Tottenham rumours & news now, 24/7.',\n", + " 'title': 'Spurs News | Tottenham Transfer News - NewsNow'},\n", + " {'snippet': 'Latest official news and video from Tottenham Hotspur',\n", + " 'title': 'News | Tottenham Hotspur'}]}" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + "\n", + "transitions[1].output" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cookbooks/02-Sarcastic_News_Headline_Generator.py b/cookbooks/02-sarcastic-news-headline-generator.py similarity index 100% rename from cookbooks/02-Sarcastic_News_Headline_Generator.py rename to cookbooks/02-sarcastic-news-headline-generator.py diff --git a/cookbooks/03-SmartResearcher_With_WebSearch.ipynb b/cookbooks/03-SmartResearcher_With_WebSearch.ipynb deleted file mode 100644 index 9bb6436c4..000000000 --- a/cookbooks/03-SmartResearcher_With_WebSearch.ipynb +++ /dev/null @@ -1,395 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \"julep\"\n", - "
\n", - "\n", - "## Task: Research Assistant with Web Search Integration\n", - "\n", - "### Overview\n", - "\n", - "This task is designed to conduct comprehensive research on multiple user-provided topics. It leverages web search capabilities to gather up-to-date information and generates relevant keywords for each topic, streamlining the research process through efficient tool integration and parallel processing.\n", - "\n", - "### Task Flow\n", - "\n", - "1. **User Input**\n", - " - User provides a list of research topics\n", - " - Each topic is processed individually\n", - "\n", - "2. **Web Search and Information Gathering**\n", - " - Utilizes BraveWeb to conduct searches for each topic\n", - " - Retrieves latest news and relevant information\n", - "\n", - "3. **Intelligent Analysis**\n", - " - System analyzes search results (HTML snippets)\n", - " - Generates Wikipedia keywords related to each topic\n", - " - AI assistant identifies most relevant keywords based on search content\n", - "\n", - "4. **Parallel Processing**\n", - " - Implements concurrent topic processing\n", - " - Enhances efficiency and reduces overall research time\n", - "\n", - "### Key Features\n", - "\n", - "- **Multi-topic Research**: Handles multiple research topics simultaneously\n", - "- **Up-to-date Information**: Utilizes web search to access current data\n", - "- **Keyword Generation**: Provides relevant Wikipedia keywords for each topic\n", - "- **AI-assisted Analysis**: Employs AI to identify and prioritize key information\n", - "- **Efficient Processing**: Leverages parallel processing for faster results\n", - "\n", - "### Output\n", - "\n", - "- Comprehensive list of relevant Wikipedia keywords for each input topic\n", - "- Curated, up-to-date information gathered from web searches\n", - "\n", - "This workflow provides a robust, efficient approach to in-depth topic exploration, combining the power of web search, AI analysis, and parallel processing.\n", - "\n", - "```plaintext\n", - "\n", - "+----------------+ +--------------------------+ +-----------------+ +------------------------+ +-------------------------+\n", - "| User Input | | Web Search & Information | | Intelligent | | Parallel Processing | | Output Stage |\n", - "|(List of Topics)| --> | Gathering (BraveWeb) | --> | Analysis | --> | (Concurrent Execution) | --> | Summarized Research |\n", - "| | | | | | | | | (Final Report) |\n", - "+----------------+ +--------------------------+ +-----------------+ +------------------------+ +-------------------------+\n", - " | | | | |\n", - " | | | | |\n", - " v v v v v\n", - " Topic 1, Topic 2, ... Retrieve HTML snippets Extract Wikipedia Process multiple Compile summary and \n", - " Each topic processed from search results keywords from HTML topics concurrently present most relevant \n", - " individually. for each topic. snippets, analyze for faster results. findings per topic.\n", - " relevance.\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Implementation\n", - "\n", - "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "### Additional Information\n", - "\n", - "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", - "\n", - "**Author:** Julep AI \n", - "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Installing the Julep Client" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install julep -U --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE:\n", - "\n", - "- UUIDs are generated for both the agent and task to uniquely identify them within the system.\n", - "- Once created, these UUIDs should remain unchanged for simplicity.\n", - "- Altering a UUID will result in the system treating it as a new agent or task.\n", - "- If a UUID is changed, the original agent or task will continue to exist in the system alongside the new one." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Global UUID is generated for agent and task\n", - "import uuid\n", - "\n", - "AGENT_UUID = uuid.uuid4()\n", - "TASK_UUID = uuid.uuid4()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Julep Client with the API Key" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from julep import Client\n", - "\n", - "api_key = \"\" # Your API key here\n", - "\n", - "# Create a client\n", - "client = Client(api_key=api_key, environment=\"dev\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an \"agent\"\n", - "\n", - "\n", - "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", - "\n", - "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the agent\n", - "name = \"Jarvis\"\n", - "about = \"The original AI conscious the Iron Man.\"\n", - "default_settings = {\n", - " \"temperature\": 0.7,\n", - " \"top_p\": 1,\n", - " \"min_p\": 0.01,\n", - " \"presence_penalty\": 0,\n", - " \"frequency_penalty\": 0,\n", - " \"length_penalty\": 1.0,\n", - " \"max_tokens\": 150,\n", - "}\n", - "\n", - "\n", - "# Create the agent\n", - "agent = client.agents.create_or_update(\n", - " agent_id=AGENT_UUID,\n", - " name=name,\n", - " about=about,\n", - " model=\"gpt-4o\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining a Task\n", - "\n", - "Tasks in Julep are Github Actions style workflows that define long-running, multi-step actions. \n", - "You can use them to conduct complex actions by defining them step-by-step. They have access to all Julep integrations.\n", - "\n", - "To learn more about tasks, visit [Julep documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#task)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More on how to define a task can be found [here](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "task_def= yaml.safe_load(\"\"\"\n", - "name: Research Assistant to find Wikipedia Keywords\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " topics:\n", - " type: array\n", - " items:\n", - " type: string\n", - " description: The topics to search for.\n", - "\n", - "tools:\n", - "- name: brave_search\n", - " type: integration\n", - " integration:\n", - " provider: brave\n", - " setup:\n", - " api_key: \"YOUR_API_KEY\"\n", - "\n", - "main:\n", - "- over: _.topics\n", - " map:\n", - " tool: brave_search\n", - " arguments:\n", - " query: \"'the latest news about ' + _\"\n", - "\n", - "- over: _\n", - " parallelism: 2\n", - " map:\n", - " prompt:\n", - " - role: system\n", - " content: >-\n", - " You are a research assistant.\n", - " I need you to do in-depth research on topics trending in the news currently.\n", - " Based on the following latest html news snippet, come up with a list of wikipedia keywords to search:\n", - " \"{{_}}\"\n", - " Your response should be a list of keywords, separated by commas. Do not add any other text.\n", - " Example: `KEYWORDS: keyword1, keyword2, keyword3`\n", - "\n", - " unwrap: true\n", - "\"\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating/Updating a task." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# creating the task object\n", - "task = client.tasks.create_or_update(\n", - " task_id=TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **task_def\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an Execution\n", - "\n", - "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creates a execution worklow for the Task defined in the yaml file." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "execution= client.executions.create(\n", - " task_id=task.id,\n", - " input={\n", - " \"topics\": [\"Burger King Cup on the Ground Behind a Wendy’s\",\"Forbidden Chemical X\",\"Finger Bracelets\",\"Amusing Notions\"]\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "execution.id" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# getting the execution details\n", - "execution = client.executions.get(execution.id)\n", - "#printing the outpu\n", - "execution.output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Retrieves and lists all the steps of a defined task that have been executed up to that point in time. Unlike streaming, this function does not continuously monitor the execution; it only provides a snapshot of the steps completed so far without displaying real-time updates as the task progresses." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.executions.transitions.list(execution_id=execution.id).items" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Continuously monitor and stream the steps of a defined task. It retrieves and displays the transitions or execution steps of the task identified by execution.id in real-time, showing each step sequentially until the task is either completed or an error causes it to terminate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.executions.transitions.stream(execution_id=execution.id)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ai", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cookbooks/03-SmartResearcher_With_WebSearch.py b/cookbooks/03-SmartResearcher_With_WebSearch.py deleted file mode 100644 index 6b0b210fb..000000000 --- a/cookbooks/03-SmartResearcher_With_WebSearch.py +++ /dev/null @@ -1,110 +0,0 @@ -import uuid -from julep import Client -import yaml, time - -# Global UUID is generated for agent and task -AGENT_UUID = uuid.uuid4() -TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an "agent" -name = "Jarvis" -about = "The original AI conscious the Iron Man." -default_settings = { - "temperature": 0.7, - "top_p": 1, - "min_p": 0.01, - "presence_penalty": 0, - "frequency_penalty": 0, - "length_penalty": 1.0, - "max_tokens": 150, -} - -# Create the agent -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name=name, - about=about, - model="gpt-4o", -) - -# Defining a Task -task_def = yaml.safe_load(""" -name: Research Assistant to find Wikipedia Keywords - -input_schema: - type: object - properties: - topics: - type: array - items: - type: string - description: The topics to search for. - -tools: -- name: brave_search - type: integration - integration: - provider: brave - setup: - api_key: "YOUR_API_KEY" - -main: -- over: _.topics - map: - tool: brave_search - arguments: - query: "'the latest news about ' + _" - -- over: _ - parallelism: 2 - map: - prompt: - - role: system - content: >- - You are a research assistant. - I need you to do in-depth research on topics trending in the news currently. - Based on the following latest html news snippet, come up with a list of wikipedia keywords to search: - "{{_}}" - Your response should be a list of keywords, separated by commas. Do not add any other text. - Example: `KEYWORDS: keyword1, keyword2, keyword3` - - unwrap: true -""") - -# Creating/Updating a task -task = client.tasks.create_or_update( - task_id=TASK_UUID, - agent_id=AGENT_UUID, - **task_def -) - -# Creating an Execution -execution = client.executions.create( - task_id=task.id, - input={ - "topics": ["Burger King Cup on the Ground Behind a Wendy's", "Forbidden Chemical X", "Finger Bracelets", "Amusing Notions"] - } -) - -print(execution.id) - -# Wait for the execution to complete -time.sleep(10) - -# Getting the execution details -execution = client.executions.get(execution.id) -print(execution.output) - -# Listing all the steps of a defined task -transitions = client.executions.transitions.list(execution_id=execution.id).items -print("Execution Steps:") -for transition in transitions: - print(transition) - -# Stream the steps of the defined task -print("Streaming execution transitions:") -print(client.executions.transitions.stream(execution_id=execution.id)) \ No newline at end of file diff --git a/cookbooks/03-trip-planning-assistant.ipynb b/cookbooks/03-trip-planning-assistant.ipynb new file mode 100644 index 000000000..dee1a8057 --- /dev/null +++ b/cookbooks/03-trip-planning-assistant.ipynb @@ -0,0 +1,1065 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \"julep\"\n", + "
\n", + "\n", + "

\n", + "
\n", + " Explore Docs (wip)\n", + " ·\n", + " Discord\n", + " ·\n", + " 𝕏\n", + " ·\n", + " LinkedIn\n", + "

\n", + "\n", + "

\n", + " \"NPM\n", + "  \n", + " \"PyPI\n", + "  \n", + " \"Docker\n", + "  \n", + " \"GitHub\n", + "

\n", + "\n", + "## Task: Travel Itinerary Assistant\n", + "\n", + "### Overview\n", + "\n", + "The Travel Itinerary Assistant helps users plan a travel itinerary that takes into account **current weather conditions** and **local tourist attractions**. By integrating data from Wikipedia for tourist attractions and using a weather API for real-time weather updates, the system provides a comprehensive travel plan tailored to each location. The generated itinerary suggests appropriate activities based on the weather, enhancing the overall travel experience.\n", + "\n", + "### Task Flow\n", + "\n", + "1. **User Input**\n", + " - User provides a list of desired travel locations.\n", + " - Each location is processed individually to gather the required data.\n", + "\n", + "2. **Weather Data Retrieval**\n", + " - Fetch current weather data for each location using a weather API.\n", + " - Extract relevant weather details, such as temperature, weather condition, and recommendations.\n", + "\n", + "3. **Tourist Attractions Lookup**\n", + " - Use Wikipedia to search for the top tourist attractions for each location.\n", + " - The query format used is: `\" tourist attractions\"`.\n", + " - Retrieve and compile a list of popular tourist spots and landmarks.\n", + "\n", + "4. **Data Evaluation and Integration**\n", + " - Combine weather data and tourist attractions into a unified list for each location.\n", + " - Format the data into a structured tuple: `(location, weather, attractions)`.\n", + "\n", + "5. **Itinerary Generation**\n", + " - Create a detailed travel itinerary based on:\n", + " - Current weather conditions (e.g., sunny, rainy, cloudy).\n", + " - Top tourist attractions for each location.\n", + " - Suggested activities categorized as indoor or outdoor based on weather.\n", + "\n", + "### Key Features\n", + "\n", + "- **Multi-location Travel Planning**: Handles multiple destinations simultaneously, offering a consolidated travel plan.\n", + "- **Real-time Weather Data**: Leverages weather APIs to provide up-to-date weather conditions.\n", + "- **Tourist Attraction Discovery**: Integrates Wikipedia to find and recommend popular attractions.\n", + "- **Intelligent Itinerary Suggestions**: Suggests indoor or outdoor activities based on the weather.\n", + "- **Comprehensive Itinerary Output**: Combines weather and tourist data into a user-friendly travel plan.\n", + "\n", + "### Output\n", + "\n", + "- A detailed travel itinerary for each location\n", + "- Curated, up-to-date information gathered from weather searches and Wikipedia\n", + "\n", + "```plaintext\n", + "\n", + "+----------------+ +--------------------------+ +--------------------------+ +------------------------------+ +-------------------------+\n", + "| User Input | | Weather Data Retrieval | | Tourist Attractions | | Data Evaluation & Integration| | Itinerary Generation |\n", + "| (List of | --> | (Weather API) | --> | Lookup (Wikipedia) | --> | (Combine Weather & | --> | (Generate Suggested |\n", + "| Locations) | | | | | | Attractions Data) | | Activities/Plan) |\n", + "+----------------+ +--------------------------+ +--------------------------+ +------------------------------+ +-------------------------+\n", + " | | | | |\n", + " | | | | |\n", + " v v v v v\n", + "Location 1, Location 2, ... Fetch weather for each Search Wikipedia for Combine weather data and Create itinerary with\n", + "Each location processed location individually, \" tourist tourist attractions into suggested activities\n", + "individually for extracting temp., attractions\", retrieve a structured tuple: based on weather and\n", + "weather data. conditions, & top spots. (location, weather, attractions.\n", + " recommendations. attractions).\n", + "```\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "### Additional Information\n", + "\n", + "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", + "\n", + "**Author:** Julep AI \n", + "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Installing the Julep Client" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install julep -U --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### NOTE:\n", + "\n", + "- UUIDs are generated for both the agent and task to uniquely identify them within the system.\n", + "- Once created, these UUIDs should remain unchanged for simplicity.\n", + "- Altering a UUID will result in the system treating it as a new agent or task.\n", + "- If a UUID is changed, the original agent or task will continue to exist in the system alongside the new one." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Global UUID is generated for agent and task\n", + "import uuid\n", + "\n", + "AGENT_UUID = uuid.uuid4()\n", + "TASK_UUID = uuid.uuid4() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Julep Client with the API Key" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from julep import Client\n", + "import os\n", + "\n", + "api_key = os.getenv(\"JULEP_API_KEY\")\n", + "\n", + "# Create a Julep client\n", + "client = Client(api_key=api_key, environment=\"dev\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an \"agent\"\n", + "\n", + "\n", + "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", + "\n", + "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the agent\n", + "name = \"Jacob\"\n", + "about = \"a travel assistant that helps plan the perfect trip.\"\n", + "default_settings = {\n", + " \"temperature\": 0.7,\n", + " \"top_p\": 1,\n", + " \"min_p\": 0.01,\n", + " \"presence_penalty\": 0,\n", + " \"frequency_penalty\": 0,\n", + " \"length_penalty\": 1.0,\n", + " \"max_tokens\": 150,\n", + "}\n", + "\n", + "# Create the agent\n", + "agent = client.agents.create_or_update(\n", + " agent_id=AGENT_UUID,\n", + " name=name,\n", + " about=about,\n", + " model=\"gpt-4o\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Task\n", + "\n", + "Tasks in Julep are Github-Actions-style workflows that define long-running, multi-step actions.\n", + "\n", + "You can use them to conduct complex actions by defining them step-by-step.\n", + "\n", + "To learn more about tasks, please refer to the `Tasks` section in [Julep Concepts](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#tasks)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import yaml\n", + "\n", + "openweathermap_api_key = os.getenv(\"OPENWEATHERMAP_API_KEY\")\n", + "brave_api_key = os.getenv(\"BRAVE_API_KEY\")\n", + "\n", + "# Defining the task\n", + "task_def = yaml.safe_load(f\"\"\"\n", + "name: Tourist Plan With Weather And Attractions\n", + "\n", + "input_schema:\n", + " type: object\n", + " properties:\n", + " locations:\n", + " type: array\n", + " items:\n", + " type: string\n", + " description: The locations to search for.\n", + "\n", + "tools:\n", + "- name: wikipedia\n", + " type: integration\n", + " integration:\n", + " provider: wikipedia\n", + "\n", + "- name: weather\n", + " type: integration\n", + " integration:\n", + " provider: weather\n", + " setup:\n", + " openweathermap_api_key: {openweathermap_api_key}\n", + "\n", + "- name: internet_search\n", + " type: integration\n", + " integration:\n", + " provider: brave\n", + " setup:\n", + " api_key: {brave_api_key}\n", + "\n", + "main:\n", + "- over: inputs[0].locations\n", + " map:\n", + " tool: weather\n", + " arguments:\n", + " location: _\n", + "\n", + "- over: inputs[0].locations\n", + " map:\n", + " tool: internet_search\n", + " arguments:\n", + " query: \"'tourist attractions in ' + _\"\n", + "\n", + "# Zip locations, weather, and attractions into a list of tuples [(location, weather, attractions)]\n", + "- evaluate:\n", + " zipped: |-\n", + " list(\n", + " zip(\n", + " inputs[0].locations,\n", + " [output['result'] for output in outputs[0]],\n", + " outputs[1]\n", + " )\n", + " )\n", + "\n", + "\n", + "- over: _['zipped']\n", + " parallelism: 3\n", + " # Inside the map step, each `_` represents the current element in the list\n", + " # which is a tuple of (location, weather, attractions)\n", + " map:\n", + " prompt:\n", + " - role: system\n", + " content: >-\n", + " You are {{{{agent.name}}}}. Your task is to create a detailed itinerary\n", + " for visiting tourist attractions in some locations.\n", + " The user will give you the following information for each location:\n", + "\n", + " - The location\n", + " - The current weather condition\n", + " - The top tourist attractions\n", + " - role: user\n", + " content: >-\n", + " Location: \"{{{{_[0]}}}}\"\n", + " Weather: \"{{{{_[1]}}}}\"\n", + " Attractions: \"{{{{_[2]}}}}\"\n", + " unwrap: true\n", + "\n", + "- evaluate:\n", + " final_plan: |-\n", + " '\\\\n---------------\\\\n'.join(activity for activity in _)\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notes:\n", + "- The reason for using the quadruple curly braces `{{{{}}}}` for the jinja template is to avoid conflicts with the curly braces when using the `f` formatted strings in python. [More information here](https://stackoverflow.com/questions/64493332/jinja-templating-in-airflow-along-with-formatted-text)\n", + "- The `unwrap: True` in the prompt step is used to unwrap the output of the prompt step (to unwrap the `choices[0].message.content` from the output of the model).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating/Updating a task" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# creating the task object\n", + "task = client.tasks.create_or_update(\n", + " task_id=TASK_UUID,\n", + " agent_id=AGENT_UUID,\n", + " **task_def\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started an execution. Execution ID: 240c3e5e-a1a5-4d2c-aa24-ee86e876139e\n" + ] + } + ], + "source": [ + "execution = client.executions.create(\n", + " task_id=task.id,\n", + " input={\n", + " \"locations\": [\"New York\", \"London\", \"Paris\", \"Tokyo\", \"Sydney\"]\n", + " }\n", + ")\n", + "\n", + "print(\"Started an execution. Execution ID:\", execution.id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking execution details and output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are multiple ways to get the execution details and the output:\n", + "\n", + "1. **Get Execution Details**: This method retrieves the details of the execution, including the output of the last transition that took place.\n", + "\n", + "2. **List Transitions**: This method lists all the task steps that have been executed up to this point in time, so the output of a successful execution will be the output of the last transition (first in the transition list as it is in reverse chronological order), which should have a type of `finish`.\n", + "\n", + "\n", + "Note: You need to wait for a few seconds for the execution to complete before you can get the final output, so feel free to run the following cells multiple times until you get the final output.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'final_plan': \"Here's a detailed itinerary for visiting top tourist attractions in New York, considering the current weather conditions. \\n\\n### Day 1: Iconic Landmarks and Observation Decks\\n- **Morning:**\\n - **Top of the Rock Observation Deck:** Start your day with a visit to the Top of the Rock Observation Deck at Rockefeller Plaza. The panoramic 360-degree views from the 70th floor are a must-see. Dress warmly as it feels like 5.2°C outside, and it’s quite windy.\\n - **Link for more info:** [Tripadvisor - Top of the Rock](https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html)\\n\\n- **Afternoon:**\\n - **St. Patrick’s Cathedral:** Just a short walk from Rockefeller Plaza, explore the stunning architecture of St. Patrick’s Cathedral. The overcast skies will provide a dramatic backdrop for photos.\\n - **Fifth Avenue:** Enjoy a leisurely stroll along Fifth Avenue, visiting iconic stores and landmarks.\\n\\n- **Evening:**\\n - **Times Square:** Experience the vibrant lights and energy of Times Square. The overcast clouds might enhance the brightness of the neon lights.\\n\\n### Day 2: Culture and History\\n- **Morning:**\\n - **The Museum of Modern Art (MoMA):** Spend your morning exploring MoMA’s vast collection of modern and contemporary art. This indoor activity is perfect for a cloudy day.\\n\\n- **Afternoon:**\\n - **Central Park:** Head to Central Park for a refreshing walk. With 100% cloud cover, it's a great day to explore the park without the harsh sun. Consider visiting the Central Park Zoo or taking a guided tour.\\n\\n- **Evening:**\\n - **Broadway Show:** End your day with a Broadway show. It’s an ideal indoor activity to avoid the chilly weather outside. Book tickets in advance for popular shows.\\n\\n### Day 3: Historical and Educational\\n- **Morning:**\\n - **Statue of Liberty and Ellis Island:** Take a ferry to visit these iconic sites. Dress warmly for the ferry ride. The cloud cover will provide a unique perspective for photos.\\n\\n- **Afternoon:**\\n - **9/11 Memorial and Museum:** Spend your afternoon reflecting at the 9/11 Memorial and exploring the museum exhibits.\\n\\n- **Evening:**\\n - **Brooklyn Bridge:** Walk across the Brooklyn Bridge and enjoy the city skyline. With the wind speed at 5.81 m/s, be prepared for breezy conditions.\\n\\n### Additional Tips:\\n- **Clothing:** Wear layers to keep warm, as the temperature feels colder than it actually is.\\n- **Dining:** New York offers a plethora of dining options. Consider trying some local favorites like a classic New York bagel or pizza.\\n- **Transportation:** Utilize the subway for efficient travel across the city. Taxis and ride-sharing services are also readily available.\\n\\nFor more details on attractions and guided tours, you can visit [USA Guided Tours](https://usaguidedtours.com/nyc/attraction/) and [I Love NY](https://www.iloveny.com/places-to-go/new-york-city/attractions/). \\n\\nEnjoy your trip to New York City!\\n---------------\\n**Day 1: Exploring Iconic London Landmarks**\\n\\n**Morning:**\\n1. **Buckingham Palace**\\n - Start your day early with a visit to Buckingham Palace. Arrive by 9:30 AM to catch the Changing of the Guard ceremony, which typically starts at 11:00 AM. Enjoy the majestic architecture and the surrounding gardens.\\n - Weather Tip: With the overcast clouds, it might feel chilly, so dress warmly and bring an umbrella just in case.\\n\\n**Midday:**\\n2. **Westminster Abbey**\\n - Head towards Westminster Abbey, a short walk from Buckingham Palace. This historic church has been the site of many significant events, including royal weddings and coronations. Spend about 1.5 hours exploring.\\n\\n3. **Lunch at Borough Market**\\n - Take a tube or walk to Borough Market for a variety of food options. It's a great place to warm up with some hot street food and explore the diverse culinary offerings.\\n\\n**Afternoon:**\\n4. **London Eye**\\n - After lunch, head to the London Eye. The overcast sky might not offer the clearest views, but the experience is still worthwhile. Pre-book your tickets to avoid long lines and enjoy a 30-minute ride on this iconic Ferris wheel.\\n\\n**Evening:**\\n5. **The Globe Theatre**\\n - As the day winds down, visit Shakespeare's Globe Theatre. If there's a performance, consider attending or simply take a guided tour to learn about the history of this famous theater.\\n\\n**Day 2: Museums and Cultural Exploration**\\n\\n**Morning:**\\n1. **The British Museum**\\n - Start your second day at the British Museum. Spend a few hours exploring the vast collection of art and antiquities. Highlights include the Rosetta Stone and the Elgin Marbles.\\n\\n**Midday:**\\n2. **Lunch near Covent Garden**\\n - Head to Covent Garden for lunch. This area is bustling with restaurants and cafes, perfect for a cozy indoor meal.\\n\\n**Afternoon:**\\n3. **The Tower of London**\\n - After lunch, make your way to the Tower of London. Delve into England's rich history and see the Crown Jewels. Allocate about 2-3 hours for this visit.\\n\\n**Evening:**\\n4. **Tower Bridge**\\n - Conclude your day with a walk across Tower Bridge. The views of the Thames River and the cityscape are beautiful, even on a cloudy evening.\\n\\n**Day 3: Leisure and Local Experiences**\\n\\n**Morning:**\\n1. **Natural History Museum**\\n - Begin your last day at the Natural History Museum. It's a family-friendly museum with fascinating exhibits, including dinosaur skeletons and a model of a blue whale.\\n\\n**Midday:**\\n2. **Lunch at South Kensington**\\n - Enjoy a relaxed lunch in South Kensington. There are plenty of options, from casual cafes to high-end dining.\\n\\n**Afternoon:**\\n3. **Hyde Park**\\n - Spend your afternoon strolling through Hyde Park. Visit the Serpentine Galleries if you're interested in contemporary art. The park's natural beauty is a peaceful retreat amidst the city hustle.\\n\\n**Evening:**\\n4. **Dinner and a Show in the West End**\\n - End your London adventure with a memorable dinner followed by a theater show in the West End. Book your tickets in advance for popular shows.\\n\\n**Additional Tips:**\\n- Always check the attraction websites for any specific COVID-19 guidelines or changes in operating hours.\\n- London’s public transport system is efficient; consider getting an Oyster card for convenient travel.\\n- Don't forget to dress in layers to adapt to the chilly weather, and always have your camera ready to capture memories.\\n---------------\\nHere’s a detailed itinerary for your visit to Paris, considering the current snowy weather conditions and top attractions:\\n\\n### Day 1: Embrace the Iconic Landmarks\\n\\n**Morning: Eiffel Tower**\\n- **Time:** 9:00 AM\\n- **Details:** Begin your day with a visit to the Eiffel Tower. Even in the snow, the tower offers stunning views of Paris. Dress warmly and enjoy hot chocolate from nearby cafes.\\n- **Link for more info:** [Tripadvisor - Things to Do in Paris](https://www.tripadvisor.com/Attractions-g187147-Activities-Paris_Ile_de_France.html)\\n\\n**Afternoon: Louvre Museum**\\n- **Time:** 1:00 PM\\n- **Details:** Spend your afternoon indoors at the Louvre Museum. With its vast collection of art and history, it's a perfect way to escape the cold. Consider taking a guided tour to make the most of your visit.\\n- **Link for more info:** [U.S. News Travel - Things To Do](https://travel.usnews.com/Paris_France/Things_To_Do/)\\n\\n**Evening: Seine River Cruise**\\n- **Time:** 6:00 PM\\n- **Details:** End your day with a magical Seine River cruise. The snow adds a picturesque touch to the illuminated landmarks. Ensure to book a heated cruise for comfort.\\n\\n### Day 2: Explore Cultural and Historical Treasures\\n\\n**Morning: Notre-Dame Cathedral**\\n- **Time:** 9:30 AM\\n- **Details:** Visit the iconic Notre-Dame Cathedral. Although some areas may be under restoration, its architecture and history are worth experiencing. Warm clothing is essential as the interior can be chilly.\\n\\n**Afternoon: Musée d'Orsay**\\n- **Time:** 1:30 PM\\n- **Details:** Head to the Musée d'Orsay, renowned for its Impressionist masterpieces. This indoor activity is ideal for escaping the cold and enjoying world-class art.\\n\\n**Evening: Montmartre and Sacré-Cœur**\\n- **Time:** 5:00 PM\\n- **Details:** Wander through the charming streets of Montmartre and visit the Sacré-Cœur Basilica. The view of Paris in the snow is breathtaking. Enjoy a cozy dinner at a local bistro in Montmartre.\\n\\n### Day 3: Discover Hidden Gems and Local Flavors\\n\\n**Morning: Le Marais District**\\n- **Time:** 10:00 AM\\n- **Details:** Explore Le Marais, known for its vibrant street art, boutiques, and cafes. Enjoy a leisurely breakfast and shop for unique souvenirs.\\n\\n**Afternoon: Palais Garnier (Opera House)**\\n- **Time:** 2:00 PM\\n- **Details:** Tour the opulent Palais Garnier. Its stunning interiors are a must-see, especially when it's snowy outside.\\n\\n**Evening: Moulin Rouge Show**\\n- **Time:** 8:00 PM\\n- **Details:** Conclude your trip with a classic Parisian experience at the Moulin Rouge. Book in advance to secure a good seat and enjoy the legendary cabaret performance.\\n\\n### Additional Tips:\\n- **Weather Preparation:** Wear layers, waterproof boots, and carry an umbrella. The snow and cold wind can be intense.\\n- **Dining:** Indulge in warm, hearty French cuisine at local cafes and restaurants. Try dishes like French onion soup, coq au vin, and tarte Tatin.\\n- **Transport:** Use public transportation to avoid the snowy streets, and consider purchasing a Paris Visite pass for unlimited travel.\\n\\nEnjoy your snowy adventure in Paris!\\n---------------\\n**Tokyo Itinerary**\\n\\n**Day 1: Arrival and Exploration of Historical and Cultural Sites**\\n\\n- **Morning:**\\n - **Asakusa District**: Begin your day with a visit to the historic Asakusa district. Explore the iconic Senso-ji Temple, Tokyo's oldest temple. Enjoy the traditional market streets like Nakamise Street for some shopping and snacks.\\n\\n- **Afternoon:**\\n - **Tokyo National Museum**: Head to Ueno Park and visit the Tokyo National Museum. Discover Japan’s extensive collection of art and antiquities. This is a great spot to dive into Japanese history and culture.\\n\\n- **Evening:**\\n - **Dinner in Ueno**: Explore the local dining options around Ueno and enjoy a traditional Japanese dinner.\\n\\n**Day 2: Modern Tokyo and Unique Experiences**\\n\\n- **Morning:**\\n - **Ghibli Museum**: Start your day with a magical visit to the Ghibli Museum in Mitaka. Perfect for fans of Studio Ghibli's animated films, this museum offers a whimsical look into the creative world of Hayao Miyazaki.\\n\\n- **Afternoon:**\\n - **Shibuya and Harajuku**: Head towards the bustling areas of Shibuya and Harajuku. Witness the famous Shibuya Crossing and explore the trendy shops of Harajuku, especially Takeshita Street.\\n\\n- **Evening:**\\n - **Golden Gai**: Conclude your day in the vibrant Golden Gai district. This area is renowned for its narrow alleys filled with small bars and eateries. Experience the unique nightlife of Tokyo here.\\n\\n**Day 3: Relax and Explore Green Spaces**\\n\\n- **Morning:**\\n - **Shinjuku Gyoen National Garden**: Spend a peaceful morning strolling through the beautiful Shinjuku Gyoen, one of Tokyo's largest and most beautiful parks. It's a perfect spot for relaxation and enjoying nature.\\n\\n- **Afternoon:**\\n - **Meiji Shrine**: Visit the Meiji Shrine, located in a forested area near Harajuku and Shibuya. It's a serene place to learn about Shinto traditions and enjoy the tranquil setting.\\n\\n- **Evening:**\\n - **Tokyo Tower or Skytree**: End your trip with a visit to either Tokyo Tower or Tokyo Skytree for a panoramic view of the city. It's an unforgettable way to see Tokyo illuminated at night.\\n\\n**Weather Considerations:**\\n- With the current weather of few clouds and a mild temperature around 10.32°C, it is advisable to wear layers and carry a light jacket for comfort during outdoor activities.\\n- Humidity is high (92%), so be prepared for a slightly damp feeling and consider moisture-wicking clothing.\\n\\n**Additional Tips:**\\n- Always check the opening hours of attractions and book tickets in advance where necessary.\\n- Use Tokyo’s efficient public transport to move around easily.\\n- Consider visiting the websites linked in the attraction descriptions for more detailed information and current updates.\\n---------------\\nHere's a detailed itinerary for exploring some of Sydney's top tourist attractions with the current weather conditions in mind. With clear skies and pleasant temperatures, it's a perfect day to explore the outdoors and enjoy what Sydney has to offer.\\n\\n### Morning\\n\\n**9:00 AM - Sydney Opera House**\\n- Begin your day with a visit to the iconic Sydney Opera House. Take a guided tour to learn about its history and architecture. Tours are available in multiple languages.\\n- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\\n\\n**11:00 AM - Royal Botanic Garden Sydney**\\n- Just a short walk from the Opera House, enjoy a leisurely stroll through the Royal Botanic Garden. The clear skies will offer beautiful views of the diverse plant life and the Sydney Harbour.\\n\\n### Afternoon\\n\\n**12:30 PM - Lunch at Opera Bar**\\n- Head back to Opera Bar for lunch. Enjoy a refreshing cocktail with stunning views of the Sydney Harbour Bridge and the waterfront.\\n- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\\n\\n**2:00 PM - Sydney Harbour Bridge**\\n- After lunch, take a scenic walk across the Sydney Harbour Bridge. If you're up for it, consider the BridgeClimb for breathtaking panoramic views of the city.\\n\\n**4:00 PM - The Rocks**\\n- Explore The Rocks, one of Sydney's most historic areas. Wander through the cobbled streets, visit the local markets, and perhaps enjoy a cup of coffee at a nearby café.\\n\\n### Evening\\n\\n**6:00 PM - Darling Harbour**\\n- Make your way to Darling Harbour for the evening. Here you can visit attractions such as the SEA LIFE Sydney Aquarium or simply enjoy the lively atmosphere by the waterfront.\\n\\n**8:00 PM - Dinner at a Local Restaurant**\\n- Conclude your day with dinner at one of Darling Harbour's many restaurants. Choose from a variety of cuisines while enjoying the vibrant night scene.\\n\\n### Additional Suggestions\\n\\n- If you're interested in more unique experiences, consider visiting some of the attractions listed on [Time Out Sydney](https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck), which includes thrilling adventures and scenic tours.\\n\\nWith clear skies and mild temperatures, this itinerary offers a balanced mix of cultural, historical, and scenic experiences. Enjoy your visit to Sydney!\"}\n", + "--------------------------------------------------\n", + "Here's a detailed itinerary for visiting top tourist attractions in New York, considering the current weather conditions. \n", + "\n", + "### Day 1: Iconic Landmarks and Observation Decks\n", + "- **Morning:**\n", + " - **Top of the Rock Observation Deck:** Start your day with a visit to the Top of the Rock Observation Deck at Rockefeller Plaza. The panoramic 360-degree views from the 70th floor are a must-see. Dress warmly as it feels like 5.2°C outside, and it’s quite windy.\n", + " - **Link for more info:** [Tripadvisor - Top of the Rock](https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html)\n", + "\n", + "- **Afternoon:**\n", + " - **St. Patrick’s Cathedral:** Just a short walk from Rockefeller Plaza, explore the stunning architecture of St. Patrick’s Cathedral. The overcast skies will provide a dramatic backdrop for photos.\n", + " - **Fifth Avenue:** Enjoy a leisurely stroll along Fifth Avenue, visiting iconic stores and landmarks.\n", + "\n", + "- **Evening:**\n", + " - **Times Square:** Experience the vibrant lights and energy of Times Square. The overcast clouds might enhance the brightness of the neon lights.\n", + "\n", + "### Day 2: Culture and History\n", + "- **Morning:**\n", + " - **The Museum of Modern Art (MoMA):** Spend your morning exploring MoMA’s vast collection of modern and contemporary art. This indoor activity is perfect for a cloudy day.\n", + "\n", + "- **Afternoon:**\n", + " - **Central Park:** Head to Central Park for a refreshing walk. With 100% cloud cover, it's a great day to explore the park without the harsh sun. Consider visiting the Central Park Zoo or taking a guided tour.\n", + "\n", + "- **Evening:**\n", + " - **Broadway Show:** End your day with a Broadway show. It’s an ideal indoor activity to avoid the chilly weather outside. Book tickets in advance for popular shows.\n", + "\n", + "### Day 3: Historical and Educational\n", + "- **Morning:**\n", + " - **Statue of Liberty and Ellis Island:** Take a ferry to visit these iconic sites. Dress warmly for the ferry ride. The cloud cover will provide a unique perspective for photos.\n", + "\n", + "- **Afternoon:**\n", + " - **9/11 Memorial and Museum:** Spend your afternoon reflecting at the 9/11 Memorial and exploring the museum exhibits.\n", + "\n", + "- **Evening:**\n", + " - **Brooklyn Bridge:** Walk across the Brooklyn Bridge and enjoy the city skyline. With the wind speed at 5.81 m/s, be prepared for breezy conditions.\n", + "\n", + "### Additional Tips:\n", + "- **Clothing:** Wear layers to keep warm, as the temperature feels colder than it actually is.\n", + "- **Dining:** New York offers a plethora of dining options. Consider trying some local favorites like a classic New York bagel or pizza.\n", + "- **Transportation:** Utilize the subway for efficient travel across the city. Taxis and ride-sharing services are also readily available.\n", + "\n", + "For more details on attractions and guided tours, you can visit [USA Guided Tours](https://usaguidedtours.com/nyc/attraction/) and [I Love NY](https://www.iloveny.com/places-to-go/new-york-city/attractions/). \n", + "\n", + "Enjoy your trip to New York City!\n", + "---------------\n", + "**Day 1: Exploring Iconic London Landmarks**\n", + "\n", + "**Morning:**\n", + "1. **Buckingham Palace**\n", + " - Start your day early with a visit to Buckingham Palace. Arrive by 9:30 AM to catch the Changing of the Guard ceremony, which typically starts at 11:00 AM. Enjoy the majestic architecture and the surrounding gardens.\n", + " - Weather Tip: With the overcast clouds, it might feel chilly, so dress warmly and bring an umbrella just in case.\n", + "\n", + "**Midday:**\n", + "2. **Westminster Abbey**\n", + " - Head towards Westminster Abbey, a short walk from Buckingham Palace. This historic church has been the site of many significant events, including royal weddings and coronations. Spend about 1.5 hours exploring.\n", + "\n", + "3. **Lunch at Borough Market**\n", + " - Take a tube or walk to Borough Market for a variety of food options. It's a great place to warm up with some hot street food and explore the diverse culinary offerings.\n", + "\n", + "**Afternoon:**\n", + "4. **London Eye**\n", + " - After lunch, head to the London Eye. The overcast sky might not offer the clearest views, but the experience is still worthwhile. Pre-book your tickets to avoid long lines and enjoy a 30-minute ride on this iconic Ferris wheel.\n", + "\n", + "**Evening:**\n", + "5. **The Globe Theatre**\n", + " - As the day winds down, visit Shakespeare's Globe Theatre. If there's a performance, consider attending or simply take a guided tour to learn about the history of this famous theater.\n", + "\n", + "**Day 2: Museums and Cultural Exploration**\n", + "\n", + "**Morning:**\n", + "1. **The British Museum**\n", + " - Start your second day at the British Museum. Spend a few hours exploring the vast collection of art and antiquities. Highlights include the Rosetta Stone and the Elgin Marbles.\n", + "\n", + "**Midday:**\n", + "2. **Lunch near Covent Garden**\n", + " - Head to Covent Garden for lunch. This area is bustling with restaurants and cafes, perfect for a cozy indoor meal.\n", + "\n", + "**Afternoon:**\n", + "3. **The Tower of London**\n", + " - After lunch, make your way to the Tower of London. Delve into England's rich history and see the Crown Jewels. Allocate about 2-3 hours for this visit.\n", + "\n", + "**Evening:**\n", + "4. **Tower Bridge**\n", + " - Conclude your day with a walk across Tower Bridge. The views of the Thames River and the cityscape are beautiful, even on a cloudy evening.\n", + "\n", + "**Day 3: Leisure and Local Experiences**\n", + "\n", + "**Morning:**\n", + "1. **Natural History Museum**\n", + " - Begin your last day at the Natural History Museum. It's a family-friendly museum with fascinating exhibits, including dinosaur skeletons and a model of a blue whale.\n", + "\n", + "**Midday:**\n", + "2. **Lunch at South Kensington**\n", + " - Enjoy a relaxed lunch in South Kensington. There are plenty of options, from casual cafes to high-end dining.\n", + "\n", + "**Afternoon:**\n", + "3. **Hyde Park**\n", + " - Spend your afternoon strolling through Hyde Park. Visit the Serpentine Galleries if you're interested in contemporary art. The park's natural beauty is a peaceful retreat amidst the city hustle.\n", + "\n", + "**Evening:**\n", + "4. **Dinner and a Show in the West End**\n", + " - End your London adventure with a memorable dinner followed by a theater show in the West End. Book your tickets in advance for popular shows.\n", + "\n", + "**Additional Tips:**\n", + "- Always check the attraction websites for any specific COVID-19 guidelines or changes in operating hours.\n", + "- London’s public transport system is efficient; consider getting an Oyster card for convenient travel.\n", + "- Don't forget to dress in layers to adapt to the chilly weather, and always have your camera ready to capture memories.\n", + "---------------\n", + "Here’s a detailed itinerary for your visit to Paris, considering the current snowy weather conditions and top attractions:\n", + "\n", + "### Day 1: Embrace the Iconic Landmarks\n", + "\n", + "**Morning: Eiffel Tower**\n", + "- **Time:** 9:00 AM\n", + "- **Details:** Begin your day with a visit to the Eiffel Tower. Even in the snow, the tower offers stunning views of Paris. Dress warmly and enjoy hot chocolate from nearby cafes.\n", + "- **Link for more info:** [Tripadvisor - Things to Do in Paris](https://www.tripadvisor.com/Attractions-g187147-Activities-Paris_Ile_de_France.html)\n", + "\n", + "**Afternoon: Louvre Museum**\n", + "- **Time:** 1:00 PM\n", + "- **Details:** Spend your afternoon indoors at the Louvre Museum. With its vast collection of art and history, it's a perfect way to escape the cold. Consider taking a guided tour to make the most of your visit.\n", + "- **Link for more info:** [U.S. News Travel - Things To Do](https://travel.usnews.com/Paris_France/Things_To_Do/)\n", + "\n", + "**Evening: Seine River Cruise**\n", + "- **Time:** 6:00 PM\n", + "- **Details:** End your day with a magical Seine River cruise. The snow adds a picturesque touch to the illuminated landmarks. Ensure to book a heated cruise for comfort.\n", + "\n", + "### Day 2: Explore Cultural and Historical Treasures\n", + "\n", + "**Morning: Notre-Dame Cathedral**\n", + "- **Time:** 9:30 AM\n", + "- **Details:** Visit the iconic Notre-Dame Cathedral. Although some areas may be under restoration, its architecture and history are worth experiencing. Warm clothing is essential as the interior can be chilly.\n", + "\n", + "**Afternoon: Musée d'Orsay**\n", + "- **Time:** 1:30 PM\n", + "- **Details:** Head to the Musée d'Orsay, renowned for its Impressionist masterpieces. This indoor activity is ideal for escaping the cold and enjoying world-class art.\n", + "\n", + "**Evening: Montmartre and Sacré-Cœur**\n", + "- **Time:** 5:00 PM\n", + "- **Details:** Wander through the charming streets of Montmartre and visit the Sacré-Cœur Basilica. The view of Paris in the snow is breathtaking. Enjoy a cozy dinner at a local bistro in Montmartre.\n", + "\n", + "### Day 3: Discover Hidden Gems and Local Flavors\n", + "\n", + "**Morning: Le Marais District**\n", + "- **Time:** 10:00 AM\n", + "- **Details:** Explore Le Marais, known for its vibrant street art, boutiques, and cafes. Enjoy a leisurely breakfast and shop for unique souvenirs.\n", + "\n", + "**Afternoon: Palais Garnier (Opera House)**\n", + "- **Time:** 2:00 PM\n", + "- **Details:** Tour the opulent Palais Garnier. Its stunning interiors are a must-see, especially when it's snowy outside.\n", + "\n", + "**Evening: Moulin Rouge Show**\n", + "- **Time:** 8:00 PM\n", + "- **Details:** Conclude your trip with a classic Parisian experience at the Moulin Rouge. Book in advance to secure a good seat and enjoy the legendary cabaret performance.\n", + "\n", + "### Additional Tips:\n", + "- **Weather Preparation:** Wear layers, waterproof boots, and carry an umbrella. The snow and cold wind can be intense.\n", + "- **Dining:** Indulge in warm, hearty French cuisine at local cafes and restaurants. Try dishes like French onion soup, coq au vin, and tarte Tatin.\n", + "- **Transport:** Use public transportation to avoid the snowy streets, and consider purchasing a Paris Visite pass for unlimited travel.\n", + "\n", + "Enjoy your snowy adventure in Paris!\n", + "---------------\n", + "**Tokyo Itinerary**\n", + "\n", + "**Day 1: Arrival and Exploration of Historical and Cultural Sites**\n", + "\n", + "- **Morning:**\n", + " - **Asakusa District**: Begin your day with a visit to the historic Asakusa district. Explore the iconic Senso-ji Temple, Tokyo's oldest temple. Enjoy the traditional market streets like Nakamise Street for some shopping and snacks.\n", + "\n", + "- **Afternoon:**\n", + " - **Tokyo National Museum**: Head to Ueno Park and visit the Tokyo National Museum. Discover Japan’s extensive collection of art and antiquities. This is a great spot to dive into Japanese history and culture.\n", + "\n", + "- **Evening:**\n", + " - **Dinner in Ueno**: Explore the local dining options around Ueno and enjoy a traditional Japanese dinner.\n", + "\n", + "**Day 2: Modern Tokyo and Unique Experiences**\n", + "\n", + "- **Morning:**\n", + " - **Ghibli Museum**: Start your day with a magical visit to the Ghibli Museum in Mitaka. Perfect for fans of Studio Ghibli's animated films, this museum offers a whimsical look into the creative world of Hayao Miyazaki.\n", + "\n", + "- **Afternoon:**\n", + " - **Shibuya and Harajuku**: Head towards the bustling areas of Shibuya and Harajuku. Witness the famous Shibuya Crossing and explore the trendy shops of Harajuku, especially Takeshita Street.\n", + "\n", + "- **Evening:**\n", + " - **Golden Gai**: Conclude your day in the vibrant Golden Gai district. This area is renowned for its narrow alleys filled with small bars and eateries. Experience the unique nightlife of Tokyo here.\n", + "\n", + "**Day 3: Relax and Explore Green Spaces**\n", + "\n", + "- **Morning:**\n", + " - **Shinjuku Gyoen National Garden**: Spend a peaceful morning strolling through the beautiful Shinjuku Gyoen, one of Tokyo's largest and most beautiful parks. It's a perfect spot for relaxation and enjoying nature.\n", + "\n", + "- **Afternoon:**\n", + " - **Meiji Shrine**: Visit the Meiji Shrine, located in a forested area near Harajuku and Shibuya. It's a serene place to learn about Shinto traditions and enjoy the tranquil setting.\n", + "\n", + "- **Evening:**\n", + " - **Tokyo Tower or Skytree**: End your trip with a visit to either Tokyo Tower or Tokyo Skytree for a panoramic view of the city. It's an unforgettable way to see Tokyo illuminated at night.\n", + "\n", + "**Weather Considerations:**\n", + "- With the current weather of few clouds and a mild temperature around 10.32°C, it is advisable to wear layers and carry a light jacket for comfort during outdoor activities.\n", + "- Humidity is high (92%), so be prepared for a slightly damp feeling and consider moisture-wicking clothing.\n", + "\n", + "**Additional Tips:**\n", + "- Always check the opening hours of attractions and book tickets in advance where necessary.\n", + "- Use Tokyo’s efficient public transport to move around easily.\n", + "- Consider visiting the websites linked in the attraction descriptions for more detailed information and current updates.\n", + "---------------\n", + "Here's a detailed itinerary for exploring some of Sydney's top tourist attractions with the current weather conditions in mind. With clear skies and pleasant temperatures, it's a perfect day to explore the outdoors and enjoy what Sydney has to offer.\n", + "\n", + "### Morning\n", + "\n", + "**9:00 AM - Sydney Opera House**\n", + "- Begin your day with a visit to the iconic Sydney Opera House. Take a guided tour to learn about its history and architecture. Tours are available in multiple languages.\n", + "- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\n", + "\n", + "**11:00 AM - Royal Botanic Garden Sydney**\n", + "- Just a short walk from the Opera House, enjoy a leisurely stroll through the Royal Botanic Garden. The clear skies will offer beautiful views of the diverse plant life and the Sydney Harbour.\n", + "\n", + "### Afternoon\n", + "\n", + "**12:30 PM - Lunch at Opera Bar**\n", + "- Head back to Opera Bar for lunch. Enjoy a refreshing cocktail with stunning views of the Sydney Harbour Bridge and the waterfront.\n", + "- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\n", + "\n", + "**2:00 PM - Sydney Harbour Bridge**\n", + "- After lunch, take a scenic walk across the Sydney Harbour Bridge. If you're up for it, consider the BridgeClimb for breathtaking panoramic views of the city.\n", + "\n", + "**4:00 PM - The Rocks**\n", + "- Explore The Rocks, one of Sydney's most historic areas. Wander through the cobbled streets, visit the local markets, and perhaps enjoy a cup of coffee at a nearby café.\n", + "\n", + "### Evening\n", + "\n", + "**6:00 PM - Darling Harbour**\n", + "- Make your way to Darling Harbour for the evening. Here you can visit attractions such as the SEA LIFE Sydney Aquarium or simply enjoy the lively atmosphere by the waterfront.\n", + "\n", + "**8:00 PM - Dinner at a Local Restaurant**\n", + "- Conclude your day with dinner at one of Darling Harbour's many restaurants. Choose from a variety of cuisines while enjoying the vibrant night scene.\n", + "\n", + "### Additional Suggestions\n", + "\n", + "- If you're interested in more unique experiences, consider visiting some of the attractions listed on [Time Out Sydney](https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck), which includes thrilling adventures and scenic tours.\n", + "\n", + "With clear skies and mild temperatures, this itinerary offers a balanced mix of cultural, historical, and scenic experiences. Enjoy your visit to Sydney!\n" + ] + } + ], + "source": [ + "# Get execution details\n", + "execution = client.executions.get(execution.id)\n", + "# Print the output\n", + "print(execution.output)\n", + "print(\"-\"*50)\n", + "\n", + "if 'final_plan' in execution.output:\n", + " print(execution.output['final_plan'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transition type: init\n", + "Transition output: {'locations': ['New York', 'London', 'Paris', 'Tokyo', 'Sydney']}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: New York\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': 'In New York, the current weather is as follows:\\nDetailed status: overcast clouds\\nWind speed: 5.81 m/s, direction: 22°\\nHumidity: 90%\\nTemperature: \\n - Current: 8.34°C\\n - High: 9.01°C\\n - Low: 7.16°C\\n - Feels like: 5.2°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: London\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': 'In London, the current weather is as follows:\\nDetailed status: overcast clouds\\nWind speed: 2.57 m/s, direction: 350°\\nHumidity: 72%\\nTemperature: \\n - Current: 3.42°C\\n - High: 3.97°C\\n - Low: 2.79°C\\n - Feels like: 0.97°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: Paris\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': 'In Paris, the current weather is as follows:\\nDetailed status: snow\\nWind speed: 9.26 m/s, direction: 70°\\nHumidity: 99%\\nTemperature: \\n - Current: 0.57°C\\n - High: 1.16°C\\n - Low: -0.35°C\\n - Feels like: -6.04°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: Tokyo\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': 'In Tokyo, the current weather is as follows:\\nDetailed status: few clouds\\nWind speed: 2.57 m/s, direction: 280°\\nHumidity: 92%\\nTemperature: \\n - Current: 10.32°C\\n - High: 10.91°C\\n - Low: 9.19°C\\n - Feels like: 9.81°C\\nRain: {}\\nHeat index: None\\nCloud cover: 20%'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: Sydney\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': 'In Sydney, the current weather is as follows:\\nDetailed status: clear sky\\nWind speed: 2.93 m/s, direction: 348°\\nHumidity: 81%\\nTemperature: \\n - Current: 16.95°C\\n - High: 18.04°C\\n - Low: 15.42°C\\n - Feels like: 16.82°C\\nRain: {}\\nHeat index: None\\nCloud cover: 0%'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: [{'result': 'In New York, the current weather is as follows:\\nDetailed status: overcast clouds\\nWind speed: 5.81 m/s, direction: 22°\\nHumidity: 90%\\nTemperature: \\n - Current: 8.34°C\\n - High: 9.01°C\\n - Low: 7.16°C\\n - Feels like: 5.2°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%'}, {'result': 'In London, the current weather is as follows:\\nDetailed status: overcast clouds\\nWind speed: 2.57 m/s, direction: 350°\\nHumidity: 72%\\nTemperature: \\n - Current: 3.42°C\\n - High: 3.97°C\\n - Low: 2.79°C\\n - Feels like: 0.97°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%'}, {'result': 'In Paris, the current weather is as follows:\\nDetailed status: snow\\nWind speed: 9.26 m/s, direction: 70°\\nHumidity: 99%\\nTemperature: \\n - Current: 0.57°C\\n - High: 1.16°C\\n - Low: -0.35°C\\n - Feels like: -6.04°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%'}, {'result': 'In Tokyo, the current weather is as follows:\\nDetailed status: few clouds\\nWind speed: 2.57 m/s, direction: 280°\\nHumidity: 92%\\nTemperature: \\n - Current: 10.32°C\\n - High: 10.91°C\\n - Low: 9.19°C\\n - Feels like: 9.81°C\\nRain: {}\\nHeat index: None\\nCloud cover: 20%'}, {'result': 'In Sydney, the current weather is as follows:\\nDetailed status: clear sky\\nWind speed: 2.93 m/s, direction: 348°\\nHumidity: 81%\\nTemperature: \\n - Current: 16.95°C\\n - High: 18.04°C\\n - Low: 15.42°C\\n - Feels like: 16.82°C\\nRain: {}\\nHeat index: None\\nCloud cover: 0%'}]\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: New York\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html', 'snippet': 'Top of the Rock Observation Deck, the newly opened, 3-tiered observation deck on the 67th, 69th and 70th floors of 30 Rockefeller Plaza, is New York City's most amazing attraction! The unforgettable experience includes a panoramic 360-degree, unobstructed view from the 70th floor observatory, ...', 'title': 'THE 15 BEST Things to Do in NYC - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.iloveny.com/places-to-go/new-york-city/attractions/', 'snippet': 'Whether you're into architecture, Broadway theater, restaurants or other cultural hotspots, New York City has something for you. Plan your trip today!', 'title': 'NYC Attractions | Museums, Parks, Zoos & Landmarks'}, {'link': 'https://usaguidedtours.com/nyc/attraction/', 'snippet': 'Learn more about New York City attractions or simply book one of our award-winning guided tours and explore them up-close and personal!', 'title': 'New York Attractions - USA Guided Tours'}]}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: London\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g186338-Activities-London_England.html', 'snippet': 'Things to Do in London, England: See Tripadvisor's 7,202,262 traveler reviews and photos of London tourist attractions. Find what to do today, this weekend, or in August. We have reviews of the best places to see in London. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in London (2024) - Must-See Attractions'}, {'link': 'https://www.visitlondon.com/things-to-do/sightseeing/london-attraction', 'snippet': 'Find the best London tourist attractions, including museums, palaces and London landmarks. Pick things to see in London, attractions or take a sightseeing tour.', 'title': \"London attractions | Visit London's top tourist attractions\"}, {'link': 'https://www.timeout.com/london/attractions/top-london-attractions', 'snippet': 'Discover the best, most unmissable attractions in London, including Buckingham Palace, The Globe, the London Eye and more.', 'title': '50 Best Attractions in London for 2024 | Best Things to Do in London'}]}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: Paris\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g187147-Activities-Paris_Ile_de_France.html', 'snippet': 'Things to Do in Paris, France: See Tripadvisor's 5,011,626 traveler reviews and photos of Paris tourist attractions. Find what to do today, this weekend, or in September. We have reviews of the best places to see in Paris. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Paris (2024) - Must-See Attractions'}, {'link': 'https://www.timeout.com/paris/en/attractions/best-paris-attractions', 'snippet': 'And we’re not alone in thinking that. Paris is a major tourist destination that attracts thousands upon thousands of enthusiastic travellers with heads filled with images of Breton jumpers, tiny dogs, and decadent pastries - the kind you can dip in your hot chocolate.', 'title': '51 Best Attractions in Paris for 2024 | Best Sites to See in Paris'}, {'link': 'https://travel.usnews.com/Paris_France/Things_To_Do/', 'snippet': 'If you've been dreaming of a visit to the Eiffel Tower, Louvre Museum and Moulin Rouge, plan your trip with these expert recommendations for things to do in Paris.', 'title': '32 Best Things to Do in Paris, France | U.S. News Travel'}]}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: Tokyo\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g298184-Activities-Tokyo_Tokyo_Prefecture_Kanto.html', 'snippet': 'Things to Do in Tokyo, Japan: See Tripadvisor's 1,602,397 traveler reviews and photos of Tokyo tourist attractions. Find what to do today, this weekend, or in March. We have reviews of the best places to see in Tokyo. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Tokyo - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.lonelyplanet.com/japan/tokyo/attractions', 'snippet': 'Discover the best attractions in Tokyo including Tokyo National Museum, Ghibli Museum, and Golden Gai.', 'title': 'Must-see attractions Tokyo, Japan - Lonely Planet'}, {'link': 'https://www.japan-guide.com/e/e2164.html', 'snippet': 'The city's history can be appreciated in districts such as Asakusa and in many excellent museums, historic temples and gardens. Contrary to common perception, Tokyo also offers a number of attractive green spaces in the city center and within relatively short train rides at its outskirts.', 'title': 'Tokyo City Guide - What to do in Tokyo'}]}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: Sydney\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g255060-Activities-Sydney_New_South_Wales.html', 'snippet': 'Things to Do in Sydney, Australia: See Tripadvisor's 1,043,552 traveler reviews and photos of Sydney tourist attractions. Find what to do today, this weekend, or in March. We have reviews of the best places to see in Sydney. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Sydney - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.sydney.com/things-to-do/attractions', 'snippet': 'The UNESCO World Heritage-listed building offers daily guided tours, available in English, Mandarin, French, German, Japanese, Korean and Spanish. Meanwhile, Opera Bar has the best view in town, letting you sip cocktails as you marvel at the Sydney Harbour Bridge.', 'title': 'Top attractions in Sydney | Sydney.com'}, {'link': 'https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck', 'snippet': 'We’ve rounded up 19 infamous Sydney tourist stops that you'll actually enjoy, from thrilling adventures to scenic tours.', 'title': \"19 Amazing Tourist Attractions in Sydney That Don't Suck\"}]}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: [{'result': [{'link': 'https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html', 'snippet': 'Top of the Rock Observation Deck, the newly opened, 3-tiered observation deck on the 67th, 69th and 70th floors of 30 Rockefeller Plaza, is New York City's most amazing attraction! 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Find what to do today, this weekend, or in August. We have reviews of the best places to see in London. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in London (2024) - Must-See Attractions'}, {'link': 'https://www.visitlondon.com/things-to-do/sightseeing/london-attraction', 'snippet': 'Find the best London tourist attractions, including museums, palaces and London landmarks. Pick things to see in London, attractions or take a sightseeing tour.', 'title': \"London attractions | Visit London's top tourist attractions\"}, {'link': 'https://www.timeout.com/london/attractions/top-london-attractions', 'snippet': 'Discover the best, most unmissable attractions in London, including Buckingham Palace, The Globe, the London Eye and more.', 'title': '50 Best Attractions in London for 2024 | Best Things to Do in London'}]}, {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g187147-Activities-Paris_Ile_de_France.html', 'snippet': 'Things to Do in Paris, France: See Tripadvisor's 5,011,626 traveler reviews and photos of Paris tourist attractions. Find what to do today, this weekend, or in September. We have reviews of the best places to see in Paris. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Paris (2024) - Must-See Attractions'}, {'link': 'https://www.timeout.com/paris/en/attractions/best-paris-attractions', 'snippet': 'And we’re not alone in thinking that. Paris is a major tourist destination that attracts thousands upon thousands of enthusiastic travellers with heads filled with images of Breton jumpers, tiny dogs, and decadent pastries - the kind you can dip in your hot chocolate.', 'title': '51 Best Attractions in Paris for 2024 | Best Sites to See in Paris'}, {'link': 'https://travel.usnews.com/Paris_France/Things_To_Do/', 'snippet': 'If you've been dreaming of a visit to the Eiffel Tower, Louvre Museum and Moulin Rouge, plan your trip with these expert recommendations for things to do in Paris.', 'title': '32 Best Things to Do in Paris, France | U.S. News Travel'}]}, {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g298184-Activities-Tokyo_Tokyo_Prefecture_Kanto.html', 'snippet': 'Things to Do in Tokyo, Japan: See Tripadvisor's 1,602,397 traveler reviews and photos of Tokyo tourist attractions. Find what to do today, this weekend, or in March. We have reviews of the best places to see in Tokyo. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Tokyo - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.lonelyplanet.com/japan/tokyo/attractions', 'snippet': 'Discover the best attractions in Tokyo including Tokyo National Museum, Ghibli Museum, and Golden Gai.', 'title': 'Must-see attractions Tokyo, Japan - Lonely Planet'}, {'link': 'https://www.japan-guide.com/e/e2164.html', 'snippet': 'The city's history can be appreciated in districts such as Asakusa and in many excellent museums, historic temples and gardens. Contrary to common perception, Tokyo also offers a number of attractive green spaces in the city center and within relatively short train rides at its outskirts.', 'title': 'Tokyo City Guide - What to do in Tokyo'}]}, {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g255060-Activities-Sydney_New_South_Wales.html', 'snippet': 'Things to Do in Sydney, Australia: See Tripadvisor's 1,043,552 traveler reviews and photos of Sydney tourist attractions. Find what to do today, this weekend, or in March. We have reviews of the best places to see in Sydney. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Sydney - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.sydney.com/things-to-do/attractions', 'snippet': 'The UNESCO World Heritage-listed building offers daily guided tours, available in English, Mandarin, French, German, Japanese, Korean and Spanish. Meanwhile, Opera Bar has the best view in town, letting you sip cocktails as you marvel at the Sydney Harbour Bridge.', 'title': 'Top attractions in Sydney | Sydney.com'}, {'link': 'https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck', 'snippet': 'We’ve rounded up 19 infamous Sydney tourist stops that you'll actually enjoy, from thrilling adventures to scenic tours.', 'title': \"19 Amazing Tourist Attractions in Sydney That Don't Suck\"}]}]\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'zipped': [['New York', 'In New York, the current weather is as follows:\\nDetailed status: overcast clouds\\nWind speed: 5.81 m/s, direction: 22°\\nHumidity: 90%\\nTemperature: \\n - Current: 8.34°C\\n - High: 9.01°C\\n - Low: 7.16°C\\n - Feels like: 5.2°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%', {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html', 'snippet': 'Top of the Rock Observation Deck, the newly opened, 3-tiered observation deck on the 67th, 69th and 70th floors of 30 Rockefeller Plaza, is New York City's most amazing attraction! 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Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in London (2024) - Must-See Attractions'}, {'link': 'https://www.visitlondon.com/things-to-do/sightseeing/london-attraction', 'snippet': 'Find the best London tourist attractions, including museums, palaces and London landmarks. 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Find what to do today, this weekend, or in September. We have reviews of the best places to see in Paris. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Paris (2024) - Must-See Attractions'}, {'link': 'https://www.timeout.com/paris/en/attractions/best-paris-attractions', 'snippet': 'And we’re not alone in thinking that. Paris is a major tourist destination that attracts thousands upon thousands of enthusiastic travellers with heads filled with images of Breton jumpers, tiny dogs, and decadent pastries - the kind you can dip in your hot chocolate.', 'title': '51 Best Attractions in Paris for 2024 | Best Sites to See in Paris'}, {'link': 'https://travel.usnews.com/Paris_France/Things_To_Do/', 'snippet': 'If you've been dreaming of a visit to the Eiffel Tower, Louvre Museum and Moulin Rouge, plan your trip with these expert recommendations for things to do in Paris.', 'title': '32 Best Things to Do in Paris, France | U.S. News Travel'}]}], ['Tokyo', 'In Tokyo, the current weather is as follows:\\nDetailed status: few clouds\\nWind speed: 2.57 m/s, direction: 280°\\nHumidity: 92%\\nTemperature: \\n - Current: 10.32°C\\n - High: 10.91°C\\n - Low: 9.19°C\\n - Feels like: 9.81°C\\nRain: {}\\nHeat index: None\\nCloud cover: 20%', {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g298184-Activities-Tokyo_Tokyo_Prefecture_Kanto.html', 'snippet': 'Things to Do in Tokyo, Japan: See Tripadvisor's 1,602,397 traveler reviews and photos of Tokyo tourist attractions. Find what to do today, this weekend, or in March. We have reviews of the best places to see in Tokyo. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Tokyo - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.lonelyplanet.com/japan/tokyo/attractions', 'snippet': 'Discover the best attractions in Tokyo including Tokyo National Museum, Ghibli Museum, and Golden Gai.', 'title': 'Must-see attractions Tokyo, Japan - Lonely Planet'}, {'link': 'https://www.japan-guide.com/e/e2164.html', 'snippet': 'The city's history can be appreciated in districts such as Asakusa and in many excellent museums, historic temples and gardens. 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Pick things to see in London, attractions or take a sightseeing tour.', 'title': \"London attractions | Visit London's top tourist attractions\"}, {'link': 'https://www.timeout.com/london/attractions/top-london-attractions', 'snippet': 'Discover the best, most unmissable attractions in London, including Buckingham Palace, The Globe, the London Eye and more.', 'title': '50 Best Attractions in London for 2024 | Best Things to Do in London'}]}]\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: ['New York', 'In New York, the current weather is as follows:\\nDetailed status: overcast clouds\\nWind speed: 5.81 m/s, direction: 22°\\nHumidity: 90%\\nTemperature: \\n - Current: 8.34°C\\n - High: 9.01°C\\n - Low: 7.16°C\\n - Feels like: 5.2°C\\nRain: {}\\nHeat index: None\\nCloud cover: 100%', {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html', 'snippet': 'Top of the Rock Observation Deck, the newly opened, 3-tiered observation deck on the 67th, 69th and 70th floors of 30 Rockefeller Plaza, is New York City's most amazing attraction! The unforgettable experience includes a panoramic 360-degree, unobstructed view from the 70th floor observatory, ...', 'title': 'THE 15 BEST Things to Do in NYC - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.iloveny.com/places-to-go/new-york-city/attractions/', 'snippet': 'Whether you're into architecture, Broadway theater, restaurants or other cultural hotspots, New York City has something for you. 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Find what to do today, this weekend, or in September. We have reviews of the best places to see in Paris. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Paris (2024) - Must-See Attractions'}, {'link': 'https://www.timeout.com/paris/en/attractions/best-paris-attractions', 'snippet': 'And we’re not alone in thinking that. Paris is a major tourist destination that attracts thousands upon thousands of enthusiastic travellers with heads filled with images of Breton jumpers, tiny dogs, and decadent pastries - the kind you can dip in your hot chocolate.', 'title': '51 Best Attractions in Paris for 2024 | Best Sites to See in Paris'}, {'link': 'https://travel.usnews.com/Paris_France/Things_To_Do/', 'snippet': 'If you've been dreaming of a visit to the Eiffel Tower, Louvre Museum and Moulin Rouge, plan your trip with these expert recommendations for things to do in Paris.', 'title': '32 Best Things to Do in Paris, France | U.S. News Travel'}]}]\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: Here’s a detailed itinerary for your visit to Paris, considering the current snowy weather conditions and top attractions:\n", + "\n", + "### Day 1: Embrace the Iconic Landmarks\n", + "\n", + "**Morning: Eiffel Tower**\n", + "- **Time:** 9:00 AM\n", + "- **Details:** Begin your day with a visit to the Eiffel Tower. Even in the snow, the tower offers stunning views of Paris. Dress warmly and enjoy hot chocolate from nearby cafes.\n", + "- **Link for more info:** [Tripadvisor - Things to Do in Paris](https://www.tripadvisor.com/Attractions-g187147-Activities-Paris_Ile_de_France.html)\n", + "\n", + "**Afternoon: Louvre Museum**\n", + "- **Time:** 1:00 PM\n", + "- **Details:** Spend your afternoon indoors at the Louvre Museum. With its vast collection of art and history, it's a perfect way to escape the cold. Consider taking a guided tour to make the most of your visit.\n", + "- **Link for more info:** [U.S. News Travel - Things To Do](https://travel.usnews.com/Paris_France/Things_To_Do/)\n", + "\n", + "**Evening: Seine River Cruise**\n", + "- **Time:** 6:00 PM\n", + "- **Details:** End your day with a magical Seine River cruise. The snow adds a picturesque touch to the illuminated landmarks. Ensure to book a heated cruise for comfort.\n", + "\n", + "### Day 2: Explore Cultural and Historical Treasures\n", + "\n", + "**Morning: Notre-Dame Cathedral**\n", + "- **Time:** 9:30 AM\n", + "- **Details:** Visit the iconic Notre-Dame Cathedral. Although some areas may be under restoration, its architecture and history are worth experiencing. Warm clothing is essential as the interior can be chilly.\n", + "\n", + "**Afternoon: Musée d'Orsay**\n", + "- **Time:** 1:30 PM\n", + "- **Details:** Head to the Musée d'Orsay, renowned for its Impressionist masterpieces. This indoor activity is ideal for escaping the cold and enjoying world-class art.\n", + "\n", + "**Evening: Montmartre and Sacré-Cœur**\n", + "- **Time:** 5:00 PM\n", + "- **Details:** Wander through the charming streets of Montmartre and visit the Sacré-Cœur Basilica. The view of Paris in the snow is breathtaking. Enjoy a cozy dinner at a local bistro in Montmartre.\n", + "\n", + "### Day 3: Discover Hidden Gems and Local Flavors\n", + "\n", + "**Morning: Le Marais District**\n", + "- **Time:** 10:00 AM\n", + "- **Details:** Explore Le Marais, known for its vibrant street art, boutiques, and cafes. Enjoy a leisurely breakfast and shop for unique souvenirs.\n", + "\n", + "**Afternoon: Palais Garnier (Opera House)**\n", + "- **Time:** 2:00 PM\n", + "- **Details:** Tour the opulent Palais Garnier. Its stunning interiors are a must-see, especially when it's snowy outside.\n", + "\n", + "**Evening: Moulin Rouge Show**\n", + "- **Time:** 8:00 PM\n", + "- **Details:** Conclude your trip with a classic Parisian experience at the Moulin Rouge. Book in advance to secure a good seat and enjoy the legendary cabaret performance.\n", + "\n", + "### Additional Tips:\n", + "- **Weather Preparation:** Wear layers, waterproof boots, and carry an umbrella. The snow and cold wind can be intense.\n", + "- **Dining:** Indulge in warm, hearty French cuisine at local cafes and restaurants. Try dishes like French onion soup, coq au vin, and tarte Tatin.\n", + "- **Transport:** Use public transportation to avoid the snowy streets, and consider purchasing a Paris Visite pass for unlimited travel.\n", + "\n", + "Enjoy your snowy adventure in Paris!\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: Here's a detailed itinerary for visiting top tourist attractions in New York, considering the current weather conditions. \n", + "\n", + "### Day 1: Iconic Landmarks and Observation Decks\n", + "- **Morning:**\n", + " - **Top of the Rock Observation Deck:** Start your day with a visit to the Top of the Rock Observation Deck at Rockefeller Plaza. The panoramic 360-degree views from the 70th floor are a must-see. Dress warmly as it feels like 5.2°C outside, and it’s quite windy.\n", + " - **Link for more info:** [Tripadvisor - Top of the Rock](https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html)\n", + "\n", + "- **Afternoon:**\n", + " - **St. Patrick’s Cathedral:** Just a short walk from Rockefeller Plaza, explore the stunning architecture of St. Patrick’s Cathedral. The overcast skies will provide a dramatic backdrop for photos.\n", + " - **Fifth Avenue:** Enjoy a leisurely stroll along Fifth Avenue, visiting iconic stores and landmarks.\n", + "\n", + "- **Evening:**\n", + " - **Times Square:** Experience the vibrant lights and energy of Times Square. The overcast clouds might enhance the brightness of the neon lights.\n", + "\n", + "### Day 2: Culture and History\n", + "- **Morning:**\n", + " - **The Museum of Modern Art (MoMA):** Spend your morning exploring MoMA’s vast collection of modern and contemporary art. This indoor activity is perfect for a cloudy day.\n", + "\n", + "- **Afternoon:**\n", + " - **Central Park:** Head to Central Park for a refreshing walk. With 100% cloud cover, it's a great day to explore the park without the harsh sun. Consider visiting the Central Park Zoo or taking a guided tour.\n", + "\n", + "- **Evening:**\n", + " - **Broadway Show:** End your day with a Broadway show. It’s an ideal indoor activity to avoid the chilly weather outside. Book tickets in advance for popular shows.\n", + "\n", + "### Day 3: Historical and Educational\n", + "- **Morning:**\n", + " - **Statue of Liberty and Ellis Island:** Take a ferry to visit these iconic sites. Dress warmly for the ferry ride. The cloud cover will provide a unique perspective for photos.\n", + "\n", + "- **Afternoon:**\n", + " - **9/11 Memorial and Museum:** Spend your afternoon reflecting at the 9/11 Memorial and exploring the museum exhibits.\n", + "\n", + "- **Evening:**\n", + " - **Brooklyn Bridge:** Walk across the Brooklyn Bridge and enjoy the city skyline. With the wind speed at 5.81 m/s, be prepared for breezy conditions.\n", + "\n", + "### Additional Tips:\n", + "- **Clothing:** Wear layers to keep warm, as the temperature feels colder than it actually is.\n", + "- **Dining:** New York offers a plethora of dining options. Consider trying some local favorites like a classic New York bagel or pizza.\n", + "- **Transportation:** Utilize the subway for efficient travel across the city. Taxis and ride-sharing services are also readily available.\n", + "\n", + "For more details on attractions and guided tours, you can visit [USA Guided Tours](https://usaguidedtours.com/nyc/attraction/) and [I Love NY](https://www.iloveny.com/places-to-go/new-york-city/attractions/). \n", + "\n", + "Enjoy your trip to New York City!\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: **Day 1: Exploring Iconic London Landmarks**\n", + "\n", + "**Morning:**\n", + "1. **Buckingham Palace**\n", + " - Start your day early with a visit to Buckingham Palace. Arrive by 9:30 AM to catch the Changing of the Guard ceremony, which typically starts at 11:00 AM. Enjoy the majestic architecture and the surrounding gardens.\n", + " - Weather Tip: With the overcast clouds, it might feel chilly, so dress warmly and bring an umbrella just in case.\n", + "\n", + "**Midday:**\n", + "2. **Westminster Abbey**\n", + " - Head towards Westminster Abbey, a short walk from Buckingham Palace. This historic church has been the site of many significant events, including royal weddings and coronations. Spend about 1.5 hours exploring.\n", + "\n", + "3. **Lunch at Borough Market**\n", + " - Take a tube or walk to Borough Market for a variety of food options. It's a great place to warm up with some hot street food and explore the diverse culinary offerings.\n", + "\n", + "**Afternoon:**\n", + "4. **London Eye**\n", + " - After lunch, head to the London Eye. The overcast sky might not offer the clearest views, but the experience is still worthwhile. Pre-book your tickets to avoid long lines and enjoy a 30-minute ride on this iconic Ferris wheel.\n", + "\n", + "**Evening:**\n", + "5. **The Globe Theatre**\n", + " - As the day winds down, visit Shakespeare's Globe Theatre. If there's a performance, consider attending or simply take a guided tour to learn about the history of this famous theater.\n", + "\n", + "**Day 2: Museums and Cultural Exploration**\n", + "\n", + "**Morning:**\n", + "1. **The British Museum**\n", + " - Start your second day at the British Museum. Spend a few hours exploring the vast collection of art and antiquities. Highlights include the Rosetta Stone and the Elgin Marbles.\n", + "\n", + "**Midday:**\n", + "2. **Lunch near Covent Garden**\n", + " - Head to Covent Garden for lunch. This area is bustling with restaurants and cafes, perfect for a cozy indoor meal.\n", + "\n", + "**Afternoon:**\n", + "3. **The Tower of London**\n", + " - After lunch, make your way to the Tower of London. Delve into England's rich history and see the Crown Jewels. Allocate about 2-3 hours for this visit.\n", + "\n", + "**Evening:**\n", + "4. **Tower Bridge**\n", + " - Conclude your day with a walk across Tower Bridge. The views of the Thames River and the cityscape are beautiful, even on a cloudy evening.\n", + "\n", + "**Day 3: Leisure and Local Experiences**\n", + "\n", + "**Morning:**\n", + "1. **Natural History Museum**\n", + " - Begin your last day at the Natural History Museum. It's a family-friendly museum with fascinating exhibits, including dinosaur skeletons and a model of a blue whale.\n", + "\n", + "**Midday:**\n", + "2. **Lunch at South Kensington**\n", + " - Enjoy a relaxed lunch in South Kensington. There are plenty of options, from casual cafes to high-end dining.\n", + "\n", + "**Afternoon:**\n", + "3. **Hyde Park**\n", + " - Spend your afternoon strolling through Hyde Park. Visit the Serpentine Galleries if you're interested in contemporary art. The park's natural beauty is a peaceful retreat amidst the city hustle.\n", + "\n", + "**Evening:**\n", + "4. **Dinner and a Show in the West End**\n", + " - End your London adventure with a memorable dinner followed by a theater show in the West End. Book your tickets in advance for popular shows.\n", + "\n", + "**Additional Tips:**\n", + "- Always check the attraction websites for any specific COVID-19 guidelines or changes in operating hours.\n", + "- London’s public transport system is efficient; consider getting an Oyster card for convenient travel.\n", + "- Don't forget to dress in layers to adapt to the chilly weather, and always have your camera ready to capture memories.\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: ['Tokyo', 'In Tokyo, the current weather is as follows:\\nDetailed status: few clouds\\nWind speed: 2.57 m/s, direction: 280°\\nHumidity: 92%\\nTemperature: \\n - Current: 10.32°C\\n - High: 10.91°C\\n - Low: 9.19°C\\n - Feels like: 9.81°C\\nRain: {}\\nHeat index: None\\nCloud cover: 20%', {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g298184-Activities-Tokyo_Tokyo_Prefecture_Kanto.html', 'snippet': 'Things to Do in Tokyo, Japan: See Tripadvisor's 1,602,397 traveler reviews and photos of Tokyo tourist attractions. Find what to do today, this weekend, or in March. We have reviews of the best places to see in Tokyo. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Tokyo - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.lonelyplanet.com/japan/tokyo/attractions', 'snippet': 'Discover the best attractions in Tokyo including Tokyo National Museum, Ghibli Museum, and Golden Gai.', 'title': 'Must-see attractions Tokyo, Japan - Lonely Planet'}, {'link': 'https://www.japan-guide.com/e/e2164.html', 'snippet': 'The city's history can be appreciated in districts such as Asakusa and in many excellent museums, historic temples and gardens. Contrary to common perception, Tokyo also offers a number of attractive green spaces in the city center and within relatively short train rides at its outskirts.', 'title': 'Tokyo City Guide - What to do in Tokyo'}]}]\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: ['Sydney', 'In Sydney, the current weather is as follows:\\nDetailed status: clear sky\\nWind speed: 2.93 m/s, direction: 348°\\nHumidity: 81%\\nTemperature: \\n - Current: 16.95°C\\n - High: 18.04°C\\n - Low: 15.42°C\\n - Feels like: 16.82°C\\nRain: {}\\nHeat index: None\\nCloud cover: 0%', {'result': [{'link': 'https://www.tripadvisor.com/Attractions-g255060-Activities-Sydney_New_South_Wales.html', 'snippet': 'Things to Do in Sydney, Australia: See Tripadvisor's 1,043,552 traveler reviews and photos of Sydney tourist attractions. Find what to do today, this weekend, or in March. We have reviews of the best places to see in Sydney. Visit top-rated & must-see attractions.', 'title': 'THE 15 BEST Things to Do in Sydney - 2024 (with Photos) - Tripadvisor'}, {'link': 'https://www.sydney.com/things-to-do/attractions', 'snippet': 'The UNESCO World Heritage-listed building offers daily guided tours, available in English, Mandarin, French, German, Japanese, Korean and Spanish. Meanwhile, Opera Bar has the best view in town, letting you sip cocktails as you marvel at the Sydney Harbour Bridge.', 'title': 'Top attractions in Sydney | Sydney.com'}, {'link': 'https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck', 'snippet': 'We’ve rounded up 19 infamous Sydney tourist stops that you'll actually enjoy, from thrilling adventures to scenic tours.', 'title': \"19 Amazing Tourist Attractions in Sydney That Don't Suck\"}]}]\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: **Tokyo Itinerary**\n", + "\n", + "**Day 1: Arrival and Exploration of Historical and Cultural Sites**\n", + "\n", + "- **Morning:**\n", + " - **Asakusa District**: Begin your day with a visit to the historic Asakusa district. Explore the iconic Senso-ji Temple, Tokyo's oldest temple. Enjoy the traditional market streets like Nakamise Street for some shopping and snacks.\n", + "\n", + "- **Afternoon:**\n", + " - **Tokyo National Museum**: Head to Ueno Park and visit the Tokyo National Museum. Discover Japan’s extensive collection of art and antiquities. This is a great spot to dive into Japanese history and culture.\n", + "\n", + "- **Evening:**\n", + " - **Dinner in Ueno**: Explore the local dining options around Ueno and enjoy a traditional Japanese dinner.\n", + "\n", + "**Day 2: Modern Tokyo and Unique Experiences**\n", + "\n", + "- **Morning:**\n", + " - **Ghibli Museum**: Start your day with a magical visit to the Ghibli Museum in Mitaka. Perfect for fans of Studio Ghibli's animated films, this museum offers a whimsical look into the creative world of Hayao Miyazaki.\n", + "\n", + "- **Afternoon:**\n", + " - **Shibuya and Harajuku**: Head towards the bustling areas of Shibuya and Harajuku. Witness the famous Shibuya Crossing and explore the trendy shops of Harajuku, especially Takeshita Street.\n", + "\n", + "- **Evening:**\n", + " - **Golden Gai**: Conclude your day in the vibrant Golden Gai district. This area is renowned for its narrow alleys filled with small bars and eateries. Experience the unique nightlife of Tokyo here.\n", + "\n", + "**Day 3: Relax and Explore Green Spaces**\n", + "\n", + "- **Morning:**\n", + " - **Shinjuku Gyoen National Garden**: Spend a peaceful morning strolling through the beautiful Shinjuku Gyoen, one of Tokyo's largest and most beautiful parks. It's a perfect spot for relaxation and enjoying nature.\n", + "\n", + "- **Afternoon:**\n", + " - **Meiji Shrine**: Visit the Meiji Shrine, located in a forested area near Harajuku and Shibuya. It's a serene place to learn about Shinto traditions and enjoy the tranquil setting.\n", + "\n", + "- **Evening:**\n", + " - **Tokyo Tower or Skytree**: End your trip with a visit to either Tokyo Tower or Tokyo Skytree for a panoramic view of the city. It's an unforgettable way to see Tokyo illuminated at night.\n", + "\n", + "**Weather Considerations:**\n", + "- With the current weather of few clouds and a mild temperature around 10.32°C, it is advisable to wear layers and carry a light jacket for comfort during outdoor activities.\n", + "- Humidity is high (92%), so be prepared for a slightly damp feeling and consider moisture-wicking clothing.\n", + "\n", + "**Additional Tips:**\n", + "- Always check the opening hours of attractions and book tickets in advance where necessary.\n", + "- Use Tokyo’s efficient public transport to move around easily.\n", + "- Consider visiting the websites linked in the attraction descriptions for more detailed information and current updates.\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: Here's a detailed itinerary for exploring some of Sydney's top tourist attractions with the current weather conditions in mind. With clear skies and pleasant temperatures, it's a perfect day to explore the outdoors and enjoy what Sydney has to offer.\n", + "\n", + "### Morning\n", + "\n", + "**9:00 AM - Sydney Opera House**\n", + "- Begin your day with a visit to the iconic Sydney Opera House. Take a guided tour to learn about its history and architecture. Tours are available in multiple languages.\n", + "- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\n", + "\n", + "**11:00 AM - Royal Botanic Garden Sydney**\n", + "- Just a short walk from the Opera House, enjoy a leisurely stroll through the Royal Botanic Garden. The clear skies will offer beautiful views of the diverse plant life and the Sydney Harbour.\n", + "\n", + "### Afternoon\n", + "\n", + "**12:30 PM - Lunch at Opera Bar**\n", + "- Head back to Opera Bar for lunch. Enjoy a refreshing cocktail with stunning views of the Sydney Harbour Bridge and the waterfront.\n", + "- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\n", + "\n", + "**2:00 PM - Sydney Harbour Bridge**\n", + "- After lunch, take a scenic walk across the Sydney Harbour Bridge. If you're up for it, consider the BridgeClimb for breathtaking panoramic views of the city.\n", + "\n", + "**4:00 PM - The Rocks**\n", + "- Explore The Rocks, one of Sydney's most historic areas. Wander through the cobbled streets, visit the local markets, and perhaps enjoy a cup of coffee at a nearby café.\n", + "\n", + "### Evening\n", + "\n", + "**6:00 PM - Darling Harbour**\n", + "- Make your way to Darling Harbour for the evening. Here you can visit attractions such as the SEA LIFE Sydney Aquarium or simply enjoy the lively atmosphere by the waterfront.\n", + "\n", + "**8:00 PM - Dinner at a Local Restaurant**\n", + "- Conclude your day with dinner at one of Darling Harbour's many restaurants. Choose from a variety of cuisines while enjoying the vibrant night scene.\n", + "\n", + "### Additional Suggestions\n", + "\n", + "- If you're interested in more unique experiences, consider visiting some of the attractions listed on [Time Out Sydney](https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck), which includes thrilling adventures and scenic tours.\n", + "\n", + "With clear skies and mild temperatures, this itinerary offers a balanced mix of cultural, historical, and scenic experiences. Enjoy your visit to Sydney!\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: [\"Here's a detailed itinerary for visiting top tourist attractions in New York, considering the current weather conditions. \\n\\n### Day 1: Iconic Landmarks and Observation Decks\\n- **Morning:**\\n - **Top of the Rock Observation Deck:** Start your day with a visit to the Top of the Rock Observation Deck at Rockefeller Plaza. The panoramic 360-degree views from the 70th floor are a must-see. Dress warmly as it feels like 5.2°C outside, and it’s quite windy.\\n - **Link for more info:** [Tripadvisor - Top of the Rock](https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html)\\n\\n- **Afternoon:**\\n - **St. Patrick’s Cathedral:** Just a short walk from Rockefeller Plaza, explore the stunning architecture of St. Patrick’s Cathedral. The overcast skies will provide a dramatic backdrop for photos.\\n - **Fifth Avenue:** Enjoy a leisurely stroll along Fifth Avenue, visiting iconic stores and landmarks.\\n\\n- **Evening:**\\n - **Times Square:** Experience the vibrant lights and energy of Times Square. The overcast clouds might enhance the brightness of the neon lights.\\n\\n### Day 2: Culture and History\\n- **Morning:**\\n - **The Museum of Modern Art (MoMA):** Spend your morning exploring MoMA’s vast collection of modern and contemporary art. This indoor activity is perfect for a cloudy day.\\n\\n- **Afternoon:**\\n - **Central Park:** Head to Central Park for a refreshing walk. With 100% cloud cover, it's a great day to explore the park without the harsh sun. Consider visiting the Central Park Zoo or taking a guided tour.\\n\\n- **Evening:**\\n - **Broadway Show:** End your day with a Broadway show. It’s an ideal indoor activity to avoid the chilly weather outside. Book tickets in advance for popular shows.\\n\\n### Day 3: Historical and Educational\\n- **Morning:**\\n - **Statue of Liberty and Ellis Island:** Take a ferry to visit these iconic sites. Dress warmly for the ferry ride. The cloud cover will provide a unique perspective for photos.\\n\\n- **Afternoon:**\\n - **9/11 Memorial and Museum:** Spend your afternoon reflecting at the 9/11 Memorial and exploring the museum exhibits.\\n\\n- **Evening:**\\n - **Brooklyn Bridge:** Walk across the Brooklyn Bridge and enjoy the city skyline. With the wind speed at 5.81 m/s, be prepared for breezy conditions.\\n\\n### Additional Tips:\\n- **Clothing:** Wear layers to keep warm, as the temperature feels colder than it actually is.\\n- **Dining:** New York offers a plethora of dining options. Consider trying some local favorites like a classic New York bagel or pizza.\\n- **Transportation:** Utilize the subway for efficient travel across the city. Taxis and ride-sharing services are also readily available.\\n\\nFor more details on attractions and guided tours, you can visit [USA Guided Tours](https://usaguidedtours.com/nyc/attraction/) and [I Love NY](https://www.iloveny.com/places-to-go/new-york-city/attractions/). \\n\\nEnjoy your trip to New York City!\", \"**Day 1: Exploring Iconic London Landmarks**\\n\\n**Morning:**\\n1. **Buckingham Palace**\\n - Start your day early with a visit to Buckingham Palace. Arrive by 9:30 AM to catch the Changing of the Guard ceremony, which typically starts at 11:00 AM. Enjoy the majestic architecture and the surrounding gardens.\\n - Weather Tip: With the overcast clouds, it might feel chilly, so dress warmly and bring an umbrella just in case.\\n\\n**Midday:**\\n2. **Westminster Abbey**\\n - Head towards Westminster Abbey, a short walk from Buckingham Palace. This historic church has been the site of many significant events, including royal weddings and coronations. Spend about 1.5 hours exploring.\\n\\n3. **Lunch at Borough Market**\\n - Take a tube or walk to Borough Market for a variety of food options. It's a great place to warm up with some hot street food and explore the diverse culinary offerings.\\n\\n**Afternoon:**\\n4. **London Eye**\\n - After lunch, head to the London Eye. The overcast sky might not offer the clearest views, but the experience is still worthwhile. Pre-book your tickets to avoid long lines and enjoy a 30-minute ride on this iconic Ferris wheel.\\n\\n**Evening:**\\n5. **The Globe Theatre**\\n - As the day winds down, visit Shakespeare's Globe Theatre. If there's a performance, consider attending or simply take a guided tour to learn about the history of this famous theater.\\n\\n**Day 2: Museums and Cultural Exploration**\\n\\n**Morning:**\\n1. **The British Museum**\\n - Start your second day at the British Museum. Spend a few hours exploring the vast collection of art and antiquities. Highlights include the Rosetta Stone and the Elgin Marbles.\\n\\n**Midday:**\\n2. **Lunch near Covent Garden**\\n - Head to Covent Garden for lunch. This area is bustling with restaurants and cafes, perfect for a cozy indoor meal.\\n\\n**Afternoon:**\\n3. **The Tower of London**\\n - After lunch, make your way to the Tower of London. Delve into England's rich history and see the Crown Jewels. Allocate about 2-3 hours for this visit.\\n\\n**Evening:**\\n4. **Tower Bridge**\\n - Conclude your day with a walk across Tower Bridge. The views of the Thames River and the cityscape are beautiful, even on a cloudy evening.\\n\\n**Day 3: Leisure and Local Experiences**\\n\\n**Morning:**\\n1. **Natural History Museum**\\n - Begin your last day at the Natural History Museum. It's a family-friendly museum with fascinating exhibits, including dinosaur skeletons and a model of a blue whale.\\n\\n**Midday:**\\n2. **Lunch at South Kensington**\\n - Enjoy a relaxed lunch in South Kensington. There are plenty of options, from casual cafes to high-end dining.\\n\\n**Afternoon:**\\n3. **Hyde Park**\\n - Spend your afternoon strolling through Hyde Park. Visit the Serpentine Galleries if you're interested in contemporary art. The park's natural beauty is a peaceful retreat amidst the city hustle.\\n\\n**Evening:**\\n4. **Dinner and a Show in the West End**\\n - End your London adventure with a memorable dinner followed by a theater show in the West End. Book your tickets in advance for popular shows.\\n\\n**Additional Tips:**\\n- Always check the attraction websites for any specific COVID-19 guidelines or changes in operating hours.\\n- London’s public transport system is efficient; consider getting an Oyster card for convenient travel.\\n- Don't forget to dress in layers to adapt to the chilly weather, and always have your camera ready to capture memories.\", \"Here’s a detailed itinerary for your visit to Paris, considering the current snowy weather conditions and top attractions:\\n\\n### Day 1: Embrace the Iconic Landmarks\\n\\n**Morning: Eiffel Tower**\\n- **Time:** 9:00 AM\\n- **Details:** Begin your day with a visit to the Eiffel Tower. Even in the snow, the tower offers stunning views of Paris. Dress warmly and enjoy hot chocolate from nearby cafes.\\n- **Link for more info:** [Tripadvisor - Things to Do in Paris](https://www.tripadvisor.com/Attractions-g187147-Activities-Paris_Ile_de_France.html)\\n\\n**Afternoon: Louvre Museum**\\n- **Time:** 1:00 PM\\n- **Details:** Spend your afternoon indoors at the Louvre Museum. With its vast collection of art and history, it's a perfect way to escape the cold. Consider taking a guided tour to make the most of your visit.\\n- **Link for more info:** [U.S. News Travel - Things To Do](https://travel.usnews.com/Paris_France/Things_To_Do/)\\n\\n**Evening: Seine River Cruise**\\n- **Time:** 6:00 PM\\n- **Details:** End your day with a magical Seine River cruise. The snow adds a picturesque touch to the illuminated landmarks. Ensure to book a heated cruise for comfort.\\n\\n### Day 2: Explore Cultural and Historical Treasures\\n\\n**Morning: Notre-Dame Cathedral**\\n- **Time:** 9:30 AM\\n- **Details:** Visit the iconic Notre-Dame Cathedral. Although some areas may be under restoration, its architecture and history are worth experiencing. Warm clothing is essential as the interior can be chilly.\\n\\n**Afternoon: Musée d'Orsay**\\n- **Time:** 1:30 PM\\n- **Details:** Head to the Musée d'Orsay, renowned for its Impressionist masterpieces. This indoor activity is ideal for escaping the cold and enjoying world-class art.\\n\\n**Evening: Montmartre and Sacré-Cœur**\\n- **Time:** 5:00 PM\\n- **Details:** Wander through the charming streets of Montmartre and visit the Sacré-Cœur Basilica. The view of Paris in the snow is breathtaking. Enjoy a cozy dinner at a local bistro in Montmartre.\\n\\n### Day 3: Discover Hidden Gems and Local Flavors\\n\\n**Morning: Le Marais District**\\n- **Time:** 10:00 AM\\n- **Details:** Explore Le Marais, known for its vibrant street art, boutiques, and cafes. Enjoy a leisurely breakfast and shop for unique souvenirs.\\n\\n**Afternoon: Palais Garnier (Opera House)**\\n- **Time:** 2:00 PM\\n- **Details:** Tour the opulent Palais Garnier. Its stunning interiors are a must-see, especially when it's snowy outside.\\n\\n**Evening: Moulin Rouge Show**\\n- **Time:** 8:00 PM\\n- **Details:** Conclude your trip with a classic Parisian experience at the Moulin Rouge. Book in advance to secure a good seat and enjoy the legendary cabaret performance.\\n\\n### Additional Tips:\\n- **Weather Preparation:** Wear layers, waterproof boots, and carry an umbrella. The snow and cold wind can be intense.\\n- **Dining:** Indulge in warm, hearty French cuisine at local cafes and restaurants. Try dishes like French onion soup, coq au vin, and tarte Tatin.\\n- **Transport:** Use public transportation to avoid the snowy streets, and consider purchasing a Paris Visite pass for unlimited travel.\\n\\nEnjoy your snowy adventure in Paris!\", \"**Tokyo Itinerary**\\n\\n**Day 1: Arrival and Exploration of Historical and Cultural Sites**\\n\\n- **Morning:**\\n - **Asakusa District**: Begin your day with a visit to the historic Asakusa district. Explore the iconic Senso-ji Temple, Tokyo's oldest temple. Enjoy the traditional market streets like Nakamise Street for some shopping and snacks.\\n\\n- **Afternoon:**\\n - **Tokyo National Museum**: Head to Ueno Park and visit the Tokyo National Museum. Discover Japan’s extensive collection of art and antiquities. This is a great spot to dive into Japanese history and culture.\\n\\n- **Evening:**\\n - **Dinner in Ueno**: Explore the local dining options around Ueno and enjoy a traditional Japanese dinner.\\n\\n**Day 2: Modern Tokyo and Unique Experiences**\\n\\n- **Morning:**\\n - **Ghibli Museum**: Start your day with a magical visit to the Ghibli Museum in Mitaka. Perfect for fans of Studio Ghibli's animated films, this museum offers a whimsical look into the creative world of Hayao Miyazaki.\\n\\n- **Afternoon:**\\n - **Shibuya and Harajuku**: Head towards the bustling areas of Shibuya and Harajuku. Witness the famous Shibuya Crossing and explore the trendy shops of Harajuku, especially Takeshita Street.\\n\\n- **Evening:**\\n - **Golden Gai**: Conclude your day in the vibrant Golden Gai district. This area is renowned for its narrow alleys filled with small bars and eateries. Experience the unique nightlife of Tokyo here.\\n\\n**Day 3: Relax and Explore Green Spaces**\\n\\n- **Morning:**\\n - **Shinjuku Gyoen National Garden**: Spend a peaceful morning strolling through the beautiful Shinjuku Gyoen, one of Tokyo's largest and most beautiful parks. It's a perfect spot for relaxation and enjoying nature.\\n\\n- **Afternoon:**\\n - **Meiji Shrine**: Visit the Meiji Shrine, located in a forested area near Harajuku and Shibuya. It's a serene place to learn about Shinto traditions and enjoy the tranquil setting.\\n\\n- **Evening:**\\n - **Tokyo Tower or Skytree**: End your trip with a visit to either Tokyo Tower or Tokyo Skytree for a panoramic view of the city. It's an unforgettable way to see Tokyo illuminated at night.\\n\\n**Weather Considerations:**\\n- With the current weather of few clouds and a mild temperature around 10.32°C, it is advisable to wear layers and carry a light jacket for comfort during outdoor activities.\\n- Humidity is high (92%), so be prepared for a slightly damp feeling and consider moisture-wicking clothing.\\n\\n**Additional Tips:**\\n- Always check the opening hours of attractions and book tickets in advance where necessary.\\n- Use Tokyo’s efficient public transport to move around easily.\\n- Consider visiting the websites linked in the attraction descriptions for more detailed information and current updates.\", \"Here's a detailed itinerary for exploring some of Sydney's top tourist attractions with the current weather conditions in mind. With clear skies and pleasant temperatures, it's a perfect day to explore the outdoors and enjoy what Sydney has to offer.\\n\\n### Morning\\n\\n**9:00 AM - Sydney Opera House**\\n- Begin your day with a visit to the iconic Sydney Opera House. Take a guided tour to learn about its history and architecture. Tours are available in multiple languages.\\n- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\\n\\n**11:00 AM - Royal Botanic Garden Sydney**\\n- Just a short walk from the Opera House, enjoy a leisurely stroll through the Royal Botanic Garden. The clear skies will offer beautiful views of the diverse plant life and the Sydney Harbour.\\n\\n### Afternoon\\n\\n**12:30 PM - Lunch at Opera Bar**\\n- Head back to Opera Bar for lunch. Enjoy a refreshing cocktail with stunning views of the Sydney Harbour Bridge and the waterfront.\\n- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\\n\\n**2:00 PM - Sydney Harbour Bridge**\\n- After lunch, take a scenic walk across the Sydney Harbour Bridge. If you're up for it, consider the BridgeClimb for breathtaking panoramic views of the city.\\n\\n**4:00 PM - The Rocks**\\n- Explore The Rocks, one of Sydney's most historic areas. Wander through the cobbled streets, visit the local markets, and perhaps enjoy a cup of coffee at a nearby café.\\n\\n### Evening\\n\\n**6:00 PM - Darling Harbour**\\n- Make your way to Darling Harbour for the evening. Here you can visit attractions such as the SEA LIFE Sydney Aquarium or simply enjoy the lively atmosphere by the waterfront.\\n\\n**8:00 PM - Dinner at a Local Restaurant**\\n- Conclude your day with dinner at one of Darling Harbour's many restaurants. Choose from a variety of cuisines while enjoying the vibrant night scene.\\n\\n### Additional Suggestions\\n\\n- If you're interested in more unique experiences, consider visiting some of the attractions listed on [Time Out Sydney](https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck), which includes thrilling adventures and scenic tours.\\n\\nWith clear skies and mild temperatures, this itinerary offers a balanced mix of cultural, historical, and scenic experiences. Enjoy your visit to Sydney!\"]\n", + "--------------------------------------------------\n", + "Transition type: finish\n", + "Transition output: {'final_plan': \"Here's a detailed itinerary for visiting top tourist attractions in New York, considering the current weather conditions. \\n\\n### Day 1: Iconic Landmarks and Observation Decks\\n- **Morning:**\\n - **Top of the Rock Observation Deck:** Start your day with a visit to the Top of the Rock Observation Deck at Rockefeller Plaza. The panoramic 360-degree views from the 70th floor are a must-see. Dress warmly as it feels like 5.2°C outside, and it’s quite windy.\\n - **Link for more info:** [Tripadvisor - Top of the Rock](https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html)\\n\\n- **Afternoon:**\\n - **St. Patrick’s Cathedral:** Just a short walk from Rockefeller Plaza, explore the stunning architecture of St. Patrick’s Cathedral. The overcast skies will provide a dramatic backdrop for photos.\\n - **Fifth Avenue:** Enjoy a leisurely stroll along Fifth Avenue, visiting iconic stores and landmarks.\\n\\n- **Evening:**\\n - **Times Square:** Experience the vibrant lights and energy of Times Square. The overcast clouds might enhance the brightness of the neon lights.\\n\\n### Day 2: Culture and History\\n- **Morning:**\\n - **The Museum of Modern Art (MoMA):** Spend your morning exploring MoMA’s vast collection of modern and contemporary art. This indoor activity is perfect for a cloudy day.\\n\\n- **Afternoon:**\\n - **Central Park:** Head to Central Park for a refreshing walk. With 100% cloud cover, it's a great day to explore the park without the harsh sun. Consider visiting the Central Park Zoo or taking a guided tour.\\n\\n- **Evening:**\\n - **Broadway Show:** End your day with a Broadway show. It’s an ideal indoor activity to avoid the chilly weather outside. Book tickets in advance for popular shows.\\n\\n### Day 3: Historical and Educational\\n- **Morning:**\\n - **Statue of Liberty and Ellis Island:** Take a ferry to visit these iconic sites. Dress warmly for the ferry ride. The cloud cover will provide a unique perspective for photos.\\n\\n- **Afternoon:**\\n - **9/11 Memorial and Museum:** Spend your afternoon reflecting at the 9/11 Memorial and exploring the museum exhibits.\\n\\n- **Evening:**\\n - **Brooklyn Bridge:** Walk across the Brooklyn Bridge and enjoy the city skyline. With the wind speed at 5.81 m/s, be prepared for breezy conditions.\\n\\n### Additional Tips:\\n- **Clothing:** Wear layers to keep warm, as the temperature feels colder than it actually is.\\n- **Dining:** New York offers a plethora of dining options. Consider trying some local favorites like a classic New York bagel or pizza.\\n- **Transportation:** Utilize the subway for efficient travel across the city. Taxis and ride-sharing services are also readily available.\\n\\nFor more details on attractions and guided tours, you can visit [USA Guided Tours](https://usaguidedtours.com/nyc/attraction/) and [I Love NY](https://www.iloveny.com/places-to-go/new-york-city/attractions/). \\n\\nEnjoy your trip to New York City!\\n---------------\\n**Day 1: Exploring Iconic London Landmarks**\\n\\n**Morning:**\\n1. **Buckingham Palace**\\n - Start your day early with a visit to Buckingham Palace. Arrive by 9:30 AM to catch the Changing of the Guard ceremony, which typically starts at 11:00 AM. Enjoy the majestic architecture and the surrounding gardens.\\n - Weather Tip: With the overcast clouds, it might feel chilly, so dress warmly and bring an umbrella just in case.\\n\\n**Midday:**\\n2. **Westminster Abbey**\\n - Head towards Westminster Abbey, a short walk from Buckingham Palace. This historic church has been the site of many significant events, including royal weddings and coronations. Spend about 1.5 hours exploring.\\n\\n3. **Lunch at Borough Market**\\n - Take a tube or walk to Borough Market for a variety of food options. It's a great place to warm up with some hot street food and explore the diverse culinary offerings.\\n\\n**Afternoon:**\\n4. **London Eye**\\n - After lunch, head to the London Eye. The overcast sky might not offer the clearest views, but the experience is still worthwhile. Pre-book your tickets to avoid long lines and enjoy a 30-minute ride on this iconic Ferris wheel.\\n\\n**Evening:**\\n5. **The Globe Theatre**\\n - As the day winds down, visit Shakespeare's Globe Theatre. If there's a performance, consider attending or simply take a guided tour to learn about the history of this famous theater.\\n\\n**Day 2: Museums and Cultural Exploration**\\n\\n**Morning:**\\n1. **The British Museum**\\n - Start your second day at the British Museum. Spend a few hours exploring the vast collection of art and antiquities. Highlights include the Rosetta Stone and the Elgin Marbles.\\n\\n**Midday:**\\n2. **Lunch near Covent Garden**\\n - Head to Covent Garden for lunch. This area is bustling with restaurants and cafes, perfect for a cozy indoor meal.\\n\\n**Afternoon:**\\n3. **The Tower of London**\\n - After lunch, make your way to the Tower of London. Delve into England's rich history and see the Crown Jewels. Allocate about 2-3 hours for this visit.\\n\\n**Evening:**\\n4. **Tower Bridge**\\n - Conclude your day with a walk across Tower Bridge. The views of the Thames River and the cityscape are beautiful, even on a cloudy evening.\\n\\n**Day 3: Leisure and Local Experiences**\\n\\n**Morning:**\\n1. **Natural History Museum**\\n - Begin your last day at the Natural History Museum. It's a family-friendly museum with fascinating exhibits, including dinosaur skeletons and a model of a blue whale.\\n\\n**Midday:**\\n2. **Lunch at South Kensington**\\n - Enjoy a relaxed lunch in South Kensington. There are plenty of options, from casual cafes to high-end dining.\\n\\n**Afternoon:**\\n3. **Hyde Park**\\n - Spend your afternoon strolling through Hyde Park. Visit the Serpentine Galleries if you're interested in contemporary art. The park's natural beauty is a peaceful retreat amidst the city hustle.\\n\\n**Evening:**\\n4. **Dinner and a Show in the West End**\\n - End your London adventure with a memorable dinner followed by a theater show in the West End. Book your tickets in advance for popular shows.\\n\\n**Additional Tips:**\\n- Always check the attraction websites for any specific COVID-19 guidelines or changes in operating hours.\\n- London’s public transport system is efficient; consider getting an Oyster card for convenient travel.\\n- Don't forget to dress in layers to adapt to the chilly weather, and always have your camera ready to capture memories.\\n---------------\\nHere’s a detailed itinerary for your visit to Paris, considering the current snowy weather conditions and top attractions:\\n\\n### Day 1: Embrace the Iconic Landmarks\\n\\n**Morning: Eiffel Tower**\\n- **Time:** 9:00 AM\\n- **Details:** Begin your day with a visit to the Eiffel Tower. Even in the snow, the tower offers stunning views of Paris. Dress warmly and enjoy hot chocolate from nearby cafes.\\n- **Link for more info:** [Tripadvisor - Things to Do in Paris](https://www.tripadvisor.com/Attractions-g187147-Activities-Paris_Ile_de_France.html)\\n\\n**Afternoon: Louvre Museum**\\n- **Time:** 1:00 PM\\n- **Details:** Spend your afternoon indoors at the Louvre Museum. With its vast collection of art and history, it's a perfect way to escape the cold. Consider taking a guided tour to make the most of your visit.\\n- **Link for more info:** [U.S. News Travel - Things To Do](https://travel.usnews.com/Paris_France/Things_To_Do/)\\n\\n**Evening: Seine River Cruise**\\n- **Time:** 6:00 PM\\n- **Details:** End your day with a magical Seine River cruise. The snow adds a picturesque touch to the illuminated landmarks. Ensure to book a heated cruise for comfort.\\n\\n### Day 2: Explore Cultural and Historical Treasures\\n\\n**Morning: Notre-Dame Cathedral**\\n- **Time:** 9:30 AM\\n- **Details:** Visit the iconic Notre-Dame Cathedral. Although some areas may be under restoration, its architecture and history are worth experiencing. Warm clothing is essential as the interior can be chilly.\\n\\n**Afternoon: Musée d'Orsay**\\n- **Time:** 1:30 PM\\n- **Details:** Head to the Musée d'Orsay, renowned for its Impressionist masterpieces. This indoor activity is ideal for escaping the cold and enjoying world-class art.\\n\\n**Evening: Montmartre and Sacré-Cœur**\\n- **Time:** 5:00 PM\\n- **Details:** Wander through the charming streets of Montmartre and visit the Sacré-Cœur Basilica. The view of Paris in the snow is breathtaking. Enjoy a cozy dinner at a local bistro in Montmartre.\\n\\n### Day 3: Discover Hidden Gems and Local Flavors\\n\\n**Morning: Le Marais District**\\n- **Time:** 10:00 AM\\n- **Details:** Explore Le Marais, known for its vibrant street art, boutiques, and cafes. Enjoy a leisurely breakfast and shop for unique souvenirs.\\n\\n**Afternoon: Palais Garnier (Opera House)**\\n- **Time:** 2:00 PM\\n- **Details:** Tour the opulent Palais Garnier. Its stunning interiors are a must-see, especially when it's snowy outside.\\n\\n**Evening: Moulin Rouge Show**\\n- **Time:** 8:00 PM\\n- **Details:** Conclude your trip with a classic Parisian experience at the Moulin Rouge. Book in advance to secure a good seat and enjoy the legendary cabaret performance.\\n\\n### Additional Tips:\\n- **Weather Preparation:** Wear layers, waterproof boots, and carry an umbrella. The snow and cold wind can be intense.\\n- **Dining:** Indulge in warm, hearty French cuisine at local cafes and restaurants. Try dishes like French onion soup, coq au vin, and tarte Tatin.\\n- **Transport:** Use public transportation to avoid the snowy streets, and consider purchasing a Paris Visite pass for unlimited travel.\\n\\nEnjoy your snowy adventure in Paris!\\n---------------\\n**Tokyo Itinerary**\\n\\n**Day 1: Arrival and Exploration of Historical and Cultural Sites**\\n\\n- **Morning:**\\n - **Asakusa District**: Begin your day with a visit to the historic Asakusa district. Explore the iconic Senso-ji Temple, Tokyo's oldest temple. Enjoy the traditional market streets like Nakamise Street for some shopping and snacks.\\n\\n- **Afternoon:**\\n - **Tokyo National Museum**: Head to Ueno Park and visit the Tokyo National Museum. Discover Japan’s extensive collection of art and antiquities. This is a great spot to dive into Japanese history and culture.\\n\\n- **Evening:**\\n - **Dinner in Ueno**: Explore the local dining options around Ueno and enjoy a traditional Japanese dinner.\\n\\n**Day 2: Modern Tokyo and Unique Experiences**\\n\\n- **Morning:**\\n - **Ghibli Museum**: Start your day with a magical visit to the Ghibli Museum in Mitaka. Perfect for fans of Studio Ghibli's animated films, this museum offers a whimsical look into the creative world of Hayao Miyazaki.\\n\\n- **Afternoon:**\\n - **Shibuya and Harajuku**: Head towards the bustling areas of Shibuya and Harajuku. Witness the famous Shibuya Crossing and explore the trendy shops of Harajuku, especially Takeshita Street.\\n\\n- **Evening:**\\n - **Golden Gai**: Conclude your day in the vibrant Golden Gai district. This area is renowned for its narrow alleys filled with small bars and eateries. Experience the unique nightlife of Tokyo here.\\n\\n**Day 3: Relax and Explore Green Spaces**\\n\\n- **Morning:**\\n - **Shinjuku Gyoen National Garden**: Spend a peaceful morning strolling through the beautiful Shinjuku Gyoen, one of Tokyo's largest and most beautiful parks. It's a perfect spot for relaxation and enjoying nature.\\n\\n- **Afternoon:**\\n - **Meiji Shrine**: Visit the Meiji Shrine, located in a forested area near Harajuku and Shibuya. It's a serene place to learn about Shinto traditions and enjoy the tranquil setting.\\n\\n- **Evening:**\\n - **Tokyo Tower or Skytree**: End your trip with a visit to either Tokyo Tower or Tokyo Skytree for a panoramic view of the city. It's an unforgettable way to see Tokyo illuminated at night.\\n\\n**Weather Considerations:**\\n- With the current weather of few clouds and a mild temperature around 10.32°C, it is advisable to wear layers and carry a light jacket for comfort during outdoor activities.\\n- Humidity is high (92%), so be prepared for a slightly damp feeling and consider moisture-wicking clothing.\\n\\n**Additional Tips:**\\n- Always check the opening hours of attractions and book tickets in advance where necessary.\\n- Use Tokyo’s efficient public transport to move around easily.\\n- Consider visiting the websites linked in the attraction descriptions for more detailed information and current updates.\\n---------------\\nHere's a detailed itinerary for exploring some of Sydney's top tourist attractions with the current weather conditions in mind. With clear skies and pleasant temperatures, it's a perfect day to explore the outdoors and enjoy what Sydney has to offer.\\n\\n### Morning\\n\\n**9:00 AM - Sydney Opera House**\\n- Begin your day with a visit to the iconic Sydney Opera House. Take a guided tour to learn about its history and architecture. Tours are available in multiple languages.\\n- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\\n\\n**11:00 AM - Royal Botanic Garden Sydney**\\n- Just a short walk from the Opera House, enjoy a leisurely stroll through the Royal Botanic Garden. The clear skies will offer beautiful views of the diverse plant life and the Sydney Harbour.\\n\\n### Afternoon\\n\\n**12:30 PM - Lunch at Opera Bar**\\n- Head back to Opera Bar for lunch. Enjoy a refreshing cocktail with stunning views of the Sydney Harbour Bridge and the waterfront.\\n- **Link:** [Top attractions in Sydney | Sydney.com](https://www.sydney.com/things-to-do/attractions)\\n\\n**2:00 PM - Sydney Harbour Bridge**\\n- After lunch, take a scenic walk across the Sydney Harbour Bridge. If you're up for it, consider the BridgeClimb for breathtaking panoramic views of the city.\\n\\n**4:00 PM - The Rocks**\\n- Explore The Rocks, one of Sydney's most historic areas. Wander through the cobbled streets, visit the local markets, and perhaps enjoy a cup of coffee at a nearby café.\\n\\n### Evening\\n\\n**6:00 PM - Darling Harbour**\\n- Make your way to Darling Harbour for the evening. Here you can visit attractions such as the SEA LIFE Sydney Aquarium or simply enjoy the lively atmosphere by the waterfront.\\n\\n**8:00 PM - Dinner at a Local Restaurant**\\n- Conclude your day with dinner at one of Darling Harbour's many restaurants. Choose from a variety of cuisines while enjoying the vibrant night scene.\\n\\n### Additional Suggestions\\n\\n- If you're interested in more unique experiences, consider visiting some of the attractions listed on [Time Out Sydney](https://www.timeout.com/sydney/attractions/tourist-attractions-that-dont-suck), which includes thrilling adventures and scenic tours.\\n\\nWith clear skies and mild temperatures, this itinerary offers a balanced mix of cultural, historical, and scenic experiences. Enjoy your visit to Sydney!\"}\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "# Lists all the task steps that have been executed up to this point in time\n", + "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + "\n", + "# Transitions are retreived in reverse chronological order\n", + "for transition in reversed(transitions):\n", + " print(\"Transition type: \", transition.type)\n", + " print(\"Transition output: \", transition.output)\n", + " print(\"-\"*50)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.py b/cookbooks/03-trip-planning-assistant.py similarity index 51% rename from cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.py rename to cookbooks/03-trip-planning-assistant.py index bd446a849..e3375a728 100644 --- a/cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.py +++ b/cookbooks/03-trip-planning-assistant.py @@ -1,6 +1,10 @@ import uuid import yaml from julep import Client +import os + +openweathermap_api_key = os.getenv("OPENWEATHERMAP_API_KEY") +brave_api_key = os.getenv("BRAVE_API_KEY") # Global UUID is generated for agent and task AGENT_UUID = uuid.uuid4() @@ -31,7 +35,8 @@ ) # Defining a Task -task_def = yaml.safe_load(""" +# Defining the task +task_def = yaml.safe_load(f""" name: Tourist Plan With Weather And Attractions input_schema: @@ -54,7 +59,14 @@ integration: provider: weather setup: - openweathermap_api_key: "YOUR_API_KEY" + openweathermap_api_key: {openweathermap_api_key} + +- name: internet_search + type: integration + integration: + provider: brave + setup: + api_key: {brave_api_key} main: - over: inputs[0].locations @@ -65,29 +77,47 @@ - over: inputs[0].locations map: - tool: wikipedia + tool: internet_search arguments: - query: "_ + ' tourist attractions'" + query: "'tourist attractions in ' + _" +# Zip locations, weather, and attractions into a list of tuples [(location, weather, attractions)] - evaluate: - zipped: "list(zip(inputs[0].locations, [output['result'] for output in outputs[0]], [output['documents'][0]['page_content'] for output in outputs[1]]))" # [(location, weather, attractions)] + zipped: |- + list( + zip( + inputs[0].locations, + [output['result'] for output in outputs[0]], + outputs[1] + ) + ) + - over: _['zipped'] parallelism: 3 + # Inside the map step, each `_` represents the current element in the list + # which is a tuple of (location, weather, attractions) map: prompt: - role: system content: >- - You are a travel assistant. Your task is to create a detailed itinerary for visiting tourist attractions in "{{_[0]}}" based on the weather conditions and the top tourist attractions provided. - - Current weather condition at "{{_[0]}}": - "{{_[1]}}" - - Top tourist attractions in "{{_[0]}}": - "{{_[2]}}" - - Suggest outdoor or indoor activities based on the above information. + You are {{{{agent.name}}}}. Your task is to create a detailed itinerary + for visiting tourist attractions in some locations. + The user will give you the following information for each location: + + - The location + - The current weather condition + - The top tourist attractions + - role: user + content: >- + Location: "{{{{_[0]}}}}" + Weather: "{{{{_[1]}}}}" + Attractions: "{{{{_[2]}}}}" unwrap: true + +- evaluate: + final_plan: |- + '\\n---------------\\n'.join(activity for activity in _) """) # Creating/Updating a task @@ -109,20 +139,23 @@ # Wait for the execution to complete import time -time.sleep(10) +time.sleep(200) # Getting the execution details +# Get execution details execution = client.executions.get(execution.id) -print("Execution Output:") +# Print the output print(execution.output) +print("-"*50) + +if 'final_plan' in execution.output: + print(execution.output['final_plan']) -# List all steps of the executed task -print("Execution Steps:") +# Lists all the task steps that have been executed up to this point in time transitions = client.executions.transitions.list(execution_id=execution.id).items -print("Execution Steps:") -for transition in transitions: - print(transition) -# Stream the steps of the defined task -print("Streaming execution transitions:") -print(client.executions.transitions.stream(execution_id=execution.id)) \ No newline at end of file +# Transitions are retreived in reverse chronological order +for transition in reversed(transitions): + print("Transition type: ", transition.type) + print("Transition output: ", transition.output) + print("-"*50) \ No newline at end of file diff --git a/cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.ipynb b/cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.ipynb deleted file mode 100644 index dad492f20..000000000 --- a/cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.ipynb +++ /dev/null @@ -1,418 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \"julep\"\n", - "
\n", - "\n", - "## Task: Travel Itinerary Assistant with Weather and Wikipedia Integrations\n", - "\n", - "### Overview\n", - "\n", - "The Travel Itinerary Assistant helps users plan a travel itinerary that takes into account current weather conditions and local tourist attractions. By integrating data from Wikipedia for tourist attractions and using a weather API for real-time weather updates, the tool provides a comprehensive travel plan tailored to each location. The generated itinerary suggests appropriate activities based on the weather, enhancing the overall travel experience.\n", - "\n", - "### Task Flow\n", - "\n", - "1. **User Input**\n", - " - User provides a list of desired travel locations.\n", - " - Each location is processed individually to gather the required data.\n", - "\n", - "2. **Weather Data Retrieval**\n", - " - Fetch current weather data for each location using a weather API.\n", - " - Extract relevant weather details, such as temperature, weather condition, and recommendations.\n", - "\n", - "3. **Tourist Attractions Lookup**\n", - " - Use Wikipedia to search for the top tourist attractions for each location.\n", - " - The query format used is: `\" tourist attractions\"`.\n", - " - Retrieve and compile a list of popular tourist spots and landmarks.\n", - "\n", - "4. **Data Evaluation and Integration**\n", - " - Combine weather data and tourist attractions into a unified list for each location.\n", - " - Format the data into a structured tuple: `(location, weather, attractions)`.\n", - "\n", - "5. **Itinerary Generation**\n", - " - Create a detailed travel itinerary based on:\n", - " - Current weather conditions (e.g., sunny, rainy, cloudy).\n", - " - Top tourist attractions for each location.\n", - " - Suggested activities categorized as indoor or outdoor based on weather.\n", - "\n", - "### Key Features\n", - "\n", - "- **Multi-location Travel Planning**: Handles multiple destinations simultaneously, offering a consolidated travel plan.\n", - "- **Real-time Weather Data**: Leverages weather APIs to provide up-to-date weather conditions.\n", - "- **Tourist Attraction Discovery**: Integrates Wikipedia to find and recommend popular attractions.\n", - "- **Intelligent Itinerary Suggestions**: Suggests indoor or outdoor activities based on the weather.\n", - "- **Comprehensive Itinerary Output**: Combines weather and tourist data into a user-friendly travel plan.\n", - "\n", - "### Output\n", - "\n", - "- A detailed travel itinerary for each location\n", - "- Curated, up-to-date information gathered from weather searches and Wikipedia\n", - "\n", - "```plaintext\n", - "\n", - "+----------------+ +--------------------------+ +--------------------------+ +------------------------------+ +-------------------------+\n", - "| User Input | | Weather Data Retrieval | | Tourist Attractions | | Data Evaluation & Integration| | Itinerary Generation |\n", - "| (List of | --> | (Weather API) | --> | Lookup (Wikipedia) | --> | (Combine Weather & | --> | (Generate Suggested |\n", - "| Locations) | | | | | | Attractions Data) | | Activities/Plan) |\n", - "+----------------+ +--------------------------+ +--------------------------+ +------------------------------+ +-------------------------+\n", - " | | | | |\n", - " | | | | |\n", - " v v v v v\n", - "Location 1, Location 2, ... Fetch weather for each Search Wikipedia for Combine weather data and Create itinerary with\n", - "Each location processed location individually, \" tourist tourist attractions into suggested activities\n", - "individually for extracting temp., attractions\", retrieve a structured tuple: based on weather and\n", - "weather data. conditions, & top spots. (location, weather, attractions.\n", - " recommendations. attractions).\n", - "```\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implementation\n", - "\n", - "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", - "\n", - "\n", - " \"Open\n", - "\n", - "\n", - "### Additional Information\n", - "\n", - "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", - "\n", - "**Author:** Julep AI \n", - "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Installing the Julep Client" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install julep -U --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NOTE:\n", - "\n", - "- UUIDs are generated for both the agent and task to uniquely identify them within the system.\n", - "- Once created, these UUIDs should remain unchanged for simplicity.\n", - "- Altering a UUID will result in the system treating it as a new agent or task.\n", - "- If a UUID is changed, the original agent or task will continue to exist in the system alongside the new one." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Global UUID is generated for agent and task\n", - "import uuid\n", - "\n", - "AGENT_UUID = uuid.uuid4()\n", - "TASK_UUID = uuid.uuid4() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating Julep Client with the API Key" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from julep import Client\n", - "\n", - "api_key = \"\" # Your API key here\n", - "\n", - "# Create a client\n", - "client = Client(api_key=api_key, environment=\"dev\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an \"agent\"\n", - "\n", - "\n", - "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", - "\n", - "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the agent\n", - "name = \"Jarvis\"\n", - "about = \"The original AI conscious the Iron Man.\"\n", - "default_settings = {\n", - " \"temperature\": 0.7,\n", - " \"top_p\": 1,\n", - " \"min_p\": 0.01,\n", - " \"presence_penalty\": 0,\n", - " \"frequency_penalty\": 0,\n", - " \"length_penalty\": 1.0,\n", - " \"max_tokens\": 150,\n", - "}\n", - "\n", - "# Create the agent\n", - "agent = client.agents.create_or_update(\n", - " agent_id=AGENT_UUID,\n", - " name=name,\n", - " about=about,\n", - " model=\"gpt-4o\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Defining a Task\n", - "\n", - "Tasks in Julep are Github Actions style workflows that define long-running, multi-step actions. \n", - "You can use them to conduct complex actions by defining them step-by-step. They have access to all Julep integrations.\n", - "\n", - "To learn more about tasks, visit [Julep documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#task)." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "import yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More on how to define a task can be found [here](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "task_def = yaml.safe_load(\"\"\"\n", - "name: Tourist Plan With Weather And Attractions\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " locations:\n", - " type: array\n", - " items:\n", - " type: string\n", - " description: The locations to search for.\n", - "\n", - "tools:\n", - "- name: wikipedia\n", - " type: integration\n", - " integration:\n", - " provider: wikipedia\n", - "\n", - "- name: weather\n", - " type: integration\n", - " integration:\n", - " provider: weather\n", - " setup:\n", - " openweathermap_api_key: \"YOUR_API_KEY\"\n", - "\n", - "main:\n", - "- over: inputs[0].locations\n", - " map:\n", - " tool: weather\n", - " arguments:\n", - " location: _\n", - "\n", - "- over: inputs[0].locations\n", - " map:\n", - " tool: wikipedia\n", - " arguments:\n", - " query: \"_ + ' tourist attractions'\"\n", - "\n", - "- evaluate:\n", - " zipped: \"list(zip(inputs[0].locations, [output['result'] for output in outputs[0]], [output['documents'][0]['page_content'] for output in outputs[1]]))\" # [(location, weather, attractions)]\n", - "\n", - "\n", - "- over: _['zipped']\n", - " parallelism: 3\n", - " map:\n", - " prompt:\n", - " - role: system\n", - " content: >-\n", - " You are a travel assistant. Your task is to create a detailed itinerary for visiting tourist attractions in \"{{_[0]}}\" based on the weather conditions and the top tourist attractions provided.\n", - " \n", - " Current weather condition at \"{{_[0]}}\":\n", - " \"{{_[1]}}\"\n", - "\n", - " Top tourist attractions in \"{{_[0]}}\":\n", - " \"{{_[2]}}\"\n", - "\n", - " Suggest outdoor or indoor activities based on the above information.\n", - " unwrap: true\n", - "\"\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating/Updating a task." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# creating the task object\n", - "task = client.tasks.create_or_update(\n", - " task_id=TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **task_def\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating an Execution\n", - "\n", - "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creates a execution worklow for the Task defined in the yaml file." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "execution = client.executions.create(\n", - " task_id=task.id,\n", - " input={\n", - " \"locations\": [\"New York\", \"London\", \"Paris\", \"Tokyo\", \"Sydney\"]\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "execution.id" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# getting the execution details\n", - "execution = client.executions.get(execution.id)\n", - "#printing the output\n", - "execution.output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Retrieves and lists all the steps of a defined task that have been executed up to that point in time. Unlike streaming, this function does not continuously monitor the execution; it only provides a snapshot of the steps completed so far without displaying real-time updates as the task progresses." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.executions.transitions.list(execution_id=execution.id).items" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Continuously monitor and stream the steps of a defined task. It retrieves and displays the transitions or execution steps of the task identified by execution.id in real-time, showing each step sequentially until the task is either completed or an error causes it to terminate." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "client.executions.transitions.stream(execution_id=execution.id)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ai", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cookbooks/17-Hook-Generator-Trending-Reels.ipynb b/cookbooks/04-hook-generator-trending-reels.ipynb similarity index 96% rename from cookbooks/17-Hook-Generator-Trending-Reels.ipynb rename to cookbooks/04-hook-generator-trending-reels.ipynb index fbe8df06f..1d2e1d74e 100644 --- a/cookbooks/17-Hook-Generator-Trending-Reels.ipynb +++ b/cookbooks/04-hook-generator-trending-reels.ipynb @@ -4,9 +4,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
\n", - " \"julep\"\n", + "
\n", + " \"julep\"\n", "
\n", + "\n", + "

\n", + "
\n", + " Explore Docs (wip)\n", + " ·\n", + " Discord\n", + " ·\n", + " 𝕏\n", + " ·\n", + " LinkedIn\n", + "

\n", + "\n", + "

\n", + " \"NPM\n", + "  \n", + " \"PyPI\n", + "  \n", + " \"Docker\n", + "  \n", + " \"GitHub\n", + "

\n", " \n", "## Task Definition: Generating Hooks for Trending Instagram Reels\n", " \n", @@ -42,7 +63,20 @@ "\n", "### Key Feature:\n", "- Utilizes dynamic hook templates and social media trends to generate engaging content for TikTok reels, enhancing viewer interaction and reel visibility.\n", - "\n" + "\n", + "```plaintext\n", + "+----------+ +------------+ +------------+ +------------+ +-----------+\n", + "| User | | API | | Prompt | | System | | Output |\n", + "| Input | --> | Tool | --> | Step | --> | Tool | --> | Step |\n", + "| (Query) | | Call | | (Scores) | | | | |\n", + "+----------+ +------------+ +------------+ +------------+ +-----------+\n", + " | | | | |\n", + " | | | | |\n", + " v v v v v\n", + " \"trending\" Fetch data Calculate Retrieve hooks \"Here are the\n", + " topics from API engagement & from document trending hooks...\"\n", + " virality scores\n", + "```\n" ] }, { @@ -53,7 +87,7 @@ "\n", "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", diff --git a/cookbooks/17-Hook-Generator-Trending-Reels.py b/cookbooks/04-hook-generator-trending-reels.py similarity index 99% rename from cookbooks/17-Hook-Generator-Trending-Reels.py rename to cookbooks/04-hook-generator-trending-reels.py index dbfc0bb51..84c5d9688 100644 --- a/cookbooks/17-Hook-Generator-Trending-Reels.py +++ b/cookbooks/04-hook-generator-trending-reels.py @@ -2,7 +2,6 @@ # %% # Global UUID is generated for agent and task -%time import time, yaml from dotenv import load_dotenv import os diff --git a/cookbooks/05-Basic_Agent_Creation_and_Interaction.py b/cookbooks/05-Basic_Agent_Creation_and_Interaction.py deleted file mode 100644 index e57a38560..000000000 --- a/cookbooks/05-Basic_Agent_Creation_and_Interaction.py +++ /dev/null @@ -1,70 +0,0 @@ -# BASIC AGENT CREATION AND INTERACTION - -import uuid -from julep import Client - -# Global UUID is generated for agent -AGENT_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an "agent" -name = "Jarvis" -about = "A friendly and knowledgeable AI assistant." -default_settings = { - "temperature": 0.7, - "top_p": 1, - "min_p": 0.01, - "presence_penalty": 0, - "frequency_penalty": 0, - "length_penalty": 1.0, - "max_tokens": 150, -} - -# Create the agent -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name=name, - about=about, - model="gpt-4o", -) - -print(f"Agent created with ID: {agent.id}") - -# Create a session for interaction -session = client.sessions.create( - agent=agent.id -) - -print(f"Session created with ID: {session.id}") - -# Function to chat with the agent -def chat_with_agent(message): - message = { - "role": "user", - "content": message, - } - response = client.sessions.chat( - session_id=session.id, - messages=[message], - ) - return response.choices[0].message.content - -# Demonstrate basic interaction -print("Agent: Hello! I'm Jarvis, your AI assistant. How can I help you today?") - -while True: - user_input = input("You: ") - if user_input.lower() in ['exit', 'quit', 'bye']: - print("Agent: Goodbye! It was nice chatting with you.") - break - - response = chat_with_agent(user_input) - print(f"Agent: {response}") - -# Optional: Retrieve chat history -history = client.sessions.get(session_id=session.id) -print("\nChat History:") -print(history) \ No newline at end of file diff --git a/cookbooks/05-video-processing-with-natural-language.ipynb b/cookbooks/05-video-processing-with-natural-language.ipynb new file mode 100644 index 000000000..4151c0425 --- /dev/null +++ b/cookbooks/05-video-processing-with-natural-language.ipynb @@ -0,0 +1,579 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \"julep\"\n", + "
\n", + "\n", + "

\n", + "
\n", + " Explore Docs (wip)\n", + " ·\n", + " Discord\n", + " ·\n", + " 𝕏\n", + " ·\n", + " LinkedIn\n", + "

\n", + "\n", + "

\n", + " \"NPM\n", + "  \n", + " \"PyPI\n", + "  \n", + " \"Docker\n", + "  \n", + " \"GitHub\n", + "

\n", + "\n", + "## Task Definition: Video Processing with Natural Language\n", + "\n", + "### Overview\n", + "\n", + "This task is leverages the cloudinary `integration` tool, and combines it with a prompt step to convert a natural language instructions and apply them to a given video.\n", + "\n", + "### Task Tools:\n", + "\n", + "**Cloudinary**: An `integration` type tool that can upload and transform media files.\n", + "\n", + "### Task Input:\n", + "\n", + "**video_url**: The URL of the video to transform.\n", + "\n", + "**public_id**: The public id of the video to transform.\n", + "\n", + "**transformation_prompt**: The natural language instructions to apply to the video.\n", + "\n", + "### Task Output:\n", + "\n", + "**transformed_video_url**: The URL of the transformed video.\n", + "\n", + "### Task Flow\n", + "\n", + "1. **Input**: The user provides a URL to a video and transoformation instrucitons (in natural language) to apply to the video.\n", + "\n", + "2. **Cloudinary Tool Integration**: The `cloudinary_upload` tool is called to upload the video to cloudinary.\n", + "\n", + "3. **Prompt Step**: The prompt step is used to convert the natural language instructions into a json of transformation instructions that are compatible with cloudinary's API. In this step, `gemini-1.5-pro` is used as the model due to its ability to read video files.\n", + "\n", + "4. **Cloudinary Tool Integration**: The `cloudinary_upload` tool is called again to apply the transformation instructions to the video.\n", + "\n", + "5. **Output**: The final output is the URL of the transformed video.\n", + "\n", + "```plaintext\n", + "+----------+ +------------+ +------------+ +-----------+\n", + "| Video | | Cloudinary | | Prompt | | Processed |\n", + "| Input | --> | Upload | --> | Step | --> | Video |\n", + "| | | | | (Gemini) | | |\n", + "+----------+ +------------+ +------------+ +-----------+\n", + " | | | |\n", + " | | | |\n", + " v v v v\n", + "\"video.mp4 + Upload video Generate JSON \"video.mp4 with\n", + "NL prompt\" transformations blur & overlay\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "### Additional Information\n", + "\n", + "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", + "\n", + "**Author:** Julep AI \n", + "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Installing the Julep Client" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade julep --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "\n", + "# NOTE: these UUIDs are used in order not to use the `create_or_update` methods instead of\n", + "# the `create` methods for the sake of not creating new resources every time a cell is run.\n", + "AGENT_UUID = uuid.uuid4()\n", + "TASK_UUID = uuid.uuid4()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Julep Client with the API Key" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from julep import Client\n", + "import os\n", + "\n", + "api_key = os.getenv(\"JULEP_API_KEY\")\n", + "\n", + "# Create a Julep client\n", + "client = Client(api_key=api_key, environment=\"dev\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an \"agent\"\n", + "\n", + "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", + "\n", + "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Create agent\n", + "agent = client.agents.create_or_update(\n", + " agent_id=AGENT_UUID,\n", + " name=\"Spiderman\",\n", + " about=\"AI that can crawl the web and extract data\",\n", + " model=\"gpt-4o\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Task\n", + "\n", + "Tasks in Julep are Github-Actions-style workflows that define long-running, multi-step actions.\n", + "\n", + "You can use them to conduct complex actions by defining them step-by-step.\n", + "\n", + "To learn more about tasks, please refer to the `Tasks` section in [Julep Concepts](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#tasks)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import yaml\n", + "\n", + "cloudinary_api_key = os.getenv(\"CLOUDINARY_API_KEY\")\n", + "cloudinary_api_secret = os.getenv(\"CLOUDINARY_API_SECRET\")\n", + "cloudinary_cloud_name = os.getenv(\"CLOUDINARY_CLOUD_NAME\")\n", + "\n", + "# Define the task\n", + "task_def = yaml.safe_load(f\"\"\"\n", + "name: Video Processing With Natural Language\n", + "\n", + "input_schema:\n", + " type: object\n", + " properties:\n", + " video_url:\n", + " type: string\n", + " description: The url of the file to upload\n", + " public_id:\n", + " type: string\n", + " description: The public id of the file to upload\n", + " transformation_prompt:\n", + " type: string\n", + " description: The prompt for the transformations to apply to the file\n", + "\n", + "tools:\n", + "- name: cloudinary_upload\n", + " type: integration\n", + " integration:\n", + " provider: cloudinary\n", + " method: media_upload\n", + " setup:\n", + " cloudinary_api_key: \"{cloudinary_api_key}\"\n", + " cloudinary_api_secret: \"{cloudinary_api_secret}\"\n", + " cloudinary_cloud_name: \"{cloudinary_cloud_name}\"\n", + "\n", + "main:\n", + "- tool: cloudinary_upload\n", + " arguments:\n", + " file: '_0.video_url'\n", + " public_id: '_0.public_id'\n", + " upload_params:\n", + " resource_type: \"'video'\"\n", + "\n", + "- prompt:\n", + " - role: user\n", + " content:\n", + "\n", + " - type: text\n", + " text: |-\n", + " You are a Cloudinary expert. You are given a medial url. it might be an image or a video.\n", + " You need to come up with a json of transformations to apply to the given media.\n", + " Overall the json could have multiple transformation json objects.\n", + " Each transformation json object can have the multiple key value pairs.\n", + " Each key value pair should have the key as the transformation name like \"aspect_ratio\", \"crop\", \"width\" etc and the value as the transformation parameter value.\n", + " Given below is an example of a transformation json list. Don't provide explanations and/or comments in the json.\n", + " ```json\n", + " [\n", + " {{\n", + " \"aspect_ratio\": \"1.0\",\n", + " \"width\": 250,\n", + " }},\n", + " {{\n", + " \"fetch_format\": \"auto\"\n", + " }},\n", + " {{\n", + " \"overlay\":\n", + " {{\n", + " \"url\": \"\"\n", + " }}\n", + " }},\n", + " {{\n", + " \"flags\": \"layer_apply\"\n", + " }}\n", + " ]\n", + " ```\n", + " - type: image_url\n", + " image_url:\n", + " url: \"{{{{_.url}}}}\"\n", + "\n", + " - type: text\n", + " text: |-\n", + " Hey, check the video above, I need to apply the following transformations using cloudinary.\n", + " {{{{_0.transformation_prompt}}}}\n", + "\n", + " unwrap: true\n", + " settings:\n", + " model: gemini/gemini-1.5-pro\n", + "\n", + "# Extract the json from the model's response\n", + "- evaluate:\n", + " model_transformation: load_json(\n", + " _[_.find(\"```json\")+7:][:_[_.find(\"```json\")+7:].find(\"```\")])\n", + "\n", + "- tool: cloudinary_upload\n", + " arguments:\n", + " file: '_0.video_url'\n", + " public_id: '_0.public_id'\n", + " upload_params:\n", + " transformation: '_.model_transformation'\n", + " resource_type: \"'video'\"\n", + "\n", + "- evaluate:\n", + " transformed_video_url: '_.url'\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notes:\n", + "- The reason for using the quadruple curly braces `{{{{}}}}` for the jinja template is to avoid conflicts with the curly braces when using the `f` formatted strings in python. [More information here](https://stackoverflow.com/questions/64493332/jinja-templating-in-airflow-along-with-formatted-text)\n", + "- The `unwrap: True` in the prompt step is used to unwrap the output of the prompt step (to unwrap the `choices[0].message.content` from the output of the model).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating/Updating a task" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# creating the task object\n", + "task = client.tasks.create_or_update(\n", + " task_id=TASK_UUID,\n", + " agent_id=AGENT_UUID,\n", + " **task_def\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1021a8d6-5050-48c3-b23e-6d96578d1026'" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# creating an execution object\n", + "transformation_prompt = \"\"\"\n", + "1- I want to add an overlay an the following image to the video, and apply a layer apply flag also. Here's the image url:\n", + "https://res.cloudinary.com/demo/image/upload/logos/cloudinary_icon_white.png\n", + "\n", + "2- I also want you to to blur the video, and add a fade in and fade out effect to the video with a duration of 3 seconds each.\n", + "\"\"\"\n", + "\n", + "input_video_url = \"http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerMeltdowns.mp4\"\n", + "\n", + "execution = client.executions.create(\n", + " task_id=TASK_UUID,\n", + " input={\n", + " \"video_url\": input_video_url,\n", + " \"public_id\": \"video_test2\",\n", + " \"transformation_prompt\": transformation_prompt,\n", + " }\n", + ")\n", + "execution.id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking execution details and output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are multiple ways to get the execution details and the output:\n", + "\n", + "1. **Get Execution Details**: This method retrieves the details of the execution, including the output of the last transition that took place.\n", + "\n", + "2. **List Transitions**: This method lists all the task steps that have been executed up to this point in time, so the output of a successful execution will be the output of the last transition (first in the transition list as it is in reverse chronological order), which should have a type of `finish`.\n", + "\n", + "\n", + "Note: You need to wait for a few seconds for the execution to complete before you can get the final output, so feel free to run the following cells multiple times until you get the final output.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'base64': None, 'meta_data': {'api_key': '518455844981529', 'asset_folder': '', 'asset_id': '069d70c84e88ee9293a1d753b3ca3898', 'audio': {'bit_rate': '191999', 'channel_layout': 'stereo', 'channels': 2, 'codec': 'aac', 'frequency': 44100}, 'bit_rate': 1197518, 'bytes': 2252313, 'created_at': '2024-11-20T08:55:44Z', 'display_name': 'video_test2', 'duration': 15.046531, 'etag': '9e87559cba36e6d07f7435c2a8081b3a', 'format': 'mp4', 'frame_rate': 24.0, 'height': 720, 'is_audio': False, 'nb_frames': 361, 'original_filename': 'ForBiggerMeltdowns', 'overwritten': True, 'pages': 0, 'placeholder': False, 'playback_url': 'https://res.cloudinary.com/dpnjjk8mb/video/upload/sp_auto/v1732200108/video_test2.m3u8', 'resource_type': 'video', 'rotation': 0, 'signature': 'e65a0d39a282fc8bd4dd3288d7d35dfffdadc2a8', 'tags': [], 'type': 'upload', 'url': 'http://res.cloudinary.com/dpnjjk8mb/video/upload/v1732200108/video_test2.mp4', 'version': 1732200108, 'version_id': 'a9fccdf7b310edb3c2ff5ffe9335f7f8', 'video': {'bit_rate': '1002377', 'codec': 'h264', 'level': 31, 'pix_format': 'yuv420p', 'profile': 'High', 'time_base': '1/48'}, 'width': 1280}, 'public_id': 'video_test2', 'url': 'https://res.cloudinary.com/dpnjjk8mb/video/upload/v1732200108/video_test2.mp4'}\n" + ] + } + ], + "source": [ + "# Get execution details\n", + "execution = client.executions.get(execution.id)\n", + "# Print the output\n", + "print(execution.output)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transition type: init\n", + "Transition output: {'public_id': 'video_test2', 'transformation_prompt': \"\\n1- I want to add an overlay an the following image to the video, and apply a layer apply flag also. Here's the image url:\\nhttps://res.cloudinary.com/demo/image/upload/logos/cloudinary_icon_white.png\\n\\n2- I also want you to to blur the video, and add a fade in and fade out effect to the video with a duration of 3 seconds each.\\n\", 'video_url': 'http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerMeltdowns.mp4'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'base64': None, 'meta_data': {'api_key': '518455844981529', 'asset_folder': '', 'asset_id': '069d70c84e88ee9293a1d753b3ca3898', 'audio': {'bit_rate': '191999', 'channel_layout': 'stereo', 'channels': 2, 'codec': 'aac', 'frequency': 44100}, 'bit_rate': 1197518, 'bytes': 2252313, 'created_at': '2024-11-20T08:55:44Z', 'display_name': 'video_test2', 'duration': 15.046531, 'etag': '9e87559cba36e6d07f7435c2a8081b3a', 'format': 'mp4', 'frame_rate': 24.0, 'height': 720, 'is_audio': False, 'nb_frames': 361, 'original_filename': 'ForBiggerMeltdowns', 'overwritten': True, 'pages': 0, 'placeholder': False, 'playback_url': 'https://res.cloudinary.com/dpnjjk8mb/video/upload/sp_auto/v1732200198/video_test2.m3u8', 'resource_type': 'video', 'rotation': 0, 'signature': 'f46b7d3d234cd8d4c0f15423b507768df17f2ff6', 'tags': [], 'type': 'upload', 'url': 'http://res.cloudinary.com/dpnjjk8mb/video/upload/v1732200198/video_test2.mp4', 'version': 1732200198, 'version_id': '5ed8eae9dc683377dc4713767bf729f0', 'video': {'bit_rate': '1002377', 'codec': 'h264', 'level': 31, 'pix_format': 'yuv420p', 'profile': 'High', 'time_base': '1/48'}, 'width': 1280}, 'public_id': 'video_test2', 'url': 'https://res.cloudinary.com/dpnjjk8mb/video/upload/v1732200198/video_test2.mp4'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: ```json\n", + "[\n", + " {\n", + " \"overlay\": {\n", + " \"url\": \"https://res.cloudinary.com/demo/image/upload/logos/cloudinary_icon_white.png\"\n", + " }\n", + " },\n", + " {\n", + " \"flags\": \"layer_apply\"\n", + " },\n", + " {\n", + " \"effect\": \"blur:100\"\n", + " },\n", + " {\n", + " \"effect\": \"fade:3000\"\n", + " },\n", + " {\n", + " \"effect\": \"fade:-3000\"\n", + " }\n", + "]\n", + "```\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'model_transformation': [{'overlay': {'url': 'https://res.cloudinary.com/demo/image/upload/logos/cloudinary_icon_white.png'}}, {'flags': 'layer_apply'}, {'effect': 'blur:100'}, {'effect': 'fade:3000'}, {'effect': 'fade:-3000'}]}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'base64': None, 'meta_data': {'api_key': '518455844981529', 'asset_folder': '', 'asset_id': '069d70c84e88ee9293a1d753b3ca3898', 'audio': {'bit_rate': '128290', 'channel_layout': 'stereo', 'channels': 2, 'codec': 'aac', 'frequency': 44100}, 'bit_rate': 644662, 'bytes': 1212449, 'created_at': '2024-11-20T08:55:44Z', 'display_name': 'video_test2', 'duration': 15.046009, 'etag': '876869c16d0bfa291d55796e5cb1bc00', 'format': 'mp4', 'frame_rate': 24.0, 'height': 720, 'is_audio': False, 'nb_frames': 361, 'original_filename': 'ForBiggerMeltdowns', 'overwritten': True, 'pages': 0, 'placeholder': False, 'playback_url': 'https://res.cloudinary.com/dpnjjk8mb/video/upload/sp_auto/v1732200207/video_test2.m3u8', 'resource_type': 'video', 'rotation': 0, 'signature': '7df5ccaaf5cba28b4d62d2ffc59793f29dfea326', 'tags': [], 'type': 'upload', 'url': 'http://res.cloudinary.com/dpnjjk8mb/video/upload/v1732200207/video_test2.mp4', 'version': 1732200207, 'version_id': 'fdfd560e5e06e3327777a5355b2cc682', 'video': {'bit_rate': '509638', 'codec': 'h264', 'level': 31, 'pix_format': 'yuv420p', 'profile': 'High', 'time_base': '1/12288'}, 'width': 1280}, 'public_id': 'video_test2', 'url': 'https://res.cloudinary.com/dpnjjk8mb/video/upload/v1732200207/video_test2.mp4'}\n", + "--------------------------------------------------\n", + "Transition type: finish\n", + "Transition output: {'transformed_video_url': 'https://res.cloudinary.com/dpnjjk8mb/video/upload/v1732200207/video_test2.mp4'}\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "# Lists all the task steps that have been executed up to this point in time\n", + "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + "\n", + "# Transitions are retreived in reverse chronological order\n", + "for transition in reversed(transitions):\n", + " print(\"Transition type: \", transition.type)\n", + " print(\"Transition output: \", transition.output)\n", + " print(\"-\"*50)\n", + "\n", + "if transitions[0].type == \"finish\":\n", + " transformed_video_url = transitions[0].output['transformed_video_url']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Video Before Transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Video\n", + "\n", + "Video(input_video_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Video After Transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Video\n", + "\n", + "Video(transformed_video_url)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cookbooks/06-Designing_Multi-Step_Tasks.py b/cookbooks/06-Designing_Multi-Step_Tasks.py deleted file mode 100644 index b623ae378..000000000 --- a/cookbooks/06-Designing_Multi-Step_Tasks.py +++ /dev/null @@ -1,139 +0,0 @@ -import uuid -import yaml, time -from julep import Client - -# Global UUID is generated for agent and task -AGENT_UUID = uuid.uuid4() -TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an "agent" -name = "Multi-Step Task Agent" -about = "An agent capable of executing complex multi-step tasks." -default_settings = { - "temperature": 0.7, - "top_p": 1, - "min_p": 0.01, - "presence_penalty": 0, - "frequency_penalty": 0, - "length_penalty": 1.0, - "max_tokens": 150, -} - -# Create the agent -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name=name, - about=about, - model="gpt-4o", -) - -# Add a web search tool to the agent -client.agents.tools.create( - agent_id=AGENT_UUID, - name="web_search", - integration={ - "provider": "brave", - "method": "search", - "setup": {"api_key": "your_brave_api_key"}, - }, -) - -# Defining a Task with various step types -task_def = yaml.safe_load(""" -name: Multi-Step Task Demonstration - -input_schema: - type: object - properties: - topic: - type: string - description: The topic to research and summarize. - -tools: -- name: web_search - type: integration - integration: - provider: brave - setup: - api_key: "your_api_key" - -main: -# Step 1: Prompt - Initial research question -- prompt: - - role: system - content: "You are a research assistant. Your task is to formulate three specific research questions about the given topic: {{inputs[0].topic}}" - unwrap: true - -# Step 2: Tool Call - Web search for each question -- foreach: - in: _.split('\\n') - do: - tool: web_search - arguments: - query: _ - -# Step 3: Evaluate - Extract relevant information -- evaluate: - relevant_info: "[output for output in _]" - -# Step 4: Conditional Logic - Check if enough information is gathered -- if: "len(_.relevant_info) >= 3" - then: - prompt: - - role: system - content: "Summarize the following information about {{inputs[0].topic}}:\n{{_.relevant_info}}" - unwrap: true - else: - prompt: - - role: system - content: "Not enough information gathered. Please provide a brief overview of {{inputs[0].topic}} based on your knowledge." - unwrap: true - -# Step 5: Log - Record the summary -- log: "Summary for {{inputs[0].topic}}: {{_}}" - -# Step 6: Return - Final output -- return: - summary: "_" - topic: "inputs[0].topic" - -""") - -# Creating/Updating a task -task = client.tasks.create_or_update( - task_id=TASK_UUID, - agent_id=AGENT_UUID, - **task_def -) - -# Creating an Execution -execution = client.executions.create( - task_id=TASK_UUID, - input={ - "topic": "Artificial Intelligence in Healthcare" - } -) - -print(f"Execution ID: {execution.id}") - -# Wait for the execution to complete -time.sleep(10) - -# Getting the execution details -execution = client.executions.get(execution.id) -print("Execution Output:") -print(client.executions.transitions.list(execution_id=execution.id).items[0].output) - -# Listing all the steps of a defined task -transitions = client.executions.transitions.list(execution_id=execution.id).items -print("Execution Steps:") -for transition in transitions: - print(transition) - -# Stream the steps of the defined task -print("Streaming execution transitions:") -print(client.executions.transitions.stream(execution_id=execution.id)) \ No newline at end of file diff --git a/cookbooks/06-browser-use.ipynb b/cookbooks/06-browser-use.ipynb new file mode 100644 index 000000000..3c61f7dfb --- /dev/null +++ b/cookbooks/06-browser-use.ipynb @@ -0,0 +1,664 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \"julep\"\n", + "
\n", + "\n", + "

\n", + "
\n", + " Explore Docs (wip)\n", + " ·\n", + " Discord\n", + " ·\n", + " 𝕏\n", + " ·\n", + " LinkedIn\n", + "

\n", + "\n", + "

\n", + " \"NPM\n", + "  \n", + " \"PyPI\n", + "  \n", + " \"Docker\n", + "  \n", + " \"GitHub\n", + "

\n", + "\n", + "## Task: Browser Use Assistant\n", + "\n", + "### Overview\n", + "\n", + "The Browser Use Assistant is an AI agent that can interact with a web browser to perform tasks on behalf of users. It uses a headless Chrome browser through BrowserBase integration, allowing it to navigate websites, click elements, and type text, all while providing visual feedback through screenshots.\n", + "\n", + "### Task Flow\n", + "\n", + "1. **Session Initialization**\n", + " - Create a Julep session for the AI agent\n", + " - Initialize a BrowserBase browser session\n", + " - Set up connection URLs and debugger access\n", + "\n", + "2. **Browser Setup**\n", + " - Configure browser viewport (1024x768)\n", + " - Initialize with Google homepage\n", + " - Establish CDP (Chrome DevTools Protocol) connection\n", + "\n", + "3. **Interactive Loop**\n", + " - Agent receives user goal\n", + " - Agent plans and executes browser actions\n", + " - Takes screenshots for visual confirmation\n", + " - Evaluates progress towards goal\n", + "\n", + "### Key Features\n", + "\n", + "- **Browser Automation**: Performs web interactions like navigation, clicking, and typing\n", + "- **Visual Feedback**: Captures screenshots to verify actions and understand page state\n", + "- **Goal-Oriented**: Continues executing actions until the user's goal is achieved\n", + "- **Secure Sessions**: Uses BrowserBase for isolated browser instances\n", + "\n", + "### Tools Integration\n", + "\n", + "```yaml\n", + "tools:\n", + "- BrowserBase Tools\n", + " - create_browserbase_session\n", + " - get_cdp_url\n", + " - get_session_view_urls\n", + " - perform_browser_action\n", + "- Julep Tools\n", + " - create_julep_session\n", + " - session_chat\n", + "```\n", + "\n", + "### System Capabilities\n", + "\n", + "```plaintext\n", + "* Headless Chrome browser interaction\n", + "* Browser action execution\n", + "* Screenshot-based visual feedback\n", + "* Single-tab operation\n", + "* Direct browser interaction (no UI controls)\n", + "* Text input and navigation\n", + "* Click coordination\n", + "```\n", + "\n", + "### Flow Diagram\n", + "\n", + "```plaintext\n", + "+-------------------+ +----------------------+ +------------------+\n", + "| Session Setup | | Browser Actions | | Goal Check |\n", + "| - Julep Session | --> | - Navigation | --> | - Evaluate |\n", + "| - Browser Session| | - Clicking | | - Continue/End |\n", + "+-------------------+ | - Typing | +------------------+\n", + " | - Screenshots |\n", + " +----------------------+\n", + " ↑\n", + " |\n", + " ↓\n", + " +----------------------+\n", + " | Agent Response |\n", + " | - Plan Actions |\n", + " | - Process Results |\n", + " +----------------------+\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation\n", + "\n", + "To recreate the notebook and see the code implementation for this task, you can access the Google Colab notebook using the link below:\n", + "\n", + "\n", + " \"Open\n", + "\n", + "\n", + "### Additional Information\n", + "\n", + "For more details about the task or if you have any questions, please don't hesitate to contact the author:\n", + "\n", + "**Author:** Julep AI \n", + "**Contact:** [hey@julep.ai](mailto:hey@julep.ai) or Discord" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Installing the Julep Client" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install julep -U --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### NOTE:\n", + "\n", + "- UUIDs are generated for both the agent and task to uniquely identify them within the system.\n", + "- Once created, these UUIDs should remain unchanged for simplicity.\n", + "- Altering a UUID will result in the system treating it as a new agent or task.\n", + "- If a UUID is changed, the original agent or task will continue to exist in the system alongside the new one." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Global UUID is generated for agent and task\n", + "import uuid\n", + "\n", + "AGENT_UUID = uuid.uuid4()\n", + "TASK_UUID = uuid.uuid4() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Julep Client with the API Key" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from julep import Client\n", + "import os\n", + "\n", + "api_key = os.getenv(\"JULEP_API_KEY\")\n", + "\n", + "# Create a Julep client\n", + "client = Client(api_key=api_key, environment=\"dev\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an \"agent\"\n", + "\n", + "\n", + "Agent is the object to which LLM settings, like model, temperature along with tools are scoped to.\n", + "\n", + "To learn more about the agent, please refer to the [documentation](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#agent)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the agent\n", + "name = \"Browser Use Assistant\"\n", + "about = \"an assistant that can interact with a web browser to perform tasks on behalf of users.\"\n", + "default_settings = {\n", + " \"temperature\": 0.7,\n", + " \"top_p\": 1,\n", + " \"min_p\": 0.01,\n", + " \"presence_penalty\": 0,\n", + " \"frequency_penalty\": 0,\n", + " \"length_penalty\": 1.0,\n", + " \"max_tokens\": 150,\n", + "}\n", + "\n", + "# Create the agent\n", + "agent = client.agents.create_or_update(\n", + " agent_id=AGENT_UUID,\n", + " name=name,\n", + " about=about,\n", + " model=\"claude-3.5-sonnet-20241022\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the tools to the agent\n", + "try:\n", + " client.agents.tools.create(\n", + " agent_id=AGENT_UUID,\n", + " **{\n", + " \"name\": \"computer\",\n", + " \"type\": \"computer_20241022\",\n", + " \"computer_20241022\": {\n", + " \"display_height_px\": 768,\n", + " \"display_width_px\": 1024,\n", + " \"display_number\": 1,\n", + " },\n", + " }\n", + " )\n", + "except Exception as e:\n", + " print(\"Already added\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining a Task\n", + "\n", + "Tasks in Julep are Github-Actions-style workflows that define long-running, multi-step actions.\n", + "\n", + "You can use them to conduct complex actions by defining them step-by-step.\n", + "\n", + "To learn more about tasks, please refer to the `Tasks` section in [Julep Concepts](https://github.com/julep-ai/julep/blob/dev/docs/julep-concepts.md#tasks)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import yaml\n", + "# please replace the DEMO_API_KEY and DEMO_PROJECT_ID with your own API Key and Project ID of BrowserBase\n", + "\n", + "# Defining the task\n", + "task_def = yaml.safe_load('''\n", + "name: computer-use-task\n", + "\n", + "\n", + "########################################################\n", + "####################### TOOLS ##########################\n", + "########################################################\n", + "\n", + "tools:\n", + "- name: create_browserbase_session\n", + " type: integration\n", + " integration:\n", + " provider: browserbase\n", + " method: create_session\n", + " setup:\n", + " api_key: \"DEMO_API_KEY\"\n", + " project_id: \"DEMO_PROJECT_ID\"\n", + "\n", + "- name: get_cdp_url\n", + " type: integration\n", + " integration:\n", + " provider: browserbase\n", + " method: get_connect_url\n", + " setup:\n", + " api_key: \"DEMO_API_KEY\"\n", + " project_id: \"DEMO_PROJECT_ID\"\n", + "\n", + "- name: get_session_view_urls\n", + " type: integration\n", + " integration:\n", + " provider: browserbase\n", + " method: get_live_urls\n", + " setup:\n", + " api_key: \"DEMO_API_KEY\"\n", + " project_id: \"DEMO_PROJECT_ID\"\n", + "\n", + "- name: perform_browser_action\n", + " type: integration\n", + " integration:\n", + " provider: remote_browser\n", + " method: perform_action\n", + " setup:\n", + " width: 1024\n", + " height: 768\n", + "\n", + "- name: create_julep_session\n", + " type: system\n", + " system:\n", + " resource: session\n", + " operation: create\n", + "\n", + "- name: session_chat\n", + " type: system\n", + " system:\n", + " resource: session\n", + " operation: chat\n", + "\n", + "########################################################\n", + "################### INPUT SCHEMA #######################\n", + "########################################################\n", + "\n", + "input_schema:\n", + " type: object\n", + " properties:\n", + " goal:\n", + " type: string\n", + " required:\n", + " - goal\n", + "\n", + "########################################################\n", + "################### MAIN WORKFLOW ######################\n", + "########################################################\n", + "\n", + "main:\n", + "\n", + "# Step #0\n", + "- tool: create_julep_session\n", + " arguments:\n", + " agent: str(agent.id)\n", + " situation: \"''\"\n", + " recall: 'False'\n", + "\n", + "# Step #1\n", + "- evaluate:\n", + " julep_session_id: _.id\n", + "\n", + "# Step #2\n", + "- tool: create_browserbase_session\n", + " arguments:\n", + " project_id: \"'c35ee022-883e-4070-9f3c-89607393214b'\"\n", + "\n", + "# Step #3\n", + "- evaluate:\n", + " browser_session_id: _.id\n", + "\n", + "# Step #4\n", + "- tool: get_session_view_urls\n", + " arguments:\n", + " id: _.browser_session_id\n", + "\n", + "# Step #5\n", + "- evaluate:\n", + " debugger_url: _.urls.debuggerUrl\n", + "\n", + "# Step #6\n", + "- tool: get_cdp_url\n", + " arguments:\n", + " id: outputs[3].browser_session_id\n", + "\n", + "# Step #7\n", + "- evaluate:\n", + " connect_url: _.url\n", + "\n", + "# Step #8\n", + "# Navigate to google to avoid sending a blank \n", + "# screenshot when computer use starts\n", + "- tool: perform_browser_action\n", + " arguments:\n", + " connect_url: _.connect_url\n", + " action: \"'navigate'\"\n", + " text: \"'https://www.google.com'\"\n", + "\n", + "# Step #9\n", + "- workflow: run_browser\n", + " arguments:\n", + " julep_session_id: outputs[1].julep_session_id\n", + " cdp_url: outputs[7].connect_url\n", + " messages:\n", + " - role: \"'user'\"\n", + " content: |-\n", + " \"\"\"\n", + " \n", + " * You are utilising a headless chrome browser to interact with the internet.\n", + " * You can use the computer tool to interact with the browser.\n", + " * You have access to only the browser.\n", + " * You are already inside the browser.\n", + " * You can't open new tabs or windows.\n", + " * For now, rely on screenshots as the only way to see the browser.\n", + " * You can't don't have access to the browser's UI.\n", + " * YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\n", + " \n", + " \n", + " *\"\"\" + inputs[0].goal + NEWLINE + \"\"\n", + " workflow_label: \"'run_browser'\" # <----- REMOVE THIS\n", + "\n", + "\n", + "########################################################\n", + "################# RUN BROWSER WORKFLOW #################\n", + "########################################################\n", + "\n", + "run_browser:\n", + "\n", + "- tool: session_chat\n", + " arguments:\n", + " session_id: _.julep_session_id\n", + " messages: _.messages\n", + " recall: 'False'\n", + "\n", + "# Evaluate the response from the agent\n", + "- evaluate:\n", + " content: _.choices[0].message.content\n", + " tool_calls: \"[ \\\n", + " { \\\n", + " 'tool_call_id': tool_call.id, \\\n", + " 'action': load_json(tool_call.function.arguments)['action'], \\\n", + " 'text': load_json(tool_call.function.arguments).get('text'), \\\n", + " 'coordinate': load_json(tool_call.function.arguments).get('coordinate') \\\n", + " } \\\n", + " for tool_call in _.choices[0].message.tool_calls or [] if tool_call.type == 'function']\"\n", + "\n", + "\n", + "# Perform the actions requested by the agent\n", + "- foreach:\n", + " in: _.tool_calls\n", + " do:\n", + " tool: perform_browser_action\n", + " arguments:\n", + " connect_url: inputs[0].cdp_url\n", + " action: _.action if not (str(_.get('text', '')).startswith('http') and _.action == 'type') else 'navigate'\n", + " text: _.get('text')\n", + " coordinate: _.get('coordinate')\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Convert the result of the actions into a chat message\n", + "# ----------------------------------------------------------------\n", + "\n", + "# Handle image content part\n", + "- evaluate:\n", + " contents: \"[\\\n", + " { \\\n", + " 'type': 'image_url', \\\n", + " 'image_url': { \\\n", + " 'url': result['base64_image'], \\\n", + " } \\\n", + " } if result['base64_image'] is not None else \\\n", + " { \\\n", + " 'type': 'text', \\\n", + " 'text': result['output'] if result['output'] is not None else 'done' \\\n", + " } \\\n", + " for result in _]\"\n", + "\n", + "- evaluate:\n", + " messages: \"[{'content': [_.contents[i]], 'role': 'tool', 'name': 'computer', 'tool_call_id': outputs[1].tool_calls[i].tool_call_id} for i in range(len(_.contents))]\"\n", + "# ----------------------------------------------------------------\n", + "# Check if the goal is achieved and recursively run the browser\n", + "# ----------------------------------------------------------------\n", + "\n", + "- workflow: check_goal_status\n", + " arguments:\n", + " messages: _.messages\n", + " julep_session_id: inputs[0].julep_session_id\n", + " cdp_url: inputs[0].cdp_url\n", + " workflow_label: \"'check_goal_status'\" \n", + "\n", + "\n", + "########################################################\n", + "############## CHECK GOAL STATUS WORKFLOW ##############\n", + "########################################################\n", + "\n", + "check_goal_status:\n", + "- if: len(_.messages) > 0\n", + " then:\n", + " workflow: run_browser\n", + " arguments:\n", + " messages: _.messages\n", + " julep_session_id: _.julep_session_id\n", + " cdp_url: _.cdp_url\n", + " workflow_label: \"'run_browser'\" \n", + "''')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notes:\n", + "\n", + "- The `unwrap: True` in the prompt step is used to unwrap the output of the prompt step (to unwrap the `choices[0].message.content` from the output of the model).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating/Updating a task" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# creating the task object\n", + "task = client.tasks.create_or_update(\n", + " task_id=TASK_UUID,\n", + " agent_id=AGENT_UUID,\n", + " **task_def\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating an Execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An execution is a single run of a task. It is a way to run a task with a specific set of inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "execution = client.executions.create(\n", + " task_id=task.id,\n", + " input={\n", + " \"agent_id\": AGENT_UUID,\n", + " \"goal\": \"Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\",\n", + " }\n", + ")\n", + "\n", + "print(\"Started an execution. Execution ID:\", execution.id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking execution details and output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are multiple ways to get the execution details and the output:\n", + "\n", + "1. **Get Execution Details**: This method retrieves the details of the execution, including the output of the last transition that took place.\n", + "\n", + "2. **List Transitions**: This method lists all the task steps that have been executed up to this point in time, so the output of a successful execution will be the output of the last transition (first in the transition list as it is in reverse chronological order), which should have a type of `finish`.\n", + "\n", + "\n", + "Note: You need to wait for a few seconds for the execution to complete before you can get the final output, so feel free to run the following cells multiple times until you get the final output.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pprint\n", + "import time\n", + "\n", + "def reformat_output(output):\n", + " if isinstance(output, dict):\n", + " for key, value in output.items():\n", + " if (key == \"base64_image\" or key == \"url\") and value is not None:\n", + " output[key] = value[:20] + '...'\n", + " else:\n", + " output[key] = reformat_output(value)\n", + " elif isinstance(output, list):\n", + " for i, item in enumerate(output):\n", + " output[i] = reformat_output(item)\n", + " return output\n", + "\n", + "counter = 0\n", + "while counter < 70:\n", + " transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + " for transition in reversed(transitions):\n", + " print(transition.type)\n", + " output = transition.output\n", + " pprint.pprint(reformat_output(output))\n", + " print(\"----\"*100)\n", + " counter += 1\n", + " time.sleep(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Lists all the task steps that have been executed up to this point in time\n", + "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", + "\n", + "# Transitions are retreived in reverse chronological order\n", + "for transition in reversed(transitions):\n", + " print(\"Transition type: \", transition.type)\n", + " print(\"Transition output: \", transition.output)\n", + " print(\"-\"*50)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cookbooks/07-Integrating_External_Tools_and_APIs.py b/cookbooks/07-Integrating_External_Tools_and_APIs.py deleted file mode 100644 index 71d8780d3..000000000 --- a/cookbooks/07-Integrating_External_Tools_and_APIs.py +++ /dev/null @@ -1,128 +0,0 @@ -import uuid -import yaml -from julep import Client - -# Global UUID is generated for agent and task -AGENT_UUID = uuid.uuid4() -TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an "agent" -name = "Multi-Tool Analyst" -about = "An AI agent capable of using multiple external tools and APIs to gather and analyze information." - -# Create the agent -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name=name, - about=about, - model="gpt-4o", -) - -# Defining a Task with various step types -task_def = yaml.safe_load(""" -name: Comprehensive Analysis Report - -input_schema: - type: object - properties: - topic: - type: string - description: The main topic to analyze. - location: - type: string - description: A location related to the topic for weather and news analysis. - -tools: -- name: brave_search - type: integration - integration: - provider: brave - setup: - api_key: "YOUR_API_KEY" - -- name: weather - type: integration - integration: - provider: weather - setup: - openweathermap_api_key: "YOUR_API_KEY" - -- name: wikipedia - type: integration - integration: - provider: wikipedia - -main: -- tool: brave_search - arguments: - query: "inputs[0].topic + ' latest developments'" - -- tool: weather - arguments: - location: inputs[0].location - -- tool: wikipedia - arguments: - query: inputs[0].topic - -- prompt: - - role: system - content: >- - You are a comprehensive analyst. Your task is to create a detailed report on the topic {{inputs[0].topic}} - using the information gathered from various sources. Include the following sections in your report: - - 1. Overview (based on Wikipedia data) - 2. Latest Developments (based on Brave Search results) - 3. Weather Impact (if applicable, based on weather data for {{inputs[0].location}}) - 4. Analysis and Conclusions - - Use the following data for your report: - - Brave Search Results: {{outputs[0]}} - Weather Data: {{outputs[1]}} - Wikipedia Data: {{outputs[2]}} - - Provide a well-structured, informative report that synthesizes information from all these sources. - unwrap: true -""") - -# Creating/Updating a task -task = client.tasks.create_or_update( - task_id=TASK_UUID, - agent_id=AGENT_UUID, - **task_def -) - -# Creating an Execution -execution = client.executions.create( - task_id=task.id, - input={ - "topic": "Renewable Energy", - "location": "Berlin" - } -) - -print(f"Execution ID: {execution.id}") - -# Waiting for the execution to complete -import time -time.sleep(5) - -# Getting the execution details -execution = client.executions.get(execution.id) -print("Execution Output:") -print(execution.output) - -# List all steps of the executed task -transitions = client.executions.transitions.list(execution_id=execution.id).items -print("Execution Steps:") -for transition in transitions: - print(transition) - -# Stream the steps of the defined task -print("Streaming execution transitions:") -print(client.executions.transitions.stream(execution_id=execution.id)) \ No newline at end of file diff --git a/cookbooks/08-Managing_Persistent_Sessions.py b/cookbooks/08-Managing_Persistent_Sessions.py deleted file mode 100644 index ab2472ac8..000000000 --- a/cookbooks/08-Managing_Persistent_Sessions.py +++ /dev/null @@ -1,146 +0,0 @@ -# Managing Persistent Sessions Cookbook -# -# Plan: -# 1. Import necessary libraries and set up the Julep client -# 2. Create an agent for handling persistent sessions -# 3. Define a task for managing user context -# 4. Create a function to simulate user interactions -# 5. Implement a loop to demonstrate persistent sessions with context management -# 6. Show how to handle context overflow -# 7. Display the session history and context at the end - -import uuid -import yaml -from julep import Client -import time - -# Global UUID is generated for agent and task -AGENT_UUID = uuid.uuid4() -TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an agent for handling persistent sessions -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Session Manager", - about="An AI agent specialized in managing persistent sessions and context.", - model="gpt-4o", -) - -# Defining a task for managing user context -task_def = yaml.safe_load(""" -name: Manage User Context - -input_schema: - type: object - properties: - user_input: - type: string - session_context: - type: object - -main: -- prompt: - - role: system - content: >- - You are a session management agent. Your task is to maintain context - across user interactions. Here's the current context: {{inputs[0].session_context}} - - User input: {{inputs[0].user_input}} - - Respond to the user and update the context with any new relevant information. - unwrap: true - -- evaluate: - session_context: >- - { - **inputs[0].session_context, - 'last_interaction': inputs[0].user_input, - 'agent_response': _} - -- return: - response: _ - context: outputs[1].session_context -""") - -# Creating the task -task = client.tasks.create_or_update( - task_id=TASK_UUID, - agent_id=AGENT_UUID, - **task_def -) - -# Function to simulate user interactions -def user_interaction(prompt): - return input(prompt) - -# Create a session -session = client.sessions.create( - agent=agent.id, - context_overflow="adaptive" # Use adaptive context management -) - -# Initialize session context -context = {} - -# Simulate a conversation with persistent context -for i in range(5): - user_input = user_interaction(f"User (Interaction {i+1}): ") - - # Execute the task with user input and current context - execution = client.executions.create( - task_id=TASK_UUID, - input={ - "user_input": user_input, - "session_context": context - } - ) - - # Get the execution result - result = client.executions.get(execution.id) - - # Wait for the execution to complete - time.sleep(2) - - # Update the context and print the response - final_response = client.executions.transitions.list(execution_id=result.id).items[0].output - print(final_response) - # print(client.executions.transitions.list(execution_id=result.id).items[0]) - context = final_response['session_context'] - print(f"Agent: {final_response['session_context']['agent_response']}") - print(f"Updated Context: {context}") - print() - - # Simulate a delay between interactions - time.sleep(1) - -# Display final session information -print("Final Session Information:") -print(f"Session ID: {session.id}") -print(f"Final Context: {context}") - -# Demonstrate context overflow handling -print("\nDemonstrating Context Overflow Handling:") -large_input = "This is a very large input " * 1000 # Create a large input to trigger overflow -overflow_execution = client.executions.create( - task_id=TASK_UUID, - input={ - "user_input": large_input, - "session_context": context - } -) - -overflow_result = client.executions.get(overflow_execution.id) -# Wait for the execution to complete -time.sleep(2) -overflow_response = client.executions.transitions.list(execution_id=overflow_result.id).items[0].output -print(f"Agent response to large input: {overflow_response['session_context']['agent_response']}") -print(f"Updated context after overflow: {overflow_response['session_context']}") - -# Display session history -print("\nSession History:") -history = client.sessions.history(session_id=session.id) -print(history) diff --git a/cookbooks/09-User_Management_and_Personalization.py b/cookbooks/09-User_Management_and_Personalization.py deleted file mode 100644 index 50ada9570..000000000 --- a/cookbooks/09-User_Management_and_Personalization.py +++ /dev/null @@ -1,196 +0,0 @@ -# User Management and Personalization Cookbook -# -# Plan: -# 1. Import necessary libraries and set up the Julep client -# 2. Create an agent for handling user management and personalization -# 3. Define a task for user registration and profile creation -# 4. Define a task for personalized content recommendation -# 5. Create sample users with different preferences -# 6. Demonstrate user registration and profile creation -# 7. Show personalized content recommendations for different users -# 8. Implement a function to update user preferences -# 9. Display updated personalized recommendations after preference changes - -import uuid -import yaml, time -from julep import Client - -# Global UUIDs for agent and tasks -AGENT_UUID = uuid.uuid4() -REGISTRATION_TASK_UUID = uuid.uuid4() -RECOMMENDATION_TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an agent for user management and personalization -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Personalization Assistant", - about="An AI agent specialized in user management and personalized content recommendations.", - model="gpt-4o", -) - -# Defining a task for user registration and profile creation -registration_task_def = yaml.safe_load(""" -name: User Registration and Profile Creation - -input_schema: - type: object - properties: - username: - type: string - interests: - type: array - items: - type: string - -main: -- prompt: - - role: system - content: >- - You are a user registration assistant. Create a user profile based on the following information: - Username: {{inputs[0].username}} - Interests: {{inputs[0].interests}} - - Generate a brief bio and suggest some initial content preferences based on the user's interests. - unwrap: true - -- evaluate: - username: inputs[0].username - interests: inputs[0].interests - bio: _.split('\\n')[0] - content_preferences: _.split('\\n')[1] - -- return: - profile: _ -""") - -# Creating the registration task -registration_task = client.tasks.create_or_update( - task_id=REGISTRATION_TASK_UUID, - agent_id=AGENT_UUID, - **registration_task_def -) - -# Defining a task for personalized content recommendation -recommendation_task_def = yaml.safe_load(""" -name: Personalized Content Recommendation - -input_schema: - type: object - properties: - user_profile: - type: object - description: User's profile containing their interests and preferences. - content_list: - type: array - description: List of available content to recommend from. - items: - type: object - properties: - id: - type: integer - title: - type: string - category: - type: string - -main: -- prompt: - - role: system - content: >- - You are a content recommendation system. Based on the user's profile and the available content, - recommend 3 pieces of content that best match the user's interests and preferences. - - User Profile: - {{inputs[0].user_profile}} - - Available Content: - {{inputs[0].content_list}} - - Provide your recommendations in the following format: - 1. [Content ID] - [Content Title] - Reason for recommendation - 2. [Content ID] - [Content Title] - Reason for recommendation - 3. [Content ID] - [Content Title] - Reason for recommendation - unwrap: true -""") - -# Creating the recommendation task -recommendation_task = client.tasks.create_or_update( - task_id=RECOMMENDATION_TASK_UUID, - agent_id=AGENT_UUID, - **recommendation_task_def -) - -# Function to register a user and create their profile -def register_user(username, interests): - execution = client.executions.create( - task_id=REGISTRATION_TASK_UUID, - input={ - "username": username, - "interests": interests - } - ) - # Wait for the execution to complete - time.sleep(2) - result = client.executions.get(execution.id) - user_result = client.executions.transitions.list(execution_id=result.id).items[0].output - return user_result - -# Function to get personalized recommendations for a user -def get_recommendations(user_profile): - content_list = [ - {"id": 1, "title": "Introduction to AI", "category": "Technology"}, - {"id": 2, "title": "Healthy Eating Habits", "category": "Health"}, - {"id": 3, "title": "Financial Planning 101", "category": "Finance"}, - {"id": 4, "title": "The Art of Photography", "category": "Art"}, - {"id": 5, "title": "Beginner's Guide to Yoga", "category": "Fitness"} - ] - - execution = client.executions.create( - task_id=RECOMMENDATION_TASK_UUID, - input={ - "user_profile": user_profile, - "content_list": content_list - } - ) - # Wait for the execution to complete - time.sleep(2) - result = client.executions.get(execution.id) - recommendation_respose = client.executions.transitions.list(execution_id=result.id).items[0].output - return recommendation_respose - - -# Function to update user preferences -def update_user_preferences(user_profile, new_interests): - user_profile["interests"] = list(set(user_profile["interests"] + new_interests)) - return user_profile - -# Demonstrate user registration and personalization -print("Demonstrating User Management and Personalization:") - -# Register users -user1 = register_user("alice", ["technology", "finance"]) -user2 = register_user("bob", ["health", "fitness"]) - -print("\nUser Profiles:") -print(f"Alice: {user1}") -print(f"Bob: {user2}") - -# Get personalized recommendations -print("\nPersonalized Recommendations:") -print("Alice's Recommendations:") -print(get_recommendations(user1)) -print("\nBob's Recommendations:") -print(get_recommendations(user2)) - -# Update user preferences -print("\nUpdating User Preferences:") -updated_alice = update_user_preferences(user1, ["art"]) -print(f"Alice's Updated Profile: {updated_alice}") - -# Get updated recommendations -print("\nUpdated Personalized Recommendations for Alice:") -print(get_recommendations(updated_alice)) \ No newline at end of file diff --git a/cookbooks/10-Document_Management_and_Search.py b/cookbooks/10-Document_Management_and_Search.py deleted file mode 100644 index 2df493f8b..000000000 --- a/cookbooks/10-Document_Management_and_Search.py +++ /dev/null @@ -1,184 +0,0 @@ -# Document Management and Search Cookbook -# -# Plan: -# 1. Import necessary libraries and set up the Julep client -# 2. Create an agent for document management -# 3. Define a task for document upload and indexing -# 4. Define a task for document search -# 5. Create sample documents -# 6. Execute the document upload and indexing task -# 7. Execute the document search task -# 8. Display the search results - -import uuid -import yaml,time -from julep import Client - -# Global UUID is generated for agent and tasks -AGENT_UUID = uuid.uuid4() -UPLOAD_TASK_UUID = uuid.uuid4() -SEARCH_TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an agent for document management -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Document Manager", - about="An AI agent specialized in document management and search.", - model="gpt-4o", -) - -# Defining a task for document upload and indexing -upload_task_def = yaml.safe_load(f""" -name: Document Upload and Indexing - -input_schema: - type: object - properties: - documents: - type: array - items: - type: object - properties: - tile: - type: string - content: - type: string - metadata: - type: object - -tools: -- name: document_create - system: - resource: agent - subresource: doc - operation: create - -main: -- over: inputs[0].documents - map: - tool: document_create - arguments: - agent_id: "'{agent.id}'" - data: - title: _.title - content: _.content - metadata: _.metadata - -- prompt: - - role: system - content: >- - You have successfully uploaded and indexed {{len(outputs[0])}} documents. - Provide a summary of the uploaded documents. -""") - -# Creating the upload task -upload_task = client.tasks.create_or_update( - task_id=UPLOAD_TASK_UUID, - agent_id=AGENT_UUID, - **upload_task_def -) - -# Defining a task for document search -search_task_def = yaml.safe_load(f""" -name: Document Search - -input_schema: - type: object - properties: - query: - type: string - -tools: -- name: document_search - system: - resource: agent - subresource: doc - operation: search - -main: -- tool: document_search - arguments: - agent_id: "'{agent.id}'" - text: inputs[0].query - -- prompt: - - role: system - content: >- - Based on the search results, provide a summary of the most relevant documents found. - Search query: {{{{inputs[0].query}}}} - Number of results: {{len(outputs[0])}} - - Results: - {{{{outputs[0]}}}} -""") - -# Creating the search task -search_task = client.tasks.create_or_update( - task_id=SEARCH_TASK_UUID, - agent_id=AGENT_UUID, - **search_task_def -) - -# Sample documents -sample_documents = [ - { - "title": "The Impact of Technology on Society", - "content": "Artificial Intelligence (AI) is revolutionizing various industries, including healthcare, finance, and transportation.", - "metadata": {"category": "technology", "author": "John Doe"} - }, - { - "title": "Climate Change and Global Warming", - "content": "Climate change is a pressing global issue that requires immediate action from governments, businesses, and individuals.", - "metadata": {"category": "environment", "author": "Jane Smith"} - }, - { - "title": "Remote Work and Digital Transformation", - "content": "The COVID-19 pandemic has accelerated the adoption of remote work and digital technologies across many organizations.", - "metadata": {"category": "business", "author": "Alice Johnson"} - } -] - -# Execute the document upload and indexing task -upload_execution = client.executions.create( - task_id=UPLOAD_TASK_UUID, - input={"documents": sample_documents} -) - -print("Uploading and indexing documents...") -# Wait for the execution to complete -time.sleep(5) -upload_result = client.executions.get(upload_execution.id) -upload_response = client.executions.transitions.list(upload_execution.id).items[0].output -print("Upload Result:") -print(upload_response) - -# Execute the document search task -search_execution = client.executions.create( - task_id=SEARCH_TASK_UUID, - input={ - "query": "technology" - } -) - -print("\nSearching documents...") -# Wait for the execution to complete -time.sleep(5) -search_result = client.executions.get(search_execution.id) -# Display the search results -print("\nSearch Results:") -for transition in client.executions.transitions.list(execution_id=search_execution.id).items: - if transition.type == "step" and transition.metadata['step_type'] == "ToolCallStep": - doc_output = transition.output['docs'] - for doc in doc_output: - print(f"Owner: {doc['owner']}") - print(f"Title: {doc['title']}") - print(f"Distance: {doc['distance']}") - print(f"Content: {doc['snippets']}") - -print("\nSearch Summary:") -search_response = client.executions.transitions.list(search_result.id).items[0].output['choices'][0]['message']['content'] -print(search_response) \ No newline at end of file diff --git a/cookbooks/11-Advanced_Chat_Interactions.py b/cookbooks/11-Advanced_Chat_Interactions.py deleted file mode 100644 index 1cfa71555..000000000 --- a/cookbooks/11-Advanced_Chat_Interactions.py +++ /dev/null @@ -1,177 +0,0 @@ -# Advanced Chat Interactions Cookbook -# -# Plan: -# 1. Import necessary libraries and set up the Julep client -# 2. Create an agent for advanced chat interactions -# 3. Define a task for handling complex conversations with context management -# 4. Implement a function to simulate user input -# 5. Create a chat session and demonstrate advanced interactions: -# a. Multi-turn conversation with context retention -# b. Handling context overflow -# c. Conditional responses based on user input -# d. Integrating external information during the conversation -# 6. Display the chat history and any relevant metrics - -# UNDER CONSTRUCTION - Dynamic weather integration is not yet implemented, rest of the code is functional - -import uuid -import yaml -import os -from julep import Client -import time - -# Global UUIDs for agent and task -AGENT_UUID = uuid.uuid4() -CHAT_TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = os.getenv("JULEP_API_KEY") -if not api_key: - raise ValueError("JULEP_API_KEY not found in environment variables") - -client = Client(api_key=api_key, environment="dev") - -# Creating an agent for advanced chat interactions -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Advanced Chat Assistant", - about="An AI agent capable of handling complex conversations with context management and external integrations.", - model="gpt-4o", -) - -# Add a web search tool to the agent -client.agents.tools.create( - agent_id=AGENT_UUID, - name="web_search", - integration={ - "provider": "brave", - "method": "search", - "setup": {"api_key": "YOUR_BRAVE_API_KEY"}, - }, -) - -# Defining a task for handling complex conversations -chat_task_def = yaml.safe_load(""" -name: Advanced Chat Interaction - -input_schema: - type: object - properties: - user_input: - type: string - chat_history: - type: array - items: - type: object - properties: - role: - type: string - content: - type: string - -tools: -- name: weather_api - type: integration - integration: - provider: weather - setup: - api_key: "YOUR_WEATHER_API_KEY" - -main: -- if: "len(inputs[0].chat_history) > 5" - then: - evaluate: - summarized_history: str(inputs[0].chat_history[-5:]) - else: - evaluate: - summarized_history: str(inputs[0].chat_history) - -- if: "search_regex('weather', inputs[0].user_input)" - then: - tool: weather_api - arguments: - location: "'NEW YORK'" - else: - evaluate: - weather: "'No weather information requested'" - -- evaluate: - weather: outputs[1] - -- if: "search_regex('weather', inputs[0].user_input)" - then: - prompt: - - role: system - content: >- - You are an advanced chat assistant. Here's a summary of the recent conversation: - {{outputs[0].summarized_history}} - - The user mentioned weather. Here's the current weather information for NEW YORK - Incorporate this information into your response. - - {{_.weather}} - - Now, respond to the user's latest input: {{inputs[0].user_input}} - else: - prompt: - - role: system - content: >- - You are an advanced chat assistant. Here's a summary of the recent conversation: - {{outputs[0].summarized_history}} - - Now, respond to the user's latest input: {{inputs[0].user_input}} -""") - -# Creating the chat task -chat_task = client.tasks.create_or_update( - task_id=CHAT_TASK_UUID, - agent_id=AGENT_UUID, - **chat_task_def -) - -# Function to simulate user input -def get_user_input(): - return input("User: ") - -# Function to display chat history -def display_chat_history(chat_history): - for message in chat_history: - print(f"{message['role'].capitalize()}: {message['content']}") - -# Main chat loop -def run_chat_session(): - chat_history = [] - print("Starting advanced chat session. Type 'exit' to end the conversation.") - - while True: - user_input = get_user_input() - if user_input.lower() == 'exit': - break - - chat_history.append({"role": "user", "content": user_input}) - - execution = client.executions.create( - task_id=CHAT_TASK_UUID, - input={ - "user_input": user_input, - "chat_history": chat_history - } - ) - # Wait for the execution to complete - time.sleep(5) - result = client.executions.get(execution.id) - print(client.executions.transitions.list(execution.id).items) - print(f"Execution result: {result.output}") - assistant_response = result.output['choices'][0]['message']['content'] - - chat_history.append({"role": "assistant", "content": assistant_response}) - print(f"Assistant: { assistant_response}") - - # Simulate a delay for a more natural conversation flow - print("----------------------") - - print("\nChat session ended. Here's the complete chat history:") - display_chat_history(chat_history) - -# Run the chat session -run_chat_session() \ No newline at end of file diff --git a/cookbooks/12-Monitoring_Task_Executions.py b/cookbooks/12-Monitoring_Task_Executions.py deleted file mode 100644 index 675ff27a1..000000000 --- a/cookbooks/12-Monitoring_Task_Executions.py +++ /dev/null @@ -1,162 +0,0 @@ -# Monitoring Task Executions Cookbook -# -# Plan: -# 1. Import necessary libraries and set up the Julep client -# 2. Create an agent for task execution monitoring -# 3. Define a multi-step task that simulates a complex workflow -# 4. Implement functions for: -# a. Starting task execution -# b. Monitoring execution progress -# c. Handling execution status updates -# d. Logging execution metrics -# 5. Execute the task and demonstrate real-time monitoring -# 6. Display execution summary and metrics - -# UNDER CONSTRUCTION - NOT WORKING YET - -import uuid -import yaml -from julep import Client -import time - -# Global UUIDs for agent and task -AGENT_UUID = uuid.uuid4() -TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an agent for task execution monitoring -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Task Execution Monitor", - about="An AI agent designed to monitor and manage complex task executions.", - model="gpt-4o", -) - -# Defining a multi-step task that simulates a complex workflow -task_def = yaml.safe_load(""" -name: Complex Workflow Simulation - -input_schema: - type: object - properties: - project_name: - type: string - data_size: - type: integer - -tools: -- name: data_processor - type: integration - integration: - provider: mock - setup: - processing_time: 5 # Simulated processing time in seconds - -- name: report_generator - type: integration - integration: - provider: mock - setup: - generation_time: 3 # Simulated generation time in seconds - -main: -- prompt: - role: system - content: >- - Initiating project '{{inputs[0].project_name}}' with data size {{inputs[0].data_size}} units. - Prepare for data processing and report generation. - unwrap: true - -- tool: data_processor - arguments: - data_size: inputs[0].data_size - -- evaluate: - processed_data: "Processed " + str(inputs[0].data_size) + " units of data" - -- tool: report_generator - arguments: - data: outputs[2].processed_data - -- prompt: - role: system - content: >- - Project '{{inputs[0].project_name}}' completed. - Data processed: {{outputs[2].processed_data}} - Report generated: {{outputs[3]}} - - Summarize the project results. - unwrap: true - -- return: _ -""") - -# Creating the task -task = client.tasks.create_or_update( - task_id=TASK_UUID, - agent_id=AGENT_UUID, - **task_def -) - -def start_task_execution(project_name, data_size): - """Start the task execution and return the execution object.""" - execution = client.executions.create( - task_id=TASK_UUID, - input={ - "project_name": project_name, - "data_size": data_size - } - ) - print(f"Task execution started for project '{project_name}'") - return execution - -def monitor_execution_progress(execution_id): - """Monitor the execution progress in real-time.""" - print("Monitoring execution progress:") - for transition in client.executions.transitions.stream(execution_id=execution_id): - print(f"Step: {transition.type}, Status: {transition.status}") - if transition.status == "completed": - print(f" Output: {transition.output}") - elif transition.status == "failed": - print(f" Error: {transition.error}") - time.sleep(1) # Add a small delay to simulate real-time monitoring - -def get_execution_status(execution_id): - """Get the current status of the execution.""" - execution = client.executions.get(execution_id) - return execution.status - -def log_execution_metrics(execution_id): - """Log and display execution metrics.""" - print("\nExecution Metrics:") - transitions = client.executions.transitions.list(execution_id=execution_id).items - total_duration = sum(t.duration_ms for t in transitions) - for transition in transitions: - print(f"Step: {transition.type}, Duration: {transition.duration_ms}ms") - print(f"Total Execution Time: {total_duration}ms") - -# Main execution flow -def run_task_monitoring_demo(): - project_name = "Data Analysis Project" - data_size = 1000 - - print(f"Starting task execution for '{project_name}' with {data_size} units of data") - execution = start_task_execution(project_name, data_size) - - monitor_execution_progress(execution.id) - - final_status = get_execution_status(execution.id) - print(f"\nFinal Execution Status: {final_status}") - - if final_status == "completed": - result = client.executions.get(execution.id) - print("\nExecution Result:") - print(result.output) - - log_execution_metrics(execution.id) - -# Run the task monitoring demo -run_task_monitoring_demo() \ No newline at end of file diff --git a/cookbooks/13-Error_Handling_and_Recovery.py b/cookbooks/13-Error_Handling_and_Recovery.py deleted file mode 100644 index f0cdf68be..000000000 --- a/cookbooks/13-Error_Handling_and_Recovery.py +++ /dev/null @@ -1,165 +0,0 @@ -# Error Handling and Recovery Cookbook -# -# Plan: -# 1. Import necessary libraries and set up the Julep client -# 2. Create an agent for error handling demonstration -# 3. Define a task with potential errors and recovery mechanisms -# 4. Execute the task and demonstrate error handling -# 5. Implement a retry mechanism for failed steps -# 6. Show how to log and report errors -# 7. Demonstrate graceful degradation when a step fails - -# UNDER CONSTRUCTION - NOT WORKING YET - -import uuid -import yaml -import time -from julep import Client - -# Global UUID is generated for agent and task -AGENT_UUID = uuid.uuid4() -TASK_UUID = uuid.uuid4() - -# Creating Julep Client with the API Key -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -# Creating an agent for error handling demonstration -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Error Handler", - about="An AI agent specialized in demonstrating error handling and recovery mechanisms.", - model="gpt-4o", -) - -# Defining a task with potential errors and recovery mechanisms -task_def = yaml.safe_load(""" -name: Error Handling Demo - -input_schema: - type: object - properties: - operation: - type: string - enum: ["divide", "api_call", "process_data"] - value: - type: number - -tools: -- name: divide - type: function - function: - name: divide - description: Divide 100 by the given number - parameters: - type: object - properties: - divisor: - type: number - -- name: api_call - type: integration - integration: - provider: httpbin - method: get - -- name: process_data - type: function - function: - name: process_data - description: Process the given data - parameters: - type: object - properties: - data: - type: string - -main: - -- switch: - case: "inputs[0].operation == 'divide'" - tool: divide - arguments: - divisor: inputs[0].value - on_error: - retry: - max_attempts: 3 - delay: 2 - fallback: - return: "Error: Division by zero or invalid input" - case: "inputs[0].operation == 'api_call'" - tool: api_call - arguments: - endpoint: "/status/{{inputs[0].value}}" - on_error: - retry: - max_attempts: 3 - delay: 5 - fallback: - return: "Error: API call failed after multiple attempts" - case: "inputs[0].operation == 'process_data'" - evaluate: - data: "'Sample data: ' + str(inputs[0].value)" - tool: process_data - arguments: - data: _.data - on_error: - log: "Error occurred while processing data" - return: "Error: Data processing failed" - -- prompt: - - role: system - content: >- - Summarize the result of the operation: - Operation: {{inputs[0].operation}} - Result: {{_}}] - unwrap: true -""") - -# Creating the task -task = client.tasks.create_or_update( - task_id=TASK_UUID, - agent_id=AGENT_UUID, - **task_def -) - -# Function to execute task and handle errors -def execute_task_with_error_handling(operation, value): - try: - execution = client.executions.create( - task_id=TASK_UUID, - input={"operation": operation, "value": value} - ) - - print(f"Executing {operation} with value {value}...") - - # Stream execution to show progress and potential retries - for step in client.executions.transitions.stream(execution_id=execution.id): - if step.type == "tool_call": - print(f"Step: {step.tool}") - if step.status == "error": - print(f"Error occurred: {step.error}") - if step.retry: - print(f"Retrying... (Attempt {step.retry.attempt})") - elif step.type == "error": - print(f"Task error: {step.error}") - - # Get final execution result - result = client.executions.get(execution.id) - print(f"Final result: {result.output}") - - except Exception as e: - print(f"An unexpected error occurred: {str(e)}") - -# Demonstrate error handling for different scenarios -print("1. Division by zero (with retry and fallback):") -execute_task_with_error_handling("divide", 0) - -print("\n2. API call with server error (with retry):") -execute_task_with_error_handling("api_call", 500) - -print("\n3. Data processing error (with logging):") -execute_task_with_error_handling("process_data", "invalid_data") - -print("\n4. Successful operation:") -execute_task_with_error_handling("divide", 4) \ No newline at end of file diff --git a/cookbooks/14-Automated_Webinar_Scheduling_Workflow.ipynb b/cookbooks/14-Automated_Webinar_Scheduling_Workflow.ipynb deleted file mode 100644 index ec45946e9..000000000 --- a/cookbooks/14-Automated_Webinar_Scheduling_Workflow.ipynb +++ /dev/null @@ -1,306 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "\"\"\"\n", - "Automated_Webinar_Scheduling_Workflow.py\n", - "\"\"\"\n", - "\n", - "import uuid\n", - "import yaml\n", - "import time\n", - "from julep import Client\n", - "\n", - "AGENT_UUID = uuid.uuid4()\n", - "SCHEDULE_WEBINAR_TASK_UUID = uuid.uuid4()\n", - "SEND_REMINDER_TASK_UUID = uuid.uuid4()\n", - "FOLLOW_UP_TASK_UUID = uuid.uuid4()\n", - "\n", - "api_key = \"\" # Your API key here\n", - "client = Client(api_key=api_key, environment=\"dev\")\n", - "\n", - "agent = client.agents.create_or_update(\n", - " agent_id=AGENT_UUID,\n", - " name=\"Webinar Scheduler\",\n", - " about=\"An AI agent that automates webinar scheduling, reminders, and follow-ups.\",\n", - " model=\"gpt-4o\",\n", - ")\n", - "\n", - "schedule_webinar_task_def = yaml.safe_load(\"\"\"\n", - "name: Schedule Webinar\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " organizer:\n", - " type: string\n", - " participants:\n", - " type: array\n", - " items:\n", - " type: string\n", - " webinar_topic:\n", - " type: string\n", - " webinar_time:\n", - " type: string\n", - "\n", - "main:\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " You are a webinar scheduling assistant. Schedule a webinar with the following details:\n", - " Organizer: {{inputs[0].organizer}}\n", - " Participants: {{inputs[0].participants}}\n", - " Webinar Topic: {{inputs[0].webinar_topic}}\n", - " Time: {{inputs[0].webinar_time}}\n", - "\n", - " Confirm the event creation and return the webinar event ID.\n", - " unwrap: true\n", - "\n", - "- evaluate:\n", - " event_id: _.uuid()\n", - "\n", - "- return:\n", - " event_id: _\n", - "\"\"\")\n", - "\n", - "schedule_webinar_task = client.tasks.create_or_update(\n", - " task_id=SCHEDULE_WEBINAR_TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **schedule_webinar_task_def\n", - ")\n", - "\n", - "send_reminder_task_def = yaml.safe_load(\"\"\"\n", - "name: Send Reminder\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " event_id:\n", - " type: string\n", - " participants:\n", - " type: array\n", - " items:\n", - " type: string\n", - "\n", - "main:\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " You are a reminder assistant. Send a reminder for the following event:\n", - " Event ID: {{inputs[0].event_id}}\n", - " Participants: {{inputs[0].participants}}\n", - "\n", - " Confirm the reminder has been sent.\n", - " unwrap: true\n", - "\n", - "- return:\n", - " status: \"Reminder sent\"\n", - "\"\"\")\n", - "\n", - "send_reminder_task = client.tasks.create_or_update(\n", - " task_id=SEND_REMINDER_TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **send_reminder_task_def\n", - ")\n", - "\n", - "follow_up_task_def = yaml.safe_load(\"\"\"\n", - "name: Send Follow-Up\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " event_id:\n", - " type: string\n", - " participants:\n", - " type: array\n", - " items:\n", - " type: string\n", - " follow_up_message:\n", - " type: string\n", - "\n", - "main:\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " You are a follow-up assistant. Send the following follow-up message to participants after the event:\n", - " Event ID: {{inputs[0].event_id}}\n", - " Participants: {{inputs[0].participants}}\n", - " Message: {{inputs[0].follow_up_message}}\n", - "\n", - " Confirm the message has been sent.\n", - " unwrap: true\n", - "\n", - "- return:\n", - " status: \"Follow-up message sent\"\n", - "\"\"\")\n", - "\n", - "follow_up_task = client.tasks.create_or_update(\n", - " task_id=FOLLOW_UP_TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **follow_up_task_def\n", - ")\n", - "def schedule_webinar(organizer, participants, webinar_topic, webinar_time):\n", - " execution = client.executions.create(\n", - " task_id=SCHEDULE_WEBINAR_TASK_UUID,\n", - " input={\n", - " \"organizer\": organizer,\n", - " \"participants\": participants,\n", - " \"webinar_topic\": webinar_topic,\n", - " \"webinar_time\": webinar_time\n", - " }\n", - " )\n", - " time.sleep(2)\n", - " result = client.executions.get(execution.id)\n", - " output = client.executions.transitions.list(execution_id=result.id).items[0].output\n", - "\n", - " if isinstance(output, dict):\n", - " return output\n", - " else:\n", - " return {\"event_id\": output}\n", - "\n", - "def send_reminder(event_id, participants):\n", - " execution = client.executions.create(\n", - " task_id=SEND_REMINDER_TASK_UUID,\n", - " input={\n", - " \"event_id\": event_id,\n", - " \"participants\": participants\n", - " }\n", - " )\n", - " time.sleep(2)\n", - " result = client.executions.get(execution.id)\n", - " return client.executions.transitions.list(execution_id=result.id).items[0].output\n", - "\n", - "def send_follow_up(event_id, participants, message):\n", - " execution = client.executions.create(\n", - " task_id=FOLLOW_UP_TASK_UUID,\n", - " input={\n", - " \"event_id\": event_id,\n", - " \"participants\": participants,\n", - " \"follow_up_message\": message\n", - " }\n", - " )\n", - " time.sleep(2)\n", - " result = client.executions.get(execution.id)\n", - " return client.executions.transitions.list(execution_id=result.id).items[0].output\n", - "\n", - "def print_output(webinar_result, reminder_result, follow_up_result):\n", - " print(\"Demonstrating Automated Webinar Scheduling Workflow:\")\n", - "\n", - " print(\"Webinar Scheduled:\")\n", - " print(\"The webinar has been successfully scheduled with the following details:\\n\")\n", - " print(f\"- Organizer: {webinar_result['organizer']}\")\n", - " print(f\"- Participants: {', '.join(webinar_result['participants'])}\")\n", - " print(f\"- Webinar Topic: {webinar_result['webinar_topic']}\")\n", - " print(f\"- Time: {webinar_result['webinar_time']}\\n\")\n", - " print(f\"The Webinar Event ID is: {webinar_result['event_id']}\")\n", - "\n", - " print(\"Reminder Status:\")\n", - " print(\"Reminder has been sent successfully for the following webinar:\\n\")\n", - " print(f\"- Event ID: {reminder_result['event_id']}\")\n", - " print(f\"- Participants: {', '.join(reminder_result['participants'])}\\n\")\n", - "\n", - " print(\"Follow-up Status:\")\n", - " print(\"Follow-up message has been successfully sent for the following webinar:\\n\")\n", - " print(f\"- Event ID: {follow_up_result['event_id']}\")\n", - " print(f\"- Participants: {', '.join(follow_up_result['participants'])}\")\n", - " print(f\"- Follow-up Message: {follow_up_result['follow_up_message']}\\n\")\n", - "\n", - "\n", - "print(\"Demonstrating Automated Webinar Scheduling Workflow:\")\n", - "\n", - "organizer = \"organizer123\"\n", - "participants = [\"participant1\", \"participant2\", \"participant3\"]\n", - "webinar_topic = \"AI in Healthcare\"\n", - "webinar_time = \"2024-11-01 10:00:00\"\n", - "follow_up_message = \"Thank you for attending the webinar! Here is the recording link.\"\n", - "\n", - "webinar_result = schedule_webinar(organizer, participants, webinar_topic, webinar_time)\n", - "\n", - "webinar_result = {\n", - " \"organizer\": organizer,\n", - " \"participants\": participants,\n", - " \"webinar_topic\": webinar_topic,\n", - " \"webinar_time\": webinar_time,\n", - " \"event_id\": \"WBNR20241101-001\"\n", - "}\n", - "\n", - "reminder_result = {\n", - " \"event_id\": webinar_result[\"event_id\"],\n", - " \"participants\": participants\n", - "}\n", - "\n", - "follow_up_result = {\n", - " \"event_id\": webinar_result[\"event_id\"],\n", - " \"participants\": participants,\n", - " \"follow_up_message\": follow_up_message\n", - "}\n", - "\n", - "print_output(webinar_result, reminder_result, follow_up_result)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hFR_2RBzmNwB", - "outputId": "8cb4622a-8954-4f7b-e047-245426b419cd" - }, - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Demonstrating Automated Webinar Scheduling Workflow:\n", - "Demonstrating Automated Webinar Scheduling Workflow:\n", - "Webinar Scheduled:\n", - "The webinar has been successfully scheduled with the following details:\n", - "\n", - "- Organizer: organizer123\n", - "- Participants: participant1, participant2, participant3\n", - "- Webinar Topic: AI in Healthcare\n", - "- Time: 2024-11-01 10:00:00\n", - "\n", - "The Webinar Event ID is: WBNR20241101-001\n", - "Reminder Status:\n", - "Reminder has been sent successfully for the following webinar:\n", - "\n", - "- Event ID: WBNR20241101-001\n", - "- Participants: participant1, participant2, participant3\n", - "\n", - "Follow-up Status:\n", - "Follow-up message has been successfully sent for the following webinar:\n", - "\n", - "- Event ID: WBNR20241101-001\n", - "- Participants: participant1, participant2, participant3\n", - "- Follow-up Message: Thank you for attending the webinar! Here is the recording link.\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "pk1LkRXQpIgg" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/cookbooks/14-Automated_Webinar_Scheduling_Workflow.py b/cookbooks/14-Automated_Webinar_Scheduling_Workflow.py deleted file mode 100644 index 34c29a37b..000000000 --- a/cookbooks/14-Automated_Webinar_Scheduling_Workflow.py +++ /dev/null @@ -1,244 +0,0 @@ -# -*- coding: utf-8 -*- -"""14-Automated_Webinar_Scheduling_Workflow.ipynb - -Automatically generated by Colab. - -Original file is located at - https://colab.research.google.com/drive/1VH-tuBMGOVKIZMJ6I6KuvJsad0is7IIF -""" - -""" -Automated_Webinar_Scheduling_Workflow.py -""" - -import uuid -import yaml -import time -from julep import Client - -AGENT_UUID = uuid.uuid4() -SCHEDULE_WEBINAR_TASK_UUID = uuid.uuid4() -SEND_REMINDER_TASK_UUID = uuid.uuid4() -FOLLOW_UP_TASK_UUID = uuid.uuid4() - -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Webinar Scheduler", - about="An AI agent that automates webinar scheduling, reminders, and follow-ups.", - model="gpt-4o", -) - -schedule_webinar_task_def = yaml.safe_load(""" -name: Schedule Webinar - -input_schema: - type: object - properties: - organizer: - type: string - participants: - type: array - items: - type: string - webinar_topic: - type: string - webinar_time: - type: string - -main: -- prompt: - - role: system - content: >- - You are a webinar scheduling assistant. Schedule a webinar with the following details: - Organizer: {{inputs[0].organizer}} - Participants: {{inputs[0].participants}} - Webinar Topic: {{inputs[0].webinar_topic}} - Time: {{inputs[0].webinar_time}} - - Confirm the event creation and return the webinar event ID. - unwrap: true - -- evaluate: - event_id: _.uuid() - -- return: - event_id: _ -""") - -schedule_webinar_task = client.tasks.create_or_update( - task_id=SCHEDULE_WEBINAR_TASK_UUID, - agent_id=AGENT_UUID, - **schedule_webinar_task_def -) - -send_reminder_task_def = yaml.safe_load(""" -name: Send Reminder - -input_schema: - type: object - properties: - event_id: - type: string - participants: - type: array - items: - type: string - -main: -- prompt: - - role: system - content: >- - You are a reminder assistant. Send a reminder for the following event: - Event ID: {{inputs[0].event_id}} - Participants: {{inputs[0].participants}} - - Confirm the reminder has been sent. - unwrap: true - -- return: - status: "Reminder sent" -""") - -send_reminder_task = client.tasks.create_or_update( - task_id=SEND_REMINDER_TASK_UUID, - agent_id=AGENT_UUID, - **send_reminder_task_def -) - -follow_up_task_def = yaml.safe_load(""" -name: Send Follow-Up - -input_schema: - type: object - properties: - event_id: - type: string - participants: - type: array - items: - type: string - follow_up_message: - type: string - -main: -- prompt: - - role: system - content: >- - You are a follow-up assistant. Send the following follow-up message to participants after the event: - Event ID: {{inputs[0].event_id}} - Participants: {{inputs[0].participants}} - Message: {{inputs[0].follow_up_message}} - - Confirm the message has been sent. - unwrap: true - -- return: - status: "Follow-up message sent" -""") - -follow_up_task = client.tasks.create_or_update( - task_id=FOLLOW_UP_TASK_UUID, - agent_id=AGENT_UUID, - **follow_up_task_def -) -def schedule_webinar(organizer, participants, webinar_topic, webinar_time): - execution = client.executions.create( - task_id=SCHEDULE_WEBINAR_TASK_UUID, - input={ - "organizer": organizer, - "participants": participants, - "webinar_topic": webinar_topic, - "webinar_time": webinar_time - } - ) - time.sleep(2) - result = client.executions.get(execution.id) - output = client.executions.transitions.list(execution_id=result.id).items[0].output - - if isinstance(output, dict): - return output - else: - return {"event_id": output} - -def send_reminder(event_id, participants): - execution = client.executions.create( - task_id=SEND_REMINDER_TASK_UUID, - input={ - "event_id": event_id, - "participants": participants - } - ) - time.sleep(2) - result = client.executions.get(execution.id) - return client.executions.transitions.list(execution_id=result.id).items[0].output - -def send_follow_up(event_id, participants, message): - execution = client.executions.create( - task_id=FOLLOW_UP_TASK_UUID, - input={ - "event_id": event_id, - "participants": participants, - "follow_up_message": message - } - ) - time.sleep(2) - result = client.executions.get(execution.id) - return client.executions.transitions.list(execution_id=result.id).items[0].output - -def print_output(webinar_result, reminder_result, follow_up_result): - print("Demonstrating Automated Webinar Scheduling Workflow:") - - print("Webinar Scheduled:") - print("The webinar has been successfully scheduled with the following details:\n") - print(f"- Organizer: {webinar_result['organizer']}") - print(f"- Participants: {', '.join(webinar_result['participants'])}") - print(f"- Webinar Topic: {webinar_result['webinar_topic']}") - print(f"- Time: {webinar_result['webinar_time']}\n") - print(f"The Webinar Event ID is: {webinar_result['event_id']}") - - print("Reminder Status:") - print("Reminder has been sent successfully for the following webinar:\n") - print(f"- Event ID: {reminder_result['event_id']}") - print(f"- Participants: {', '.join(reminder_result['participants'])}\n") - - print("Follow-up Status:") - print("Follow-up message has been successfully sent for the following webinar:\n") - print(f"- Event ID: {follow_up_result['event_id']}") - print(f"- Participants: {', '.join(follow_up_result['participants'])}") - print(f"- Follow-up Message: {follow_up_result['follow_up_message']}\n") - - -print("Demonstrating Automated Webinar Scheduling Workflow:") - -organizer = "organizer123" -participants = ["participant1", "participant2", "participant3"] -webinar_topic = "AI in Healthcare" -webinar_time = "2024-11-01 10:00:00" -follow_up_message = "Thank you for attending the webinar! Here is the recording link." - -webinar_result = schedule_webinar(organizer, participants, webinar_topic, webinar_time) - -webinar_result = { - "organizer": organizer, - "participants": participants, - "webinar_topic": webinar_topic, - "webinar_time": webinar_time, - "event_id": "WBNR20241101-001" -} - -reminder_result = { - "event_id": webinar_result["event_id"], - "participants": participants -} - -follow_up_result = { - "event_id": webinar_result["event_id"], - "participants": participants, - "follow_up_message": follow_up_message -} - -print_output(webinar_result, reminder_result, follow_up_result) - diff --git a/cookbooks/15-Personal_Finance_Tracker.ipynb b/cookbooks/15-Personal_Finance_Tracker.ipynb deleted file mode 100644 index cd6909944..000000000 --- a/cookbooks/15-Personal_Finance_Tracker.ipynb +++ /dev/null @@ -1,208 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import uuid\n", - "import yaml\n", - "import time\n", - "from julep import Client\n", - "\n", - "TRACKER_UUID = uuid.uuid4()\n", - "BUDGET_TASK_UUID = uuid.uuid4()\n", - "ADVICE_TASK_UUID = uuid.uuid4()\n", - "\n", - "api_key = \"\" # Your API key here\n", - "client = Client(api_key=api_key, environment=\"dev\")\n", - "\n", - "agent = client.agents.create_or_update(\n", - " agent_id=TRACKER_UUID,\n", - " name=\"Personal Finance Tracker\",\n", - " about=\"Tracks user expenses, analyzes spending patterns, and provides financial advice.\",\n", - " model=\"gpt-4o\",\n", - ")\n", - "\n", - "log_expense_task_def = yaml.safe_load(f\"\"\"\n", - "name: Log Expense\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " amount:\n", - " type: number\n", - " category:\n", - " type: string\n", - " description:\n", - " type: string\n", - "\n", - "tools:\n", - "- name: finance_tool\n", - " system:\n", - " resource: agent\n", - " subresource: tool\n", - " operation: create\n", - "\n", - "main:\n", - "- tool: finance_tool\n", - " arguments:\n", - " agent_id: \"'{agent.id}'\"\n", - " data:\n", - " amount: inputs[0].amount\n", - " category: inputs[0].category\n", - " description: inputs[0].description\n", - "\"\"\")\n", - "\n", - "log_expense_task = client.tasks.create_or_update(\n", - " task_id=TRACKER_UUID,\n", - " agent_id=TRACKER_UUID,\n", - " **log_expense_task_def\n", - ")\n", - "\n", - "set_budget_task_def = yaml.safe_load(f\"\"\"\n", - "name: Set Budget\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " category:\n", - " type: string\n", - " budget_amount:\n", - " type: number\n", - "\n", - "tools:\n", - "- name: finance_tool\n", - " system:\n", - " resource: agent\n", - " subresource: tool\n", - " operation: create\n", - "\n", - "main:\n", - "- tool: finance_tool\n", - " arguments:\n", - " agent_id: \"'{agent.id}'\"\n", - " data:\n", - " category: inputs[0].category\n", - " budget_amount: inputs[0].budget_amount\n", - "\"\"\")\n", - "\n", - "set_budget_task = client.tasks.create_or_update(\n", - " task_id=BUDGET_TASK_UUID,\n", - " agent_id=TRACKER_UUID,\n", - " **set_budget_task_def\n", - ")\n", - "\n", - "send_advice_task_def = yaml.safe_load(f\"\"\"\n", - "name: Send Financial Advice\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " advice:\n", - " type: string\n", - " email:\n", - " type: string\n", - "\n", - "tools:\n", - "- name: email_tool\n", - " system:\n", - " resource: agent\n", - " subresource: tool\n", - " operation: create\n", - "\n", - "main:\n", - "- tool: email_tool\n", - " arguments:\n", - " agent_id: \"'{agent.id}'\"\n", - " to: inputs[0].email\n", - " subject: \"Financial Advice\"\n", - " body: inputs[0].advice\n", - "\"\"\")\n", - "\n", - "send_advice_task = client.tasks.create_or_update(\n", - " task_id=ADVICE_TASK_UUID,\n", - " agent_id=TRACKER_UUID,\n", - " **send_advice_task_def\n", - ")\n", - "\n", - "def log_expense(amount, category, description):\n", - " execution = client.executions.create(\n", - " task_id=TRACKER_UUID,\n", - " input={\"amount\": amount, \"category\": category, \"description\": description}\n", - " )\n", - " print(\"Logging expense...\")\n", - " time.sleep(5)\n", - " result = client.executions.get(execution.id)\n", - " print(f\"Expense logged: {description} of ${amount} in {category} category.\")\n", - "\n", - "def set_budget(category, budget_amount):\n", - " execution = client.executions.create(\n", - " task_id=BUDGET_TASK_UUID,\n", - " input={\"category\": category, \"budget_amount\": budget_amount}\n", - " )\n", - " print(\"Setting budget...\")\n", - " time.sleep(5)\n", - " result = client.executions.get(execution.id)\n", - " print(f\"Budget set for {category}: ${budget_amount}.\")\n", - "\n", - "def send_financial_advice(email, advice):\n", - " execution = client.executions.create(\n", - " task_id=ADVICE_TASK_UUID,\n", - " input={\"advice\": advice, \"email\": email}\n", - " )\n", - " print(\"Sending financial advice...\")\n", - " time.sleep(5)\n", - " result = client.executions.get(execution.id)\n", - " print(f\"Advice sent to {email}: '{advice}'.\")\n", - "\n", - "log_expense(50, \"Groceries\", \"Weekly grocery shopping\")\n", - "set_budget(\"Groceries\", 200)\n", - "send_financial_advice(\"user@example.com\", \"Consider reducing your grocery budget to save more.\")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "32Q7ccRJs_h5", - "outputId": "3ffa8354-ec10-4fed-9da8-9bf7c234a35d" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Logging expense...\n", - "Expense logged: Weekly grocery shopping of $50 in Groceries category.\n", - "Setting budget...\n", - "Budget set for Groceries: $200.\n", - "Sending financial advice...\n", - "Advice sent to user@example.com: 'Consider reducing your grocery budget to save more.'.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "k9fvuTaGtGBp" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/cookbooks/15-Personal_Finance_Tracker.py b/cookbooks/15-Personal_Finance_Tracker.py deleted file mode 100644 index ffda91b17..000000000 --- a/cookbooks/15-Personal_Finance_Tracker.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -"""15_Personal_Finance_Tracker.ipynb - -Automatically generated by Colab. - -Original file is located at - https://colab.research.google.com/drive/1jzy4evusmnkPxntEdpibh_Xm12T-m8in -""" - -import uuid -import yaml -import time -from julep import Client - -TRACKER_UUID = uuid.uuid4() -BUDGET_TASK_UUID = uuid.uuid4() -ADVICE_TASK_UUID = uuid.uuid4() - -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -agent = client.agents.create_or_update( - agent_id=TRACKER_UUID, - name="Personal Finance Tracker", - about="Tracks user expenses, analyzes spending patterns, and provides financial advice.", - model="gpt-4o", -) - -log_expense_task_def = yaml.safe_load(f""" -name: Log Expense - -input_schema: - type: object - properties: - amount: - type: number - category: - type: string - description: - type: string - -tools: -- name: finance_tool - system: - resource: agent - subresource: tool - operation: create - -main: -- tool: finance_tool - arguments: - agent_id: "'{agent.id}'" - data: - amount: inputs[0].amount - category: inputs[0].category - description: inputs[0].description -""") - -log_expense_task = client.tasks.create_or_update( - task_id=TRACKER_UUID, - agent_id=TRACKER_UUID, - **log_expense_task_def -) - -set_budget_task_def = yaml.safe_load(f""" -name: Set Budget - -input_schema: - type: object - properties: - category: - type: string - budget_amount: - type: number - -tools: -- name: finance_tool - system: - resource: agent - subresource: tool - operation: create - -main: -- tool: finance_tool - arguments: - agent_id: "'{agent.id}'" - data: - category: inputs[0].category - budget_amount: inputs[0].budget_amount -""") - -set_budget_task = client.tasks.create_or_update( - task_id=BUDGET_TASK_UUID, - agent_id=TRACKER_UUID, - **set_budget_task_def -) - -send_advice_task_def = yaml.safe_load(f""" -name: Send Financial Advice - -input_schema: - type: object - properties: - advice: - type: string - email: - type: string - -tools: -- name: email_tool - system: - resource: agent - subresource: tool - operation: create - -main: -- tool: email_tool - arguments: - agent_id: "'{agent.id}'" - to: inputs[0].email - subject: "Financial Advice" - body: inputs[0].advice -""") - -send_advice_task = client.tasks.create_or_update( - task_id=ADVICE_TASK_UUID, - agent_id=TRACKER_UUID, - **send_advice_task_def -) - -def log_expense(amount, category, description): - execution = client.executions.create( - task_id=TRACKER_UUID, - input={"amount": amount, "category": category, "description": description} - ) - print("Logging expense...") - time.sleep(5) - result = client.executions.get(execution.id) - print(f"Expense logged: {description} of ${amount} in {category} category.") - -def set_budget(category, budget_amount): - execution = client.executions.create( - task_id=BUDGET_TASK_UUID, - input={"category": category, "budget_amount": budget_amount} - ) - print("Setting budget...") - time.sleep(5) - result = client.executions.get(execution.id) - print(f"Budget set for {category}: ${budget_amount}.") - -def send_financial_advice(email, advice): - execution = client.executions.create( - task_id=ADVICE_TASK_UUID, - input={"advice": advice, "email": email} - ) - print("Sending financial advice...") - time.sleep(5) - result = client.executions.get(execution.id) - print(f"Advice sent to {email}: '{advice}'.") - -log_expense(50, "Groceries", "Weekly grocery shopping") -set_budget("Groceries", 200) -send_financial_advice("user@example.com", "Consider reducing your grocery budget to save more.") - diff --git a/cookbooks/16-E_commerce_Order_Processing_Workflow.ipynb b/cookbooks/16-E_commerce_Order_Processing_Workflow.ipynb deleted file mode 100644 index bf4e741ca..000000000 --- a/cookbooks/16-E_commerce_Order_Processing_Workflow.ipynb +++ /dev/null @@ -1,381 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "import uuid\n", - "import yaml\n", - "import time\n", - "from julep import Client" - ], - "metadata": { - "id": "QIJXVEzBYrRv" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "AGENT_UUID = uuid.uuid4()\n", - "ORDER_PLACEMENT_TASK_UUID = uuid.uuid4()\n", - "INVENTORY_CHECK_TASK_UUID = uuid.uuid4()\n", - "PAYMENT_PROCESSING_TASK_UUID = uuid.uuid4()\n", - "SHIPMENT_TRACKING_TASK_UUID = uuid.uuid4()" - ], - "metadata": { - "id": "tCqfsDHuYu4V" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "api_key = \"\" # Your API key here\n", - "client = Client(api_key=api_key, environment=\"dev\")" - ], - "metadata": { - "id": "mSBH1k6OYxUW" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "agent = client.agents.create_or_update(\n", - " agent_id=AGENT_UUID,\n", - " name=\"Order Processing Assistant\",\n", - " about=\"An AI agent specialized in automating the order processing workflow for e-commerce.\",\n", - " model=\"gpt-4o\",\n", - ")" - ], - "metadata": { - "id": "loTLYbQ8Y1i5" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "order_placement_task_def = yaml.safe_load(\"\"\"\n", - "name: Order Placement\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " user_id:\n", - " type: string\n", - " order_details:\n", - " type: object\n", - " properties:\n", - " item_id:\n", - " type: integer\n", - " quantity:\n", - " type: integer\n", - "\n", - "main:\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " You are an order placement assistant. Process the following order:\n", - " User ID: {{inputs[0].user_id}}\n", - " Order Details: {{inputs[0].order_details}}\n", - "\n", - " Confirm the order placement and return the order ID.\n", - " unwrap: true\n", - "\n", - "- evaluate:\n", - " order_id: _.uuid()\n", - "\n", - "- return:\n", - " order_id: _\n", - "\"\"\")" - ], - "metadata": { - "id": "UDsmzc_pY4Dx" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "order_placement_task = client.tasks.create_or_update(\n", - " task_id=ORDER_PLACEMENT_TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **order_placement_task_def\n", - ")\n" - ], - "metadata": { - "id": "cc76A2UxY-Z7" - }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "inventory_check_task_def = yaml.safe_load(\"\"\"\n", - "name: Inventory Check\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " item_id:\n", - " type: integer\n", - " quantity:\n", - " type: integer\n", - "\n", - "main:\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " You are an inventory checker. Check the availability of the following item:\n", - " Item ID: {{inputs[0].item_id}}\n", - " Quantity Requested: {{inputs[0].quantity}}\n", - "\n", - " Return true if available, otherwise return false.\n", - " unwrap: true\n", - "\"\"\")\n", - "\n" - ], - "metadata": { - "id": "uCdVhA98ZBPB" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "inventory_check_task = client.tasks.create_or_update(\n", - " task_id=INVENTORY_CHECK_TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **inventory_check_task_def\n", - ")\n" - ], - "metadata": { - "id": "ZICp9jXiZEgO" - }, - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "payment_processing_task_def = yaml.safe_load(\"\"\"\n", - "name: Payment Processing\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " user_id:\n", - " type: string\n", - " order_id:\n", - " type: string\n", - " amount:\n", - " type: number\n", - "\n", - "main:\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " You are a payment processor. Process payment for the following order:\n", - " User ID: {{inputs[0].user_id}}\n", - " Order ID: {{inputs[0].order_id}}\n", - " Amount: {{inputs[0].amount}}\n", - "\n", - " Confirm payment status (success or failure).\n", - " unwrap: true\n", - "\n", - "- evaluate:\n", - " payment_status: \"success\" # Simulating a successful payment\n", - "\n", - "- return:\n", - " payment_status: _\n", - "\"\"\")\n", - "\n", - "payment_processing_task = client.tasks.create_or_update(\n", - " task_id=PAYMENT_PROCESSING_TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **payment_processing_task_def\n", - ")\n", - "\n", - "shipment_tracking_task_def = yaml.safe_load(\"\"\"\n", - "name: Shipment Tracking\n", - "\n", - "input_schema:\n", - " type: object\n", - " properties:\n", - " order_id:\n", - " type: string\n", - "\n", - "main:\n", - "- prompt:\n", - " - role: system\n", - " content: >-\n", - " You are a shipment tracker. Track the shipment for the following order:\n", - " Order ID: {{inputs[0].order_id}}\n", - "\n", - " Return the current status of the shipment.\n", - " unwrap: true\n", - "\"\"\")\n" - ], - "metadata": { - "id": "lr0M9XWKZG9N" - }, - "execution_count": 11, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "shipment_tracking_task = client.tasks.create_or_update(\n", - " task_id=SHIPMENT_TRACKING_TASK_UUID,\n", - " agent_id=AGENT_UUID,\n", - " **shipment_tracking_task_def\n", - ")" - ], - "metadata": { - "id": "-k0Wl-o8ZJw7" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def place_order(user_id, item_id, quantity):\n", - " execution = client.executions.create(\n", - " task_id=ORDER_PLACEMENT_TASK_UUID,\n", - " input={\n", - " \"user_id\": user_id,\n", - " \"order_details\": {\n", - " \"item_id\": item_id,\n", - " \"quantity\": quantity\n", - " }\n", - " }\n", - " )\n", - " time.sleep(2)\n", - " result = client.executions.get(execution.id)\n", - " output = client.executions.transitions.list(execution_id=result.id).items[0].output\n", - "\n", - " if isinstance(output, dict):\n", - " return output\n", - " else:\n", - " return {\"order_id\": output}\n", - "\n", - "def check_inventory(item_id, quantity):\n", - " execution = client.executions.create(\n", - " task_id=INVENTORY_CHECK_TASK_UUID,\n", - " input={\n", - " \"item_id\": item_id,\n", - " \"quantity\": quantity\n", - " }\n", - " )\n", - " time.sleep(2)\n", - " result = client.executions.get(execution.id)\n", - " return client.executions.transitions.list(execution_id=result.id).items[0].output\n", - "\n", - "def process_payment(user_id, order_id, amount):\n", - " execution = client.executions.create(\n", - " task_id=PAYMENT_PROCESSING_TASK_UUID,\n", - " input={\n", - " \"user_id\": user_id,\n", - " \"order_id\": order_id,\n", - " \"amount\": amount\n", - " }\n", - " )\n", - " time.sleep(2)\n", - " result = client.executions.get(execution.id)\n", - " return client.executions.transitions.list(execution_id=result.id).items[0].output\n", - "\n", - "def track_shipment(order_id):\n", - " execution = client.executions.create(\n", - " task_id=SHIPMENT_TRACKING_TASK_UUID,\n", - " input={\n", - " \"order_id\": order_id\n", - " }\n", - " )\n", - " time.sleep(2)\n", - " result = client.executions.get(execution.id)\n", - " return client.executions.transitions.list(execution_id=result.id).items[0].output\n" - ], - "metadata": { - "id": "6iM6NqwlZMTD" - }, - "execution_count": 16, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "print(\"Demonstrating E-commerce Order Processing Workflow:\")\n", - "\n", - "user_id = \"user123\"\n", - "item_id = 1\n", - "quantity = 2\n", - "amount = 49.99\n", - "\n", - "is_available = check_inventory(item_id, quantity)\n", - "if is_available:\n", - " print(f\"Inventory Check: Item {item_id} is available.\")\n", - " order_result = place_order(user_id, item_id, quantity)\n", - " print(f\"Order Result: {order_result}\")\n", - " payment_result = process_payment(user_id, order_result[\"order_id\"], amount)\n", - " print(f\"Payment Status: {payment_result}\")\n", - " shipment_result = track_shipment(order_result[\"order_id\"])\n", - " print(f\"Shipment Status: {shipment_result}\")\n", - "else:\n", - " print(f\"Inventory Check: Item {item_id} is not available.\")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FTDf1VaMZQ3f", - "outputId": "a358feb6-2630-4a2c-d423-16d7f2937c07" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Demonstrating E-commerce Order Processing Workflow:\n", - "Inventory Check: Item 1 is available.\n", - "Order Result: {'order_id': 'Order confirmed for User ID: user123. The order details are as follows: Item ID: 1, Quantity: 2. Your order ID is ORD456789.'}\n", - "Payment Status: Payment processed successfully for Order ID: ORD456789. The payment amount of $49.99 has been confirmed.\n", - "Shipment Status: The current status of the shipment for Order ID ORD456789 is \"In Transit.\" The order has been picked up by the carrier and is on its way to the delivery address. Estimated delivery is within 3-5 business days.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "0NaRffiYZXGf" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/cookbooks/16-E_commerce_Order_Processing_Workflow.py b/cookbooks/16-E_commerce_Order_Processing_Workflow.py deleted file mode 100644 index e8db94556..000000000 --- a/cookbooks/16-E_commerce_Order_Processing_Workflow.py +++ /dev/null @@ -1,238 +0,0 @@ -# -*- coding: utf-8 -*- -"""E-commerce_Order_Processing_Workflow.ipynb - -Automatically generated by Colab. - -Original file is located at - https://colab.research.google.com/drive/1mP-uZV8-wMMJA0eDcF5TH6C82ZOup055 -""" - -import uuid -import yaml -import time -from julep import Client - -AGENT_UUID = uuid.uuid4() -ORDER_PLACEMENT_TASK_UUID = uuid.uuid4() -INVENTORY_CHECK_TASK_UUID = uuid.uuid4() -PAYMENT_PROCESSING_TASK_UUID = uuid.uuid4() -SHIPMENT_TRACKING_TASK_UUID = uuid.uuid4() - -api_key = "" # Your API key here -client = Client(api_key=api_key, environment="dev") - -agent = client.agents.create_or_update( - agent_id=AGENT_UUID, - name="Order Processing Assistant", - about="An AI agent specialized in automating the order processing workflow for e-commerce.", - model="gpt-4o", -) - -order_placement_task_def = yaml.safe_load(""" -name: Order Placement - -input_schema: - type: object - properties: - user_id: - type: string - order_details: - type: object - properties: - item_id: - type: integer - quantity: - type: integer - -main: -- prompt: - - role: system - content: >- - You are an order placement assistant. Process the following order: - User ID: {{inputs[0].user_id}} - Order Details: {{inputs[0].order_details}} - - Confirm the order placement and return the order ID. - unwrap: true - -- evaluate: - order_id: _.uuid() - -- return: - order_id: _ -""") - -order_placement_task = client.tasks.create_or_update( - task_id=ORDER_PLACEMENT_TASK_UUID, - agent_id=AGENT_UUID, - **order_placement_task_def -) - -inventory_check_task_def = yaml.safe_load(""" -name: Inventory Check - -input_schema: - type: object - properties: - item_id: - type: integer - quantity: - type: integer - -main: -- prompt: - - role: system - content: >- - You are an inventory checker. Check the availability of the following item: - Item ID: {{inputs[0].item_id}} - Quantity Requested: {{inputs[0].quantity}} - - Return true if available, otherwise return false. - unwrap: true -""") - -inventory_check_task = client.tasks.create_or_update( - task_id=INVENTORY_CHECK_TASK_UUID, - agent_id=AGENT_UUID, - **inventory_check_task_def -) - -payment_processing_task_def = yaml.safe_load(""" -name: Payment Processing - -input_schema: - type: object - properties: - user_id: - type: string - order_id: - type: string - amount: - type: number - -main: -- prompt: - - role: system - content: >- - You are a payment processor. Process payment for the following order: - User ID: {{inputs[0].user_id}} - Order ID: {{inputs[0].order_id}} - Amount: {{inputs[0].amount}} - - Confirm payment status (success or failure). - unwrap: true - -- evaluate: - payment_status: "success" # Simulating a successful payment - -- return: - payment_status: _ -""") - -payment_processing_task = client.tasks.create_or_update( - task_id=PAYMENT_PROCESSING_TASK_UUID, - agent_id=AGENT_UUID, - **payment_processing_task_def -) - -shipment_tracking_task_def = yaml.safe_load(""" -name: Shipment Tracking - -input_schema: - type: object - properties: - order_id: - type: string - -main: -- prompt: - - role: system - content: >- - You are a shipment tracker. Track the shipment for the following order: - Order ID: {{inputs[0].order_id}} - - Return the current status of the shipment. - unwrap: true -""") - -shipment_tracking_task = client.tasks.create_or_update( - task_id=SHIPMENT_TRACKING_TASK_UUID, - agent_id=AGENT_UUID, - **shipment_tracking_task_def -) - -def place_order(user_id, item_id, quantity): - execution = client.executions.create( - task_id=ORDER_PLACEMENT_TASK_UUID, - input={ - "user_id": user_id, - "order_details": { - "item_id": item_id, - "quantity": quantity - } - } - ) - time.sleep(2) - result = client.executions.get(execution.id) - output = client.executions.transitions.list(execution_id=result.id).items[0].output - - if isinstance(output, dict): - return output - else: - return {"order_id": output} - -def check_inventory(item_id, quantity): - execution = client.executions.create( - task_id=INVENTORY_CHECK_TASK_UUID, - input={ - "item_id": item_id, - "quantity": quantity - } - ) - time.sleep(2) - result = client.executions.get(execution.id) - return client.executions.transitions.list(execution_id=result.id).items[0].output - -def process_payment(user_id, order_id, amount): - execution = client.executions.create( - task_id=PAYMENT_PROCESSING_TASK_UUID, - input={ - "user_id": user_id, - "order_id": order_id, - "amount": amount - } - ) - time.sleep(2) - result = client.executions.get(execution.id) - return client.executions.transitions.list(execution_id=result.id).items[0].output - -def track_shipment(order_id): - execution = client.executions.create( - task_id=SHIPMENT_TRACKING_TASK_UUID, - input={ - "order_id": order_id - } - ) - time.sleep(2) - result = client.executions.get(execution.id) - return client.executions.transitions.list(execution_id=result.id).items[0].output - -print("Demonstrating E-commerce Order Processing Workflow:") - -user_id = "user123" -item_id = 1 -quantity = 2 -amount = 49.99 - -is_available = check_inventory(item_id, quantity) -if is_available: - print(f"Inventory Check: Item {item_id} is available.") - order_result = place_order(user_id, item_id, quantity) - print(f"Order Result: {order_result}") - payment_result = process_payment(user_id, order_result["order_id"], amount) - print(f"Payment Status: {payment_result}") - shipment_result = track_shipment(order_result["order_id"]) - print(f"Shipment Status: {shipment_result}") -else: - print(f"Inventory Check: Item {item_id} is not available.") - From 7ba8fd3d5db4a7497c55d566fa4d749077a05147 Mon Sep 17 00:00:00 2001 From: Vedant Sahai Date: Thu, 21 Nov 2024 19:48:15 -0500 Subject: [PATCH 18/23] update cookbooks readme (#861) > [!IMPORTANT] > Update `cookbooks/README.md` to add new notebooks and remove outdated ones, with updated descriptions and links. > > - **Notebook Updates**: > - Added `00-Devfest-Email-Assistant.ipynb`, `04-Hook_Generator_Trending_Reels.ipynb`, `05-Video_Processing_With_Natural_Language.ipynb`, `06-Browser_Use.ipynb`. > - Removed `03-SmartResearcher_With_WebSearch.ipynb`, `04-TripPlanner_With_Weather_And_WikiInfo.ipynb`, `05-Basic_Agent_Creation_and_Interaction.ipynb`, `06-Designing_Multi-Step_Tasks.ipynb`, `07-Integrating_External_Tools_and_APIs.ipynb`, `08-Managing_Persistent_Sessions.ipynb`, `09-User_Management_and_Personalization.ipynb`, `10-Document_Management_and_Search.ipynb`, `11-Advanced_Chat_Interactions.ipynb`, `12-Monitoring_Task_Executions.ipynb`, `13-Error_Handling_and_Recovery.ipynb`. > - **Misc**: > - Fixed table formatting in `cookbooks/README.md`. > - Removed trailing newline in `cookbooks/README.md`. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 5ec16c4b00a3fe6f0c38053e19f2019dcc0fb5ad. It will automatically update as commits are pushed. --- cookbooks/README.md | 27 +++++++++++---------------- 1 file changed, 11 insertions(+), 16 deletions(-) diff --git a/cookbooks/README.md b/cookbooks/README.md index 7af36aff6..9dd403a5a 100644 --- a/cookbooks/README.md +++ b/cookbooks/README.md @@ -8,21 +8,16 @@ Welcome to the **Julep AI Notebook Collection**! This directory contains a set o Each notebook explores a unique use case, demonstrating different aspects of Julep's capabilities. Below is a quick overview of the notebooks, their purpose, and a link to run each of them on Google Colab. -| **Notebook Name** | **Colab Link** | **Description** | **Implemented** | -|------------------------------------------------- |---------------------------------------------------------------------------- |--------------------------------------------------------------------|-----------------| -| `01-Website_Crawler_using_Spider.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/01-Website_Crawler_using_Spider.ipynb) | Implements a web crawler using a spider to extract website content. | Yes | -| `02-Sarcastic_News_Headline_Generator.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/02-Sarcastic_News_Headline_Generator.ipynb) | Generates sarcastic news headlines using a Brave Search Tool. | Yes | -| `03-SmartResearcher_With_WebSearch.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/03-SmartResearcher_With_WebSearch.ipynb) | Searches and aggregates web information for research purposes using Brave Search. | Yes | -| `04-TripPlanner_With_Weather_And_WikiInfo.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/04-TripPlanner_With_Weather_And_WikiInfo.ipynb) | Plans trips using weather data and Wikipedia information. | Yes | -| `05-Basic_Agent_Creation_and_Interaction.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/05-Basic_Agent_Creation_and_Interaction.ipynb) | Demonstrates how to create a basic agent and interact with it. | No | -| `06-Designing_Multi-Step_Tasks.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/06-Designing_Multi-Step_Tasks.ipynb) | Explores creating tasks with various step types. | No | -| `07-Integrating_External_Tools_and_APIs.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/07-Integrating_External_Tools_and_APIs.ipynb) | Shows how to integrate and use external tools and APIs. | No | -| `08-Managing_Persistent_Sessions.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/08-Managing_Persistent_Sessions.ipynb) | Covers creating and managing persistent sessions with context. | No | -| `09-User_Management_and_Personalization.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/09-User_Management_and_Personalization.ipynb) | Demonstrates user management and personalized interactions. | No | -| `10-Document_Management_and_Search.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/10-Document_Management_and_Search.ipynb) | Explores document upload, management, and search capabilities. | No | -| `11-Advanced_Chat_Interactions.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/11-Advanced_Chat_Interactions.ipynb) | Covers advanced chat features and context handling. | No | -| `12-Monitoring_Task_Executions.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/12-Monitoring_Task_Executions.ipynb) | Shows how to monitor and manage task executions. | No | -| `13-Error_Handling_and_Recovery.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/13-Error_Handling_and_Recovery.ipynb) | Demonstrates implementing error handling and recovery in tasks. | No | +| **Notebook Name** | **Colab Link** | **Description** | **Implemented** | +|------------------ |--------------- |----------------- |----------------- | +| `00-Devfest-Email-Assistant.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/00-Devfest-Email-Assistant.ipynb) | Email assistant for managing DevFest communications | Yes | +| `01-Website_Crawler_using_Spider.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/01-Website_Crawler_using_Spider.ipynb) | Implements a web crawler using a spider to extract website content | Yes | +| `02-Sarcastic_News_Headline_Generator.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/02-Sarcastic_News_Headline_Generator.ipynb) | Generates sarcastic news headlines using a Brave Search Tool | Yes | +| `03-Trip_Planning_Assistant.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/03-Trip_Planning_Assistant.ipynb) | Plans trips using weather data and location information | Yes | +| `04-Hook_Generator_Trending_Reels.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/04-Hook_Generator_Trending_Reels.ipynb) | Generates engaging hooks for trending social media reels | Yes | +| `05-Video_Processing_With_Natural_Language.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/05-Video_Processing_With_Natural_Language.ipynb) | Processes videos using natural language commands | Yes | +| `06-Browser_Use.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/06-Browser_Use.ipynb) | Demonstrates browser automation capabilities | Yes | + ## Potential Cookbooks for Contributors @@ -147,4 +142,4 @@ For more details about the tasks or if you have any questions, please don't hesi --- -If you have feedback or would like to contribute to the notebooks, feel free to open an issue(s) in the [repository](https://github.com/julep-ai/julep). \ No newline at end of file +If you have feedback or would like to contribute to the notebooks, feel free to open an issue(s) in the [repository](https://github.com/julep-ai/julep). From 21cc7c9db245bb8132d8fc2d9b9293ca1a2beca4 Mon Sep 17 00:00:00 2001 From: Vedant Sahai Date: Thu, 21 Nov 2024 20:17:10 -0500 Subject: [PATCH 19/23] Translate readme (#866) > [!IMPORTANT] > Update image source and "Explore Docs" link text in README files across multiple languages. > > - **Image Update**: > - Updated image source in `README-CN.md`, `README-FR.md`, `README-JA.md`, `README.md`, and `cookbooks/README.md` to `https://github.com/user-attachments/assets/10ba11e4-4ced-400e-a400-ee0f72541780`. > - **Text Changes**: > - Added "(wip)" to "Explore Docs" link in `README.md` and its translations. > - Updated "Explore Docs" link text in `README-CN.md`, `README-FR.md`, and `README-JA.md` to indicate work in progress. > - Updated "Explore Docs" link text in `README.md` to "Explore Docs (wip)". > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 7831671c479dfdc1d69eec445749bb4549f1fe1d. It will automatically update as commits are pushed. --------- Co-authored-by: Diwank Singh Tomer Co-authored-by: creatorrr <931887+creatorrr@users.noreply.github.com> --- README-CN.md | 67 ++++++++-------- README-FR.md | 68 ++++++++-------- README-JA.md | 77 ++++++++++--------- README.md | 2 +- cookbooks/00-Devfest-Email-Assistant.ipynb | 2 +- cookbooks/01-website-crawler.ipynb | 2 +- ...02-sarcastic-news-headline-generator.ipynb | 2 +- cookbooks/03-trip-planning-assistant.ipynb | 2 +- .../04-hook-generator-trending-reels.ipynb | 2 +- ...deo-processing-with-natural-language.ipynb | 2 +- cookbooks/06-browser-use.ipynb | 2 +- cookbooks/README.md | 4 +- 12 files changed, 120 insertions(+), 112 deletions(-) diff --git a/README-CN.md b/README-CN.md index e39a65bcc..be6b17772 100644 --- a/README-CN.md +++ b/README-CN.md @@ -1,12 +1,12 @@ [English](README.md) | [中文翻译](README-CN.md) | [日本語翻訳](README-JA.md) | [French](README-FR.md)
- julep + julep


- 探索文档 + 探索文档（正在开发中） · 不和谐 · @@ -62,34 +62,39 @@ -

📖 Table of Contents

- -- [主要特点](#%E4%B8%BB%E8%A6%81%E7%89%B9%E7%82%B9) -- [Python 快速入门🐍](#python-%E5%BF%AB%E9%80%9F%E5%85%A5%E9%97%A8) -- [Node.js Quick Start 🟩](#nodejs-quick-start-) - - [Step 1: Create an Agent](#step-1-create-an-agent) -- [Components](#components) - - [Mental Model](#mental-model) -- [Concepts](#concepts) -- [Understanding Tasks](#understanding-tasks) - - [Lifecycle of a Task](#lifecycle-of-a-task) - - [Types of Workflow Steps](#types-of-workflow-steps) -- [Tool Types](#tool-types) - - [User-defined `functions`](#user-defined-functions) - - [`system` tools](#system-tools) - - [Built-in `integrations`](#built-in-integrations) - - [Direct `api_calls`](#direct-api_calls) -- [Integrations](#integrations) -- [Other Features](#other-features) - - [Adding Tools to Agents](#adding-tools-to-agents) - - [Managing Sessions and Users](#managing-sessions-and-users) - - [Document Integration and Search](#document-integration-and-search) - - [SDK 参考](#sdk-%E5%8F%82%E8%80%83) - - [API 参考](#api-%E5%8F%82%E8%80%83) -- [本地快速启动](#%E6%9C%AC%E5%9C%B0%E5%BF%AB%E9%80%9F%E5%90%AF%E5%8A%A8) -- [Julep 和 LangChain 等有什么区别？](#julep-%E5%92%8C-langchain-%E7%AD%89%E6%9C%89%E4%BB%80%E4%B9%88%E5%8C%BA%E5%88%AB) - - [不同的用例](#%E4%B8%8D%E5%90%8C%E7%9A%84%E7%94%A8%E4%BE%8B) - - [不同的外形尺寸](#%E4%B8%8D%E5%90%8C%E7%9A%84%E5%A4%96%E5%BD%A2%E5%B0%BA%E5%AF%B8) +

📖 目录

+ +- [简介](#introduction) +- [主要特点](#key-features) +- [快速示例](#quick-example) +- [安装](#安装) +- [Python 快速入门 🐍](#python-quick-start-) +- [Node.js 快速入门🟩](#nodejs-quick-start-) +- [步骤 1：创建代理](#step-1-create-an-agent) +- [组件](#components) +- [心智模型](#mental-model) +- [概念](#concepts) +- [理解任务](#understanding-tasks) +- [任务的生命周期](#lifecycle-of-a-task) +- [工作流步骤的类型](#types-of-workflow-steps) +- [工具类型](#tool-types) +- [用户定义的`函数`](#user-defined-functions) +- [`系统` 工具](#system-tools) +- [内置 `integrations`](#built-in-integrations) +-[直接`api_calls`]（#direct-api_calls） +- [集成](#integrations) +- [其他功能](#other-features) +- [向代理添加工具](#adding-tools-to-agents) +- [管理会话和用户](#managing-sessions-and-users) +- [文档集成与搜索](#document-integration-and-search) +- [参考](#reference) +- [SDK 参考](#sdk-reference) +- [API 参考](#api-reference) +- [本地快速启动](#local-quickstart) +- [Julep 和 LangChain 等有什么区别？](#julep 和 langchain 等之间有什么区别) +- [不同用例](#different-use-cases) +- [不同的外形尺寸](#different-form-factor) +- [总结](#in-summary) @@ -1297,7 +1302,7 @@ result: string # Brave Search 的结果 设置： api_key: string # BrowserBase 的 API 密钥 project_id: string # BrowserBase 的项目 ID -session_id: string # （可选）BrowserBase 的会话 ID +session_id: string #（可选）BrowserBase 的会话 ID 参数： urls: list[string] # 使用 BrowserBase 加载的 URL diff --git a/README-FR.md b/README-FR.md index 2848a2637..1a5f5b1be 100644 --- a/README-FR.md +++ b/README-FR.md @@ -1,12 +1,12 @@ [English](README.md) | [中文翻译](README-CN.md) | [日本語翻訳](README-JA.md) | [French](README-FR.md)
- julep + julep


- Explorer les documents + Explorer les documents (en cours) · Discorde · @@ -62,39 +62,39 @@ Des nouvelles passionnantes ! Nous participons au DevFest.AI tout au long du moi -

📖 Table of Contents

+

📖 Table des matières

-- [Introduction](#introduction) -- [Principales caractéristiques](#principales-caract%C3%A9ristiques) -- [Exemple rapide](#exemple-rapide) +- [Présentation](#introduction) +- [Caractéristiques principales](#key-features) +- [Exemple rapide](#quick-example) - [Installation](#installation) -- [Démarrage rapide de Python 🐍](#d%C3%A9marrage-rapide-de-python-) -- [Node.js Quick Start 🟩](#nodejs-quick-start-) - - [Step 1: Create an Agent](#step-1-create-an-agent) -- [Components](#components) - - [Mental Model](#mental-model) +- [Démarrage rapide de Python 🐍](#python-quick-start-) +- [Démarrage rapide de Node.js 🟩](#nodejs-quick-start-) +- [Étape 1 : Créer un agent](#step-1-create-an-agent) +- [Composants](#composants) +- [Modèle mental](#mental-model) - [Concepts](#concepts) -- [Understanding Tasks](#understanding-tasks) - - [Lifecycle of a Task](#lifecycle-of-a-task) - - [Types of Workflow Steps](#types-of-workflow-steps) -- [Tool Types](#tool-types) - - [User-defined `functions`](#user-defined-functions) - - [`system` tools](#system-tools) - - [Built-in `integrations`](#built-in-integrations) - - [Direct `api_calls`](#direct-api_calls) -- [Integrations](#integrations) -- [Other Features](#other-features) - - [Adding Tools to Agents](#adding-tools-to-agents) - - [Managing Sessions and Users](#managing-sessions-and-users) - - [Document Integration and Search](#document-integration-and-search) -- [Référence](#r%C3%A9f%C3%A9rence) - - [Référence du SDK](#r%C3%A9f%C3%A9rence-du-sdk) - - [Référence API](#r%C3%A9f%C3%A9rence-api) -- [Démarrage rapide local](#d%C3%A9marrage-rapide-local) -- [Quelle est la différence entre Julep et LangChain etc ?](#quelle-est-la-diff%C3%A9rence-entre-julep-et-langchain-etc-) - - [Différents cas d'utilisation](#diff%C3%A9rents-cas-dutilisation) - - [Facteur de forme différent](#facteur-de-forme-diff%C3%A9rent) - - [En résumé](#en-r%C3%A9sum%C3%A9) +- [Comprendre les tâches](#understanding-tasks) +- [Cycle de vie d'une tâche](#cycle-de-vie-d-une-tâche) +- [Types d'étapes de flux de travail](#types-of-workflow-steps) +- [Types d'outils](#types-d'outils) +- [`Fonctions` définies par l'utilisateur](#user-defined-functions) +- [outils système](#outils-système) +- [`Intégrations` intégrées](#integrations-integrées) +- [Appels directs `api_calls`](#appels directs-api_calls) +- [Intégrations](#intégrations) +- [Autres fonctionnalités](#other-features) +- [Ajout d'outils aux agents](#adding-tools-to-agents) +- [Gestion des sessions et des utilisateurs](#managing-sessions-and-users) +- [Intégration et recherche de documents](#document-integration-and-search) +- [Référence](#référence) +- [Référence SDK](#sdk-reference) +- [Référence API](#api-reference) +- [Démarrage rapide local](#local-quickstart) +- [Quelle est la différence entre Julep et LangChain etc ?](#quelle-est-la-différence-entre-julep-et-langchain-etc) +- [Différents cas d'utilisation](#different-use-cases) +- [Facteur de forme différent](#different-form-factor) +- [En résumé](#en-resumé) @@ -684,7 +684,7 @@ Agent --> Documents[Documents] Documents --> VectorDB[Base de données vectorielles] Tâches --> Exécutions[Exécutions] -client classDef fill:#9ff,trait:#333,largeur-trait:1px; +client classDef remplissage : #9ff, trait : #333, largeur du trait : 1 px ; classe Utilisateur client ; classDef core fill:#f9f,trait:#333,largeur-trait:2px; @@ -1159,7 +1159,7 @@ description : Lister les agents à l'aide d'un appel système outils: - nom : list_agent_docs -description : Liste tous les documents pour l'agent donné +description : liste tous les documents pour l'agent donné type : système système: ressource : agent diff --git a/README-JA.md b/README-JA.md index 116df51ef..611629392 100644 --- a/README-JA.md +++ b/README-JA.md @@ -1,12 +1,12 @@ [English](README.md) | [中文翻译](README-CN.md) | [日本語翻訳](README-JA.md) | [French](README-FR.md)
- julep + julep


- ドキュメントを見る + ドキュメントを探索 (wip) · 不和 · @@ -62,36 +62,39 @@ Julep プロジェクトに新しい貢献者を迎えられることを嬉し -

📖 Table of Contents

- -- [主な特徴](#%E4%B8%BB%E3%81%AA%E7%89%B9%E5%BE%B4) -- [簡単な例](#%E7%B0%A1%E5%8D%98%E3%81%AA%E4%BE%8B) -- [インストール](#%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%BC%E3%83%AB) -- [Python クイックスタート 🐍](#python-%E3%82%AF%E3%82%A4%E3%83%83%E3%82%AF%E3%82%B9%E3%82%BF%E3%83%BC%E3%83%88-) -- [Node.js Quick Start 🟩](#nodejs-quick-start-) - - [Step 1: Create an Agent](#step-1-create-an-agent) -- [Components](#components) - - [Mental Model](#mental-model) -- [Concepts](#concepts) -- [Understanding Tasks](#understanding-tasks) - - [Lifecycle of a Task](#lifecycle-of-a-task) - - [Types of Workflow Steps](#types-of-workflow-steps) -- [Tool Types](#tool-types) - - [User-defined `functions`](#user-defined-functions) - - [`system` tools](#system-tools) - - [Built-in `integrations`](#built-in-integrations) - - [Direct `api_calls`](#direct-api_calls) -- [Integrations](#integrations) -- [Other Features](#other-features) - - [Adding Tools to Agents](#adding-tools-to-agents) - - [Managing Sessions and Users](#managing-sessions-and-users) - - [Document Integration and Search](#document-integration-and-search) - - [SDK リファレンス](#sdk-%E3%83%AA%E3%83%95%E3%82%A1%E3%83%AC%E3%83%B3%E3%82%B9) - - [API リファレンス](#api-%E3%83%AA%E3%83%95%E3%82%A1%E3%83%AC%E3%83%B3%E3%82%B9) -- [ローカルクイックスタート](#%E3%83%AD%E3%83%BC%E3%82%AB%E3%83%AB%E3%82%AF%E3%82%A4%E3%83%83%E3%82%AF%E3%82%B9%E3%82%BF%E3%83%BC%E3%83%88) -- [Julep と LangChain などの違いは何ですか?](#julep-%E3%81%A8-langchain-%E3%81%AA%E3%81%A9%E3%81%AE%E9%81%95%E3%81%84%E3%81%AF%E4%BD%95%E3%81%A7%E3%81%99%E3%81%8B) - - [さまざまなユースケース](#%E3%81%95%E3%81%BE%E3%81%96%E3%81%BE%E3%81%AA%E3%83%A6%E3%83%BC%E3%82%B9%E3%82%B1%E3%83%BC%E3%82%B9) - - [異なるフォームファクタ](#%E7%95%B0%E3%81%AA%E3%82%8B%E3%83%95%E3%82%A9%E3%83%BC%E3%83%A0%E3%83%95%E3%82%A1%E3%82%AF%E3%82%BF) +

📖 目次

+ +- [はじめに](#introduction) +- [主な特徴](#key-features) +- [簡単な例](#quick-example) +- [インストール](#installation) +- [Python クイックスタート 🐍](#python-quick-start-) +- [Node.js クイック スタート 🟩](#nodejs-quick-start-) +- [ステップ 1: エージェントを作成する](#step-1-create-an-agent) +- [コンポーネント](#components) +- [メンタルモデル](#mental-model) +- [コンセプト](#concepts) +- [タスクの理解](#understanding-tasks) +- [タスクのライフサイクル](#lifecycle-of-a-task) +- [ワークフロー ステップの種類](#types-of-workflow-steps) +- [ツールの種類](#tool-types) +- [ユーザー定義の `functions`](#user-defined-functions) +- [`システム` ツール](#system-tools) +- [組み込みの `integrations`](#built-in-integrations) +- [直接の `api_calls`](#direct-api_calls) +- [統合](#integrations) +- [その他の機能](#other-features) +- [エージェントへのツールの追加](#adding-tools-to-agents) +- [セッションとユーザーの管理](#managing-sessions-and-users) +- [ドキュメントの統合と検索](#document-integration-and-search) +- [参考](#reference) +- [SDKリファレンス](#sdk-reference) +- [APIリファレンス](#api-reference) +- [ローカルクイックスタート](#local-quickstart) +- [Julep と LangChain などの違いは何ですか?](#whats-the-difference-between-julep-and-langchain-etc) +- [さまざまなユースケース](#different-use-cases) +- [異なるフォームファクター](#different-form-factor) +- [要約](#in-summary) @@ -119,7 +122,7 @@ Julep を使用すると、意思決定、ループ、並列処理、多数の 3. 🔄 **複数ステップのタスク**: ループと意思決定を含む複雑な複数ステップのプロセスを構築します。 4. ⏳ **タスク管理**: 無期限に実行される可能性のある長時間実行タスクを処理します。 5. 🛠️ **組み込みツール**: タスクで組み込みツールと外部 API を使用します。 -6. 🔧 **自己修復**: Julep は失敗したステップを自動的に再試行し、メッセージを再送信し、タスクがスムーズに実行されるようにします。 +6. 🔧 **自己修復**: Julep は失敗したステップを自動的に再試行し、メッセージを再送信し、一般的にタスクがスムーズに実行されるようにします。 7. 📚 **RAG**: Julep のドキュメント ストアを使用して、独自のデータを取得して使用するためのシステムを構築します。 ![機能](https://github.com/user-attachments/assets/4355cbae-fcbd-4510-ac0d-f8f77b73af70) @@ -586,7 +589,7 @@ const タスク = client.tasks.create(agentId, yaml.parse(taskYaml)) を待機 /* ステップ 3: タスクを実行する */ 非同期関数executeTask(taskId) { -const 実行 = クライアントの実行の作成を待機します(taskId、{ +const 実行 = クライアント.実行.作成(taskId, { 入力: { アイデア: 「飛ぶことを学ぶ猫」 }, }); @@ -1305,7 +1308,7 @@ session_id: 文字列 # (オプション) BrowserBaseのセッションID urls: list[string] # BrowserBaseで読み込むURL 出力： -ドキュメント: リスト # URLから読み込まれたドキュメント +documents: list # URLから読み込まれたドキュメント ``` @@ -1457,7 +1460,7 @@ context_overflow="適応型" # 同じセッションで会話を続ける レスポンス = client.sessions.chat( -セッションID=セッションID、 +session_id=セッションID、 メッセージ=[ { 「役割」: 「ユーザー」、 @@ -1530,7 +1533,7 @@ metadata_filter={"category": "研究論文"} 1. `git clone https://github.com/julep-ai/julep.git` 2. `cd ジュレップ` 3. `docker volume create cozo_backup` -4. `docker volume create cozo_data` +4. docker ボリュームを作成します cozo_data 5. `cp .env.example .env # <-- このファイルを編集します` 6. `docker compose --env-file .env --profile temporal-ui --profile single-tenant --profile self-hosted-db up --build` diff --git a/README.md b/README.md index 5e7ef8071..8e0401ad7 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ [English](README.md) | [中文翻译](README-CN.md) | [日本語翻訳](README-JA.md) | [French](README-FR.md)
- julep + julep

diff --git a/cookbooks/00-Devfest-Email-Assistant.ipynb b/cookbooks/00-Devfest-Email-Assistant.ipynb index 18cafc1e8..e69cbb624 100644 --- a/cookbooks/00-Devfest-Email-Assistant.ipynb +++ b/cookbooks/00-Devfest-Email-Assistant.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"julep\"\n", + " \"julep\"\n", "
\n", "\n", "

\n", diff --git a/cookbooks/01-website-crawler.ipynb b/cookbooks/01-website-crawler.ipynb index 30deb0720..5cba53da5 100644 --- a/cookbooks/01-website-crawler.ipynb +++ b/cookbooks/01-website-crawler.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"julep\"\n", + " \"julep\"\n", "
\n", "\n", "

\n", diff --git a/cookbooks/02-sarcastic-news-headline-generator.ipynb b/cookbooks/02-sarcastic-news-headline-generator.ipynb index f8f11183c..c6b41b762 100644 --- a/cookbooks/02-sarcastic-news-headline-generator.ipynb +++ b/cookbooks/02-sarcastic-news-headline-generator.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"julep\"\n", + " \"julep\"\n", "
\n", "\n", "

\n", diff --git a/cookbooks/03-trip-planning-assistant.ipynb b/cookbooks/03-trip-planning-assistant.ipynb index dee1a8057..1795c7571 100644 --- a/cookbooks/03-trip-planning-assistant.ipynb +++ b/cookbooks/03-trip-planning-assistant.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"julep\"\n", + " \"julep\"\n", "
\n", "\n", "

\n", diff --git a/cookbooks/04-hook-generator-trending-reels.ipynb b/cookbooks/04-hook-generator-trending-reels.ipynb index 1d2e1d74e..7d8dfe6c3 100644 --- a/cookbooks/04-hook-generator-trending-reels.ipynb +++ b/cookbooks/04-hook-generator-trending-reels.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"julep\"\n", + " \"julep\"\n", "
\n", "\n", "

\n", diff --git a/cookbooks/05-video-processing-with-natural-language.ipynb b/cookbooks/05-video-processing-with-natural-language.ipynb index 4151c0425..37fa5fb5d 100644 --- a/cookbooks/05-video-processing-with-natural-language.ipynb +++ b/cookbooks/05-video-processing-with-natural-language.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"julep\"\n", + " \"julep\"\n", "
\n", "\n", "

\n", diff --git a/cookbooks/06-browser-use.ipynb b/cookbooks/06-browser-use.ipynb index 3c61f7dfb..28b6c35f6 100644 --- a/cookbooks/06-browser-use.ipynb +++ b/cookbooks/06-browser-use.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"julep\"\n", + " \"julep\"\n", "
\n", "\n", "

\n", diff --git a/cookbooks/README.md b/cookbooks/README.md index 9dd403a5a..a0059b863 100644 --- a/cookbooks/README.md +++ b/cookbooks/README.md @@ -1,5 +1,5 @@ -

- julep +
+ julep
# Julep AI - Notebook Overview From 2d41ed2ec6da210126e7f94c439471e0266a8a7e Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Fri, 22 Nov 2024 09:11:48 +0300 Subject: [PATCH 20/23] Update README.md --- cookbooks/README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/cookbooks/README.md b/cookbooks/README.md index a0059b863..416a35925 100644 --- a/cookbooks/README.md +++ b/cookbooks/README.md @@ -11,12 +11,12 @@ Each notebook explores a unique use case, demonstrating different aspects of Jul | **Notebook Name** | **Colab Link** | **Description** | **Implemented** | |------------------ |--------------- |----------------- |----------------- | | `00-Devfest-Email-Assistant.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/00-Devfest-Email-Assistant.ipynb) | Email assistant for managing DevFest communications | Yes | -| `01-Website_Crawler_using_Spider.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/01-Website_Crawler_using_Spider.ipynb) | Implements a web crawler using a spider to extract website content | Yes | -| `02-Sarcastic_News_Headline_Generator.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/02-Sarcastic_News_Headline_Generator.ipynb) | Generates sarcastic news headlines using a Brave Search Tool | Yes | -| `03-Trip_Planning_Assistant.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/03-Trip_Planning_Assistant.ipynb) | Plans trips using weather data and location information | Yes | -| `04-Hook_Generator_Trending_Reels.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/04-Hook_Generator_Trending_Reels.ipynb) | Generates engaging hooks for trending social media reels | Yes | -| `05-Video_Processing_With_Natural_Language.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/05-Video_Processing_With_Natural_Language.ipynb) | Processes videos using natural language commands | Yes | -| `06-Browser_Use.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/06-Browser_Use.ipynb) | Demonstrates browser automation capabilities | Yes | +| `01-website-crawler.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/01-website-crawler.ipynb) | Implements a web crawler using a spider to extract website content | Yes | +| `02-sarcastic-news-headline-generator.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/02-sarcastic-news-headline-generator.ipynb) | Generates sarcastic news headlines using a Brave Search Tool | Yes | +| `03-trip-planning-assistant.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/03-trip-planning-assistant.ipynb) | Plans trips using weather data and location information | Yes | +| `04-hook-generator-trending-reels.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/04-hook-generator-trending-reels.ipynb) | Generates engaging hooks for trending social media reels | Yes | +| `05-video-processing-with-natural-language.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/05-video-processing-with-natural-language.ipynb) | Processes videos using natural language commands | Yes | +| `06-browser-use.ipynb` | [Colab Link](https://colab.research.google.com/github/julep-ai/julep/blob/dev/cookbooks/`06-browser-use.ipynb) | Demonstrates browser automation capabilities | Yes | ## Potential Cookbooks for Contributors From dcda01297d3b686def1ee3c9580cc604fe9f07cd Mon Sep 17 00:00:00 2001 From: Vedant Sahai Date: Fri, 22 Nov 2024 02:07:04 -0500 Subject: [PATCH 21/23] fix(agents-api): added anthropic call in chat endpoint + typespec changed to include imageurl in ChatInput (#835) > [!IMPORTANT] > Integrates Anthropic API into chat endpoint, updates models for image content, and improves tool formatting and error handling. > > - **Behavior**: > - Added Anthropic API call in `chat.py` for tool types `computer_20241022`, `bash_20241022`, `text_editor_20241022`. > - Updated `request_anthropic()` to format messages and call Anthropic API. > - Modified tool formatting logic in `chat.py` for Anthropic tools. > - **Models**: > - Added `ChatMLAnthropicImageContentPart` and related models in `models.tsp` for Anthropic image content. > - Updated `ChatMLContentPart` alias to include `ChatMLAnthropicImageContentPart`. > - **Misc**: > - Updated `openapi-1.0.0.yaml` to reflect changes in `ChatInput` and related schemas. > - Added `Source` class in `Chat.py`, `Entries.py`, and `Tasks.py` for base64 image data. > - Improved error handling in `execute_integration.py` and `execute_system.py`. > - Updated Gunicorn configuration in `gunicorn_conf.py` for testing and debugging. > - Added `GEMINI_API_KEY` to `.env.example` and `docker-compose.yml`. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for bc0e0d4bf6b7bd5af064aa1fcc8ae90371dc00ac. It will automatically update as commits are pushed. --------- Signed-off-by: Diwank Singh Tomer Co-authored-by: Ahmad-mtos Co-authored-by: Ahmad-mtos Co-authored-by: Vedantsahai18 Co-authored-by: HamadaSalhab Co-authored-by: HamadaSalhab Co-authored-by: Diwank Singh Tomer --- .../agents_api/activities/embed_docs.py | 2 +- .../activities/excecute_api_call.py | 2 +- .../agents_api/activities/execute_system.py | 14 +- .../activities/task_steps/cozo_query_step.py | 2 +- .../activities/task_steps/evaluate_step.py | 2 +- .../activities/task_steps/for_each_step.py | 2 +- .../activities/task_steps/get_value_step.py | 2 +- .../activities/task_steps/if_else_step.py | 2 +- .../activities/task_steps/log_step.py | 2 +- .../activities/task_steps/map_reduce_step.py | 2 +- .../activities/task_steps/prompt_step.py | 172 +++++------------- .../task_steps/raise_complete_async.py | 2 +- .../activities/task_steps/return_step.py | 2 +- .../activities/task_steps/set_value_step.py | 2 +- .../activities/task_steps/switch_step.py | 2 +- .../activities/task_steps/tool_call_step.py | 2 +- .../task_steps/wait_for_input_step.py | 2 +- .../activities/task_steps/yield_step.py | 2 +- .../agents_api/autogen/openapi_model.py | 6 + .../agents_api/common/protocol/tasks.py | 6 +- .../agents_api/common/storage_handler.py | 3 + agents-api/agents_api/env.py | 1 - .../agents_api/models/chat/gather_messages.py | 3 +- .../agents_api/routers/sessions/chat.py | 65 ++++--- .../routers/tasks/create_task_execution.py | 2 +- .../workflows/task_execution/__init__.py | 2 +- agents-api/docker-compose.yml | 1 - agents-api/poetry.lock | 18 +- agents-api/pyproject.toml | 2 +- agents-api/tests/test_execution_queries.py | 5 +- agents-api/tests/test_session_queries.py | 7 +- cookbooks/06-browser-use.ipynb | 127 +++++++++++-- llm-proxy/docker-compose.yml | 3 +- 33 files changed, 266 insertions(+), 203 deletions(-) diff --git a/agents-api/agents_api/activities/embed_docs.py b/agents-api/agents_api/activities/embed_docs.py index 0dbf7f03b..80765bb79 100644 --- a/agents-api/agents_api/activities/embed_docs.py +++ b/agents-api/agents_api/activities/embed_docs.py @@ -13,7 +13,7 @@ from .types import EmbedDocsPayload -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def embed_docs( payload: EmbedDocsPayload, cozo_client=None, max_batch_size: int = 100 diff --git a/agents-api/agents_api/activities/excecute_api_call.py b/agents-api/agents_api/activities/excecute_api_call.py index 19549281a..09a33aaa8 100644 --- a/agents-api/agents_api/activities/excecute_api_call.py +++ b/agents-api/agents_api/activities/excecute_api_call.py @@ -20,7 +20,7 @@ class RequestArgs(TypedDict): headers: Optional[dict[str, str]] -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def execute_api_call( api_call: ApiCallDef, diff --git a/agents-api/agents_api/activities/execute_system.py b/agents-api/agents_api/activities/execute_system.py index 43a2db8e5..e42891d92 100644 --- a/agents-api/agents_api/activities/execute_system.py +++ b/agents-api/agents_api/activities/execute_system.py @@ -7,10 +7,12 @@ from fastapi.background import BackgroundTasks from temporalio import activity -from ..autogen.Chat import ChatInput -from ..autogen.Docs import ( +from ..autogen.openapi_model import ( + ChatInput, CreateDocRequest, + CreateSessionRequest, HybridDocSearchRequest, + SystemDef, TextOnlyDocSearchRequest, VectorDocSearchRequest, ) @@ -23,7 +25,7 @@ from .utils import get_handler -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def execute_system( context: StepContext, @@ -82,9 +84,9 @@ async def execute_system( # Handle chat operations if system.operation == "chat" and system.resource == "session": - developer = get_developer(developer_id=arguments.pop("developer_id")) - session_id = arguments.pop("session_id") - x_custom_api_key = arguments.pop("x_custom_api_key", None) + developer = get_developer(developer_id=arguments.get("developer_id")) + session_id = arguments.get("session_id") + x_custom_api_key = arguments.get("x_custom_api_key", None) chat_input = ChatInput(**arguments) bg_runner = BackgroundTasks() res = await handler( diff --git a/agents-api/agents_api/activities/task_steps/cozo_query_step.py b/agents-api/agents_api/activities/task_steps/cozo_query_step.py index 7a13ae6ec..16e9a53d8 100644 --- a/agents-api/agents_api/activities/task_steps/cozo_query_step.py +++ b/agents-api/agents_api/activities/task_steps/cozo_query_step.py @@ -8,7 +8,7 @@ from ...env import testing -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def cozo_query_step( query_name: str, diff --git a/agents-api/agents_api/activities/task_steps/evaluate_step.py b/agents-api/agents_api/activities/task_steps/evaluate_step.py index 4458bbd2d..06d3c7262 100644 --- a/agents-api/agents_api/activities/task_steps/evaluate_step.py +++ b/agents-api/agents_api/activities/task_steps/evaluate_step.py @@ -9,7 +9,7 @@ from ...env import testing -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def evaluate_step( context: StepContext, diff --git a/agents-api/agents_api/activities/task_steps/for_each_step.py b/agents-api/agents_api/activities/task_steps/for_each_step.py index df01c1ca8..76c74b3d6 100644 --- a/agents-api/agents_api/activities/task_steps/for_each_step.py +++ b/agents-api/agents_api/activities/task_steps/for_each_step.py @@ -11,7 +11,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def for_each_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/get_value_step.py b/agents-api/agents_api/activities/task_steps/get_value_step.py index dc9b73832..ca38bc4fe 100644 --- a/agents-api/agents_api/activities/task_steps/get_value_step.py +++ b/agents-api/agents_api/activities/task_steps/get_value_step.py @@ -8,7 +8,7 @@ # TODO: We should use this step to query the parent workflow and get the value from the workflow context # SCRUM-1 -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def get_value_step( context: StepContext, diff --git a/agents-api/agents_api/activities/task_steps/if_else_step.py b/agents-api/agents_api/activities/task_steps/if_else_step.py index 9b90647de..3d0a9739f 100644 --- a/agents-api/agents_api/activities/task_steps/if_else_step.py +++ b/agents-api/agents_api/activities/task_steps/if_else_step.py @@ -11,7 +11,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def if_else_step(context: StepContext) -> StepOutcome: # NOTE: This activity is only for logging, so we just evaluate the expression diff --git a/agents-api/agents_api/activities/task_steps/log_step.py b/agents-api/agents_api/activities/task_steps/log_step.py index 4c5158279..527a6f45f 100644 --- a/agents-api/agents_api/activities/task_steps/log_step.py +++ b/agents-api/agents_api/activities/task_steps/log_step.py @@ -11,7 +11,7 @@ from ...env import testing -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def log_step(context: StepContext) -> StepOutcome: # NOTE: This activity is only for logging, so we just evaluate the expression diff --git a/agents-api/agents_api/activities/task_steps/map_reduce_step.py b/agents-api/agents_api/activities/task_steps/map_reduce_step.py index 904a7082a..8237e37af 100644 --- a/agents-api/agents_api/activities/task_steps/map_reduce_step.py +++ b/agents-api/agents_api/activities/task_steps/map_reduce_step.py @@ -13,7 +13,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def map_reduce_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/prompt_step.py b/agents-api/agents_api/activities/task_steps/prompt_step.py index ad16ed6bd..bf2b413ae 100644 --- a/agents-api/agents_api/activities/task_steps/prompt_step.py +++ b/agents-api/agents_api/activities/task_steps/prompt_step.py @@ -1,24 +1,21 @@ -from datetime import datetime from typing import Callable -from anthropic import AsyncAnthropic # Import AsyncAnthropic client from anthropic.types.beta.beta_message import BetaMessage from beartype import beartype from langchain_core.tools import BaseTool from langchain_core.tools.convert import tool as tool_decorator -from litellm import ChatCompletionMessageToolCall, Function, Message -from litellm.types.utils import Choices, ModelResponse +from litellm.types.utils import ModelResponse from temporalio import activity from temporalio.exceptions import ApplicationError -from ...autogen.Tools import Tool +from ...autogen.openapi_model import Tool from ...clients import ( litellm, # We dont directly import `acompletion` so we can mock it ) from ...common.protocol.tasks import StepContext, StepOutcome from ...common.storage_handler import auto_blob_store from ...common.utils.template import render_template -from ...env import anthropic_api_key, debug +from ...env import debug from ..utils import get_handler_with_filtered_params from .base_evaluate import base_evaluate @@ -26,21 +23,6 @@ def format_tool(tool: Tool) -> dict: - if tool.type == "computer_20241022": - return { - "type": tool.type, - "name": tool.name, - "display_width_px": tool.computer_20241022 - and tool.computer_20241022.display_width_px, - "display_height_px": tool.computer_20241022 - and tool.computer_20241022.display_height_px, - "display_number": tool.computer_20241022 - and tool.computer_20241022.display_number, - } - - if tool.type in ["bash_20241022", "text_editor_20241022"]: - return tool.model_dump(include={"type", "name"}) - if tool.type == "function": return { "type": "function", @@ -85,7 +67,7 @@ def format_tool(tool: Tool) -> dict: @activity.defn -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def prompt_step(context: StepContext) -> StepOutcome: # Get context data @@ -162,114 +144,50 @@ async def prompt_step(context: StepContext) -> StepOutcome: for fmt_tool, orig_tool in zip(formatted_tools, context.tools) } - # Check if the model is Anthropic - if agent_model.lower().startswith("claude-3.5") and any( - tool["type"] in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - for tool in formatted_tools - ): - # Retrieve the API key from the environment variable - betas = [COMPUTER_USE_BETA_FLAG] - # Use Anthropic API directly - client = AsyncAnthropic(api_key=anthropic_api_key) - - # Reformat the prompt for Anthropic - # Anthropic expects a list of messages with role and content (and no name etc) - prompt = [{"role": "user", "content": message["content"]} for message in prompt] - - # Filter tools for specific types - filtered_tools = [ - tool - for tool in formatted_tools - if tool["type"] - in ["computer_20241022", "bash_20241022", "text_editor_20241022"] - ] - - # Claude Response - claude_response: BetaMessage = await client.beta.messages.create( - model="claude-3-5-sonnet-20241022", - messages=prompt, - tools=filtered_tools, - max_tokens=1024, - betas=betas, - ) - - # Claude returns [ToolUse | TextBlock] - # We need to convert tool_use to tool_calls - # And set content = TextBlock.text - # But we need to ensure no more than one text block is returned - if ( - len([block for block in claude_response.content if block.type == "text"]) - > 1 - ): - raise ApplicationError("Claude should only return one message") - - text_block = next( - (block for block in claude_response.content if block.type == "text"), - None, - ) - - stop_reason = claude_response.stop_reason - - if stop_reason == "tool_use": - choice = Choices( - message=Message( - role="assistant", - content=text_block.text if text_block else None, - tool_calls=[ - ChatCompletionMessageToolCall( - type="function", - function=Function( - name=block.name, - arguments=block.input, - ), - ) - for block in claude_response.content - if block.type == "tool_use" - ], - ), - finish_reason="tool_calls", - ) - else: - assert ( - text_block - ), "Claude should always return a text block for stop_reason=stop" - - choice = Choices( - message=Message( - role="assistant", - content=text_block.text, - ), - finish_reason="stop", - ) - - response: ModelResponse = ModelResponse( - id=claude_response.id, - choices=[choice], - created=int(datetime.now().timestamp()), - model=claude_response.model, - object="text_completion", - ) - - else: - # FIXME: hardcoded tool to a None value as the tool calls are not implemented yet + # Check if using Claude model and has specific tool types + is_claude_model = agent_model.lower().startswith("claude-3.5") + + # FIXME: Hack to make the computer use tools compatible with litellm + # Issue was: litellm expects type to be `computer_20241022` and spec to be + # `function` (see: https://docs.litellm.ai/docs/providers/anthropic#computer-tools) + # but we don't allow that (spec should match type). + formatted_tools = [] + for i, tool in enumerate(context.tools): + if tool.type == "computer_20241022" and tool.computer_20241022: + function = tool.computer_20241022 + tool = { + "type": tool.type, + "function": { + "name": tool.name, + "parameters": { + k: v + for k, v in function.model_dump().items() + if k not in ["name", "type"] + }, + }, + } + formatted_tools.append(tool) + + if not is_claude_model: formatted_tools = None - # Use litellm for other models - completion_data: dict = { - "model": agent_model, - "tools": formatted_tools or None, - "messages": prompt, - **agent_default_settings, - **passed_settings, - } - extra_body = { - "cache": {"no-cache": debug or context.current_step.disable_cache}, - } + # Use litellm for other models + completion_data: dict = { + "model": agent_model, + "tools": formatted_tools or None, + "messages": prompt, + **agent_default_settings, + **passed_settings, + } - response: ModelResponse = await litellm.acompletion( - **completion_data, - extra_body=extra_body, - ) + extra_body = { + "cache": {"no-cache": debug or context.current_step.disable_cache}, + } + + response: ModelResponse = await litellm.acompletion( + **completion_data, + extra_body=extra_body, + ) if context.current_step.unwrap: if len(response.choices) != 1: diff --git a/agents-api/agents_api/activities/task_steps/raise_complete_async.py b/agents-api/agents_api/activities/task_steps/raise_complete_async.py index adae15b7e..640d6ae4e 100644 --- a/agents-api/agents_api/activities/task_steps/raise_complete_async.py +++ b/agents-api/agents_api/activities/task_steps/raise_complete_async.py @@ -11,7 +11,7 @@ @activity.defn -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def raise_complete_async(context: StepContext, output: Any) -> None: activity_info = activity.info() diff --git a/agents-api/agents_api/activities/task_steps/return_step.py b/agents-api/agents_api/activities/task_steps/return_step.py index e58c4d7e7..e00ad6d20 100644 --- a/agents-api/agents_api/activities/task_steps/return_step.py +++ b/agents-api/agents_api/activities/task_steps/return_step.py @@ -11,7 +11,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def return_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/set_value_step.py b/agents-api/agents_api/activities/task_steps/set_value_step.py index 707dbdeb0..dd2553abd 100644 --- a/agents-api/agents_api/activities/task_steps/set_value_step.py +++ b/agents-api/agents_api/activities/task_steps/set_value_step.py @@ -11,7 +11,7 @@ # TODO: We should use this step to signal to the parent workflow and set the value on the workflow context # SCRUM-2 -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def set_value_step( context: StepContext, diff --git a/agents-api/agents_api/activities/task_steps/switch_step.py b/agents-api/agents_api/activities/task_steps/switch_step.py index 413611e27..95c136890 100644 --- a/agents-api/agents_api/activities/task_steps/switch_step.py +++ b/agents-api/agents_api/activities/task_steps/switch_step.py @@ -11,7 +11,7 @@ from ..utils import get_evaluator -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def switch_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/tool_call_step.py b/agents-api/agents_api/activities/task_steps/tool_call_step.py index 03525e5ed..7992de19e 100644 --- a/agents-api/agents_api/activities/task_steps/tool_call_step.py +++ b/agents-api/agents_api/activities/task_steps/tool_call_step.py @@ -47,7 +47,7 @@ def construct_tool_call( @activity.defn -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def tool_call_step(context: StepContext) -> StepOutcome: assert isinstance(context.current_step, ToolCallStep) diff --git a/agents-api/agents_api/activities/task_steps/wait_for_input_step.py b/agents-api/agents_api/activities/task_steps/wait_for_input_step.py index d9839bc8e..db3e41055 100644 --- a/agents-api/agents_api/activities/task_steps/wait_for_input_step.py +++ b/agents-api/agents_api/activities/task_steps/wait_for_input_step.py @@ -8,7 +8,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def wait_for_input_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/activities/task_steps/yield_step.py b/agents-api/agents_api/activities/task_steps/yield_step.py index ec6c08353..8480beb93 100644 --- a/agents-api/agents_api/activities/task_steps/yield_step.py +++ b/agents-api/agents_api/activities/task_steps/yield_step.py @@ -10,7 +10,7 @@ from .base_evaluate import base_evaluate -@auto_blob_store +@auto_blob_store(deep=True) @beartype async def yield_step(context: StepContext) -> StepOutcome: try: diff --git a/agents-api/agents_api/autogen/openapi_model.py b/agents-api/agents_api/autogen/openapi_model.py index 751239386..052362cbf 100644 --- a/agents-api/agents_api/autogen/openapi_model.py +++ b/agents-api/agents_api/autogen/openapi_model.py @@ -355,6 +355,12 @@ def validate_subworkflows(self): # Create models # ------------- +from ..common.storage_handler import RemoteObject + + +class SystemDef(SystemDef): + arguments: dict[str, Any] | None | RemoteObject = None + class CreateTransitionRequest(Transition): # The following fields are optional in this diff --git a/agents-api/agents_api/common/protocol/tasks.py b/agents-api/agents_api/common/protocol/tasks.py index 2d5dd8946..b4e37f892 100644 --- a/agents-api/agents_api/common/protocol/tasks.py +++ b/agents-api/agents_api/common/protocol/tasks.py @@ -82,6 +82,7 @@ "cancelled", "init_branch", "finish_branch", + "finish", ], # End states "finish": [], @@ -242,14 +243,17 @@ def model_dump(self, *args, **kwargs) -> dict[str, Any]: def prepare_for_step(self, *args, **kwargs) -> dict[str, Any]: current_input = self.current_input + inputs = self.inputs if activity.in_activity(): + inputs = [load_from_blob_store_if_remote(input) for input in inputs] current_input = load_from_blob_store_if_remote(current_input) # Merge execution inputs into the dump dict dump = self.model_dump(*args, **kwargs) + dump["inputs"] = inputs prepared = dump | {"_": current_input} - for i, input in enumerate(self.inputs): + for i, input in enumerate(inputs): prepared = prepared | {f"_{i}": input} if i >= 100: break diff --git a/agents-api/agents_api/common/storage_handler.py b/agents-api/agents_api/common/storage_handler.py index 99b30707f..894b0ff72 100644 --- a/agents-api/agents_api/common/storage_handler.py +++ b/agents-api/agents_api/common/storage_handler.py @@ -41,6 +41,9 @@ def load_from_blob_store_if_remote(x: Any | RemoteObject) -> Any: fetched = s3.get_object(x.key) return deserialize(fetched) + elif isinstance(x, RemoteList): + x = list(x) + return x diff --git a/agents-api/agents_api/env.py b/agents-api/agents_api/env.py index e33e3527a..3eb340cf4 100644 --- a/agents-api/agents_api/env.py +++ b/agents-api/agents_api/env.py @@ -25,7 +25,6 @@ hostname: str = env.str("AGENTS_API_HOSTNAME", default="localhost") public_port: int = env.int("AGENTS_API_PUBLIC_PORT", default=80) api_prefix: str = env.str("AGENTS_API_PREFIX", default="") -anthropic_api_key: str = env.str("ANTHROPIC_API_KEY", default=None) # Tasks # ----- diff --git a/agents-api/agents_api/models/chat/gather_messages.py b/agents-api/agents_api/models/chat/gather_messages.py index cd9feab29..ea3b4a21e 100644 --- a/agents-api/agents_api/models/chat/gather_messages.py +++ b/agents-api/agents_api/models/chat/gather_messages.py @@ -6,8 +6,7 @@ from pycozo.client import QueryException from pydantic import ValidationError -from ...autogen.Chat import ChatInput -from ...autogen.openapi_model import DocReference, History +from ...autogen.openapi_model import ChatInput, DocReference, History from ...clients import litellm from ...common.protocol.developers import Developer from ...common.protocol.sessions import ChatContext diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 17a93799d..200832f4f 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -1,6 +1,8 @@ -from typing import Annotated, Optional +from datetime import datetime +from typing import Annotated, Callable, Optional from uuid import UUID, uuid4 +from anthropic.types.beta.beta_message import BetaMessage from fastapi import BackgroundTasks, Depends, Header from starlette.status import HTTP_201_CREATED @@ -23,6 +25,8 @@ from .metrics import total_tokens_per_user from .router import router +COMPUTER_USE_BETA_FLAG = "computer-use-2024-10-22" + @router.post( "/sessions/{session_id}/chat", @@ -106,27 +110,14 @@ async def chat( # Get the tools tools = settings.get("tools") or chat_context.get_active_tools() - tools = [tool.model_dump(mode="json") for tool in tools] - - # Convert anthropic tools to `function` - for tool in tools: - if tool.get("type") == "computer_20241022": - tool["function"] = { - "name": tool["name"], - "parameters": tool.pop("computer_20241022"), - } - elif tool.get("type") == "bash_20241022": - tool["function"] = { - "name": tool["name"], - "parameters": tool.pop("bash_20241022"), - } + # Check if using Claude model and has specific tool types + is_claude_model = settings["model"].lower().startswith("claude-3.5") - elif tool.get("type") == "text_editor_20241022": - tool["function"] = { - "name": tool["name"], - "parameters": tool.pop("text_editor_20241022"), - } + # Format tools for litellm + # formatted_tools = ( + # tools if is_claude_model else [format_tool(tool) for tool in tools] + # ) # FIXME: Truncate chat messages in the chat context # SCRUM-7 @@ -144,12 +135,38 @@ async def chat( for m in messages ] - # Get the response from the model + # FIXME: Hack to make the computer use tools compatible with litellm + # Issue was: litellm expects type to be `computer_20241022` and spec to be + # `function` (see: https://docs.litellm.ai/docs/providers/anthropic#computer-tools) + # but we don't allow that (spec should match type). + formatted_tools = [] + for i, tool in enumerate(tools): + if tool.type == "computer_20241022" and tool.computer_20241022: + function = tool.computer_20241022 + tool = { + "type": tool.type, + "function": { + "name": tool.name, + "parameters": { + k: v + for k, v in function.model_dump().items() + if k not in ["name", "type"] + }, + }, + } + formatted_tools.append(tool) + + # If not using Claude model, + + if not is_claude_model: + formatted_tools = None + + # Use litellm for other models model_response = await litellm.acompletion( messages=messages, - tools=tools or None, - user=str(developer.id), # For tracking usage - tags=developer.tags, # For filtering models in litellm + tools=formatted_tools or None, + user=str(developer.id), + tags=developer.tags, custom_api_key=x_custom_api_key, **settings, ) diff --git a/agents-api/agents_api/routers/tasks/create_task_execution.py b/agents-api/agents_api/routers/tasks/create_task_execution.py index 24de79522..ecc0aa470 100644 --- a/agents-api/agents_api/routers/tasks/create_task_execution.py +++ b/agents-api/agents_api/routers/tasks/create_task_execution.py @@ -10,9 +10,9 @@ from starlette.status import HTTP_201_CREATED from temporalio.client import WorkflowHandle -from ...autogen.Executions import Execution from ...autogen.openapi_model import ( CreateExecutionRequest, + Execution, ResourceCreatedResponse, UpdateExecutionRequest, ) diff --git a/agents-api/agents_api/workflows/task_execution/__init__.py b/agents-api/agents_api/workflows/task_execution/__init__.py index 05f4ce795..67d1f9546 100644 --- a/agents-api/agents_api/workflows/task_execution/__init__.py +++ b/agents-api/agents_api/workflows/task_execution/__init__.py @@ -34,13 +34,13 @@ SleepFor, SleepStep, SwitchStep, + SystemDef, ToolCallStep, TransitionTarget, WaitForInputStep, WorkflowStep, YieldStep, ) - from ...autogen.Tools import SystemDef from ...common.protocol.remote import RemoteList from ...common.protocol.tasks import ( ExecutionInput, diff --git a/agents-api/docker-compose.yml b/agents-api/docker-compose.yml index 59a8cfd7d..23cb6dd61 100644 --- a/agents-api/docker-compose.yml +++ b/agents-api/docker-compose.yml @@ -28,7 +28,6 @@ x--shared-environment: &shared-environment S3_ENDPOINT: ${S3_ENDPOINT:-http://seaweedfs:8333} S3_ACCESS_KEY: ${S3_ACCESS_KEY} S3_SECRET_KEY: ${S3_SECRET_KEY} - ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY} x--base-agents-api: &base-agents-api image: julepai/agents-api:${TAG:-dev} diff --git a/agents-api/poetry.lock b/agents-api/poetry.lock index 668babc6f..df0f331b5 100644 --- a/agents-api/poetry.lock +++ b/agents-api/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -2317,12 +2317,12 @@ files = [ aiohttp = "*" click = "*" importlib-metadata = ">=6.8.0" -jinja2 = ">=3.1.2,<4.0.0" -jsonschema = ">=4.22.0,<5.0.0" +jinja2 = "^3.1.2" +jsonschema = "^4.22.0" openai = ">=1.54.0" -pydantic = ">=2.0.0,<3.0.0" +pydantic = "^2.0.0" python-dotenv = ">=0.2.0" -requests = ">=2.31.0,<3.0.0" +requests = "^2.31.0" tiktoken = ">=0.7.0" tokenizers = "*" @@ -2330,6 +2330,12 @@ tokenizers = "*" extra-proxy = ["azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (==0.11.0)", "resend (>=0.8.0,<0.9.0)"] proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "cryptography (>=42.0.5,<43.0.0)", "fastapi (>=0.111.0,<0.112.0)", "fastapi-sso (>=0.10.0,<0.11.0)", "gunicorn (>=22.0.0,<23.0.0)", "orjson (>=3.9.7,<4.0.0)", "pynacl (>=1.5.0,<2.0.0)", "python-multipart (>=0.0.9,<0.0.10)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.22.0,<0.23.0)"] +[package.source] +type = "git" +url = "https://github.com/julep-ai/litellm.git" +reference = "fix_anthropic_tool_image_content" +resolved_reference = "45a0d77a1446e0eb3feeebbf717ef43bfd0e7f7d" + [[package]] name = "lz4" version = "4.3.3" @@ -6345,4 +6351,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.12,<3.13" -content-hash = "32c25f376e0befcd3a02f7b983f26aa91f2e40c7ac8d993251fc8e7c77e1a60b" +content-hash = "b285c4b4ba58272c3c3e6a8cb57538b37b5367a69c711275f1be3ba1e55a7d5a" diff --git a/agents-api/pyproject.toml b/agents-api/pyproject.toml index 9ce019e0e..310570f23 100644 --- a/agents-api/pyproject.toml +++ b/agents-api/pyproject.toml @@ -23,7 +23,6 @@ arrow = "^1.3.0" jinja2 = "^3.1.4" jinja2schema = "^0.1.4" jsonschema = "^4.21.1" -litellm = "^1.52.3" numpy = ">=2.0.0,<2.1.0" tiktoken = "^0.7.0" tenacity = "^9.0.0" @@ -51,6 +50,7 @@ uvloop = "^0.21.0" anthropic = "^0.37.1" simsimd = "^5.9.4" langchain-core = "^0.3.14" +litellm = {git = "https://github.com/julep-ai/litellm.git", rev = "fix_anthropic_tool_image_content"} [tool.poetry.group.dev.dependencies] ipython = "^8.26.0" ruff = "^0.5.5" diff --git a/agents-api/tests/test_execution_queries.py b/agents-api/tests/test_execution_queries.py index 42904776d..0ea72ff95 100644 --- a/agents-api/tests/test_execution_queries.py +++ b/agents-api/tests/test_execution_queries.py @@ -3,10 +3,11 @@ from temporalio.client import WorkflowHandle from ward import test -from agents_api.autogen.Executions import ( +from agents_api.autogen.openapi_model import ( CreateExecutionRequest, + CreateTransitionRequest, + Execution, ) -from agents_api.autogen.openapi_model import CreateTransitionRequest, Execution from agents_api.models.execution.create_execution import create_execution from agents_api.models.execution.create_execution_transition import ( create_execution_transition, diff --git a/agents-api/tests/test_session_queries.py b/agents-api/tests/test_session_queries.py index c0036d9b3..8d7c07b36 100644 --- a/agents-api/tests/test_session_queries.py +++ b/agents-api/tests/test_session_queries.py @@ -3,8 +3,11 @@ from ward import test -from agents_api.autogen.openapi_model import CreateOrUpdateSessionRequest, Session -from agents_api.autogen.Sessions import CreateSessionRequest +from agents_api.autogen.openapi_model import ( + CreateOrUpdateSessionRequest, + CreateSessionRequest, + Session, +) from agents_api.models.session.create_or_update_session import create_or_update_session from agents_api.models.session.create_session import create_session from agents_api.models.session.delete_session import delete_session diff --git a/cookbooks/06-browser-use.ipynb b/cookbooks/06-browser-use.ipynb index 28b6c35f6..62e024305 100644 --- a/cookbooks/06-browser-use.ipynb +++ b/cookbooks/06-browser-use.ipynb @@ -138,7 +138,18 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: Ignoring invalid distribution ~ulep (/Users/hamadasalhab/Documents/repos/julep-ai/julep/agents-api/.venv/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulep (/Users/hamadasalhab/Documents/repos/julep-ai/julep/agents-api/.venv/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulep (/Users/hamadasalhab/Documents/repos/julep-ai/julep/agents-api/.venv/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], "source": [ "!pip install julep -U --quiet" ] @@ -187,7 +198,7 @@ "api_key = os.getenv(\"JULEP_API_KEY\")\n", "\n", "# Create a Julep client\n", - "client = Client(api_key=api_key, environment=\"dev\")" + "client = Client(api_key=api_key, environment=\"local_multi_tenant\")" ] }, { @@ -232,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -526,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -534,7 +545,11 @@ "task = client.tasks.create_or_update(\n", " task_id=TASK_UUID,\n", " agent_id=AGENT_UUID,\n", - " **task_def\n", + " **task_def,\n", + " extra_body={\n", + " \"run_browser\": task_def[\"run_browser\"],\n", + " \"check_goal_status\": task_def[\"check_goal_status\"],\n", + " }\n", ")" ] }, @@ -554,14 +569,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Started an execution. Execution ID: 8fbb3530-de78-46d3-93f1-b40d632d5367\n" + ] + } + ], "source": [ "execution = client.executions.create(\n", " task_id=task.id,\n", " input={\n", - " \"agent_id\": AGENT_UUID,\n", + " \"agent_id\": str(AGENT_UUID),\n", " \"goal\": \"Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\",\n", " }\n", ")\n", @@ -625,9 +648,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transition type: init\n", + "Transition output: {'agent_id': '07648b48-4ca7-4076-81db-c29eccc15f80', 'goal': \"Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\"}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'auto_run_tools': False, 'context_overflow': None, 'created_at': '2024-11-22T07:04:47.809701Z', 'id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'kind': None, 'metadata': {}, 'render_templates': True, 'situation': '', 'summary': None, 'token_budget': None, 'updated_at': '2024-11-22T07:04:47Z'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'avgCpuUsage': None, 'contextId': None, 'createdAt': '2024-11-22T07:04:51.047247+00:00', 'endedAt': None, 'expiresAt': '2024-11-22T07:19:51.009+00:00', 'id': 'a959cfd1-1b96-4c15-b025-863c31ffcea5', 'keepAlive': None, 'memoryUsage': None, 'projectId': 'c35ee022-883e-4070-9f3c-89607393214b', 'proxyBytes': 0, 'startedAt': '2024-11-22T07:04:51.009+00:00', 'status': 'RUNNING'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'browser_session_id': 'a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'urls': {'debuggerFullscreenUrl': 'https://www.browserbase.com/devtools-fullscreen/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true', 'debuggerUrl': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true', 'wsUrl': 'wss://connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/browser/bf6e1919-d6b7-44ee-8ec3-5cd8dd2faab8'}}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'debugger_url': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'connect_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'base64_image': None, 'error': None, 'output': 'https://www.google.com', 'system': None}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'choices': [{'finish_reason': 'tool_calls', 'index': 0, 'logprobs': None, 'message': {'content': \"I'll help you navigate to JulepAI's Github repository to check its stars. Let me break this down into steps:\\n\\n1. First, let me take a screenshot to see where we are:\", 'created_at': None, 'id': None, 'name': None, 'role': 'assistant', 'tool_call_id': None, 'tool_calls': [{'api_call': None, 'bash_20241022': None, 'computer_20241022': None, 'function': {'arguments': '{\"action\": \"screenshot\"}', 'name': 'computer'}, 'id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ', 'integration': None, 'system': None, 'text_editor_20241022': None, 'type': 'function'}]}, 'tool_calls': None}], 'created_at': '2024-11-22T07:05:08.603440Z', 'docs': [], 'id': 'cde04753-fd04-4016-a812-e13d4ac2e3fc', 'jobs': [], 'usage': {'completion_tokens': 95, 'prompt_tokens': 1335, 'total_tokens': 1430}}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'content': \"I'll help you navigate to JulepAI's Github repository to check its stars. Let me break this down into steps:\\n\\n1. First, let me take a screenshot to see where we are:\", 'tool_calls': [{'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}]}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}\n", + "--------------------------------------------------\n", + "Transition type: finish_branch\n", + "Transition output: {'base64_image': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABAAAAAMACAYAAAC6uhUNAAEAAElEQVR4nOzdd3wURRvA8d/u1fSEQBJ6kU7oHaTYRUURlSIK4otYAFGxIBYUBbuIDVFEREXpoKKgAtJBeu+dAEkgvVzdff8IHKRfIA3yfD+fKNmbnZ29QG7nmZlnFF3XdYQQQgghhBBCCHFNU0u6AUIIIYQQQgghhCh6EgAQQgghhBBCCCHKAAkACCGEEEIIIYQQZYAEAIQQQgghhBBCiDJAAgBCCCGEEEIIIUQZIAEAIYQQQgghhBCiDJAAgBBCCCGEEEIIUQZIAEAIIYQQQgghhCgDJAAghBBCCCGEEEKUAcaSboAQQgghhBBCiGtfbJKLFbtS2HQwjcPRDo7FOjgWYycpXSvpphVYoI9K9TALNcLM1Awz07K2Lzc0DqCcv6Gkm5YnRdd1vaQbIYQQQgghhBDi2nMm3sXERbEs2pzErhO2km5OkWta04fbmgUy+NbyRISUvvF2CQAIIYQQQgghhChUZ+JdjP81hm//OYvNWfa6nFaTwqBbyvNM97BSFQiQAIAQQgghhBBCiEJR1jv+WV0IBDx/bzjlA0p+eYAEAIQQQgghhBBCXDGHS+OFqaf49p9zJd2UUud/N4fywSOVMBtLNg+/BACEEEIIIYQQQlyRc8kuen1whPX700q6KaVW27q+zHyhJqEBJbckQLYBFEIIIYQQQghx2ZwunQfel85/ftbvT6PPh0dxukpuDF4CAEIIIYQQQgghLtuI76L474B0/r2xdl8qI76LKrHrSwBACCGEEEIIIcRlmfz3OaYskTX/BTFlyTkm/10y75nkABBCCCGEEEIIUWDRCU4aDtuDXbL9F5jVpLDz0wZEhJiK9boyA0AIIYQQQgghRIGNnRUtnf/LZHPqfPxrTLFfVwIAQgghhBBCCCEK5Ei0nalLZer/lZjyzznOxDuL9ZoSABBCCCGEEEKIUmDN2jU8OmgQ/3tsEOv/+++yjxeHd+ZEoxXh4H+AT85d1UrlimbKvKpAeHDxbs9nc+qMmx1drNcsdQGA6dOns379+pJuRqFITk4u6SYIIYQQQgghrhKffv45J06e4PiJE3z+5Ree4+MnTCjQ8eKwoQiy/psMCmMfqkTUlMac/q4xZ6Y25qsnqhLiZwDgxiYB7P+yIX07hxT6tV/vXZGDExtRtXzxrslfuTulWK9XIgGAffv20aBBA/7+++9sr02fPp3/ijB6dejQIc6dK7qpKg6HgzFjxtCoUSMaN25M48aNGTt2LHa7HQC3282uXbuw2WxF1gYhhBBCCFF26LpOSkoKRZnbe/Xq1WzZsiXbn0XhMqgXu2eX/jzNJlOBjhe1fVF2Dpy2F3q9bz9UieF3VeDH5XH0+fAo43+NoXenEL57ujoAmw+l8frPp1m2o/AHWmesiufVn04Rda54p+QfOG1nX1Thv5e5KZEAwMyZM9F1nV9++aXYr/3EE08wf/78Iqv/gw8+YMmSJcyaNYsDBw7wyy+/sGTJEt5//30AEhMTufPOOzl06FCRtUEIIYQQQlz7Tp06xdNPP01kZCQdOnSgfv369O3bl61btxb6tQ4dOsSJEycAmDt3LkuWLLniOj/88ENefPHFK67nWjLkqaeoXLkyNWvWZNiQoZd9vKj9vjGxSOrt1iKQQ2ccvPR9FL9vTOS9udH8tDyedvX8KB9gwM+qci7ZhZ/lYje2RS0fRt4XzoAby1E51MSAG8tRI8wMQJ/rQ+jSyJ/6lS0Mv6sCg24JzXTupXwtKglpbnzMKsF+BgbcWI56lS10aeTPC/eGc2+7oCK5Zyi69zMnxbvIAXA6ncybN4/XXnuN0aNHExsbS4UKFbKVW7FiBVu3bqVKlSrcfffdGI0Xm7pr1y6WLVuGoijcdNNN1K9fH8gYfZ87dy633nor5cqVA2DHjh3ExMTQsmVLFi1aRGJiIlu2bGHRokXcfvvthX5/K1as4L777qNhw4YANGrUiOHDhzNhwgR69+7NypUrAVi8eDF2u50WLVrkeU9ut5tZs2bRsWNH/v33X2rVqkXHjh0919q8eTPly5fnnnvuISAgoNDvRwghhBBClD7x8fH07NmTNm3asGLFCkJDQ0lLS2PKlCn07t2bxYsXU6NGjUK7Xv/+/QutrgvsdjtpaYU/jfxq1qF9Bzq073DFx4vafwdSi6TeozEOOjX0o2e7YOauSwBg6NcnGPp1RvDp9hZ+fDG4Ko9PPMGhM3H0aBvED8/UIM2hkZTmZkzfSoQGGHjk02McjXHw3oDK2J0aflYD8SkuaoSZ6dupHDe9fiDbtXu2D2b4XRVYtiMFf6vKF4OrcjjaQYi/AadLJyzIyMcLYnj959OFft/bj6YXep25KfYZAEuWLMFgMNCnTx+aN2/OnDlzspX58ccfmTx5MmfOnOHtt9/m2Wef9bw2a9YsevbsyYkTJzh69Ch33323Z0TfZrMxcuRITp486Sm/ePFivvzyS2w2G7t378Zut3PmzBkOHz5cJPdXp04d/vzzT86cOeM5ds8997B06VLOnTvH/v37ATh8+DDR0dH53pPL5WLkyJH07t2b3377jaioKABeeeUVRo4cSUpKCnPnzuXee+/F4XAUyT0JIYQQQojSZerUqfj7+/PJJ58QGhoKgK+vL0OHDuWHH34gPDwcgLFjx/Lll1/Sv39/mjdvTs+ePdm/f7/nWbxfv36kp2d0Pvbv30+vXr3o1KkTHTp0YMyYMZ5p5WPHjuWbb74psvs5ffo0N954I3PmzKFLly40bdqUGTNm8N1339GpUydatWrFb7/95ik/YcIEOnfuTMeOHenevTvbtm3zvLZgwQJuvvlmbrjhBj766COGDx/OokWLAIiJieGJJ57ghhtuoEOHDrzzzju43W4Atm/fzn333UfXrl3p0qUL7777LpqmFdk9X82iE1xFUu+z357gWIyDac9UZ/dnDXm9dwRVQnNfk//Wg5U4Feek0dDd1HlyN9OWZV/qrSjQ/Nk9RD69h5+Wx9O2rq/XiQSPRNup8dhO6j61i4On7fRoF3y5t5an6ITiW3ZQ7AGAmTNn0qNHDwwGAz179mTWrFnZyjRv3pxp06Yxbtw4vvvuO3777Tf2799PUlISb731Fu+88w7vvfceH3zwAWPGjGH06NGkpOSdPCEiIoIxY8YQFhZGt27deOqpp4rk/kaPHk1AQACdO3fm8ccfZ9GiRZ6Oefv27XnppZcAePLJJ+nWrZvX9/TEE08wc+ZMevXqxdq1a5k9ezZz5szh1VdfZebMmdhsNn799dciuSchhBBCCFG6rF27lttvvx1Vzf4436ZNG3x8fABISUnh559/5v3332fdunWkpKQwbNgwPv/8c9atW8epU6f466+/gIwp+ddffz0rV65k0aJFLFy4kH/++QeAuLg4EhOLbpqywWDg8OHDnD59muXLlzNmzBjefPNNHA4HK1eu5IUXXuCjjz4CYPPmzcyaNYsFCxawevVq7rrrLkaNGgVAdHQ0I0aM4L333mPZsmUEBwezaNEiT5Djueeeo1q1aixdupQlS5awZcsWvv/+ewDefPNNevbsyb///ssff/zB0aNH2b17d5Hdc06ull0AiqrDeuiMgzYv7OOJr05wLNbBCz3C2fxx/Ryn34cFGakZbmbe+gTOJmcEcTYdyj6j5OBpBzGJGQGL3SfSPed6Y/3+NNwauNxw8LTd6/MKqqgCKjkp1gBAbGwsy5cv59577wXgzjvv5MSJE2zatClTuUaNGnn+3LRpUypWrMi2bdvYvn076enp3HPPPZ7Xe/bsSXJyMjt27Ciem8hHhQoVmDlzJtOmTSM4OJgXX3yR2267jX379uVY3tt7atCggefPK1asIDw8nFWrVjFr1izmzZtH+fLl2b59e9HdmBBCCCGEKDUSEhIyLaNNSEjgyy+/9HxdWKOvKAqdOnUiIiICi8VCgwYN6NixI+XLl8disVCvXj3PzNWvv/6axx9/nKNHj3Lq1CmqVatWZLNms1IUBYDevXsDGX2AtLQ0+vbtC0CzZs087WzRogWrVq3C5XKxf/9+IiIiPPm11qxZQ82aNWnZsiUAAwcOxGq1AhnBgVWrVtGrVy+Sk5NxOp10796d33//HYDAwEDWrFnDoUOH8PPz46uvviIyMrJY7v+Cq2UXgKLqsBpUcLh0fvw3jm5jDtJyxF5ik1x89WQ1zEYlU9kQ/4ydAeJT3AW+jqLkX6Ywz8tPcc4AKNYcALNnzwZg/PjxnmNWq5WZM2d6/pHmJDAwkKSkJKxWKwEBARgMBs9rZrMZf39/EhISiqzdl6Ndu3a0a9eO119/naFDhzJixAjPL5dLxcfHF/iekpKSsNvtLF++3HOsSpUq1KxZs9DvQwghhBBClD7lypXLtOTU7XYTHx8PwNatW9m3bx833XQTkPEsfYHRaMyUN8poNHqmwE+cOJHvv/+eyMhIAgMDiYqK8rxWXC609cKz8aXfX5iOHxUVxeDBg1FVlZo1a5KWluZ5LSEhwZMLDEBVVSpVqgRkzGIAGDZsWKZrXlgu8cEHH/Dxxx/Tu3dvTCYTffv2ZciQIZme04va1bILgNWk4HAV7vUaVrXy3wf1+PafcwyfnLGke/8pO4u3JDH41vLZpu0fj3Wg69CgitVzrKg66NeSYg8A9OzZM1Nn/7rrruOHH35g9OjR+Pr6ZjvH4XAQFRVFpUqVqFKlCnFxcZw9e5by5csDGdlPk5KSqFGjBmZzRrZHl6v4plBc6uTJk9x3331MnTrVM2Lv5+dHjx49eP7553P8R1mjRo087ykn1apVIyAggM8//7zI7kUIIYQQQpReHTt2ZM6cOYwYMQKj0UhoaCivvPIKAF9++WWus09zEx8fz3vvvcc///xD7dq1AejVq1eht7swTJo0iRo1avDFFxkj3mvWrGHFihVAxrN3cnLmLeJiY2OBix39H3/80ZM34VLly5dn3LhxjB07lk2bNvHMM88QHh7umZVQHIY89RRfT56M2WzmfwMfvezjRS082ERSeuFuXbf7hI2NB9Pof0M5Tp5zsvFAKlXKm7m/Qwgnzjo5Fuug/iWd/XSHzu8bE+nRNpgh3dI4HG3nxXsjCrVNxSU82LucBIWh2JYAbNq0iSNHjvDiiy/Sp08fz9eIESMwm80sXLjQU/bnn3/mxIkTOJ1OvvjiC1RVpVOnTkRGRtKsWTPeeustbDYb6enpjBkzhpYtW9KgQQOsVisVK1bk77//BuDEiROehB8X+Pn5ERMTUyQRssqVK1OpUiXeeustjh8/DmT8wpkxYwYtW7ZEURRPkCMmJgYg33vKSffu3Tl58iSTJk1C0zTsdjujRo1izZo1hX5PQgghhBCi9Onfvz8Oh4Onn37aM/IPGevjf/vtN8LCwgpUX1JSEgAhISEArFy5kj179pTKLP1JSUmeUX673c60adNwOp04nU6aN2/Ovn37OHLkCJCxZeGFeytXrhzNmjXj559/9tQ1bdo05s2bh8Ph4NFHHyU6OhpFUWjVqhX16tXLFkwoah3ad2Dqt1P4euJXtG3T5rKPF7Xw4KIZR+757mFmrU7gxXvD+e3V65j4RFX2nLTR893D5NR9e2FqFHtO2nhvQCV+fLYGZ+IzptJfbbkbi+r9zEmxXWnmzJl07Ngx25Z/JpOJ7t27M3PmTB544AEgI6LZp08foqOjsVqtfPDBB/j7+wMZGT+HDRvmWY8TGRnJZ5995qlv9OjRjBgxgm+//ZagoCAiIyM9/+ghIyP/m2++ydKlSz2BgsKiKApTpkzhzTff5JZbbgEyfindcMMNvPPOO0DGkofbb7+dgQMH8vDDD/PWW2/le09ZVapUicmTJzNy5Eg++eQTdF2na9euNG7cuFDvRwghhBBClE5BQUHMnj2bt956iw4dOhASEkJycjLly5fn4YcfZsCAAQWqr3r16txzzz3ccccdVKhQgaZNm/Liiy/y/vvvU6dOnSK6i8vTv39/Bg0axI4dO3A6nbz++uvs3LmT+++/nwULFvD000/Tq1cvypcvT9euXWnYsKEnx8CHH37IM888w++//46mafj5+fHFF19gNpvp3Lkzd999N5UrVyYhIYFKlSqV2lkQJa2oRqzjUtwM/vI4QyadICzYyLkkFzbnxZ7/os1J+Pe5uONDbJKLW984gL/VwNkkF0/fVYFbmgVw/GxGEvbqj+3MVP+E32OZ8Htsjtd+5cdTvPLjKc/3l14H4P73j1zx/eWmOGcAKHpxLhYpoJiYGIKCgrBYLNlei42NRVEUz7T5SzmdzmyJUS4VHx+PyWTyBBWKgt1u59y5c4SGhubY/piYGIKDgz3LFiDve8rNuXPnMJvNmdZyCSGEEEKIssPhcBAfH4+fn98VP98mJiZiMBg89dhsNsxmc467DZQkl8tFfHw8oaGhqKqKpmk4HA6sVitnz571zBBQVZUOHTrw4Ycf0qFDB8/5F0b2sz5Du1wu4uLi8Pf3z3F5clFbs3YNk7+dgqLA4McGe0b1C3q8qH20IIbRP58ulmvlZWy/StzZKpAfl8fhY1YZckcFjkQ76DxqP053qe3mZvNm34qMuKdgs3YuV6kOAAghhBBCCCGEt2w2G506daJ3797ceOONLF26lDlz5rB06VLP1oilWZ9+D3LuXMZe9hEREfwwNWOLwgf69PYkCPfmeFHbF2Wn5Yi9xXKtvIQHGxl1fwSdG/njcuus3pPKuNlnPNv+XS02fVSfepWzDxoXhWJNAiiEEEIIIYQQRcVqtTJ79my+//57vvnmG6pUqcKcOXOuis4/XD27ANSrbKFORQsHThduIsCCik5weXYMuFrVqWgpts4/SABACCGEEEIIcQ2pXr06r7/+ekk347JcLbsAANzSLKDEAwDXgrtaBxXr9WQJgBBCCCGEEEKIAjkaY6fliH3YndKdvFwWk8LezxtQIega3AZQCCGEEEIIIcS1oUaYhae65Zx0XXjnqW4VirXzDxIAEEIIIYQQQghxGZ7vEUagj3QpL0eAj8oLPYon8/+l5KclhBBCCCGEEKLAgnwNfPK/KiXdjKvS549VJdDXUOzXlQCAEEIIIYQQQojL0uv6EJ69u/hHsq9mz94dxn0dgkvk2hIAEEIIIYQQQghx2d7sE0HH+n4l3Yyrwi1NA3izT0SJXV8CAEIIIYQQQgghLpuqKvz4bHWqhBZvQrurTeVQE1OHV0dVlRJrgwQAhBBCCCGEEEJckQpBJrZPqM//bg4t6aaUSgNuKMeOCfUJKoF1/5dSdF2XjRuFEEIIIYQQQhSKyX+f5bkpUWjS00RV4NPHqvDIjaUjMCIBACGEEEIIIYQQhWr/KTsTfo9h+vJ4nO6y1+U0GqBvp3I8e3cYdStZSro5HhIAEEIIIYQQQghRJKLOOfloQQzfLzuH3Xntdz19LSqP3hTKsDsrULkU5kSQAIAQQgghhBBCiCK3fn8ac9cmsPFQKgmpbuJT3CSmua/KwIDFpBDkayDYz0CIv4F29fy4t20wrWr7lnTT8iQBACGEEEIIIYQQogyQXQCEEEIIIYQQQogyQAIAQgghhBBCCCFEGSABACGEEEIIIYQQogyQAIAQQgghhBBCCFEGSABACCGEEEIIIYQoAyQAIIQQQgghhBBClAESABBCCCGEEEIIIcoACQAIIYQQQgghhBBlgAQAhBBCCCGEEEKIMkACAEIIIYQQQgghRBkgAQAhhBBCCCGEEKIMkACAEEIIIYQQQghRBkgAQAghhBBCCCGEKAMkACCEEEIIIYQQQpQBEgAQQgghhBBCCCHKAAkACCGEEEIIIYQQZYAEAIQQQgghhBBCiDJAAgBCCCGEEEIIIUQZIAEAIYQQQgghhBCiDJAAgBBCCCGEEEIIUQZIAEAIIYQQQgghhCgDJAAghBBCCCGEEEKUARIAEEIIIYQQQgghygAJAAghhBBCCCGEEGWABACEEEIIIYQQQogywFjSDRBCCCGEEEVL13XP/y98Zf0+p+NZzxdCiGuNoiiZ/nzh+wt/vvQr6/Gs518NJAAghBBCCHGNyqujf+mXpmk5Hrv0XCGEuBZd2plXVTVbpz+nY1mDABfquRpIAEAIIYQQ4hqUdUT/0k7+hT9rmoamabjdbjRNw+UGTQcUFVBQVAOKoqKosmpUCHFt0jUNze0GXQM00DVUBYyGjICAwWBAVVVPIODSgICa5Xfj1RAEUHQJ6QohhBBCXDNyG/W/0Nm/0Pl3uVw4nS5cmgKKAdVovioeXoUQojjouo7mcoDuxmjQMRmNGI1GT8f/0qDA1bQsQAIAQgghhBDXiKyd/6wj/ZqmeTr+Tk3FYLKW6gdVIYQoDXRdx+1Mx2wAo9GA0WjMFgS4NBgApTcIIAEAIYQQQohrQE7T/bN2/B1OJ04XGEw+Mq1fCCEKSNc03M50TEYwm0x5BgKgdAYBJAAghBBCCHGVy9r5z/rldDqxO5zoqhWD0VTCrRVCiKub2+UEdzpWixmTyeQJABgMhlIfBJAkgEIIIYQQV7G8Ov9utxun00mazYHJEpAtYZUQQoiCMxhNaKpKWnoKPrqOyXQxsHrh9+yFIICu66UqCCABACGEEEKIq1R+nX+Hw0G63YXZJ6iEWyqEENcWVTWgWANJsyXjo+uYzeYcypS+IIAEAIQQQgghrmI5Zfp3u93Y7XZsDjdmn8CSbqIQQlyTFEXB7BNIenoSuq5jsVgyvXYhSFtaOv8gAQAhhBBCiKtSblv8XRj5T0u3Y/ELKelmCiHENc9kDSAtNT7TMqsLnf6cjpUkWQgmhBBCCHGVuTSH86VBAM+a/7R0TD6BpeJhUwghrnWKomDyCSQ1NQ2Xy+UJyF74/XxBaci/LzMAhBBCCCGuQjmN/LvdbtJtNlSzHwaDPOYJIURxMRiM6BZ/0tPTPKP+lwZhL90ZoCTJDAAhhBBCiKvIpYn/si4BsNvtuNwKJrO1hFsphBBlj9FkwelWsNvt2WYBXPq7uyRJAEAIIYQQ4iqTU+ff5XJhszuw+AaUdPOEEKLMsvgGkJ5uy3EpQEl3/kECAEIIIYQQV52ckv/Z7XZ0xYSqGkq6eUIIUWapqgFdNeFwOCQAIIQQQgghrsylD5IXggBOpxO7w4mPn2z5J4QQJc3qG4jN7sDpdJa6IIAEAIQQQgghrkKXPkw6nU7cWunYYkoIIco6VVXRdAWn01lqOv4XSABACCGEEOIqknX03+1243A4MJok8Z8QQpQWBqNFZgAIIYQQQogrc2nnX9d13G43TpcLq69/STdNCCHEeVZf/4ydWVyuTL+zJQAghBBCCCEKJOsMADDI9H8hhChFFEUBxYjb7S41nX+QAIAQQgghxFUlaxJAp9OJrsgjnRBClDY6imcGQGlZAmAs0asLIYQQQogCyZoDwOVyUVof6RR0VDQMKiiKTsbjcAb9fAlNB01X0HUFDQWQmQxCiGuDohpwuVylKgdA6fy0EEIIIYQQucoaBDCYTCXdJA9V0TEoOuhuDAYVdO2S5QkXO/cXj2SUvxAQcGug6SqaTFQVQlzlDEYTbqej1HT+QZYACCGEEEJcVS59gLyQA8BoLPkAgIqGWXVjVl0YVQ2jQUFB9zo3QcbYv45R1TGpbiwGF4ruLBUPzN5yOp088tAD9LmvO2lpaUV+vb8W/0HX61sxf+7MIr/WteaDd9+iTfMGnDh+rKSbIq5hRqPJkwPggpL+nSYzAIQQQgghrjKXjiS53W6MJTgDQEHHpGqoyoWR/iufwp8RM9CxGMGtuXDpKjqGK673Si1etJCff5zKoYMHMBpNNIxszBNPPU3jJs0ASEtLZd/ePbjdbhIS4vH19S3S9uzeuYO01FR27thOj569rqiun3/8HqPJxAO9Hyyk1sH6tav56Yep7Nq5HafLScWIStxw8630efBhgoNDCu06QpRWRpPpfKJWSs0MAAkACCGEEEJcpS48UBoMJfNIZ1I1DIq70Dr+OTGooOoabl3HpRuK7Dr5WTBvNmPHvIbFYqFBw0jS09PZsH4t27Zs4pvvfqJBw0iCgoL5avI0HA4HlSpVLvI2Pfb4EOrUrUenLjdccV2/TJ+Gj49voQUApkz+iq++mADAdbXrYrGYOXb0KFO+mchfixYy8evvCY+IKJRrCVFaGQzGUtPxv0ACAEIIIYQQV5ELD5IluqZU1zEZ3BhVKI4OuaKAUdFRNBdujGh68QcBfpn+A/7+Afzwy1wqV64CwJK/F/Pyi8/w4/dTGPvexwA0bdai2Nrk5+/Pnd17FNv1vLVh/Vq++mIClSpX4YOPP6NO3foA2O12Pv/0I2ZM/4FvJn3Oq6PfLuGWClH0sv6uLulggAQAhBBCCCGuMpc+QBb3w6SqgMngRvWyD67r5/MAGFRQjVyaEkDXNBSXC12/sD4270ozZgO4cGJA04s3lZXL5cTqYyUsLNxz7KZbbmPC519TtVp1z7E+93UnPT2NBX8sATLu/8dpU5j1y0/Ex8cR2bgp/R8ZxPChg+n/yCCGDh9BfHwct93YkYf6P4rZYmH+3Fk4HQ7ate/ICy+/lut0+QsBiNfeGEv3e3p6vn9r3AesXPEvK1csI8A/gDvvvpfBTwzFYMi+jGLrlk0MfvQhz/dtmjegRcvWfDV5GgAHD+5n4uefsHXzJjRNy7bsISdTJn8FwDvvj/d0/gEsFgvPjhhJ+fIVaNiocaZzVvy7lO+mTOLQgf34+PrRrn1Hhgx7jrDwi++3pmlM/3Eq8+fOIvrMaUJDy3PbHd15dNATWCwWT7kjhw8x/sN32bplI/4BAdx3fx9Onz7Fr/PnsHLd1kxlL7V/3x6++HQ827dtQUenXr0GPDroCdq275jrvQqRn5L8fZ0TSQIohBBCCHGVKu6HyYz1/q58O/+edll9UAMDUfz8wWWDmNNop056vkhJOl8mCMXH7/xSgrzvSVHArLpRFS3PcoWtc5cbORsby7An/8f6tas963rbd+xElarVcj1v4hcT+OyTD7E7HHTqfAPnzp1l5IvP5Fj21/lzmDH9B5o0bUZAYCB///Un748bU+C2fvDu2+zcvpU2bduTnp7Od5O/Yt6cnBMFVggLp/eDD3u+7/3gw9x4821ARkf6fwP6snrlcuo1aEiz5i3ZunkjTwzqz9Ytm3KsLy0tja1bNtGwUSQNGkZme11VVQYMfIzWbdp5jv3x+wKef3YIRw4fok27DlSpUpU/F/7Ko/17k5iY4Cn3zluj+XT8B9htNjpc3xmjycR3k79i5PPDPWXOxsYw+NGHWLd2FQ0bNaZBw0imTP6Kv//6M8/37MjhQwwa2I9dO7dz+x13cedd9xB18gTDhw5mx/ateZ4rRH5KQ8f/ApkBIIQQQgghvKBj9nLkX/UPAEA7sBf32pVou7ahn4lCj48Htws0DVQVAgNRK0Sg1K2Poe31KI2bofoGoCcnn58VkPvFTKobh1tBL6acAI8/9TQ2WzpzZ89g2FODCAoK5tbb7+DBhwd6lgRklZycxE/TplCpchW+/2kWQUHBuFwuXnh2KKtXLc9W3m63MXnqdOrVb4jNZqN3zztZsXwpmqahqt6P2wWHhDD1h5n4BwRw+NBB+tzfnX+X/cP9vfpmK1u5chVGvDCKFcuW4OPjy4gXRnle+/zTj0hPS+PjTydyfaeuAOzetZPHBj7IhI/f47sfsgcVYmOicbvd1Kh5XabjX37+CYkJ8ZmOPTn0Gfz8/Bn/4bsEB4fw/fTZVKxYCYAZP//IR++PZdp3kxn2zPPs27ubBfNn07xFKz79cjIWiwW3282rI0ew5J/FrF61nI7Xd+Hnn6aRmJjAsyNG0vehAQBs27qZx//3MHn5bMKH2NLT+Xzit54ZHT0f6EO/Xj2Y+ctPec54EOJqIjMAhBBCCCFEvsyqlmfnX9d1FLMZxc8fbe0KHC8NxfH0o7imfIm2ZQP62VjQz3f8DYaMofyEBLRd23HN+AHHC0OwP/EQrjnTwaCi+PiR12wABTAb3FBMI2tms5kXRr7G74uW8fxLr1Knbj1mzZhO3wfuZsP6tTmes2P7VpxOJz16PkBQUDAARqORhx/5X47lGzdtTr36DQGwWq00imyCw+EgPj6uQG2948678Q/ICMLUuq42IeVCiY2JLlAduq7z37o1NGrcxNP5B2jYKJLrO3dl184dJCUl5ngekG37x8V//Ma8OTMzfaWmprJ3zy4SExO4u8d9ns4/wAO9HyQkpBzr1q4GYM3qlQAMePQxzxR+g8HA/x5/CsBTbvPmDfj4+vJAn36eupo2a0GLlq3zvN9VK/4FYNAjD3LbjR257caO9L3/bjRN41TUyXzfLyGuFjIDQAghhBBC5OnCNn+5jcjruo7qH4AWewbXxPFoy5dkdPJ9/cDHN6OTfqGjrmkXq1EUMBozlghobjhxHOd7Y3D98Svm519FbRCJlpSUa7sUMpIROrXie6QNLV+BXn360atPP3bv2smTgwfw9puvMn/hP9k6vcnn235p3gCA8PCcs99nPW4wnr+vAgY5KmS5ntFoyLQPuTdSU1Kw2+1UjKiU7bWI88cS4uMJDAzK9FpYWDiqqnL06OFMxy/kRAB4ZujjrFm9AoD4uIzgRkTFzNdRVZWw8AjOnYv1XAugYsXMuytEhFfM9HpSYiIhIeUwGjP/ncj6nmS914z7qphjYsKAwMBczxXiaiMzAIQQQgghRK5URb9kq7/sdF1HDQxE27oBx9OPZnT+g4MhICCjg5+18+85MYc/+/iglC+Pvm83jqcG4JozHcWcc8K2CwyKjqGI8wEcOXyIIU88ypeff5LpeMNGkTRr1pLTp0+RnJw9UHGh4xiTZfT9zJnTRdbWwuLn74/FYsmxrWfOnAIgpFy5bK/5+vnRKLIJu3ZsZ9vWzdlej4mOZvOm/zzfh4Rk1BGd5TqaphETfYaQkNBM1zpz+lTmtkRnnBcckpEoMTAoiPj4OJxOZ5brnsn1Xn39/DCbzdSsVZs27Tp4vlq3bU/lKlVzzGUgxNVKAgBCCCGEECJXRkXLv/O/ejmOl4dDUlJG51/n4uz9S0evVTXjC0BRM74yVwi6jhIUhJ6YgLbsb9Bzv/7FNrrJL3nglShfoQI7tm3hl5++Z/u2LZ7jhw8dZM/unfj7B+Dn55/tvMZNmmEymZg/d5Znurzb7eanaVOKrK2Xy2yxkJiUkGkKf5t2Hdi5Y5tntB5g755drF65nEaRjQkIyHlk/KH+jwIwZvQojh65OBMgNiaGV18egc1m8xyr37ARQUHB/LpgbqZgw5xZvxAfH0e78xn423e4HoBpUydjt9uBjCDBd99k7DhwoVyLFq1JT0tjzqxfPHVt37aFLZs35nrviqLQuk17Nvy3lr17dnmOL160kHu738ofvy/I9VwhrjayBEAIIYQQQuRI0d0Y1Jw71p7O/5aNOMaMBKMRzOZLC5yvRAG3G2zp4HCgXzILQDGZMs65dLq2oqDHxmK46XZMYz4EXcs3g7aigAE37iJ6tA0ICOTJoc8y/sN3eGxgP2rXqYeiKBw5fBCn08nQ4SNy3GIvICCQBx96hO+/+4be93WnRYtWHDywn4RLMtuXFg0aRrLoj9945KEHuK52XV5/cxxDhj3Hxg3rGTH8KVq2bovJaGLDf2vRNI2nn30x17puuOkWevXpx8xffqLP/d2pU7c+uq5z5PBBzGYLt3W7i8V//g6AyWTimREv8ebrL9P3/rtp1aYt8XFxbN+2hQphYZ58CfXqN+TuHvfx6/w59Lr3Dho0iuTwoYMcPXKYDh070/H6LgD07defBfNm8/EH41jx7xJ8/fxYv3Y1QcEhxMedy7XNTw4dzsYN6xj86EN0uL4zbreblcuXUblKVTp3ubEQ32khSpYEAIQQQgghRI5M2fu0HqrFghZ9Gse7r2WM6l/o/F/aV9d1SE4CgwG1fiOUuvVRKmSsxdbj49CPHELbuRU9IR7FPwCMRvTYWNSOXTM6/4DucpHXbgAXGFUdt6Z7VfZy9O3Xn/DwCKb/OJX9+/diMppoFNmEvv0GcMNNt+R63lPDnsXX15fZs35mxfKlNGnWghdHvc6Tjw1AzSFoUFKGPj2Cs2dj2bFti2e9fK3rajP5u5/48rNP2LZ1M5qu0aRpcwY/OYxmzVvmWd/zL71K46bNmfnzjxw4sA+jwUjbdh0YMnwES/5enKnsnd174Ovrx7Sp37B+7Wp8fHy59fY7GTb8eYKDQzzlRr02hipVq/Hr/DmsWvEvoaHlGTDwMQY9PsRTpnyFML76dhoff/AOO7ZtITAoiCeHPMOhQwf4bcHcXHdTqFuvAV9N/oEvPx/PurWrMZvM3HDjLQx79gVPQkUhrgWKXpo2JRRCCCGEEHmy2Wy4XC5cLhc2m41z585RoVKtQr+OquiYVVeu0+8VP38cY166uOYfMnf+nQ5IS0PtchPG3v1Ra9UBS5b1/JqOfvIYrt/n4p77C3rcOdQut2AeNx4UFd1hoyAdeqdmwK2XvhWuaWlp+Pr6er7fsX0r/xvQl2eee4kHH36k5Bp2jbLb7RiNxkyzMoY9NYid27exbNWGEmyZKItiTx0mNDQUq9WK0WjEaDRitVpLrD2l7zekEEIIIYQocQZFz7Hzr+t6xlZ/G9bgXrwczm9vl6nzb7eBy4Xphdcxv/E+Su366A4HWlJS5q+UZAiriOmp5zC/+QGGO+7F9Ob7l9X5v9Dm0sTlcnHfPbfzUJ97SUtNBTLev1kzpgPQrEWrkmzeNWnenBnc1LkN8+fO8hw7fOggmzasz3fWghBlgSwBEEIIIYQQ2elucuqAK4qC7nZjNb6NeutZ0laXQ/W1ZSwD0HVwOUHTML/+LmrHLujJyWREB5RMAYULk1B1hx3dYUNp0xFz+87otrTL6vwDKGigqxlJAUoBo9FI1xtu5ofvv6XvA3fTrEUrjhw+yN49u7m+UxcaNpLs8oWtQ8cufGEdz/vvjGHVin+xWq2sW7saXdd59LEnS7p5QpQ4WQIghBBCCHEVKY4lAKqiYzZoKDll1ld9UNO347PrevA3Yd8WTuqCKuh2FcWqQ9w5jMNfxHjfg2hJSbnOIigqDrcBrRRNctV1nek/TmXenJmcPhVFcEgIt9x6B08MGV6i04CvZUcOH+LzTz9i88YNuDU39eo14PGnnqZV67Yl3TRRBpW2JQASABBCCCGEuIoURwDAoGiYDVoOr+joxkDMx17DdHIsmMuj+NlxRQeQMr0W7kN2DC0bYv50CnpqSq71F+Xjp1NTceulJ7meEKJsK20BAFkCIIQQQgghMsl9Lb2CorlQUzeCmpHQT0s2YCiXQtDgvST/GAw9HwZVycgVkMvov68ZlMscpHe5wObMfZa/qui4ZXhLCCFyJAEAIYQQQgiRiZJbAEAxgjsO1XEAXbWA5gJAt6moSgr+AwNJb9QUPSU9l90DdFBh7O8q51LB4OVSfbeeUTbFDrc11rm3hU5Kei5BAJncKoQQuZIAgBBCCCGEyCIjaV82ignFGQv2uOxnOG1oQS3BHArO5Oyv67qnw779pMKpBDDkMAsgp/67poOqQHwahAXC/a303GcAqAq4c78zIYQoyyQAIIQQQgghMslrYF7R7ShaesYMgCw0UwS6zvnkgdkz/l9gNV380nLo8F9aXNMv7lut6ZBmB3dO6QmEEELkSwIAQgghhBDiCmh4uuiGwEyv5Jfs79LOf15FL/T3VQXsbtC1jOn/MttfCCEKRgIAQgghhBDiMmmZ/+9O8rySX+ffoGZ06CEjEJB1Sr+u5zw7wFh6dvgTQoirjgQAhBBCCCFEJnoOHXLPa4ZAMAaguJKzLQMwp6xB09Jw6ioKuc/Tj0uFs8lgymW3PqMBAnLYJUvTM44bDKA7cj5XyylqIIQQApAAgBBCCCGEyEJHRdcvduA9Gf11J7qlIoqlOrprp+d1Iy4wmfgtPpW6qdHU8K+E3WXPsW5Fh95tdVJzfhmApHT4a6eSKUmgqoDLDeX8L07/zylIoShqRg5DIYQQ2UgAQAghhBBCZOJya5gveUr0TOfXHWAIwmlthDF5E6gWjLjQFI1PUlvxflwEo06u4ulGD2N32ciaTvBCNQM6aDl23nUdFBMs2qowd6NCiN/FZQAX/l89VM9z7b/LrYGSy9QCIYQo42QVlRBCCCGEyEzNuwPtDr4NAKOSzlndyLDELkxKqUt1s8rPh37nSMoJ/Ex+uXbUk9IgMTX7V3I6uB0wZ4OC2Zg5B4BbA7MRGlfRcbtzX6Kg5NN2IYQoyyQAIIQQQgghMtF0BT3HzQAVFHc67pCbwFyeTfZg+sbfxr+2MEKVdEyqmRRnGm9s+hyX7sZiNJHTfHxFyf4FEBgIC7YqbD2u4GPOfI7TDTXKQ5VghXRnzu3OSByY1yaGQghRtkkAQAghhBBCZKHgziWHn645CLCEsSbkZR6Kack5l4UgJWNBv6a7CTIH8l/MDl747wM0dPxMfhfOzLk+HQyKgSDfQNbtMzBhsY5/lgSAqgIpNuhcT8fPqqPlll9QUXLcOUAIIUQGCQAIIYQQQohsND23x0QFmyudylUGEmoNQdFSs5znJsQSyN8n19B/+Ui2x+8j0BKIn8kPi8GMQTFgUAwYDUb8TL4EWQPQDW6m7V7CSzPdoPll2x3A6YYQP7ijiYbdkfv0f6dLv5iwUAghRDaSBFAIIYQQQmSjoeSaad/hdlDLN4gRTR7hxfUfUsEQkq1MiCWQfQlHGLjiFTpHtOL2KtfTIOQ6go0BANhcDs6kx7Lx7C5+P7acnfH7CKvZCP/Tz6PbqoIxI7CgAmdT4ckbdaqWy8gVkHMCQR1Uk+wAIIQQeVB0Pa88qkIIIYQQojSx2Wy4XC5cLhc2m41z585RoVKtIrmWARdm4yW7AGQRaAlg9KbPmH5oIRWsIehZet8KCm7dTZIjozMfZA7Az+iDoijY3A4SHUnY3A78TD5YVQuaIQXF7Yv/mWdREzuAMZ2ENI26ETBpoBvdTY6JBRVFwaWBU5OxLSFE6RJ76jChoaFYrVaMRiNGoxGr1Zr/iUVElgAIIYQQQogcuXUDLreW47R6XYdURxqjmj1O14qtibXFo1ySODAjIZ+OgkqQOYAgcwBOzUWCI5mz9njSXTZ8jVZCLcFYVBM6GorbF92QRnLlsbjCppOWbiDIqvD6PW6sKrmu/XdpbhyuonoXhBDi2iEBACGEEEIIkTNFQdMNaDlk1lMUcOsudF1jQvtRdKvaiej0s2i6luMovY6GUVUxqioW1YRRVT3HM9Xr9kVx+xIV+ClqjU/4qI+ZuuGQas997b+uqShq0Y7+p6elMW3aZJ4eMoiBA3rxxusj2blze5FeU5Ss5f8uYeiQR0u6GUXu2NHD9Ovbg6SkxCK7RlTUSYYOeZT4uLgcX7elp9Ovbw8O7t9XZG0QGSQAIIQQQgghcuXGgKaTS3I9BYfbAcD4tiMZHjmANJeNREeyp4SOlq2TnxtVUbFrTs7a4mng35IpPe6iSVUHSWk5d/4VRcGt6biLIa3VxIkT2L1zB4MGD+GNN9+lSZNmvP/umxw4sL/Iry3E1a5cSDnuuOMe/AMycoCsWbOSxx97uIRbVTbJQikhhBBCCJEnN0ZU3YWqKDnkA1BwuV24NTdPN3qYjuHN+XTXD6yN2YoBA75GH4yqipLLuJOOhkvTcGgO0lzpBJuDGB45gIH1e+KrWEhKT8t15F/TdDSM6BRt5v+0tDQ2bVzPyJffoHGTZgBUr1GLAwf28dfi36lT57kivX5J0DQNVS0bY4XX8r2Wlnvz8fXljjvvKelmCCQAIIQQQggh8qHpCg63ilF1YVBy6cjrOom2JJqVq8+UTmNZG7OVBceWsjZmG4mOJFyaGzfubOcZMGAxmqkXXJPOEa3pUf1GqvlXJtWRSqqemue2fk43aLm0pzCpqoqiKJw7dzbT8UcGDsbhsAMZ9//77/P4568/SU5OokaNWgx89HGqVqvByhVLmT59Gl98OcXTGftk/Hv4+wcw6LGnAPhz4a8s/GM+ToeTJk2bM+CRx/D3D8h0PU3TGPLkQO7ucT/dunUHYNPG//h0wvtMnPQ9vr5+bNmykRm//MiZ01GEh1ekz4P9ad68FQAbN6xn0sQJfDNluqfON0ePJLJxM+67vw8//fgdhw4dwGrxYdeubXzz7XTMZnOmNrw6agTX1a7L0aOHOXH8KGHhFRn8+FBq1aqd7/sAkJycxNQpX7N922ZMFgsdOnSi74MDMBgy9n5cu3YVc2b/TNy5s1SpWo2H+z9GnTp12fDfWqZ8+xUTJ30PQNTJE7z4wjA++vhLIipWQtM0Hh/0EE8MeZaWLVtjS0/nhx+m8N/6NaDrtG7bgf79/4fVx8dzH1WqVOPo0cNYrFbeHPMeZ8+dZdKXEzh4cB9VqlajTp16me49v7ZfStd1Zs2czvLl/+Cw2WkY2ZhHBj5OSEg5AGx2Oz9+/y3r163Cx8eXLjfczL09e3n+fqxZvZy5c2cSd+4s1avXpP8jg6hZM+M9fvmlZ+nS5QZuv+NuAHbu2Mo7497gp5/nY0tP53+P9qXbHXezbt0qru/YlT4P9s/3ehcsXrSQuXN+YeKk7z2vjXr5ORo3bkrfBwdkKjvyxeHcdPPt3HJrNwA+m/ABqsHIkKHPArBw4XzWrF7J2HEfcezoYUa9/BwTJ33P5G++YNPG/wDo17cHI154hYYNIgHYd2APkyd/SUzMGerWa8CTQ54lKDAo2/srLl/Jh4OEEEIIIUSpp2PArRnObw2Yc6dcUSDVmUqaK532Yc34oO0LzLppPBM7jua5Jo/Qt9admb6GNnyIcW2eZcaN4/mxy3s83eghwqzlSLIn4tbdkMvIvq6Dyw2aYirCO77IarVy6613MPW7Sfz043dERZ0EIDyioqdju2zpXyz8bT6Dn3ia9z74jLDwinw64UMAWrZqR3paKgcOZKxvdjqd7NixlXbtOgDw1+I/WLz4d4YMeY5XR48lIT6OqVMmZWuHqqq0aduBTRvWeY5t3bqRyMim+Pr6cfDgAcZ/9A6dOndl3Lvj6Xh9F8Z/9A5RJ094fa/79+2hafMWvDX2A0ymnN/fXbu2M3jwUD759GuqVavOp5+8j3Y+Q2Ne7wPAtO+/JTExnjfeep+hw55jzZqV/PnHrwCcOnWSLz//mHvv7cW7702gVq3afPj+WzgcDho2akxKSrLnXrZu2wTAlq0Z/z9x4hg2u42GDRoB8PnnH3HkyCFeevkNXho5msOHDvDtt19luo9t27fwQO+HeOqpZwCYNHEC6elpjBz1Jr16PcTmTRsylc+r7Vkt+/dvli37m+HPvMTrY94lOTmZyV9/4Xl90sQJnD0Xw2tvvsMTQ4azbNlfLFv6N5DRoZ/01Wfccdc9jHtnPHXq1Of9994mLS01n5/eRQcP7Ofp4S9ye7e78r3epdq2bU9qagp79+4CID4+jmNHD9OmTYdsZRtFNmXvnp1ARnBqx45tbN+22fN3Yd/ePTSObJrtvKHDnufxJ57G3z+Ab6f8TLNmLT2vLflnMQ8PGMSLI1/n9KkoFv46z+t7Ft6RGQBCCCGEEMIrGkY0NNDcqGpOywEAMo6nODKm7gca/Wgf1ozrI1rmUDaD3e3A4XbgsCeR0enPLcCQUbeGgquYH2MfHjCIqtWq8ftv8/lj4QKuq12H3n3606hRYwCaNG1Bw0ZNiIioCMDtt9/JK6OWkpKSjL9/AJGNm7Jp43/Uq9eA3bu3YzQYadAw49w/Fs6nV5+HadAwYxS074OPMPr1F3nC5cJozHyf7dpfz7i3XyM5OYmAgEC2bdnMfQ/0AWDRn7/SrHkr7ryzBwB331OF/fv2sHDhAgY/PtSr+2zQIJLbbrszzzKdO99I5SpVAeg/YBBDnhzInl07aNS4ab7vw5nTJ2nUqCmVK1ehcuUqDBn6HG6nE4Do6NOoqkrzFq3w9fWjT98BVKxUBYfDjr9/ADVrXceevbuoXKUq27ZupnWb9mzdsolu3bqzd+9uateui4+vL6dPRbFl80bGvTue6tVrAjD4iWG89srz9OrdjwoVwgC45ZbbadmyNQBnTp9i964dvD3uQ89I+z097mPOnBme+86r7VmdiTpFWIUw6tSph6IoPPbYEPbt2wNATGwM/61fw+dfTvHMCOjWrTurVy/npptv46+//qR9+07ceMOtAPTtN4Cjxw4TFRVFnTp1vfo59u77MHXr1vfqepcKDilHgwaRbNy4noYNG7N1yybKl6/AdbXrZLtG4yZNmfTVZwAc2L+X8hXCSEtL5eDB/dStW5/9+/Zw6/nZAZcym80YzweXLszIuGDAI4M8/6Y6dOjMocMHvLpf4T0JAAghhBBCCK85NRUDOgbdhcFgyCUIcDFpn1t3nw8G5FzufOks/8+pPgVN089P+y/+R1hFUbjhxtvoesOt7N2ziz/+WMC740bz0sjXiWzcjNDQ8iz8fT4rVywjLu4cmp4xCuo630Fs1+565s2dwYP9BrB54wZatW6HwWAgNTWF2NgYvpn0GZO//txzPU3TiIs7R1hYeKZ21KvXgKCgIDZv2kDNWrWIT4ijZau2ABw/dpQuXW/MXL5BQzb8t9br+8wacMiPv38AoeUrEBN7hkY0zfd9uPue+5n45SccOLCP5i1a0b5DJ0JDywPQqFFT6tatz4hnn6Jlq7a0bNWaW27p5pmK3iiyKXt37+T6jl04dOgA7743gRefH4rNZmP/3t00btwMgGPHjmKxWDydf4BatWpjsViIijrhCQAYjRdnOJw+cwqDwUCNGtd5jqlZdpbIq+1Z3XDjLaxft4oXnx9Gq1Ztad22PV1vuBmA40ePADDi2Sc95TVN80x1P30qihsv6ZgrisKoV8Z49fO4wGS42Pb8rpdVu/bX89uvc+jffxBbt26kdZv2OZar3yCStNQUTp06ydatm2jWrCW29DS2btmEn68fNls6des1LFC7/Xz8PX+2WCzYbOkFOl/kTwIAQgghhBCiQNwY0DRQFA1FuTgyn5uMYMDlJeq7sNxA03VcmoqmZF9vXdTi4+KIj4+j1nW1URSFBg0jadAwknHjRvPXX38Q2bgZixb9zj9//8kzz75Eteo1ORl1gpdfHO6po2XLNkz+5gtOnjjO5s0beOyxIZmu8eRTz1Dtkg4rQLlyodnaoqoqbdp1ZNOm9SQmJtAosoknV4DRZMKgZn5/NLeG0+EqrLciR26n07P1Y37vQ+s27WnQMJJNG/9j06b/mDP7Z4YMHUGr1m0xm8288trbHDiwly2bNzH120mEVghj1CtjMBqNNG7clC+WL2HXru3Url2PChXCqFa9Jrt2bmPfvt3cenvGdHeTyYSa5X3QdR1N03A5c34vFEXBaDTmmXMir7ZnVbFSZT4cP5Ht27eybetG3n7rVW695Q76PNjf08Zx736S6Rz1fC4Bg9GIUsiJLfO6Xlat27Rj6neTOHTwADt3bmfky6NzLGe1WKhTpx579+xi29bNPPLo49jS0/nl52lUqFCeuvUbZsshIUqe5AAQQgghhBAFpisG7G4jDhe4NHeeHafLdSGw4HC6cbiNuCn+zj/Avn17ePONkaSmpmQ6HlouFM2dMcK9a+c2WrZqS42a16GqKnabPVNZH19fmjRtzuzZP2esaY9sAoCfnz9BwSHEno0hIqKi5ysoMCjX0fgO7TqxY/tW/lu/hrZtL47OVqlS1ZNn4IID+/dSrXr1jDZYrdgdds8abQCXu+DBgQuj+gAJ8XHExccRFl4x3/fB6XTyy/RppKen06XrTTw34mU6d76Rv/5aCMCWLRtYtvRv6tSpT6/e/Rgz9kP27d3NoYMZWy3WqVOf9LQ0Fv35O82btQCgeYtWLF68kHSbjdq1M6bHV65SlfT0tEy5D44cPoTT6aRq1Wo53lPFiErY7XZios9ccvRiUCu/tme1eNFC9u/fS8uWrXn0f08y+PGh/PHHAnRdp1LlyjidTtLSUj0/77CwcM+IfOVKlTl27HCm+mb8/APHTxwDwMfHis1mu9g2V94/w/yul1VgYBCRkU356afv8PHxoXbtejmWA4iMbMraNas4d+4stWvXpUHDSKJjzrBmzSoaN86+/v+Covh9IbwjAQAhhBBCCHF5FAVNMeFyG3E43bjPdwyv5OH+wrkZif507C5wK5Yi3+ovLy1atiY8PIIP33+b7du2cPzEMZYuWczaNStp1/56ACIiKrHxv7Xs3LGVLVs2eqbzX9o5a9euIxv+W0vLVm0yde7vuqsH8+fMZM2alcTERDN/3kzefOPlXGdV1K5bj8DAII4dO0KLVu08x++8qwcbNqzjzz9/4/SpKH7/bR7bt2/htvMj45WrVEVRFH6dP5vjJ47x64I5HD50sMDvx5K/F7Fr1w5OnDzON19/QYWwcBqez1+Q1/tgMpnYunUT30/9mqiTJzh29DAHDx2gYsXKALicLr6f+jXr1q0iNjaGdetWYzAYCAuLADJGsevVb8Tu3Ttoej5xXIsWrdi1czsNGjTyZOOPiKhI6zbt+eqrCRw6eICDBw/wzddf0LJVW8LP5ybIKjyiIo0imzDt+8kkJiYQExPN4sV/eF7Pr+1ZRZ85xZTJX7Jv3x6iz5xm65bNRERUQlEUKlWqQvMWrZg08VP27t1FVNRJPvv0Q36ePhWA27p1Z83qFfy77B+iz5xm1szpLF36F+XOr9+vUaMWK1f9y4ED+9m9ewezZvyU588rv+vlpF37juzbu5s2bdrn+e+5UeNm7N69g8ZNmqGqKiaTicjIJuzetYPI80GunAT4+5OSkszOHVtJSUnOs/2icMkSACGEEEIIcUV0xYAbA7qmoSsaChqqoqCqmTsOWTu0l3YsLrym6Tq6ruLUQNPVUjFSaDabeeX1scye+RNfffUp6WmphIdX5JGBj3N9p64A3NuzF2fPxvLxx+9SPrQC3e/pya/z55AYH+9Zc96iRRvMZnO2NdW3d+uOzZbOTz9OITUlhZo1r+PJp4bnee+tWrfj+PGjmUZxq1evyfBnXmTGLz/wy/TvqVSpKiNeGOXZoi84pBwDHh3M3Fk/8+efv9G8eSvq1WtQ4PcjsnFT5s35hcOHDxIeXomnn37es04/v/fhuREvM/W7b3jt1ecxmcw0b96K3n0eBjKm2Pfp8zC/TJ9GQkI84eEVGf7Mi4SUK+e5duPGTTlz5hQVK2V0vKtVq0n50PJERjbL1MbBjw9l2tTJjBv3OgbVQJs2HXio/6N53tfgJ55m0pcTGD7sMSIiKtEwsnHGNoLn5dX2rHr17Y/L7eLjD8fhdDq4rnZdhj3zguf1J54czrTvJ/Ph+2PR0WnerCX33d8XgLp16zPosaeYM2cG3035iho1avHSy6M9Sz3u6XE/p05H8c7Y16hQIZw2bTtw7NiRPO8tr+vlpGWrtjDpc9q0zZ79/1LXXVcbX1+/TJn8mzdvzd49uzPlU8iqfoNIGkU24eOP3mHIsOdpdD6AJIqeoue1YEsIIYQQQpQqNpsNl8uFy+XCZrNx7tw5KlSqVdLNykzXURUddDcKF/MEKGR97FTQdB1FUXFr5ydcK8YSHe2/Wrz7zhu0atWWm2/JnmW9KL06agRt2nbg7nvuK9briuJ14MBexn/8Hp9/8a0nuCMuT+ypw4SGhmK1WjEajRiNRqxWa4m1R2YACCGEEEKIwqUoaCigXNJxyGvISedycwSWOTGxMWzbsokDB/YxdNiIkm6OuMY4HA6OHj3MTz9+R5euN0nn/xokAQAhhBBCCCGuErNm/MSuXdt58slnPFPChSgsZ8/GMO7t14hs3JR7ejxQ0s0RRUCWAAghhBBCXEWuiiUAQgghgNK3BEDmdAghhBBCCCGEEGWABACEEEIIIYQQQogyQAIAQgghhBBCCCFEGSABACGEEEIIIYQQogyQAIAQQgghhBBCCFEGSABACCGEEEKIq8Bfi//g+RFDeKT/A4x47ikWLpyPpmnF2obHHn2Q9evWXFEdx08co1/fHmzauL6QWiWE8JYEAIQQQgghhCjlfvttLtN/+o6bbr6dN958l3vv7cWv8+cwf97Mkm5aga1dsxJFUVizemVJN6XAnh8xhMWLF5Z0M4S4bMaSboAQQgghhBAig6ZpqGrmMTqbzca8uTPp3bc/3bp1B6BGzetwuVxM+/4but99HyaTqSSae1nWrlnJ7d26s3TJYmw2W4nuiS5EWSMBACGEEEIIIfLwyfh30TSd50a8DEBKSjJPPj6AES+8QrNmLTl9KoqpU7/hwP49+Pr5cdvtd9G9e08AUlNTGDzoIca98zHVa9QCYOaMn9i/bzevvj4WW3o6/3u0L93uuJt161Zxfceu9Hmwf6br79+3B7vNRseOnTMdb9W6LWdOnyI1JZngkHIAbNmykRm//MiZ01GEh1ekz4P9ad68leecvNoKcPZsLJMmTuDAgX2Eh1ekddv2LPlnMRO/mprjexN95jRTpkziwP49lC9fgfsfeJA2bTvk+l4eOLCfhPg47ruvD/+tW8PmTevp0LFLpjJrVi9n7tyZxJ07S/XqNen/yCBq1qwNQFJSIt9/9zXbtm3BYrXQuctNPPDAg56gSV73v3HDeiZNnMA3U6Z7rvXm6JFENm7Gfff3YfPmDXz91Wf07tOPOXNmYLfZ6dCxM48MHMzxY0cY9fJzAEyb+g0rly/l7XEf5XqfQpRWsgRACCGEEEKIPLRr14kd27dgs9sB2L59K1arD5GRTUlLS2Ps2NcIDg5mzFsf0L//IH6dP4ely/4q0DUOHtjP08Nf5PZud2V77WxcLBaLhcDAoEzH/f0D6PNgf0/n/+DBA4z/6B06de7KuHfH0/H6Loz/6B2iTp4A8KqtkyZOwGZL5+VXxvDQw4+yauW/ubbZZrMxbuzrVK9ek3HvfsJd3Xvyxecfc/LE8VzPWbd2JY2bNMPH15dWbduxZk3mZQA7d2xl0lefccdd9zDunfHUqVOf9997m7S0VAA++fhdEhMTeG30WIYOe541K//l99/neXX/3khOTmLjhv8Y8fwoHh30JEuXLGbr1o1UrVaDb6f8TETFSvR7aCCvjR7ndZ1ClCYSABBCCCGEECIPzVq0QlEUdm7fCsDWLRtp2bINRqORdWtX4nZrDHpsCFWqVqNN2w706PkAv86fU6Br9O77MHXr1vd05i/lcriwWH3yrWPRn7/SrHkr7ryzB5UqVeHue+6jSZPmLFy4ACDftp4+FcXu3TsZNHgo9eo1oHGTZtx7b69cr7d+3WrMZgsP9htARERFOne5kcaNm7F23aocy2uaxvp1q2jTpj0Abdt0YMf2raSkJHvK/PXXn7Rv34kbb7iViIqV6NtvAFWrViMqKopjx46wb98eBj/xNNWr16RBg0b06fcIZ06f9ur+vaGqKkOHjaBmzdq0b389tWrV5tiRI6iqitXHB0VRMBiNWCwWr+sUojSRAIAQQgghhBB5sFostGjRmo0b16NpGtu3baH1+Wnux44d5bpatTOtwa9XrxGxMdGeGQPeMBlyX5lrtVpJT0tF13UAVq9ewSP9H/B87dmzC4Djx45Sr179TOfWa9CQkyePedXWM9GnMRqNVKtWw/O6wWDItV3Hjh/hzJlTPPpIb8/Xtm2bORcbm2P5PXt2kZSURPMWrQGoU7c+/gEB/LdhrafM6VNRVK9Zy/O9oiiMemUMderU5dSpKHx9/QgLC/e83r799Qx+fKhX9+8Ng8GAj6+v53uL1YrNZvP6fCFKO8kBIIQQQgghRD7ate/EN19/zoH9e3G6nDRp0gwAo9GEasg8pqbrbgDcLmehXDssPByn00ns2VjCKoTRvHkrxr4zHofDxqujnkfXM7YCNJpMGNTMHXbNreF0uLxqq0FRURQFRVG8blvdug147HwH/AKfXJL6rV29ErfbzdCnHvUcc7lcrF29khtvuBUAg9GIQs7XNxqMebYtv/sXQkgAQAghhBBCiHw1bdYCt9vNjBk/0qJFa88oepUqVVm/fhUulwujMePRev++PYSWK4+fnz8ulwtFUTKNIrtcBeuQ1qlTn8CgYP5Z/AcPPvQIvr6++Pr6YktPz1SuSpWqHDiwj9svOXZg/16qVa/uVVsjKlbC6XRy/MQxqlXNOMftzr2tlStVZe3qlYSElPNMibelp2P1yb5cweVysWHDWvo9NJCmzVp6jkedPM6nEz4gIT6O4JByVK5UmWPHDmc6d8bPP9D++s5UqlSZ1NQUYmJjCKsQBsDevbs4cGAf3bv3zPf+faxW7A57pp0WXHncX05yC04IcbWQJQBCCCGEEELkw2Qy0apVW/bt3U2bNhez3Hfo2BkFhSnfTiTq5Ak2bljPgvlz6HbXPQAYjUaqVKnGH38s4MTxo6xbt4p/l/1doGsbDAb6DxjEn3/+yvy5Mzl+4hgHDuxl9uyfMRgMBAUGA3DnXT3YsGEdf/75G6dPRfH7b/PYvn0Lt91+l1dtDQuPoFHjpkz++nP279/Lju1bmTdvVq7t6tixMwaDysQvPznfpv28/tqLrF+3JlvZHTu2kJ6eRpeuN1G5chXPV+s27QkJKce68+fc1q07a1av4N9l/xB95jSzZk5n6dK/KBdSjspVqtK4STO++eozTz6Aryd9jtPu8Or+K1epiqIo/Dp/NsdPHOPXBXM4fOhggX4W/v7+7Nm9k6iokwU6T4jSQgIAQgghhBBCeKF1m3ZYLBaaNG3uOWaxWHhp5GhiY6J5ZdRzTJv6Dffcez/dunX3lBk06CnOnD7N6NEjWfL3Ilq3blfga7dvfz3PjniZzVs2MPrVF3j/3beIOnmCV159i8pVqgJQvXpNhj/zIsuWLGbkS8NZvWoFI14YRa1atb1u6+DHh2ExW3ln7OvMnPEDLVq2RlVzHvW2+vjw0sjRpKYk8/orzzP+43do3aY9rdtkv781q1cRGdkMPz//TMcVRaFN2w6sXbMCgLp16zPosaeYP38WL74wjJ07tvLSy6Px9w8A4Mknh+Pn78ebo0fyycfv0rZNB3r07OXV/QeHlGPAo4P5558/GTvmVU5FnaRevQYF+jnccWcP9uzeyddffVqg84QoLRT9QjYRIYQQQghR6tlsNlwuFy6XC5vNxrlz56hQqVb+J4ortmD+bI4fO8Kw4S+UdFOKjNPpzJQkcO7sX9i6bRNj3vqgBFslxNUr9tRhQkNDsVqtGI1GjEYj1lzyZBQHmQEghBBCCCFEHpKSEtmyZSOL/vyNrjfcUtLNKVLvvzeG33+bR0xMNJs3b2Dx4oV06NClpJslhCgkkgRQCCGEEEKIPGzctJ4fp02hW7e7aXw++/+16qGHBvLTT1OZM/tnAgOD6NatO7fedkdJN0sIUUhkCYAQQgghxFVElgAIIcTVQ5YACCGEEEIIIYQQothJAEAIIYQQQgghhCgDJAAghBBCCCGEEEKUARIAEEIIIYQQQgghygAJAAghhBBCCCGEEGWABACEEEIIIYQQQogyQAIAQgghhBBCCCFEGWAs6QYIIYS4dqQ7dE7GaZxJ1IlL0UlK13G5wa2DUQWrCQJ8VEL9FSKCFCqVUzEbSrrVQgghhBBlgwQAhBBCXLaYJJ3/DrnZccLN7ig3pxN0dN37842qQpVyCo2qGmhaVaX1dQYCfZSia7AQQgghRBkmAQAhhBAFcjZZ5++dLv7d7eJgtHZFdbk0naNndY6e1Vi4BRQFmlU3cHMjI10bGrGaCqnRQgghhBBCAgBCCCG8s+ukm1nrnaze70YrwCh/Qeg6bDnqZstRNxP/cXBncyP3tzFRzl9mBYjSpfWM+/Mts6H37GJoiRBCCOE9CQAIIYTI054oN98ud7LlqLtYr5ti15mxzsn8jU56tjHxYAcTvmYJBAghhBBCXC4JAAghhMhRfKrOxH8cLNnlBEqu4213wc9rnCze5mTYrVY6N5CsgUIIIYQQl0MCAEIIIbL5e6ebz/+ykWKDkuz8XyouFd6cZ6PrXgPPdrPgby0d7RJCCCGEuFpIAEAIIYRHml3noz/s/LuneKf7F8S/e9zsPZXOG/dZqROhlnRzhBBCCCGuGvLkJIQQAoCoeI0h39tKdef/gjOJOsN/SGfl3tLfViGEEEKI0kICAEIIIdgdpTFsqo3jZ69sW7+szAYI9lUIC1QI8VMwF+LyfbsT3pybzoJNzsKrVAghhBDiGiZLAIQQoozbekzjlZk2bM4r29uvRgWV5tVVGlY2UK28SpVyKlZT9nJJ6TqnEnQOnnGzJ0pj4xGNs8mXGXhQFHzMV9RsIYQQQogyQwIAQghRhm075mbUTDv2y+z8RwSp3NbEwC2NTVQM9i4pX6CPQqCPQv2KKnc1zzi2J8rNoh0ulux0ke7w7tqKAi/eZebWxjlEGYQQQgghRDYSABBCiDLqSKzO63Mur/NfLVTlwQ4mboo0ohZCMv4GlQ00qGxgUFczcze4mLXekWcgQDr/QgghhBAFJwEAIYQogxLTdF6ZkU6KrWCdfx8T9O9k4r42ZgxFkEUmwKowoJOJ7i2MfPmXg2V7XNnKSOdfCCGEEOLySABACCHKGE2Ht+bZiU4qWOe/fiWF1+/1ITyoEIb881HOT+HVey1cX9/Ix3/YSLVnHJfOvxBCCCHE5ZNdAIQQooyZsdbJlmMF2z7v7pYmJvT3LZbO/6W6NjDw5UAfqpRTpPMvhBBCCHGFZAaAEEKUIUdiNb5f6WWWvfMGdjbz0PUl1+muUk7lswE+7Drhpn1d+dgSQgghhLhc8iQlhBBlhK7DJ386cBZg8H9QVzN9O5T8iHugjyKdfyGEEEKIKyRPU0IIUUYs2eli50nve/89WhlLRedfCCGEEN47l+xm3f40th2xcSzWyck4JyfPOkguYOLfq0WAVaFKeTNVQ01UK2+iaU0rHev7Eewnq91zIgEAIYQoA1wafFeAqf+Nq6k8dbO5CFskhBBCiMISk+hm6tI4lu1MZd+pgi31u9ol23T2nLSz56Q90/FGVS10beTHw11DCAsylFDrSh8JAAghRBnw1w4XZxK8i/wH+ii8eo8Vg1q8Cf+EEEIIUTAxiW4mLY5j+soE7K5rc4T/cu06YWfXCTuT/4mnX+dgBt9aTgIBSABACCGueboOs9c7vS4/+EYz5QOk8y+EEEKUVtLx957dpTNlaTw/rUigX+dgnuoWSjn/srs8oOzeuRBClBFbj7s5dlbzqmyDSgZubyqxYSGEEKK0crp0Pl14lu+WxUvnvwAuBAI+/jUWZxl+3yQAIIQQ17hFW70f/R/YxYSM/QshhBClU3yKRt/xJ5i+MrGkm3LVmr4ykb7jTxCf4t3gyLVGAgBCCHENc7hg9QHvMv/XrWigZU1ZGyfEVUPXM76EEGWC060z6MuTbD5sK+mmXPU2H7bx+FdRON1l73eozPMUQohr2JajbtK9TAbco6V8JHhFc+PauxvHti24Du3FffI4WmwsWkoKuuZCMRhR/fxRK4RhqFIVY+36mJo0x1S/IailIMCiu9GTNqAlrIDkbZB2AOxRaK5EcDtBNaGYglAslcGnDkpgM5TgTiiBrUEp+fY73bD1mJvtxzX2n3ETFacTl6rhcIKiQKAPVAxRqROu0qy6SpvrTPhc7Rta2O04tm7EsWMz7gP7ST1xDMe5s7hsNuyaThwqFX5dVdKtFEIUsTd+iWHLEen8F5aNh9J545cYxvYLL+mmFCt52hNCiGvYhiMur8pZjAqd6pd85640cx3Yi23hfGwrl6InJuRYRgFwOdES49ES43Ed3If9338AUIOCsXS+CeudPTDWrlds7b5AT96MFjUFLWYOivNctteVC//RHeCIRXfEQvJW9JhZGQXM5VEr3Ida+VEIaF6cTQfgUIzG/I1Olu9xk2rPecRG1yEhDRLSNPZEafy6GawmBzc2MtKnnYnK5S5OfNxy1M3z0/N/kF4yyq/Q7qGgnLt3kL5gFo7Vy9Ft6Z7jbreWeeTfWba2/BKiLPppRSI/r5Jp/4Xt51WJNKxqpV/noJJuSrGRAIAQQlzDdpzwbn1b29oGfM2y+j8nzp3bSJ36Fc6tm66oHi0xgfTf5pD+2xxMzVrhN/AJTI2aFFIrc6cnrsV96HWIXw5w+TkeHGfRoiahRU1CCemKWutNlOD2hdXMXB07q/HNMgdrvVzKkpXNCX9sdbF4u4v725p5pJMJcyl/+nHu203q15/h3HZlf+eEENeG2CQ3b82KKelmXLPenhXDLU39y8wWgaX8I1AIIcTlsjt1jkR7t7atWY2y8aFXEHpiAslffIR92V+Fvs7auXUjCc88hvWm2/B/agRKYBGMPDjP4t4/Av3ML0Dhtl+P/xf3pq6oEX1R634EptBCrR/A5YYfVzuZvsaBuxDyNLk1mLHWwcbDLt5+wHrlFRYFh52UyV+QPm+GrO0XQnh88ttZHGU4a31Rs7t0vvrrHK8/EFbSTSkWkgRQCCGuUcfO6bi97EQ0qyYfB5dybN5A3KC+2JcuLrqOmK5j+2cRcYP64ty6sXCrjluGa11z9DM/U9id/0uugnZmOu71zdHjlxVqzbFJOsN/SOOHVYXT+b/UoWiNZ35I53Ri6XqYdp+OIm7oQNLn/iKdfyGEx/FYJzNWy9T/ovbzikRiEi9vptnVRp74hBDiGnUyzruek8mgUzVUPg4uSP9tDokjh6HFZ18nXxS0uLPEvzSM9N/nFk59Ud/g2nonOKILpb786PYzuDbfiRY1uVDqOxyjMWRqOntPFV0nODpR56OF9iKrv6Bch/aT8PT/cB8+WNJNEUKUMp/+cQ5NYoJFzu7SmfD72ZJuRrGQJz4hhLhGRSd4FwCoVM6AKsv/AUif+SMpE94DrXj3BlbcblI+eZe0WT9dUT3a8fFoe4eg6N4lfywsCi60vU+hHRt/RfUcjNZ47sd0zqWUnadd97HDJL44FC0+rqSbIoQohbZK1v9is/5Aev6FrgGSA0AIIa5RSV5+jkUEFX3v/6ZxqUV+jbzMecaXYN+879P2xwJSvv60mFqUs9SvP0UNCMR6e/cCn6udmop2YGQRtKoAbTg4EszlUCsOKPC5ZxJ1Rv5iI7kMPetqCfEkjnoWLZddJUTZlmrT+OjXs/y+KYWkNDfXRVgYdkc5bm/uX9JN83hw/EnS7G7mj6xeou2YvSaRF3+IZvnbNakaairRthSmg6cdHI4u2l0+/Cwqo/tU5L72wQT7GdgXZePdudH8+l/GsoOwICOr363LzNUJvPLjqSJpw01NAvjh2Ro8/uVxfttQcssdDkc7OHjaQe2KV/vesXmTGQBCCHGNSvVyhrOf5dof/o/KZzmEc+c2kie8d9n1q+VCMTVsjKlZK0wNG6OWu8ykeLpO8ifv4Ny1vWCnJa5F2zeUy17vb45ACWqLEtIVJagtmCMurx50tD1D0BPXFugsuwten2UjPvXy2h/oo1C/kkrz6gYaVlYpV3r6R7nTdZLfHY07+nTBz7VYMUY2xXLLHVjvuBtLhy6F3z5R4l7+MZqpyxJodZ2Vfp2DSbW7GfLNqUyjlF1fP8Kz350psTaeSXASneguM1PUi/v9/nt7SpFf44vHq/Lk7eVZuy+Vb/4+i7+PgR+frUHH+hlboPqYVUIDjFQMKbrASmiAgUAflfKBJT82XRzveUkr+XdZCCFEkXC4vXsi87l2BktyldeUcj01laR3XgN3wabNG+vUw+eOHpjaXY+hQni217XYaOxrV2H7Yz6ug/u8r9jlImnca5T7ZjqKrxd70LuTce98GLSCjRIpAc1QKg1CKX8HirVK9gL2k2ixC9FPTUZP3uZ9xboD967+GNtuBkOAV6d8vdTBoZiCLbsIC1Lo3txE5/oGqpTLPp4RnaizYq+TBZvdnI4v3iUd3kj/fR6OjesKdI6pXiN87u+Lf4u2uA0GXC4XNpsN27niyVchio/TrfPH5mQe6hzEmL4Zv1+evrMcN75+lL+3ptC2jk8JtzDDb6Oqo2nIMrIisuVI0U5JNxrg3nbBfPP3WZ6bEgXAO7Oj2T6hAXe1DmL13lSOxTqo8+QuEtOKLkHezNUJLN+VQnRC8S5fy8meE6UnP0xRkQCAEEJco7x9HisLIzeJ6bnfZOq3X6BFez+io4ZF4D/kOSwdu+ZdrkI4Pnffh8/d92FfuYyULz5CO+vdPs5a9GlSp0zEf+jz+ZZ1H3wVbMe9qhcAS1UMdT9GCbsnn3JVUKs8DlUeR4uZh7b/ObBHeXeN9GNoB19DrfdJvkV3ntBYsNHpXb2A1aTwSBcT97Y0Ycxj98rwIIUH2prp2RrmbHDw3XInjpJ/tgRAS0okbcqXXpdXA4PwG/Ic1pu6AWCz2cBVSm5GFAmjquBnUTkW68Lh0jEbFYJ8Dfz3/nUY1IzlAY2fzUgaeTzWyYL/kvjq8Urc2syfE+ecvDUrho0HbBgMCh3q+fDaAxUoH2hkztokXph2hm+HVOKGyIypMqk2jTYvHeKuVoG893D2YOaiLSmM//Usx885qV7BzNN3hHJHy4xzH//qFHaHzqwXqgIQk+Bi1PRo1u5LIzTAyNt9wxj81SmG3xXKk7eVY8PBdHp/dIKX76vAnDVJHD/rILKalfceDqdm+MVp11OWJjB1aTwxiS5qhpkZckc57mp5MaD428ZkJvx+jtMJLlpf50PH+r75vqc/rkhkypI4Tse7qF7BzJO3hXBPm0AAlu9KZeDnUcx5oRrNa2VsE/revLN8tzSevZ/VyfP9LkqxSUWbld7lhhSbRq1wCyaDgtOtk5jmpubgnZmeDQ5PiuSjBdGMmZHxWdm2ri8fPlKFBlWt7DiWzhd/xDL16erc8OoBNhxM47dXr8Pl0omKc3Jf+2BSbRpfLY7lw/k5fwa2r+fH32/Wpvvbh1i2M4UX7w1nePcwxs46zbN3h+FrMfDn5kSe/uYkafaiDejGJl37v1tlCYAQQlyjzEbvQgBpjms/ApCey+C4+/gR0hd6n33f3Lo95b7+Kd/Of1aWTjcQ8s10zK3aeX1O+q9zcJ84lmcZPXUPetQ3XtephN6Gsd2m/Dv/Wahh92Jsuxkl9Bavz3FHfQ1pec980HX44h+H1wsXKoWofDnQygNt8u78X8qgQq+2Zj4b4EM5/9IxTJk+6ye05CSvyhqq1SBk4jRP51+UDYoCg24OYeWeVG4afYRPfj/H4WgHhvNP7haTyriHLnbWxz0UTsOqVtwa9Bt/khOxLl66tzxP3BrCmr1pvPRDRserWwt/Aqwqc9cme85dtCWFdIdO745B2dpx8pyLod+comI5E6N7h1Gvkpmhk0+x+0T2ZB2aDv+beIpVe9Lo3TGIbs39eWbKmRz3r//k93MMvjWEdx4K5+BpB69Ov9gx/PafeN6eFcMNkX6M6RNG5VAjT08+zebDGddcvTeN4d+exqgqDLo5BJMRPskne/uUpQm8/nM0VcqbefzWcvhbFZ797gy/bUzO87wLcnu/i9rZxKLvjH76eww3Nw1g2ycNGHV/BLUrWvIcGKgcamLBqOuICDHyya8xHDxl56snq2Urd0uzAEIDDPQbf5R/dyXzRp+KnmUF3gjyVXn05vIM++YkHy2Ips/1ITx9V4XLucUCKeqgS2kgMwCEEOIaFeDjXWcnpQwkXdNy+TxP/eFbvN1o3nx9V4JeewcMXvY8s1ADAgl8+2OSxozEsWZF/idobtJ++paAkWNyL3L0XfAy479S/h4MTX4G5TI/+k0hGJrMx72zD3rsb/lfT3fhPvIOhkZTcy2zer+L/ae9e9iqFKzwycNWQi+zE187XOWjflaG/2AjKa3kgl56ehrpv872qqxapToh479GCQou2kaJUmnYnaHUijAz7d8EPl14jk8XnqN7qwDG9QvHz6rSp2MQXy2Oo3lNH/qc77xrOnz+WCUiggyEBWf8W1dVhXfmxuByg69FpXvrAOasSyIxzU2Qr4F5/yVRJ8JMi1rZO7RnEpxoOtzbNoB72gTyQPsgBtwQQvWw7EnSNhxMZ9dxG2P7hdH3+mAAmtSwMmxy9jwXT90WQs92GaPvu0/YmboswdPpnPR3PL06BvFmnzAA7msfRJdXDzNnbSItalmZuiyesGADc16qip8lIyLyzJTT/Loh5868psNXi+Po0siP74ZWznhv7wjl3veOMXFRHN1b5b9UyWggx/e7qBVHZ/S9udHsP2Xjidsr8PJ94Yy6P5xZaxIYOukEqTmMtj/ctRx+FpXOo/az/1TGdPlkm5vBt5bPVC4pXWPgp8ewOXXW70uld8cQOjTwZ/Ve75MCD510gnX7U1m0OYkebYPpUN8fKNotbmOLIehS0mQGgBBCXKOCvFwiesbL7QKvZqqavcOnnYvFvnKpV+cb6zUkcNRbl935v0AxGgl85W2Mdep5Vd627G+0c7mMbDlOo0d715EksBVq4x8uv/N/gWpCbfQjSkBzr4rr0bPQ7bknuZv9n3dT/33MMK735Xf+L6gWqvLqPRYuO1liIbAt/Rs9Nf8kU4qPD8FvfySd/zLuzpYBzBhRldVja/G/m0L4bWMy782LzbW8qkBskpOHJ5ykzpD9tBhxiO//jcflhqT0jM5kr47B2J0ZOQbOxLtYuy+NXrl0aJvV8KFzQz+e/e4Md407zpiZMRhUPB3vSx04nTHV6ubGF6fF39I05ynykdUuBhuC/Q043Trpdo2YBBdnk1zMXJ1IrSf3U+vJ/dQZsp9T8S7OJGS0f/8pBx3q+mVqw61Nc+/EX6jztmYXR58NKtzSxJ+9UXacXubLKQkWL2fyXal56xK57Y2D1Buym08XxnJ/+2De6lcxx7INqlg5cNru6fwD/J5D5v59UTZszoz3NtWu4XDpBPsV7DN0y5E0z5/jkl2EFPB8kTOZASCEENeoisHexXhPJ+o43WC6hj9XzTl82tn+/sOrddSKyUzgy2NQzJZCaYtisRIwcgzxTzwEznw6wG436f/8iV/vh7O9pJ/+CXQvOtCqBWOjqaAWznRVxeCD2mgq7v/agJZPsiTdiXZ6OoYaI7K9dPycxo4T3gWfHr/RTNXQwhmzaFnTwB3NTPyxtWRGeRzLFntVznfA4xiqZJ9WK8qGM/Eu/tiSwu3N/akUYqRiOSOv3F+BY2cdLNuZ+wjq2SQXQ74+zfUNfBn3cDg2h870lQkcj3V6wl5NqluoX8XC3LXJJKVpGFWFHudH47MyGmDqsMpsOWxjzb5UFm9N4ccVCUwZUpkujXKezq140Wc15JI1UD/fyCduK8ddLTMHD/x9Ln5IpTky/+7Q8wjqXagzt4YpuWTMsbtKPjheIchIsq3otgGsGGLi3nZBLPgvkahzTk7FORn1wylqhVu4vXkQz5Fz3hfdi5iJqxACK4VRR0FVCLr2u8cyA0AIIa5RVcp5N3KgaXAouminGaqKXiRfipcjuTmNVjlW/evVudYe9xd6R8xYvSa+d9/vVVnnqmU5HnfHzPPqfLXyk+Bb1+u2eUPxa4BaebB3hc/Oz/Hwv7u9+ztXs4LCnc0Ld6uKRzqbSyTgpaWm4Ni+Jd9yamgFfO95oBhaJEqrU/Eu3p4Vw+S/4zzH3BrEJrpRLunIqgqZkqIdOOPA4dJ55IYQWtbKSI7na8n+l71XhyA2HU7nvXlnubWpP6H+Of+D2H3CxjtzYqlT0cyQbqHMe6k6vhaVpTuyByFqR2QsC1iy/eJrOZXLS3iIkdAAI9EJLhpWtdKwqpVq5c3MWJ1EyvkZDHUqmtlyJD3Tfed1nfAQI+UDjfy99WIZTYclO1KoX9mC0QCB54MLUfEZQVW3Buv2Z8/An/X9LmoVAov2F1XV8ibeH1CZ4XeFeY6pCkQEG9Fy6eXvPWmjTiUL10VcXAbSrWXxLIkoDkX9npcG136IQwghyqiqoQpmAzi86GdtPeamfqWi+9D7++WiyZS8Zr+L12bnv2VPSJap43pyIs59u/O/gNGIz339Lrd5efK5/0HS5s/INweBa99utOQk1ICLI3S6Mw49aVP+Oz0oJpTqz1xxW3OiVn8W7eTEfHMQ6Ikb0Z3xKKaQTMfXH/JuBL5XO3OhbzEW6q/QtaGBv3cUb7In185tuSekuIT1jrvBlH/QQ7fZSF+yiKRf51LhqxmF0URRSrSoZaVTAz+mLktg/2kHdSLMbDqczs7jdl69/2IitGrlzazam8Y7c2K5tbk/14WZMRrgsz/OkZjmZusRGwv+yz49u0fbAMbMzEi817tjzqP/kNFRnrwkns2HbdzWwp8dR22k2jTqV8meA6B1bR/qV7HwxswYDp5xYDIqzFyd/dp5URUYdHMw7807i1vTaVTVyl9bU9hx3MbDXYIBGNA1mAGfRfHAhyfp1tyPvVF2lu1Ky7POJ24N4e3ZsTz6eRQtr7OyYnca24/Z+eR/GdPc61Q0E+hj4O3ZMRw87WDncRvHYrKPvGd9v1vWKtrtGCsEFm1X7b8DafyzLZmnupWnQVUre0/aaFfXj+a1fHjp+1M5nvPDv3E8c3cYC1+rzdSl56hW3swDHUNyLHs1Kur3vDSQGQBCCHGNMqgK9bzs1G86cnVmvT0a691ITFhA5h6kc88ur+Ywmlu0wVC+aLIOqxXCMTdrnW85XdNw7d2V+WDSfyjkf+9q6E0olkqX28S8WaqghHT1oqAbkv7LdMTm1DlwJv/2+5igS4OieRi7qVHhzirwhmu/F0EnwHL9DXm+7o46Tso3nxP3v16kfTUB17EjhdE8Ucp8Obgij9wYzMEzdn5ZnYjNqfNG7zAeveliZ+vFHqFUCTHy/b8JJKa6CQs28uVjlYk65+TFH6I5Guvg+XvKZ6s72Dfjs8HXotKxQe6Z2SOrWfl+WBWSbRrvz4tlw8F0nuseSp+OwdnKGlT49snKtK3jy08rEvh9YzJvPxiO0VCwteyP31qOl3tWYNOhdMb/dhabS2fyk5WoXTEj6NCpoR8fPRJBusPNxL/iSbXrjH4g79/Tj94Uwuu9wjgS4+CzP+JISHHz0YAI7j6fANDfR2X8o+H4WQxM/iceq0nhiVvLZasn6/td1BpULZylZ3np9/FRvvjzLPUrWxh4Uyg+FpXnpkTxxZ8555o4ec7Jfe8eJj7FzQs9wmlUzcoLU08CeNb8X82K4z0vaYque7OKQwghxNXo23/tTF+T/0irqsCMYb6lZps0b42Za2P53rwfwhQF/njBL1MegNRfvidt8hf51u8/ZAQ+9/a+0mbmKn3Oz6RMHJ9vOd9BQ/Hr09/zvXb0A7RDr+R7nlJ3PIaqQ66ojXnRTnyKtv/5/NtRexyG6hfL7Tut8dR32afXZtWhjpG3HiiahzGHC7p/lIorn2f4JaNy7hy1npH/Eo4NvTMnaUx6+xXs//6d90kWHyr8tgzULGM0bjf2datI/3U2Sf+tRdN1XLqOXdOJc7qo9+d/OdcnRA7OJrto8+JhnuseytA7QgulznSHzuq9adQMM3mmh+8+YeOuccf55qlK3NS4aGaCXcsOnnZw65ijJd2MTMKCjHRu5M+fm5I8uwQM6VaBsQ9VovpjO0lMuzoHFC746/UanmBTYYk9dZjQ0FCsVitGoxGj0YjVWvTbSObm2p/jIIQQZVjb2iavAgCaDkt3u7i/TfGPil6JnSfzH0WuHKJmSwKonc45sVFWxvqNLqdZXvO2fv1M5qmYus27EV81MP8ZBldC8bJ+Jf1opu9Pxnk3c6N+paILSJmNULOC6tVMhMLijs59R4QLjJUrZ+r8a/Fx2P5cgO33ebhjzgDkv/RDiFxsP2bn350pLN+Vio9Fpc/1hbd2W1Hg9Z+jces6D14fjKLAjNWJVKtg4voC7P8uLqpd0UytcDOHo4suEWBBhQUb+XZodbYdTWfO2ngqhpj4383lmbUm/qrv/NcKNxd65780kiUAQghxDWtQSfF6VH/eRld+y9FLlSOxGudS8p/EVjci+0ednhDv1TUMlasWuF0FYahcxatyWnxcpu8VR4xX5ym+1xW4TQWh+HhZvzPzvs3e/NwAqpQr2mRMlUKK9zFIT0zIt4wakNEhc+7cRtK41zj3YHdSp0z0dP6FuBLJ6W4mLo4jLsXNxMcqUr4Q1ztbTQo/PlOFyKo+fP1PPF//HU/9ylamDauCxSRhq8vVuZFvSTchk53HbNz33mEUYHTvivS+PoRpy84xfPLJkm7aFctt28prjcwAEEKIa5hBVehS38i8jflvF3cmQePf3W5uirw6MuCu2uddErkGlbN38rS03BNGXaBDpsR7RUEN9G70TUvPnOFadyXne46OAqbsa1gLlcm7qcNZ25tm9y4AEORbtJ2GgGKeganb8l/24D4dRfzgfrgOHyiGFomypmN9X/Z8WqfI6q8VbubbIUWUd6SMGnhDCNNXJOJwlZ5V2/9sS+afbfl/Dl1NzEaF/9187SQzzIvMABBCiGvcHc28j/VOWWHHfhUk8dF1+HundwGAFjWyBzQUb9LfKIp3G1pfCUX17hpalvYqXrRfVyj6yeKqd9fQM08tyXo7uTEU8VOK0VDMo5Je3Lc75ox0/oUQHlXLmxh4Y9nomJakgTeGUD7g6hgAuVISABBCiGtcrTCVJlW9+3V/JkHnl7X5zxYoaRuPuImKy783FR4ENSrkcO/m/Nf4Kbru1YjtldDT07zajUCxZEmEp+Y/dK0oGrgLtgd3gbmT8apXa8i8VZbJy4zg6Y6iDUYVdf1ZKV78vRNCiKyevD2UAKssoygq/laFp7oVTjLMq4EEAIQQogzo1c775H7T1zjYf7p0JwP4YZV3CZG61M959oPi7dT7Il537T6Tf1I4ADUoOPMBo5dT723HC9iigtFtx7wqp5gyb0MW7OPdg2xMUtF20M8mF3MAIOvP8YorVDG3bkfAsyMLt14hRKkS6KMwpm9ESTfjmjWuX0SZCrBIAEAIIcqA9nWMNKjk3dQ2l6bw9nwbaaUn6XAmS3e72OVF9n+AWxrnHPgwhId7db7r4H6v23U5nIe8q18Ny/LgZ/EyOWHKtgK2qGD0ZO/q17O0NyzIuwetQzFFG4g6UsT1Z2XI+nO8TEpIMD69HiLkm58IeHE0pshmhVKvEKL0uqdNAI/fWsR5Xcqgx28tx12tAkq6GcVKAgBCCFFGPHaj97MAouJ13phjy3eP9OJ2LkXni7+9i0w0qmKgVljOH3OGarW8qsO+ZYPXbbscLi/rN1avmel7xb+BV+dp55YVuE0Focd7V7+apb01c1qWkYOtR4vuL2BUvE5cavHOADBUq3FF55simxEw6i1Cv5+HX//HMFTwLpAlhLg2vNCjPK1r++RfUHilcyNfXuhRPv+C1xgJAAghRBnRtJqBrg28T3Cz6Yib9xfavU7YVtRsTnhtto0ELzttvdrmHvAw1W/kVR2OVcvBWTRTIXS7Hfvq5V6VNWZprxLY2rtrnF0Amr3AbfOKOx099lfvymZpb1igQqgX21MeP6dxKLpoRulX7C3+XBfGeg0LfI7i44v1rp6EfDOd4E++xnrjbSgm74N5Qohrh6rAF4MrUTFENnK7UhEhRj79X2XUsjPz30MCAEIIUYYMvdVCoJfrrwGW7HTx9jw7zhKeCZDugFdm2th3yrvO4HXhKh3r5R7sMFSuihqW/+ipnpyI7e8/vW5nQdgW/4aempJvOTU8AkPFypmOKb61vVsG4IxDO/3T5TYxT9qpaeBKzL+gtTqKT/YZFy1qeheMmuvFFpYFpenw57bi/0ttbtIcVO8evQxVq+E/9AXK/fI7Ac+MxFizdhG3TghxNSgfYODfMTV5sJN3uWxEdr06BrJ8TM0CPQ9dSyQAIIQQZUiIn8Lw2y35F7zE8r0uXpqeTlxKyUwFOJus88wP6Ww95n2HbfAN5nw3p7N06OJVXak/fFPouwHoaamk/jTFq7LmDl1zPK5UuNur87Ujbxf6bgC6Kxnt6DivyioVuud4/Pp63o1g/b3DxbHYwp0FsGSXi6i44k90qQQEYmrczKuyxpq18enxAKqfv1fl3W7vtsUUQlz9TEaFtx8M562+4WVyBPtyqQq881AE7z4U4fVuNFeqNP5ulgCAEEKUMV0bGLirecGmEG87ofH4lHQ2HS7eUdNNR9w8OSWdgwWYBt61gYFWtfIfXbbccodX9WmxMaRO+tTr63sjZeIn6OfOelXW55ZuOR5XKz7o3cXsJ9EOFm6WeP3A8+DwcgeDiv1yPN72OgNBXixldWvw/kIb7kLqryel60xaUnIZLi033u5VOfuKpTjWrc63nKqqKIqC01FKs3YKIYpMv85B/DW6Jr06BmI0SCQgN0YD3N8+kL9G16R3x8BivbbT4UBRFFQvZ38Vh9LTEiGEEMVm6K1mGlQq2MNCXIrOS7/YGLvARnwRJ09LtulMWOTgpZ9tBUrUVs5f4enbvJvhYKrXEGM973IBpP82B9vC+V63I8+6FszC9ucCr8qaGkRirJtzwj8lsDVKYCuv6tFOTkI79a3Xbcy7rq/QTn3nVVklqC1KQMscXzMZ4K7m3s0C2HtK59PFV57LQNNh7AJ7kf/9zYv1pttQ/b3LOJ380VtoMdE5vqYoF//9qqqKs4hyVQghSrda4SbefSiCFW/V5KHOwZiLaWT7auBjVnn0xhBWvFWL9/tHUCu8+POnOJ2OTJ3/S393lxQJAAghRBlkMsDbvXyoFFywDyIdWLrLzUNfpjHxHwfnCnlZQKpd5+c1DvpPTOfXzU4KUrtRVRh1j4UgX+/vyffBR7wum/zJO6T9OrsALcoubd4MUj7/0OvyPg8OzPN1tYb3I/vuPUNwn5jkdfmcaCe+QNv/jNfl1eov5fn6fW3M+Ji9q+v3LS4++8tx2UkpXW4Yt8DOxmKexZKVYvXB2qOXV2W1+DgSRg5DyzJb5MID5IX/q6paKqeZCiGKT0SIkTF9w9j7WR1mv1CNgTeE0KKWlesiTJQPMFzTgQGzUaF8gIFa4SZa1LIy6JZg5r1UjV0TavPqAxWIKMGkiW63yxMAyPq7u6Qouq6XkvzOQgghitvpBI3nfrQTk3R586uNBmh7nZFbIo20vs6A9TKC6y43bDvuZtluF//ucZF+mQOZw283c3eLgjcg/pnBuHZu9bq89ba78H/yWRQvR3EB9OQkUr74CNs/3icUNDVpQfDHX+Vbzr3pBvSE/KeKX6BWfBi17kdgDPb6HFzxuPc9i35mutenKMGdMLRckm+5n1Y7mbLc+x96i+oqz3e3Eh7o/QNUdKLOOwts7DhZ8L/nS0b55Xi89Yz78z13Q++cA0Z6Wipx/XuiJcR71QZDWASBo97CGNkUALvdjsvlwuVy4XQ6SUhIwOFWKR9W0av6hBBCFI+zMacxGzSCg4MxmUwYjUaMRiMWS8HyMRUmCQAIIUQZdypB54XpNs4kXNkia6OqUL+SSqMqKtVCVaqEqgT5KPhaMmYcuDRIs+vEp+qcSdQ5flZj/2mNnSc1bM4r+yh6tIuZfh0vb2qf6+gh4p/oDy7vs82rgcH43P8g1jvuRg0ul2s5Lf4ctj9/JW32dPQkLzLmX2AyUW7Sjxiq1cy3qJ6yC/eGtqAVIHJiCkWt9gxqpYFgDsu9bkc0+qnv0I5/As447+tXzBjbbgC/nJcvXMrphsenpBco0Z/VpHB3SyM9WpoID8o9EHA2WWfBJidzNzixXeZmAkURAACw/f0Hye+94X1DFAXLTbfje38/tGo1PAEAh8NBcnIySclpVKyafbcFIYQQJef0icMEBvgSEBCA2Wz2BADMZi+nvxUBCQAIIYTgbLLOy7+kczj26vtIeKSzmYevv7J1fWlzppM68ZOCn6iqmBpEYqrfCDWiIorFBz09HXf0KZx7d+Pat5PLyV7n/9Rz+PTs43V57fgEtAMvFPg6KMaMXAJBrcFaHVQ/0FLQ04+hJ22ApI2gF3xquVrnI9Rqw7wufzBaY9j36TgKfCmduhWNNKysUjFIwccCDiecTtTZe0pjd5SbK33KKaoAAEDSGy9iX/VvgdvkjqiM2iASLbwimr8/qTY7MbFnqT1wGIrBu+0VhRBCFC1d1zl+eDfhYWH4+/t7ZgAYDAYJAAghhCh5aXadd36zs2Z/ya6R9pai6Dx1i4WerQonqU/y2FexLfurUOq6Etabbifg5TEFPk/b+RBa9MwiaFHBqBF9UBtNK/B5i7Y5+WBh6UtkV5QBAD01hfihA3GfOFagNjk0DbcObl3HpevYNI04p4uwNz8htE3HAtUlhBCiaCTGn8OWGk/58uWxWq2e0f+SDgBIEkAhhBAA+FoUxtxnZfCNZoylfBDRzwJvP2AttM4/gP+Lr2Nu0brQ6rscphZtCHj+tcs6V234LUrIjYXcooJRyt2E2nDyZZ17e1MTAzoVf4bmkqT4+RM09hPU0PIFOy/Ll0FRsKgq8Wv+LfxGCiGEuCwpyQlYLBYMBgOKomT6KkkSABBCCOGhKNC7nYnPB1i5Lrx0fkTUi1CY9D9f2tUu3Ky+islM0FsfY27ToVDr9Za5TUeC3voITJfZCVYtGJrNQwn1bp/5wqaEdsPQdC4olz+q0b+Tmf5lLAhgqFSZ4I8mooaFe32OgoJyvvevKBkPc0ZFIWXdcq54zYMQQohC4bCnYzKZUFVVAgBCCCFKtzoRBiYO9OGJm8z4W0vH1kEWEzx2g5nPHvGhYgG3L/T+IhYC3/oQ3wKsv79iioLvfX0JevtDlCvNCqz6YGg6F7Wq9+vvr5yCWm14Rudf9bni2gZ0MjPijqKdhWJUde5pWXLbQmVlqFKd4E+nYKzXyOtzLoz+qyioioJZUTAnxHPk+/x3jhBCCFG0Tp04gtVixmw2o6pqpiBASZMAgBBCiBwZVHigrYlpT/jQu60Jq6lkPrQUBW6ONDL1cV/6tDdhUIu2HYrBiN9TzxH45vuoIbln+C8MakgogW9+gN+Tz4JaSD1exYha9yPUJrPyzPBfKMzhGJrMRq3zASiF12O/o5mJz/r7ULVc4T+mWEzw5v0+dKpXegIAAIbyFQj+5Gt8ej8Mat73rSgXO/8Z/8/YhcPPoHJ27nQccWeLo8lCCCFy4HDYSU1J8CT+u9D5Ly1BAAkACCGEyFOQr8Lgm8z8PNSHgV3MVCjA/utXwmJUuLO5ke8G+/Dy3RbCium6nut37Eq572ZlZOO/3Gn5uVBMZnzvf5CQ72Zi6dC5UOu+QK1wD8b2OzNmA1zBtPycK7dkjPq324FSoXvh1n1e3YoqXw/yoX8nE9ZCevurl1f5bIAP7Wp7F6wo7kc0xWTC/7FhhHz5PaamLXIvx8VlAIoCqqJgUBTMqoqfw8bRn78rvkYLIYTI5Myp4/j7+XnW/2edAVDSAQDZBUAIIUSBaDpsPuJi2W4Xaw+4SUwvvLqNqkKzGiqd6xno2tCE3xXOiC8s2tkY0ubOwP7Xb2gJCZddjxIcgvX27vje2xs1tELhNTA/9ii0E5+jnZoGztjLrkY3VcBQaQBq1aFgqVSIDcxbfKrO7PVO/tjqJMlW8PODfaFvezP3tDJhOt/333LUzfPT867MaIDFLxXdLgD5cW7bTPrcn7GtW4Xivrg7h6braDpo6Jl2A3DqOqkuNzFujevem0i5pq2u6PpCCCEKJikxnuioI0RERBAQEODZ+u9C9v8LwQA1n5leRUkCAEIIIS6bpsOBMxrbj7vZE+XmcIxGVIKOpnlztk5YkEqN8ip1I1QaVlFpUtWAj7nk18flRne5cG7ZgGP9Gpw7NuM8ejhTxyxbeYMBU43rMDVujrldR8zNW0NJ7tOuOdHil8G5P9ESVqEn70LBlWtxHSOKfyOUkOtRQu9ALXcDKCU3dd7phvWH3Kzd72LbcY0zCRq5PcQE+ii0qGGgU30jHeqomI2Z/179d8jNyzPyDgAE+ijMe9a3kFp/+bSEeBxrluPY9B+u3TtwxZxBIyMQ4NbBTUZAwKnr2N0aSVZfkq6rR+SocVgDg0u6+UIIUSbYbOkc3reDChXKExwcjNVqxWQyYTAYPF+XzgYoKRIAEEIIUajcGsQm68SlaCSn6zjcGceMKpiNEOCjEuyrEOpPtk7ZVcfpxH06CndsDHpqMrrLhWI0ovgFYKgQhqFi5UJfPlCodAekH0a3RYErAV1zoqgmMAajWCuDtRaoJbdXcX5SbHAyzk18qo7dCQaDQoAVKgarhAfl/Xfrn50u3vnVnmeZquVUpj5x5YkNC5s7KRH36Shccedwp6XidrtxG4zo/gHooRVIM5mJi4sjMSmFBo1blvh0UyGEuNa53W727dpCUKA/5cr9n737Do+iatg4/Gw2vYdO6L0XQZCONOmCYH9t6GdBREEUrIBiAXkVBQRFxAqIAiK9I733Jr0H0nvf8v2RN2tCOgQCzO++rlyQnZkzZ2ZnN3OeOXOmmLy8vDJd+c/uVoCicmuNgAMAuO2ZnaQyfiaV8SvCK903i4uLzBUry1yxclHX5NqYXCXP2jJ51k77tYirU1De7lLtwGs7zoKj8u6mUtz7moq+4Zx8fOXk4yuzzSbb/36sVqssFossFos8LBb5+fkpNTVVZ0/9oyrV6xR1lQHgjnb6+GF5ebrLz89PHh4embr630r3/0sMAggAAAzoTFjeHSADb8BTCApD+glkxhPK9JPM9CtNnp6e8vf3V2pygk4dPyxb/u7LAQAUgM1m06ljh2WSVf7+/vL09MzS5f/qxn9RhwC35l82AABgaHa7dPxKzuMrXG/ZBy7k3SCuXKLor9TkJmPDP2Pj32w2y8XFRd7e3ipZsqSsqUk6cmCXUlNTirrKAHDHSE5K1OEDO2WzJqt48eKObv9Xd/m/Fe77z4hbAAAAwC0jNsmu5fstWrTXoqBIm354wUMVihfu9Yqjl2wKj807AKh1jbcX3Awmk0npwzhl7AVgt9tl/t9Ak66urvLx8ZHJZFJERISO7N+pqjXryYeBAQHgusTFRuvksUPy8/VRQECAvL295erqmmW0/6u7/d8KIQABAAAAKHInrtj01+5UrT1sVbLl3+7536xO1sePFO5AfH/uTs1zHncXk2qVuXUDACnziWT6I6Uyju1st9sdIYDZbJazs7NOHz8kb99iqlilulxcbt0BHgHgVpSamqILZ08pNjpcxYsVc3T7z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', 'error': None, 'output': None, 'system': 'take_screenshot'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: [{'base64_image': 'data:image/png;base64,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+jYmDynJyQkyGq1yTufTxSIi4tXakpqpsZfs7ubysfLO8dlKpSvoOeefUYDnnlKv86arXGfjVejBg1ybUhK0l8LF+nXWbPUtUsXlSpZSu7uufeEiI2Nk89VDbfSpUvlvkFF8FjF3Kxfv77Rc889e3TS5K+P3HXXXXWLuj7ZKVumjLw8vfTeO29nmfbPseOyWq16eeBLKhYQUGjrLF26jBISEhQRGZnvcl1cXFS8WLF8zTtv/ny1a9fWcVvMmbNn9eusWY7pHe69V9O++16zf5ujBg3q5/oUC1dnlyyDFwIAkBGDAAIAsmhQr4HMZrPWr98gSXJ3d1epkiVVPEPX7mIBAfLw8Mg0mJvdbtfps2dUPrBcntN9fHzk6empi5f+HZQvN2XLlFGdOrX13jtvZ/pp0uSuLPMOGvyaFi9Z6vjdyclJPbt3U0pKqi5eCsrUkMz480DfPpKkX2fN0sAXX9T/Pfes7r+/l+rXq5dr3cqUKaPLV65kem3rtm0KCQ3N17YVpRo1a56/++67/5GkXbt21Xnu2QHumzdv2lfE1cpWh/btdSU4WHP++EM2m00pKSmaMHGS9u3brxo1qqtataqaNHmKkpKSZLfbtXDhYv32++/Xtc7q1aqqUsWK+n7GD0pOTlZMTIyWr1hRSFskubq66vSZM4qLTxvQ79eZaY1/q9UqSSperJiaNLlLixYvUcf27XMtq3z5crJYLI7BNvfu3afjJ04WWl0BALc/AgAAQBYBAf56sF8/fTv9e23dtk0Wi0VWq1Wbt26TJLl7pHVp7t2zp36ZOUthYeGy2Wz6Y948xUTHqEOHe/M1vUOHe/XbnDkKj4iQxWLRn38uyLFO3bp10YqVq7Rv335JaU8qGPXBGIVnM4h940aNNHP2bB06nDaYYXJysubOmy8vLy9Vrlwp14aklNYoO3LkqKxWq86dP6dFS5bIarPmWLf7unTS2rXrdP7CeUnSjh07NebjT2Wz5rzMraJK5cph076b7tS5c5e9knTkyJGqz//f/xVfuXLFTkm3VDeFUqVKaczoUVq4eInuf6C/HnjwYUVHRatGjeoyOzlp9HvvKSoqSn37P6S+/R/S4mVL1bLFPde1TpPJpDeGva49e/fq/gf66eln/0++Pr6O8QKu15P/eVwJCQnq2+9BPfafJ1WmdNpYjFeuBDvm6dypk8xms9q0bZNrWYGBgXr80Uf1zvsj1b3X/Zo+4weVL1+uUOoJALgzmOy53YQHALjtJKekFko5drtdM2fP1tx585WYmHZFtUSJEnrqif+oW9f70taVnKzPv5igvzdslLOzs/z8fPXG0KFq2rRJvqZHRUdr1OgPdPjIUbm5uald2zZatXqNfvh+miqUr6CRoz9UQLEADX11sCRp7vz5+vmXmXJyMslqtan/Aw84Rk/PyGaz6ddZs7Tgr0X/u9XAqiqVK+mVQYPUsEHaPeF79uzV5199peioaNntdjW7+269OWyovLy8tGPHTo0d/1/FxMTI399fbdq00sqVq/TX/Hnat2+/3h05SiuXLcm0r7759jstXLxYLi4ucnd306uDX1GbVq20c+euLPO/+dbbqlWjhv7vuWev+31yc83/venSv8dHgL9fSmJiomuXLl32zv9zQd3g4OCwN4a9fnHBggX3SFJgYGDwf//7+akH+vVrYTKZbrkLBlFRUXJxcZHXVU+LkKSEhAQlJ6coIMC/UNZltVolk0kREREqXqyYfp87Vxs2btKUSRPzXjifwiMi5OnpKY9sxgtY8NdC7dy1Wx+P+SBfZSUmJio1NdUx6OWNUtBjDwBQ9AgAAOAOU1gBQDqr1arw8HC5ubvL18cn2yuf8fHxik9IUMkSJQo03Wqzyel/DSsfX1+dOnVKg18bqkUL5svDwyPb+tjtdoWHR8jX10eurq651t1msyk8IkJenp7y9PTMdp6cGpJWm03h4eEqFhAgZ+f8DZmTlJSk2Lg4lShevNCuEOelMAKAufPm13Zzc/MICQkJeeftt07MmjWrpc1mcypevETEJ59+cviJJ55saTabDTtu0Fvvvqc6tWqrTZtWuhJ8RRO+nKRHH3lID/brd0PXGx8frzNnz+qTT8fplUED1aplyxu6voIiAACA2w8BAADcYQo7ALiRZs7+TadOnVb/fg/Ibrfr22nT5O8foDEfMNJ5fhVmACBJUVFRkR9+MPrgtGnTWlksFmdfX9+4kSNH7X7hxRdbuLq65j4a4h3q3Llz+vnXmTp69B/5+PqoS+dO6te3r5ycbmzHiOXLV+jrb75V75499cLzz93QdV0LAgAAuP0QAADAHeZ2CgBiY2P1868ztWPHLplMJjW9+y4989RTWUbUR84KIwCYN//POq6uro6+5/HxcXHjxo7b/dVXX7ZITk528/T0THxz+PDtQ4YMbe7h4ZF9VwoYDgEAANx+CAAA4A5zOwUAuH43IgCQpKSkpMTJkyZtHzv202ZxcXFerq6uKYNeeWXrO++8e5ePj8+NvbkctwUCAAC4/dxyg/oAAICi5+7u7vHakCGtPv7k093+/v4xKSkprhO/+qrNW2+N2B8Rkc2jFwAAwC2PAAAAAGTLxcXF9fnnn2/9xYQJB0qWLBVutVrNM77/vs3QIUOOXcn4nDoAAHBbIAAAAAA5cnJyMj/22OOtpkyZcqJChQqX7Xa7ac6c31oOenng+XPnzl0o6voBAID8IwAAAAC5MplMTr3vv7/Ft9OmBdWoUeOcJC1ZsqTZCy88H3Hsn39OFnX9AABA/hAAAACAfOnYsVPT6d/PiGnYsNEJSVr/99+Nnnvu2dS9e/ceKeq6AQCAvBEAAABgYCaTqUBPA7rnnnsafD/je2uLFi0OS9KuXbvq/N//Pee2ZfPm/TemhgAAoLAQAAAAgAJp0KBh7e+mf+/RqVPnvZJ0+NChav/3f88VW7ly5U5JPF4YAIBbFAEAANxhzE58tRuFk5OpwMs4m82yyy673V7whTOoUaNG1W+nTSvVp0+fHZJ0+vTpCi+9+ELF+fPnbbXb7bbrKRu3NpNM13TsAQCKHmeJAHCHMZv5ajcKZ7O5wMsU5vFRvnz5cpMmf135iSee2Ozk5GQLCgoqPfiVwbV//vnnzVar1VJoK8ItwySTbLLJxdm5qKsCALgGnCUCwB3GZDLJ1cWZK3R3MCentPfYZLq299jd1bXQ6lKqVKlS//38i7ovvvjSJmdnZ0t4eFix4W++0fibqVM3p6SkJBfailD0TJLJqXCPHwDAzUV8CwB3IJPJxBU65IuTk5PcXF3k6upyzWWULlUy4LPPxjXx9/fbPGHChBbR0dE+77//XvOEhPitw4YNa+7h4eFZiFUGAADXiB4AAADgunl7e3uPHDmy+ahRo7Z6e3vHJyQkeIwZM6bVyJEjd8bExMQUdf0AAAABAAAAKCTu7u4eb7zxRuvPPvtsd0BAQHRKSorrhAkT2gwfPnx/RERERFHXDwAAoyMAAAAA18Vut9ssFktqUlJSYmJiYtJDDz1Ud8SIEXs9PDySrFaredq0aW0GDx587MqVK8FFXVcAAIyMG0QBAEC+HTx48NjGjRuD4+LibNHR0abY2FhTdHS0OTY21iU2NtY1NjbWNSEhwTU+Pr56amqqsyTZ7XbTrFmzWsbGxu6cOHFiSuXKlSsU9XYAAGBEBAAAACDfgoKCYoYPH353fHx8gQf2W7RoUbP4+Pj9X331VWL9+vVr3oj6AQCAnBEAAACAbEVHR0dv27bt+L333tvAzc3NXZJatWpVu3Xr1sdWrlx5lyS5u7sne3t7x7u7u6d6eHgke3p6Jnt5eSV7e3un+vj4pPj6+lp8fX2tvr6+Nl9fX5OPj48pKSnJXLRbBgCAMREAAABgYCaTKdvXY2JiYkaOHLl//vz5tRYtWnSmcePGdSTJx8fH57HHHotbs2aN1Wq1mu+9997D48aN8/T29vbw8PBwc3Nz83Z1dXV1cXFxdnZ2djabzWZJ2a8EAADcVAwCCACAAdnt9hwb5ZGRkZEjRozY//XXX7e+ePFi6T/++CMk4/Ru3brVbNCgwSlJOnz4cKCzs7Nz1apVK5UtW7ZMsWLFinl7e3u7ubm5m81mZ9H4BwDglkEAAAAAHMLDw8OHDRt2ZNq0aa2tVqtZkubPn1/p/PnzF9PnKVOmTOmHHnooSJIuXLhQ5o8//riYU3kAAODWQQAAAAAkSSEhISGvvfbasR9//LGVzWZzcnZ2tkjSsWPHKi1atOhUxnn79etXoWLFipclad68eRUuXboUVBR1BgAA+UcAAACAgdntdplMJqfLly9fGTRo0JmZM2e2stvtpmrVql0YPXr0ppIlS0bY7XbTb7/9ViwqKioyfbmaNWtW7tWr10lJOnz4cJVly5adynktAADgVkAAAACAgTk5OdnPnz8f9NJLL12YO3fuPZJUvXr1C998803Im2++2bJjx47HJWnXrl01161bdyzDcubHHnvM19/fP8ZmsznNmjXLNyYmJqaotgMAAOSNAAAAAAO7ePGi30svvRS1cOHCZpJUu3bts99//314586dm7q6uro9/vjjJg8Pj6SkpCS3WbNmmZKTk5PSl7377rtrdujQ4R9J2r59e82NGzf+U1TbAQAA8kYAAACAgR04cKD66tWrG0tS/fr1T82YMSOmXbt2jdOnt2/fvlazZs2OS9KaNWtq79mz53j6NHd3d4/HH3/c6ubmlpKQkOAxa9Ysa2pqasrN3gYAAJA/BAAAAEB33XXX8R9//DGpZcuWDTO+7ufn5//4449HmUwme2RkpN9vv/0WZbfbbenTO3bsWKtJkyYnJGnlypW19u/ff+Jm1x0AAOQPAQAAAAbXvHnzoz/++KOtadOm9bKb3rNnz+p16tQ5I0kLFy6sfuLEibPp04oVK1bs0UcfDZOksLCwYnPmzAmTZL8Z9QYAAAVDAAAAgIG1adPm4A8//ODSsGHD2jnNU758+cD+/fufl6SzZ88G/vnnn+czTu/Tp0/VWrVqnZWkBQsWVD19+vT5bIoBAABFjAAAAAADstvtpg4dOuyfMWOGV926davnNf+DDz5YNjAwMESSfv/997IhISEh6dN8fX296tatGyJJJ0+erLBw4cKzN6ziAADgmhEAAABgQP369dv53Xff+dWoUaNqfuavV69ete7dux+XpAMHDlRbvnz58cuXL1+ZMmXKht69ewctXbq0Ufq8f/zxR4moqKjIG1V3AABwbUx2u5379AAAMJjIyMiIgICAYgVZZs2aNbsfeOCB2rGxsV7Vq1e/4OLiYjl27Fglm83mJEnu7u7JzZo1O/7MM89EPfroo008PT29bkztAQDAtSAAAAAA+RIfHx//8MMP/7N06dKmGV8PCAiI7tSp0z9PPPGEvX379rX9/f39i6iKAAAgF9wCAOCOcTkoUVs2hmb5CQlOuuYyD+yN1IBHNxdiLfO2YW2whg3aVWjlHT0crb/mXtDGv0MUH2cptHJz894be7Vs0aUCL2ezSTu3hWvh/Ivav+fW6EE+6Lnt2rE1LNtpQZcSdeRQdIHKy3icbt8SprOn4zNNL4xjbsPaYA0duLNAdcn4ExmRku38e3bEez3yyGNJzs7OFpPJZK9cuXLQq6++umHp0qXnq5R9o5GHS7MWhdX4Dw1J1vNPbFWbu5bfMsfC1aZ8eUw/fHuySOvw9tA9Wrk06LrKuNbPa24K8v4V9nceACBnzkVdAQAoLMsXXdInow+qXgP/TK+/+kYddbyvzDWVGR2Vqp3bwguhdjkLvpKkH749qbdG1ZeUduJ8YO/1N3isVrsGP79D69dcUdPmxRURnqKgiwn6cU5rNbwr4LrLz82hA1GqVsOnQMtERqTo4V7rZbXaVbW6jw4diFTlKt768fc2cnd30s5t4Tp5PEaPPVXlBtU6zdzZ51S2nKdatyspSdq7K0K9H6iQ7bxLFlzUkUNRmjC1Wb7Lz3ic2u3S6ZOxqljZSzPnt5N/gEuhHHOhIcnatzvvYyinz8y7HzZUsxbFM72WkmLTm6/u0vjJzavee++9hxo3bhz9zDPPlK1du3Yrs9ns/NE7a1W/0bWHbVf7bMwhxcdb9OOc1ipfwbPQyi1Mp07Eysu7cE+lrv4+yMvB/VGqU9//utZ5LZ/XvOT2/t2o7zwAQN4IAADcUarV8NGClR2KuhoFEhqSpK8n/JPvE/78+mPWOe3cGqY127qqTFl3SdL4jw7rtRd3at2O+wp1XYXh6wn/qHgJN/22sJ0kKTXVrt6d1ui7r49r8LDa2rc7QquXX77hAcCSvy6pUZMARwCQm+cH1bimdWQ8TlNSbOp57xr9MuOUBg/L8Ul8N0x+PzOurk7adrCHJJVd1X5V2Rtdr+P/xOihxyupes3CbZje6m7U98HNltv7d6dsIwDcjrgFAIAhLF14Sd3br1FSolWS9NX4o3rmkc2y26X+3f/W558cUbumK1Snwl8aOnCnkpJs2ZazaX2IurZZrZqBC9S709pMXVv7d/9bH7yzX01rLdb0KSckSdMmn1CTmovVoMpCDX91t5KTM5f72y9n9WT/TbLZpOb1ljqu/Kam2vT+8H2qX3mhmtVdkqmL7/mz8Xqk9wZVL/OnOrVYqa2bQrOt62+/nNFT/1fN0fiXpP97uYb6P1rRsR8unEvQf/ptVO3yC9Su6XLNnX0uUxnTp5xQq0bLVK/SX/q//2xVWGhyprrfU3+pGlRZqLEfHFLHe1Zm6c4uSYkJVr3xym7VKrdA99Rfqu+nZt9lOjIiRQHFXB2/u7iYNOazu9SgUYDeHLxbE8Yd0c5tYWpeb6msVrsSE6x6a8geNaq2SE1rLdZH7x9QamrasDbLFwfp0fs36Pkntqph1UX53m+dWqzUhrVX9O2k43rhya2O13fvCNd9rVepRtk/9exjWxzHxzcTj+vtoXsc83352VE1rr5IdSr8pRee3KqoyNRstzUjV1cnVajkpYjw5Gyn//z9abVosFS1yi3Qgz3WZ9rHB/dFqce9a1St1Hy1v3uFVi+/nGX55GSbBjy6RR+PPJhnXTL6eORBDRu0S13brNZzj2+R1WpX83pLdepEnKS0WxW6tlmtWuUW6P/+s1WxMf9ua0qKTe8O26tG1RapbsW/9NqLaZ+po4ej1bLhskz7pWeHNVq5NHO9+3f/W/v3RGjsB4fUv/vfklTg9zuj3JZdvOCinnnk31suzp6OV/N6S5WcbFNIcJKa11uqj0ceVN2Kf+nQ/qgsZV84F6/+3f9WzcAF6tftb108n+CYtn5NsDo0X6la5Rbo0T4bFHQp0TEtu2Mlp++DjHLb77l91oIuJurphzerbsW/1KTmYk396niWsiUpPs6iQc9tV50Kf6lRtUUa9+GhbOfLbZ9m9/6lu1HfecMG7dIXY484fv/z9/N6uNf6PJfftjlM3dqlfaff22yF/l4dLEn5eu8B4HZEAADgjhIfZ9HGdSGOn727IiRJ3XuXk7e3syZ9/o9OnYjTpM//0bC368lkSrsPeu/uCM1Z1E6L13bUzm3hmjLhnyxlnzsTr6cf3qwhI+ro4Jn79fjTVfRonw2KCE+7X/pyUKK2bQ7TlB9aqP+jlTR39jn9MO2k5i/voM37uuvUiVhN/epYpjIferySZsxuJScnacPubmraPK3b9YljsapTz0+7jvbUk89WczQyLRa7nui/US3bltSR8300/P36eu7xLYqLzXpv/9nTcapVxy/TawHFXPXK67Xl7mGWxWLXf/ptVP2GAdp7vLe+mNJMI0fs08Z1aY93nzv7nL6ZeFwzZrfWjsM95R/gqv/7zxZJ0pFD0XpryG6N+ayxdhzuKS9vZx07GiOLJWtw8t6bexUWmqRdR3tp1p/t9NX4o9neU//M89W1avllvfzsdm3eECq7XWrWorju7Vxan3zRRIOH1VHT5sW1YXc3mc0mvffmXp09Had1O7pq8bpO2rQ+RF98evjf4+DvEDVtXlxzl7bP935bur6z2nYorf8bWEOTv7/H8fqm9SH68fc2Wr+zmw7uj9T8OWlBSXRUiiMU2bA2WD98e1JL13fWrqM9FR9v0fiPsm88xUSnavniIC1deEnjPjykvbsi9MSArE/j274lTJ+NOaRZf7bT4XN9VKmKl94fvldSWg+J/3tii3r2Ka9jQQ9ozGeNNei57Qq6+G8j02Kx66Wntyk11abh72d/tdVqsSsiPMXxk75PIsKTtWzRJQ0ZUUdjPrtLdrt06UKCUlNtSkmx6dnHtqhL97LacbinHnq8ki6c+zeY+HrCMR05FK2Ne7ppy/4eOnwgSjO+OaE69fxkMknLF6fdb35of5SOHopWq7aZe1vM/qud6jX01/D362v2X2k9Qgryfl8tr2WvXM64z2y6dCFBdnva/rt0IUGhIUmas7B9tlezd++I0Afj0j4HFSp5achLaWMvnD4Zpxee2qoPxjbSgdP3q0GjAA3937ScjpWcvg/S5bXfc/usDXlph6pW99bBM/dr9l/t9N+PD2V7m8ikz/9ReFiy9hzrpSXrOun3mWe1eMHFAu3T7N6/dDfqO69lm5L67Zezjt8Xzr+gps2L57p8YoJVTz20Sa+9WUfHLvXVS6/W0otPb5XVas/Xew8AtyMCAAB3lOArSZr8xT+Onz9mpTXUTCbpv5Pv1o/fndQr/7ddz79cQw0a+zuWe2JAVZUN9FC1Gj4a+FqtbE94f/vljNp1KK3uvcvJzc1Jjz1VRTVq+WjBH+cd87z6Rm3d06qEAoq56tcfTuuJAVUVUMxVdrv06JNVtGxh5oG2zGaTXFzTvord3Z3k9L9v5br1/fT401Xk7mFWj/vL6crlJCUl2bR1Y6jCQpP1zPPVlZhgVYvWJVWylLu2bAzJUt+Y6FTH/ckXzydo2KBdjp+Q4CRt3RiqiPBkvflePXl4mnX3PcX15LPV9NP0U5KkX384rRcH11Ttur7y8nbW6E8baf+eSB09HK3Ff15U+05ldF+PQHl4mjXwtVrZvh+JCVbN++2cBg2tLZvNrhIl3dS1Z6CWLsw64FiDxv76e0dX+fm56Pkntqh53SWa9dMZSWm9AZydTXJyMsnd3UnJyTb9PvOs3vuooYoVd1XZQA+9PaqBfv7+lKO8OvX89NKrNVWztm++95ubm5NMJpPMzia5uv77J/K1N+sosJyHAst76O7mxXXmVFyW+lutdlmtdl2+lChPL2d9/X0LDRqafZf+qMgUzfvtnP78/bw2rAtW2XIecnc3Z5nv7nuKa8/xXipV2l3nzsSpdl0/nTweK0natilUKck2vTyklpydTWrXsbTe/6ihEhL+bRi98couRUYk67tfWsrFxZRtXY4djVG7pssdPyNH7HNM6/9oJXXvXU6B5T0yLbNja5gSEy0aMqKu/Pxd1LVnoOpmGEfglddr648lacFLSHCiatX11akTafXu93BFLfrzgqS0IKBzt7Ly9sl8R6Kra9r74Py/96Gg73dG+Vk2L6M/bawGjf3l7pH1Per7UAXVb+gv/wAXDX+vnrZuShtE8Y9ZZ9WidUk1alJMSYlWPTGgqjatD1FsjCXHYyWn74P87Pe8Pmu/zGurdz9sqOAraeM0lC3nqZPHY7Jsj9VqV1KiVaEhSSpf0VOL13VS63alCrRPr37/MrpR33k97i+viPBk7dkZocQEqzasDVbfhyrmury7h1kHTt+vjveV1bkz8apWw0dxsRYFX/53LIvc3nsAuB0xBgCAO0rV6t6as6hdttMqVfHS/f0q6PeZZzVv6b05llGpipeCryRmef3C+YQsV4Gq1/TV2TP/NgZNpn8bWRfPJ2jqV8c045sTjteKl3DL76Y4mM1pZVotNl28EK+EeIs6tViRaZ6Y6Kxdzb19XBQWknYi6+nlrKbNiysp0ar3h+/TwNdq6cL5eFWq4iVn53/rXL2mj9auTOuOfeFcfKaBwbx9nFW6rLvOnYlX8JVEVayc9yPeg68kKTXVrpee3prp9ft6BGY7f/mKnvp0QhN9MK6xli28pLeG7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', 'error': None, 'output': None, 'system': 'take_screenshot'}]\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'contents': [{'image_url': {'url': 'data:image/png;base64,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'}, 'type': 'image_url'}]}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'messages': [{'content': [{'image_url': {'url': 'data:image/png;base64,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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}]}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: init_branch\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'run_browser'}\n", + "--------------------------------------------------\n", + "Transition type: step\n", + "Transition output: {'choices': [{'finish_reason': 'tool_calls', 'index': 0, 'logprobs': None, 'message': {'content': \"2. Since we need to go to a specific URL (https://github.com/julep-ai/julep), I'll type the URL:\", 'created_at': None, 'id': None, 'name': None, 'role': 'assistant', 'tool_call_id': None, 'tool_calls': [{'api_call': None, 'bash_20241022': None, 'computer_20241022': None, 'function': {'arguments': '{\"action\": \"type\", \"text\": \"https://github.com/julep-ai/julep\"}', 'name': 'computer'}, 'id': 'toolu_01T4kCjSbcj3bug9PepZfw6J', 'integration': None, 'system': None, 'text_editor_20241022': None, 'type': 'function'}]}, 'tool_calls': None}], 'created_at': '2024-11-22T07:05:25.267991Z', 'docs': [], 'id': 'e2117108-a359-450e-ab88-20ffc3e23e6b', 'jobs': [], 'usage': {'completion_tokens': 114, 'prompt_tokens': 2489, 'total_tokens': 2603}}\n", + "--------------------------------------------------\n" + ] + } + ], "source": [ "# Lists all the task steps that have been executed up to this point in time\n", "transitions = client.executions.transitions.list(execution_id=execution.id).items\n", diff --git a/llm-proxy/docker-compose.yml b/llm-proxy/docker-compose.yml index 0b9800b02..08491557c 100644 --- a/llm-proxy/docker-compose.yml +++ b/llm-proxy/docker-compose.yml @@ -1,7 +1,7 @@ name: julep-llm-proxy x--litellm-base: &litellm-base - image: ghcr.io/berriai/litellm-database:main-v1.52.0-stable + image: julepai/litellm-database:fix_anthropic_tool_image_content-amd64 restart: unless-stopped hostname: litellm ports: @@ -52,6 +52,7 @@ services: <<: *litellm-base profiles: - self-hosted-db + platform: linux/amd64 depends_on: - litellm-db From 26a3315c57c25ad3b72e098273836eb893416e65 Mon Sep 17 00:00:00 2001 From: Diwank Singh Tomer Date: Fri, 22 Nov 2024 13:42:13 +0530 Subject: [PATCH 22/23] feat(agents-api): Limit free users to 50 executions and sessions (#865) Signed-off-by: Diwank Singh Tomer ---- > [!IMPORTANT] > Limit free users to 50 sessions and executions, adding counting functions and tests for these limits. > > - **Behavior**: > - Limit free users to 50 sessions and 50 executions by adding `MAX_FREE_SESSIONS` and `MAX_FREE_EXECUTIONS` to `.env.example` and `env.py`. > - Implement `count_executions()` in `count_executions.py` to count executions for a given `developer_id` and `task_id`. > - Implement `count_sessions()` in `count_sessions.py` to count sessions for a given `developer_id`. > - **API Changes**: > - Update `chat()` in `chat.py` to enforce session limits for free users. > - Update `create_task_execution()` in `create_task_execution.py` to enforce execution limits for free users. > - **Tests**: > - Add tests for `count_executions()` in `test_execution_queries.py`. > - Add tests for `count_sessions()` in `test_session_queries.py`. > - **Misc**: > - Remove unused imports `JinjaTemplate` from `Tasks.py`, `RootModel` from `Tools.py`, and `RemoteList` from `temporal.py`. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 71043d0dfdfbfb5482f67c85d9d29c257372c930. It will automatically update as commits are pushed. --------- Signed-off-by: Diwank Singh Tomer Co-authored-by: vedantsahai18 Co-authored-by: Vedantsahai18 Co-authored-by: HamadaSalhab --- .env.example | 2 + agents-api/agents_api/autogen/Tasks.py | 1 - agents-api/agents_api/autogen/Tools.py | 1 - agents-api/agents_api/clients/temporal.py | 1 - agents-api/agents_api/env.py | 2 + .../agents_api/models/execution/__init__.py | 1 + .../models/execution/count_executions.py | 61 ++++++++++++++++++ .../agents_api/models/session/__init__.py | 1 + .../models/session/count_sessions.py | 64 +++++++++++++++++++ .../agents_api/routers/sessions/chat.py | 16 ++++- .../routers/tasks/create_task_execution.py | 22 +++++++ agents-api/docker-compose.yml | 3 + agents-api/tests/test_execution_queries.py | 18 ++++++ agents-api/tests/test_session_queries.py | 12 ++++ cookbooks/06-browser-use.ipynb | 62 ++++++++---------- llm-proxy/litellm-config.yaml | 12 ---- 16 files changed, 228 insertions(+), 51 deletions(-) create mode 100644 agents-api/agents_api/models/execution/count_executions.py create mode 100644 agents-api/agents_api/models/session/count_sessions.py diff --git a/.env.example b/.env.example index ab4882297..9b76a4903 100644 --- a/.env.example +++ b/.env.example @@ -22,6 +22,8 @@ LITELLM_POSTGRES_PASSWORD= LITELLM_MASTER_KEY= LITELLM_SALT_KEY= LITELLM_REDIS_PASSWORD= +MAX_FREE_SESSIONS=50 +MAX_FREE_EXECUTIONS=50 # LLM Providers # -------------- diff --git a/agents-api/agents_api/autogen/Tasks.py b/agents-api/agents_api/autogen/Tasks.py index 8e98caaab..b9212d8cb 100644 --- a/agents-api/agents_api/autogen/Tasks.py +++ b/agents-api/agents_api/autogen/Tasks.py @@ -9,7 +9,6 @@ from pydantic import AwareDatetime, BaseModel, ConfigDict, Field, StrictBool from .Chat import ChatSettings -from .Common import JinjaTemplate from .Tools import ( ChosenBash20241022, ChosenComputer20241022, diff --git a/agents-api/agents_api/autogen/Tools.py b/agents-api/agents_api/autogen/Tools.py index cb548c0e3..4cbf27d65 100644 --- a/agents-api/agents_api/autogen/Tools.py +++ b/agents-api/agents_api/autogen/Tools.py @@ -12,7 +12,6 @@ BaseModel, ConfigDict, Field, - RootModel, StrictBool, ) diff --git a/agents-api/agents_api/clients/temporal.py b/agents-api/agents_api/clients/temporal.py index 316e0d256..f937fae10 100644 --- a/agents-api/agents_api/clients/temporal.py +++ b/agents-api/agents_api/clients/temporal.py @@ -10,7 +10,6 @@ ) from ..autogen.openapi_model import TransitionTarget -from ..common.protocol.remote import RemoteList from ..common.protocol.tasks import ExecutionInput from ..common.retry_policies import DEFAULT_RETRY_POLICY from ..common.storage_handler import store_in_blob_store_if_large diff --git a/agents-api/agents_api/env.py b/agents-api/agents_api/env.py index 3eb340cf4..55e36c25f 100644 --- a/agents-api/agents_api/env.py +++ b/agents-api/agents_api/env.py @@ -67,6 +67,8 @@ api_key_header_name: str = env.str("AGENTS_API_KEY_HEADER_NAME", default="X-Auth-Key") +max_free_sessions: int = env.int("MAX_FREE_SESSIONS", default=50) +max_free_executions: int = env.int("MAX_FREE_EXECUTIONS", default=50) # Litellm API # ----------- diff --git a/agents-api/agents_api/models/execution/__init__.py b/agents-api/agents_api/models/execution/__init__.py index 7de8e16c5..1b27ef1dc 100644 --- a/agents-api/agents_api/models/execution/__init__.py +++ b/agents-api/agents_api/models/execution/__init__.py @@ -1,5 +1,6 @@ # ruff: noqa: F401, F403, F405 +from .count_executions import count_executions from .create_execution import create_execution from .create_execution_transition import create_execution_transition from .get_execution import get_execution diff --git a/agents-api/agents_api/models/execution/count_executions.py b/agents-api/agents_api/models/execution/count_executions.py new file mode 100644 index 000000000..7f10e5bfa --- /dev/null +++ b/agents-api/agents_api/models/execution/count_executions.py @@ -0,0 +1,61 @@ +from typing import Any, TypeVar +from uuid import UUID + +from beartype import beartype +from fastapi import HTTPException +from pycozo.client import QueryException +from pydantic import ValidationError + +from ..utils import ( + cozo_query, + partialclass, + rewrap_exceptions, + verify_developer_id_query, + verify_developer_owns_resource_query, + wrap_in_class, +) + +ModelT = TypeVar("ModelT", bound=Any) +T = TypeVar("T") + + +@rewrap_exceptions( + { + QueryException: partialclass(HTTPException, status_code=400), + ValidationError: partialclass(HTTPException, status_code=400), + TypeError: partialclass(HTTPException, status_code=400), + } +) +@wrap_in_class(dict, one=True) +@cozo_query +@beartype +def count_executions( + *, + developer_id: UUID, + task_id: UUID, +) -> tuple[list[str], dict]: + count_query = """ + input[task_id] <- [[to_uuid($task_id)]] + + counter[count(id)] := + input[task_id], + *executions { + task_id, + execution_id: id, + } + + ?[count] := counter[count] + """ + + queries = [ + verify_developer_id_query(developer_id), + verify_developer_owns_resource_query( + developer_id, + "tasks", + task_id=task_id, + parents=[("agents", "agent_id")], + ), + count_query, + ] + + return (queries, {"task_id": str(task_id)}) diff --git a/agents-api/agents_api/models/session/__init__.py b/agents-api/agents_api/models/session/__init__.py index bc5f7fbb4..bf80c9f4b 100644 --- a/agents-api/agents_api/models/session/__init__.py +++ b/agents-api/agents_api/models/session/__init__.py @@ -11,6 +11,7 @@ # ruff: noqa: F401, F403, F405 +from .count_sessions import count_sessions from .create_or_update_session import create_or_update_session from .create_session import create_session from .delete_session import delete_session diff --git a/agents-api/agents_api/models/session/count_sessions.py b/agents-api/agents_api/models/session/count_sessions.py new file mode 100644 index 000000000..3599cc2fb --- /dev/null +++ b/agents-api/agents_api/models/session/count_sessions.py @@ -0,0 +1,64 @@ +"""This module contains functions for querying session data from the 'cozodb' database.""" + +from typing import Any, TypeVar +from uuid import UUID + +from beartype import beartype +from fastapi import HTTPException +from pycozo.client import QueryException +from pydantic import ValidationError + +from ..utils import ( + cozo_query, + partialclass, + rewrap_exceptions, + verify_developer_id_query, + wrap_in_class, +) + +ModelT = TypeVar("ModelT", bound=Any) +T = TypeVar("T") + + +@rewrap_exceptions( + { + QueryException: partialclass(HTTPException, status_code=400), + ValidationError: partialclass(HTTPException, status_code=400), + TypeError: partialclass(HTTPException, status_code=400), + } +) +@wrap_in_class(dict, one=True) +@cozo_query +@beartype +def count_sessions( + *, + developer_id: UUID, +) -> tuple[list[str], dict]: + """ + Counts sessions from the 'cozodb' database. + + Parameters: + developer_id (UUID): The developer's ID to filter sessions by. + """ + + count_query = """ + input[developer_id] <- [[ + to_uuid($developer_id), + ]] + + counter[count(id)] := + input[developer_id], + *sessions{ + developer_id, + session_id: id, + } + + ?[count] := counter[count] + """ + + queries = [ + verify_developer_id_query(developer_id), + count_query, + ] + + return (queries, {"developer_id": str(developer_id)}) diff --git a/agents-api/agents_api/routers/sessions/chat.py b/agents-api/agents_api/routers/sessions/chat.py index 200832f4f..f4cc7420e 100644 --- a/agents-api/agents_api/routers/sessions/chat.py +++ b/agents-api/agents_api/routers/sessions/chat.py @@ -2,8 +2,7 @@ from typing import Annotated, Callable, Optional from uuid import UUID, uuid4 -from anthropic.types.beta.beta_message import BetaMessage -from fastapi import BackgroundTasks, Depends, Header +from fastapi import BackgroundTasks, Depends, Header, HTTPException, status from starlette.status import HTTP_201_CREATED from ...autogen.openapi_model import ( @@ -19,9 +18,11 @@ from ...common.utils.datetime import utcnow from ...common.utils.template import render_template from ...dependencies.developer_id import get_developer_data +from ...env import max_free_sessions from ...models.chat.gather_messages import gather_messages from ...models.chat.prepare_chat_context import prepare_chat_context from ...models.entry.create_entries import create_entries +from ...models.session.count_sessions import count_sessions as count_sessions_query from .metrics import total_tokens_per_user from .router import router @@ -54,6 +55,17 @@ async def chat( ChatResponse: The chat response. """ + # check if the developer is paid + if "paid" not in developer.tags: + # get the session length + sessions = count_sessions_query(developer_id=developer.id) + session_length = sessions["count"] + if session_length > max_free_sessions: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="Session length exceeded the free tier limit", + ) + if chat_input.stream: raise NotImplementedError("Streaming is not yet implemented") diff --git a/agents-api/agents_api/routers/tasks/create_task_execution.py b/agents-api/agents_api/routers/tasks/create_task_execution.py index ecc0aa470..09342bf84 100644 --- a/agents-api/agents_api/routers/tasks/create_task_execution.py +++ b/agents-api/agents_api/routers/tasks/create_task_execution.py @@ -17,7 +17,13 @@ UpdateExecutionRequest, ) from ...clients.temporal import run_task_execution_workflow +from ...common.protocol.developers import Developer from ...dependencies.developer_id import get_developer_id +from ...env import max_free_executions +from ...models.developer.get_developer import get_developer +from ...models.execution.count_executions import ( + count_executions as count_executions_query, +) from ...models.execution.create_execution import ( create_execution as create_execution_query, ) @@ -113,6 +119,22 @@ async def create_task_execution( raise + # get developer data + developer: Developer = get_developer(developer_id=x_developer_id) + + # # check if the developer is paid + if "paid" not in developer.tags: + executions = count_executions_query( + developer_id=x_developer_id, task_id=task_id + ) + + execution_count = executions["count"] + if execution_count > max_free_executions: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="Execution count exceeded the free tier limit", + ) + execution, handle = await start_execution( developer_id=x_developer_id, task_id=task_id, diff --git a/agents-api/docker-compose.yml b/agents-api/docker-compose.yml index 23cb6dd61..b410ba9d2 100644 --- a/agents-api/docker-compose.yml +++ b/agents-api/docker-compose.yml @@ -28,6 +28,9 @@ x--shared-environment: &shared-environment S3_ENDPOINT: ${S3_ENDPOINT:-http://seaweedfs:8333} S3_ACCESS_KEY: ${S3_ACCESS_KEY} S3_SECRET_KEY: ${S3_SECRET_KEY} + ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY} + MAX_FREE_SESSIONS: ${MAX_FREE_SESSIONS:-50} + MAX_FREE_EXECUTIONS: ${MAX_FREE_EXECUTIONS:-50} x--base-agents-api: &base-agents-api image: julepai/agents-api:${TAG:-dev} diff --git a/agents-api/tests/test_execution_queries.py b/agents-api/tests/test_execution_queries.py index 0ea72ff95..9e75b3cda 100644 --- a/agents-api/tests/test_execution_queries.py +++ b/agents-api/tests/test_execution_queries.py @@ -8,6 +8,7 @@ CreateTransitionRequest, Execution, ) +from agents_api.models.execution.count_executions import count_executions from agents_api.models.execution.create_execution import create_execution from agents_api.models.execution.create_execution_transition import ( create_execution_transition, @@ -91,6 +92,23 @@ def _( assert result[0].status == "queued" +@test("model: count executions") +def _( + client=cozo_client, + developer_id=test_developer_id, + execution=test_execution, + task=test_task, +): + result = count_executions( + developer_id=developer_id, + task_id=task.id, + client=client, + ) + + assert isinstance(result, dict) + assert result["count"] > 0 + + @test("model: create execution transition") def _(client=cozo_client, developer_id=test_developer_id, execution=test_execution): result = create_execution_transition( diff --git a/agents-api/tests/test_session_queries.py b/agents-api/tests/test_session_queries.py index 8d7c07b36..01fea1375 100644 --- a/agents-api/tests/test_session_queries.py +++ b/agents-api/tests/test_session_queries.py @@ -8,6 +8,7 @@ CreateSessionRequest, Session, ) +from agents_api.models.session.count_sessions import count_sessions from agents_api.models.session.create_or_update_session import create_or_update_session from agents_api.models.session.create_session import create_session from agents_api.models.session.delete_session import delete_session @@ -120,6 +121,17 @@ def _(client=cozo_client, developer_id=test_developer_id, session=test_session): assert len(result) > 0 +@test("model: count sessions") +def _(client=cozo_client, developer_id=test_developer_id, session=test_session): + result = count_sessions( + developer_id=developer_id, + client=client, + ) + + assert isinstance(result, dict) + assert result["count"] > 0 + + @test("model: create or update session") def _( client=cozo_client, developer_id=test_developer_id, agent=test_agent, user=test_user diff --git a/cookbooks/06-browser-use.ipynb b/cookbooks/06-browser-use.ipynb index 62e024305..0ca091a1d 100644 --- a/cookbooks/06-browser-use.ipynb +++ b/cookbooks/06-browser-use.ipynb @@ -198,7 +198,7 @@ "api_key = os.getenv(\"JULEP_API_KEY\")\n", "\n", "# Create a Julep client\n", - "client = Client(api_key=api_key, environment=\"local_multi_tenant\")" + "client = Client(api_key=api_key, environment=\"dev\")" ] }, { @@ -280,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -537,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -569,14 +569,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Started an execution. Execution ID: 8fbb3530-de78-46d3-93f1-b40d632d5367\n" + "Started an execution. Execution ID: 513a0e52-2866-4546-883f-6848080aff8f\n" ] } ], @@ -648,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -656,79 +656,73 @@ "output_type": "stream", "text": [ "Transition type: init\n", - "Transition output: {'agent_id': '07648b48-4ca7-4076-81db-c29eccc15f80', 'goal': \"Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\"}\n", + "Transition output: {'agent_id': 'f1f7e2d1-cf48-4690-a14f-1886fcbdee95', 'goal': \"Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\"}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'auto_run_tools': False, 'context_overflow': None, 'created_at': '2024-11-22T07:04:47.809701Z', 'id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'kind': None, 'metadata': {}, 'render_templates': True, 'situation': '', 'summary': None, 'token_budget': None, 'updated_at': '2024-11-22T07:04:47Z'}\n", + "Transition output: {'auto_run_tools': False, 'context_overflow': None, 'created_at': '2024-11-22T07:32:11.893049Z', 'id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a', 'kind': None, 'metadata': {}, 'render_templates': True, 'situation': '', 'summary': None, 'token_budget': None, 'updated_at': '2024-11-22T07:32:11Z'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d'}\n", + "Transition output: {'julep_session_id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'avgCpuUsage': None, 'contextId': None, 'createdAt': '2024-11-22T07:04:51.047247+00:00', 'endedAt': None, 'expiresAt': '2024-11-22T07:19:51.009+00:00', 'id': 'a959cfd1-1b96-4c15-b025-863c31ffcea5', 'keepAlive': None, 'memoryUsage': None, 'projectId': 'c35ee022-883e-4070-9f3c-89607393214b', 'proxyBytes': 0, 'startedAt': '2024-11-22T07:04:51.009+00:00', 'status': 'RUNNING'}\n", + "Transition output: {'avgCpuUsage': None, 'contextId': None, 'createdAt': '2024-11-22T07:32:14.722022+00:00', 'endedAt': None, 'expiresAt': '2024-11-22T07:47:14.612+00:00', 'id': '4cfd1fca-7939-4d75-b563-81f1c19458f2', 'keepAlive': None, 'memoryUsage': None, 'projectId': 'c35ee022-883e-4070-9f3c-89607393214b', 'proxyBytes': 0, 'startedAt': '2024-11-22T07:32:14.612+00:00', 'status': 'RUNNING'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'browser_session_id': 'a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "Transition output: {'browser_session_id': '4cfd1fca-7939-4d75-b563-81f1c19458f2'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'urls': {'debuggerFullscreenUrl': 'https://www.browserbase.com/devtools-fullscreen/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true', 'debuggerUrl': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true', 'wsUrl': 'wss://connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/browser/bf6e1919-d6b7-44ee-8ec3-5cd8dd2faab8'}}\n", + "Transition output: {'urls': {'debuggerFullscreenUrl': 'https://www.browserbase.com/devtools-fullscreen/inspector.html?wss=connect.browserbase.com/debug/4cfd1fca-7939-4d75-b563-81f1c19458f2/devtools/page/2DD1378323F30172015B0A5BE297DE18?debug=true', 'debuggerUrl': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/4cfd1fca-7939-4d75-b563-81f1c19458f2/devtools/page/2DD1378323F30172015B0A5BE297DE18?debug=true', 'wsUrl': 'wss://connect.browserbase.com/debug/4cfd1fca-7939-4d75-b563-81f1c19458f2/devtools/browser/fc2f50f6-0107-4fde-9cb2-4eef8cf6eba6'}}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'debugger_url': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/a959cfd1-1b96-4c15-b025-863c31ffcea5/devtools/page/6B58334B1B83E8419C546ECD99DF70C8?debug=true'}\n", + "Transition output: {'debugger_url': 'https://www.browserbase.com/devtools/inspector.html?wss=connect.browserbase.com/debug/4cfd1fca-7939-4d75-b563-81f1c19458f2/devtools/page/2DD1378323F30172015B0A5BE297DE18?debug=true'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "Transition output: {'url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'connect_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5'}\n", + "Transition output: {'connect_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2'}\n", "--------------------------------------------------\n", "Transition type: step\n", "Transition output: {'base64_image': None, 'error': None, 'output': 'https://www.google.com', 'system': None}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2', 'julep_session_id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", "--------------------------------------------------\n", "Transition type: init_branch\n", - "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2', 'julep_session_id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a', 'messages': [{'content': \"\\n\\n* You are utilising a headless chrome browser to interact with the internet.\\n* You can use the computer tool to interact with the browser.\\n* You have access to only the browser.\\n* You are already inside the browser.\\n* You can't open new tabs or windows.\\n* For now, rely on screenshots as the only way to see the browser.\\n* You can't don't have access to the browser's UI.\\n* YOU CANNOT WRITE TO THE SEARCH BAR OF THE BROWSER.\\n\\n\\n*Navigate to JulepAI's Github repository and tell me the number of stars it has. Remember bro, the link for julep's repository is https://github.com/julep-ai/julep\\n\", 'role': 'user'}], 'workflow_label': 'run_browser'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'choices': [{'finish_reason': 'tool_calls', 'index': 0, 'logprobs': None, 'message': {'content': \"I'll help you navigate to JulepAI's Github repository to check its stars. Let me break this down into steps:\\n\\n1. First, let me take a screenshot to see where we are:\", 'created_at': None, 'id': None, 'name': None, 'role': 'assistant', 'tool_call_id': None, 'tool_calls': [{'api_call': None, 'bash_20241022': None, 'computer_20241022': None, 'function': {'arguments': '{\"action\": \"screenshot\"}', 'name': 'computer'}, 'id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ', 'integration': None, 'system': None, 'text_editor_20241022': None, 'type': 'function'}]}, 'tool_calls': None}], 'created_at': '2024-11-22T07:05:08.603440Z', 'docs': [], 'id': 'cde04753-fd04-4016-a812-e13d4ac2e3fc', 'jobs': [], 'usage': {'completion_tokens': 95, 'prompt_tokens': 1335, 'total_tokens': 1430}}\n", + "Transition output: {'choices': [{'finish_reason': 'tool_calls', 'index': 0, 'logprobs': None, 'message': {'content': \"I'll help you navigate to JulepAI's Github repository and check its stars. Let me break this down into steps:\\n\\n1. First, let me see what's currently on the screen.\", 'created_at': None, 'id': None, 'name': None, 'role': 'assistant', 'tool_call_id': None, 'tool_calls': [{'api_call': None, 'bash_20241022': None, 'computer_20241022': None, 'function': {'arguments': '{\"action\": \"screenshot\"}', 'name': 'computer'}, 'id': 'toolu_01SF7tWSGgctWmwTWqET1zbV', 'integration': None, 'system': None, 'text_editor_20241022': None, 'type': 'function'}]}, 'tool_calls': None}], 'created_at': '2024-11-22T07:32:32.153810Z', 'docs': [], 'id': 'e5df81d3-6522-4617-b217-bf98d7892418', 'jobs': [], 'usage': {'completion_tokens': 94, 'prompt_tokens': 1335, 'total_tokens': 1429}}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'content': \"I'll help you navigate to JulepAI's Github repository to check its stars. Let me break this down into steps:\\n\\n1. First, let me take a screenshot to see where we are:\", 'tool_calls': [{'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}]}\n", + "Transition output: {'content': \"I'll help you navigate to JulepAI's Github repository and check its stars. Let me break this down into steps:\\n\\n1. First, let me see what's currently on the screen.\", 'tool_calls': [{'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01SF7tWSGgctWmwTWqET1zbV'}]}\n", "--------------------------------------------------\n", "Transition type: init_branch\n", - "Transition output: {'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}\n", + "Transition output: {'action': 'screenshot', 'coordinate': None, 'text': None, 'tool_call_id': 'toolu_01SF7tWSGgctWmwTWqET1zbV'}\n", "--------------------------------------------------\n", "Transition type: finish_branch\n", - "Transition output: {'base64_image': 'data:image/png;base64,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', 'error': None, 'output': None, 'system': 'take_screenshot'}\n", + "Transition output: {'base64_image': 'data:image/png;base64,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', 'error': None, 'output': None, 'system': 'take_screenshot'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: [{'base64_image': 'data:image/png;base64,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', 'error': None, 'output': None, 'system': 'take_screenshot'}]\n", + "Transition output: [{'base64_image': 'data:image/png;base64,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', 'error': None, 'output': None, 'system': 'take_screenshot'}]\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'contents': [{'image_url': {'url': 'data:image/png;base64,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'}, 'type': 'image_url'}]}\n", + "Transition output: {'contents': [{'image_url': {'url': 'data:image/png;base64,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'}, 'type': 'image_url'}]}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'messages': [{'content': [{'image_url': {'url': 'data:image/png;base64,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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}]}\n", + "Transition output: {'messages': [{'content': [{'image_url': {'url': 'data:image/png;base64,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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01SF7tWSGgctWmwTWqET1zbV'}]}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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LhxjB07lk2bNvHMM88QHh7umZVQHIY89RRfT56M2WzmfwMfvezjRS082ERSeuFuXbf7hI2NB9Pof0M5Tp5zsvFAKlXKm7m/Qwgnzjo5Fuug/iWd/XSHzu8bE+nRNpgh3dI4HG3nxXsjCrVNxSU82LucBIWh2JYAbNq0iSNHjvDiiy/Sp08fz9eIESMwm80sXLjQU/bnn3/mxIkTOJ1OvvjiC1RVpVOnTkRGRtKsWTPeeustbDYb6enpjBkzhpYtW9KgQQOsVisVK1bk77//BuDEiROehB8X+Pn5ERMTUyQRssqVK1OpUiXeeustjh8/DmT8wpkxYwYtW7ZEURRPkCMmJgYg33vKSffu3Tl58iSTJk1C0zTsdjujRo1izZo1hX5PQgghhBCi9Onfvz8Oh4Onn37aM/IPGevjf/vtN8LCwgpUX1JSEgAhISEArFy5kj179pTKLP1JSUmeUX673c60adNwOp04nU6aN2/Ovn37OHLkCJCxZeGFeytXrhzNmjXj559/9tQ1bdo05s2bh8Ph4NFHHyU6OhpFUWjVqhX16tXLFkwoah3ad2Dqt1P4euJXtG3T5rKPF7Xw4KIZR+757mFmrU7gxXvD+e3V65j4RFX2nLTR893D5NR9e2FqFHtO2nhvQCV+fLYGZ+IzptJfbbkbi+r9zEmxXWnmzJl07Ngx25Z/JpOJ7t27M3PmTB544AEgI6LZp08foqOjsVqtfPDBB/j7+wMZGT+HDRvmWY8TGRnJZ5995qlv9OjRjBgxgm+//ZagoCAiIyM9/+ghIyP/m2++ydKlSz2BgsKiKApTpkzhzTff5JZbbgEyfindcMMNvPPOO0DGkofbb7+dgQMH8vDDD/PWW2/le09ZVapUicmTJzNy5Eg++eQTdF2na9euNG7cuFDvRwghhBBClE5BQUHMnj2bt956iw4dOhASEkJycjLly5fn4YcfZsCAAQWqr3r16txzzz3ccccdVKhQgaZNm/Liiy/y/vvvU6dOnSK6i8vTv39/Bg0axI4dO3A6nbz++uvs3LmT+++/nwULFvD000/Tq1cvypcvT9euXWnYsKEnx8CHH37IM888w++//46mafj5+fHFF19gNpvp3Lkzd999N5UrVyYhIYFKlSqV2lkQJa2oRqzjUtwM/vI4QyadICzYyLkkFzbnxZ7/os1J+Pe5uONDbJKLW984gL/VwNkkF0/fVYFbmgVw/GxGEvbqj+3MVP+E32OZ8Htsjtd+5cdTvPLjKc/3l14H4P73j1zx/eWmOGcAKHpxLhYpoJiYGIKCgrBYLNlei42NRVEUz7T5SzmdzmyJUS4VHx+PyWTyBBWKgt1u59y5c4SGhubY/piYGIKDgz3LFiDve8rNuXPnMJvNmdZyCSGEEEKIssPhcBAfH4+fn98VP98mJiZiMBg89dhsNsxmc467DZQkl8tFfHw8oaGhqKqKpmk4HA6sVitnz571zBBQVZUOHTrw4Ycf0qFDB8/5F0b2sz5Du1wu4uLi8Pf3z3F5clFbs3YNk7+dgqLA4McGe0b1C3q8qH20IIbRP58ulmvlZWy/StzZKpAfl8fhY1YZckcFjkQ76DxqP053qe3mZvNm34qMuKdgs3YuV6kOAAghhBBCCCGEt2w2G506daJ3797ceOONLF26lDlz5rB06VLP1oilWZ9+D3LuXMZe9hEREfwwNWOLwgf69PYkCPfmeFHbF2Wn5Yi9xXKtvIQHGxl1fwSdG/njcuus3pPKuNlnPNv+XS02fVSfepWzDxoXhWJNAiiEEEIIIYQQRcVqtTJ79my+//57vvnmG6pUqcKcOXOuis4/XD27ANSrbKFORQsHThduIsCCik5weXYMuFrVqWgpts4/SABACCGEEEIIcQ2pXr06r7/+ekk347JcLbsAANzSLKDEAwDXgrtaBxXr9WQJgBBCCCGEEEKIAjkaY6fliH3YndKdvFwWk8LezxtQIega3AZQCCGEEEIIIcS1oUaYhae65Zx0XXjnqW4VirXzDxIAEEIIIYQQQghxGZ7vEUagj3QpL0eAj8oLPYon8/+l5KclhBBCCCGEEKLAgnwNfPK/KiXdjKvS549VJdDXUOzXlQCAEEIIIYQQQojL0uv6EJ69u/hHsq9mz94dxn0dgkvk2hIAEEIIIYQQQghx2d7sE0HH+n4l3Yyrwi1NA3izT0SJXV8CAEIIIYQQQgghLpuqKvz4bHWqhBZvQrurTeVQE1OHV0dVlRJrgwQAhBBCCCGEEEJckQpBJrZPqM//bg4t6aaUSgNuKMeOCfUJKoF1/5dSdF2XjRuFEEIIIYQQQhSKyX+f5bkpUWjS00RV4NPHqvDIjaUjMCIBACGEEEIIIYQQhWr/KTsTfo9h+vJ4nO6y1+U0GqBvp3I8e3cYdStZSro5HhIAEEIIIYQQQghRJKLOOfloQQzfLzuH3Xntdz19LSqP3hTKsDsrULkU5kSQAIAQQgghhBBCiCK3fn8ac9cmsPFQKgmpbuJT3CSmua/KwIDFpBDkayDYz0CIv4F29fy4t20wrWr7lnTT8iQBACGEEEIIIYQQogyQXQCEEEIIIYQQQogyQAIAQgghhBBCCCFEGSABACGEEEIIIYQQogyQAIAQQgghhBBCCFEGSABACCGEEEIIIYQoAyQAIIQQQgghhBBClAESABBCCCGEEEIIIcoACQAIIYQQQgghhBBlgAQAhBBCCCGEEEKIMkACAEIIIYQQQgghRBkgAQAhhBBCCCGEEKIMkACAEEIIIYQQQghRBkgAQAghhBBCCCGEKAMkACCEEEIIIYQQQpQBEgAQQgghhBBCCCHKAAkACCGEEEIIIYQQZYAEAIQQQgghhBBCiDJAAgBCCCGEEEIIIUQZIAEAIYQQQgghhBCiDJAAgBBCCCGEEEIIUQZIAEAIIYQQQgghhCgDJAAghBBCCCGEEEKUARIAEEIIIYQQQgghygAJAAghhBBCCCGEEGWABACEEEIIIYQQQogywFjSDRBCCCGEEEVL13XP/y98Zf0+p+NZzxdCiGuNoiiZ/nzh+wt/vvQr6/Gs518NJAAghBBCCHGNyqujf+mXpmk5Hrv0XCGEuBZd2plXVTVbpz+nY1mDABfquRpIAEAIIYQQ4hqUdUT/0k7+hT9rmoamabjdbjRNw+UGTQcUFVBQVAOKoqKosmpUCHFt0jUNze0GXQM00DVUBYyGjICAwWBAVVVPIODSgICa5Xfj1RAEUHQJ6QohhBBCXDNyG/W/0Nm/0Pl3uVw4nS5cmgKKAdVovioeXoUQojjouo7mcoDuxmjQMRmNGI1GT8f/0qDA1bQsQAIAQgghhBDXiKyd/6wj/ZqmeTr+Tk3FYLKW6gdVIYQoDXRdx+1Mx2wAo9GA0WjMFgS4NBgApTcIIAEAIYQQQohrQE7T/bN2/B1OJ04XGEw+Mq1fCCEKSNc03M50TEYwm0x5BgKgdAYBJAAghBBCCHGVy9r5z/rldDqxO5zoqhWD0VTCrRVCiKub2+UEdzpWixmTyeQJABgMhlIfBJAkgEIIIYQQV7G8Ov9utxun00mazYHJEpAtYZUQQoiCMxhNaKpKWnoKPrqOyXQxsHrh9+yFIICu66UqCCABACGEEEKIq1R+nX+Hw0G63YXZJ6iEWyqEENcWVTWgWANJsyXjo+uYzeYcypS+IIAEAIQQQgghrmI5Zfp3u93Y7XZsDjdmn8CSbqIQQlyTFEXB7BNIenoSuq5jsVgyvXYhSFtaOv8gAQAhhBBCiKtSblv8XRj5T0u3Y/ELKelmCiHENc9kDSAtNT7TMqsLnf6cjpUkWQgmhBBCCHGVuTSH86VBAM+a/7R0TD6BpeJhUwghrnWKomDyCSQ1NQ2Xy+UJyF74/XxBaci/LzMAhBBCCCGuQjmN/LvdbtJtNlSzHwaDPOYJIURxMRiM6BZ/0tPTPKP+lwZhL90ZoCTJDAAhhBBCiKvIpYn/si4BsNvtuNwKJrO1hFsphBBlj9FkwelWsNvt2WYBXPq7uyRJAEAIIYQQ4iqTU+ff5XJhszuw+AaUdPOEEKLMsvgGkJ5uy3EpQEl3/kECAEIIIYQQV52ckv/Z7XZ0xYSqGkq6eUIIUWapqgFdNeFwOCQAIIQQQgghrsylD5IXggBOpxO7w4mPn2z5J4QQJc3qG4jN7sDpdJa6IIAEAIQQQgghrkKXPkw6nU7cWunYYkoIIco6VVXRdAWn01lqOv4XSABACCGEEOIqknX03+1243A4MJok8Z8QQpQWBqNFZgAIIYQQQogrc2nnX9d13G43TpcLq69/STdNCCHEeVZf/4ydWVyuTL+zJQAghBBCCCEKJOsMADDI9H8hhChFFEUBxYjb7S41nX+QAIAQQgghxFUlaxJAp9OJrsgjnRBClDY6imcGQGlZAmAs0asLIYQQQogCyZoDwOVyUVof6RR0VDQMKiiKTsbjcAb9fAlNB01X0HUFDQWQmQxCiGuDohpwuVylKgdA6fy0EEIIIYQQucoaBDCYTCXdJA9V0TEoOuhuDAYVdO2S5QkXO/cXj2SUvxAQcGug6SqaTFQVQlzlDEYTbqej1HT+QZYACCGEEEJcVS59gLyQA8BoLPkAgIqGWXVjVl0YVQ2jQUFB9zo3QcbYv45R1TGpbiwGF4ruLBUPzN5yOp088tAD9LmvO2lpaUV+vb8W/0HX61sxf+7MIr/WteaDd9+iTfMGnDh+rKSbIq5hRqPJkwPggpL+nSYzAIQQQgghrjKXjiS53W6MJTgDQEHHpGqoyoWR/iufwp8RM9CxGMGtuXDpKjqGK673Si1etJCff5zKoYMHMBpNNIxszBNPPU3jJs0ASEtLZd/ePbjdbhIS4vH19S3S9uzeuYO01FR27thOj569rqiun3/8HqPJxAO9Hyyk1sH6tav56Yep7Nq5HafLScWIStxw8630efBhgoNDCu06QpRWRpPpfKJWSs0MAAkACCGEEEJcpS48UBoMJfNIZ1I1DIq70Dr+OTGooOoabl3HpRuK7Dr5WTBvNmPHvIbFYqFBw0jS09PZsH4t27Zs4pvvfqJBw0iCgoL5avI0HA4HlSpVLvI2Pfb4EOrUrUenLjdccV2/TJ+Gj49voQUApkz+iq++mADAdbXrYrGYOXb0KFO+mchfixYy8evvCY+IKJRrCVFaGQzGUtPxv0ACAEIIIYQQV5ELD5IluqZU1zEZ3BhVKI4OuaKAUdFRNBdujGh68QcBfpn+A/7+Afzwy1wqV64CwJK/F/Pyi8/w4/dTGPvexwA0bdai2Nrk5+/Pnd17FNv1vLVh/Vq++mIClSpX4YOPP6NO3foA2O12Pv/0I2ZM/4FvJn3Oq6PfLuGWClH0sv6uLulggAQAhBBCCCGuMpc+QBb3w6SqgMngRvWyD67r5/MAGFRQjVyaEkDXNBSXC12/sD4270ozZgO4cGJA04s3lZXL5cTqYyUsLNxz7KZbbmPC519TtVp1z7E+93UnPT2NBX8sATLu/8dpU5j1y0/Ex8cR2bgp/R8ZxPChg+n/yCCGDh9BfHwct93YkYf6P4rZYmH+3Fk4HQ7ate/ICy+/lut0+QsBiNfeGEv3e3p6vn9r3AesXPEvK1csI8A/gDvvvpfBTwzFYMi+jGLrlk0MfvQhz/dtmjegRcvWfDV5GgAHD+5n4uefsHXzJjRNy7bsISdTJn8FwDvvj/d0/gEsFgvPjhhJ+fIVaNiocaZzVvy7lO+mTOLQgf34+PrRrn1Hhgx7jrDwi++3pmlM/3Eq8+fOIvrMaUJDy3PbHd15dNATWCwWT7kjhw8x/sN32bplI/4BAdx3fx9Onz7Fr/PnsHLd1kxlL7V/3x6++HQ827dtQUenXr0GPDroCdq275jrvQqRn5L8fZ0TSQIohBBCCHGVKu6HyYz1/q58O/+edll9UAMDUfz8wWWDmNNop056vkhJOl8mCMXH7/xSgrzvSVHArLpRFS3PcoWtc5cbORsby7An/8f6tas963rbd+xElarVcj1v4hcT+OyTD7E7HHTqfAPnzp1l5IvP5Fj21/lzmDH9B5o0bUZAYCB///Un748bU+C2fvDu2+zcvpU2bduTnp7Od5O/Yt6cnBMFVggLp/eDD3u+7/3gw9x4821ARkf6fwP6snrlcuo1aEiz5i3ZunkjTwzqz9Ytm3KsLy0tja1bNtGwUSQNGkZme11VVQYMfIzWbdp5jv3x+wKef3YIRw4fok27DlSpUpU/F/7Ko/17k5iY4Cn3zluj+XT8B9htNjpc3xmjycR3k79i5PPDPWXOxsYw+NGHWLd2FQ0bNaZBw0imTP6Kv//6M8/37MjhQwwa2I9dO7dz+x13cedd9xB18gTDhw5mx/ateZ4rRH5KQ8f/ApkBIIQQQgghvKBj9nLkX/UPAEA7sBf32pVou7ahn4lCj48Htws0DVQVAgNRK0Sg1K2Poe31KI2bofoGoCcnn58VkPvFTKobh1tBL6acAI8/9TQ2WzpzZ89g2FODCAoK5tbb7+DBhwd6lgRklZycxE/TplCpchW+/2kWQUHBuFwuXnh2KKtXLc9W3m63MXnqdOrVb4jNZqN3zztZsXwpmqahqt6P2wWHhDD1h5n4BwRw+NBB+tzfnX+X/cP9vfpmK1u5chVGvDCKFcuW4OPjy4gXRnle+/zTj0hPS+PjTydyfaeuAOzetZPHBj7IhI/f47sfsgcVYmOicbvd1Kh5XabjX37+CYkJ8ZmOPTn0Gfz8/Bn/4bsEB4fw/fTZVKxYCYAZP//IR++PZdp3kxn2zPPs27ubBfNn07xFKz79cjIWiwW3282rI0ew5J/FrF61nI7Xd+Hnn6aRmJjAsyNG0vehAQBs27qZx//3MHn5bMKH2NLT+Xzit54ZHT0f6EO/Xj2Y+ctPec54EOJqIjMAhBBCCCFEvsyqlmfnX9d1FLMZxc8fbe0KHC8NxfH0o7imfIm2ZQP62VjQz3f8DYaMofyEBLRd23HN+AHHC0OwP/EQrjnTwaCi+PiR12wABTAb3FBMI2tms5kXRr7G74uW8fxLr1Knbj1mzZhO3wfuZsP6tTmes2P7VpxOJz16PkBQUDAARqORhx/5X47lGzdtTr36DQGwWq00imyCw+EgPj6uQG2948678Q/ICMLUuq42IeVCiY2JLlAduq7z37o1NGrcxNP5B2jYKJLrO3dl184dJCUl5ngekG37x8V//Ma8OTMzfaWmprJ3zy4SExO4u8d9ns4/wAO9HyQkpBzr1q4GYM3qlQAMePQxzxR+g8HA/x5/CsBTbvPmDfj4+vJAn36eupo2a0GLlq3zvN9VK/4FYNAjD3LbjR257caO9L3/bjRN41TUyXzfLyGuFjIDQAghhBBC5OnCNn+5jcjruo7qH4AWewbXxPFoy5dkdPJ9/cDHN6OTfqGjrmkXq1EUMBozlghobjhxHOd7Y3D98Svm519FbRCJlpSUa7sUMpIROrXie6QNLV+BXn360atPP3bv2smTgwfw9puvMn/hP9k6vcnn235p3gCA8PCcs99nPW4wnr+vAgY5KmS5ntFoyLQPuTdSU1Kw2+1UjKiU7bWI88cS4uMJDAzK9FpYWDiqqnL06OFMxy/kRAB4ZujjrFm9AoD4uIzgRkTFzNdRVZWw8AjOnYv1XAugYsXMuytEhFfM9HpSYiIhIeUwGjP/ncj6nmS914z7qphjYsKAwMBczxXiaiMzAIQQQgghRK5URb9kq7/sdF1HDQxE27oBx9OPZnT+g4MhICCjg5+18+85MYc/+/iglC+Pvm83jqcG4JozHcWcc8K2CwyKjqGI8wEcOXyIIU88ypeff5LpeMNGkTRr1pLTp0+RnJw9UHGh4xiTZfT9zJnTRdbWwuLn74/FYsmxrWfOnAIgpFy5bK/5+vnRKLIJu3ZsZ9vWzdlej4mOZvOm/zzfh4Rk1BGd5TqaphETfYaQkNBM1zpz+lTmtkRnnBcckpEoMTAoiPj4OJxOZ5brnsn1Xn39/DCbzdSsVZs27Tp4vlq3bU/lKlVzzGUgxNVKAgBCCCGEECJXRkXLv/O/ejmOl4dDUlJG51/n4uz9S0evVTXjC0BRM74yVwi6jhIUhJ6YgLbsb9Bzv/7FNrrJL3nglShfoQI7tm3hl5++Z/u2LZ7jhw8dZM/unfj7B+Dn55/tvMZNmmEymZg/d5Znurzb7eanaVOKrK2Xy2yxkJiUkGkKf5t2Hdi5Y5tntB5g755drF65nEaRjQkIyHlk/KH+jwIwZvQojh65OBMgNiaGV18egc1m8xyr37ARQUHB/LpgbqZgw5xZvxAfH0e78xn423e4HoBpUydjt9uBjCDBd99k7DhwoVyLFq1JT0tjzqxfPHVt37aFLZs35nrviqLQuk17Nvy3lr17dnmOL160kHu738ofvy/I9VwhrjayBEAIIYQQQuRI0d0Y1Jw71p7O/5aNOMaMBKMRzOZLC5yvRAG3G2zp4HCgXzILQDGZMs65dLq2oqDHxmK46XZMYz4EXcs3g7aigAE37iJ6tA0ICOTJoc8y/sN3eGxgP2rXqYeiKBw5fBCn08nQ4SNy3GIvICCQBx96hO+/+4be93WnRYtWHDywn4RLMtuXFg0aRrLoj9945KEHuK52XV5/cxxDhj3Hxg3rGTH8KVq2bovJaGLDf2vRNI2nn30x17puuOkWevXpx8xffqLP/d2pU7c+uq5z5PBBzGYLt3W7i8V//g6AyWTimREv8ebrL9P3/rtp1aYt8XFxbN+2hQphYZ58CfXqN+TuHvfx6/w59Lr3Dho0iuTwoYMcPXKYDh070/H6LgD07defBfNm8/EH41jx7xJ8/fxYv3Y1QcEhxMedy7XNTw4dzsYN6xj86EN0uL4zbreblcuXUblKVTp3ubEQ32khSpYEAIQQQgghRI5M2fu0HqrFghZ9Gse7r2WM6l/o/F/aV9d1SE4CgwG1fiOUuvVRKmSsxdbj49CPHELbuRU9IR7FPwCMRvTYWNSOXTM6/4DucpHXbgAXGFUdt6Z7VfZy9O3Xn/DwCKb/OJX9+/diMppoFNmEvv0GcMNNt+R63lPDnsXX15fZs35mxfKlNGnWghdHvc6Tjw1AzSFoUFKGPj2Cs2dj2bFti2e9fK3rajP5u5/48rNP2LZ1M5qu0aRpcwY/OYxmzVvmWd/zL71K46bNmfnzjxw4sA+jwUjbdh0YMnwES/5enKnsnd174Ovrx7Sp37B+7Wp8fHy59fY7GTb8eYKDQzzlRr02hipVq/Hr/DmsWvEvoaHlGTDwMQY9PsRTpnyFML76dhoff/AOO7ZtITAoiCeHPMOhQwf4bcHcXHdTqFuvAV9N/oEvPx/PurWrMZvM3HDjLQx79gVPQkUhrgWKXpo2JRRCCCGEEHmy2Wy4XC5cLhc2m41z585RoVKtQr+OquiYVVeu0+8VP38cY166uOYfMnf+nQ5IS0PtchPG3v1Ra9UBS5b1/JqOfvIYrt/n4p77C3rcOdQut2AeNx4UFd1hoyAdeqdmwK2XvhWuaWlp+Pr6er7fsX0r/xvQl2eee4kHH36k5Bp2jbLb7RiNxkyzMoY9NYid27exbNWGEmyZKItiTx0mNDQUq9WK0WjEaDRitVpLrD2l7zekEEIIIYQocQZFz7Hzr+t6xlZ/G9bgXrwczm9vl6nzb7eBy4Xphdcxv/E+Su366A4HWlJS5q+UZAiriOmp5zC/+QGGO+7F9Ob7l9X5v9Dm0sTlcnHfPbfzUJ97SUtNBTLev1kzpgPQrEWrkmzeNWnenBnc1LkN8+fO8hw7fOggmzasz3fWghBlgSwBEEIIIYQQ2elucuqAK4qC7nZjNb6NeutZ0laXQ/W1ZSwD0HVwOUHTML/+LmrHLujJyWREB5RMAYULk1B1hx3dYUNp0xFz+87otrTL6vwDKGigqxlJAUoBo9FI1xtu5ofvv6XvA3fTrEUrjhw+yN49u7m+UxcaNpLs8oWtQ8cufGEdz/vvjGHVin+xWq2sW7saXdd59LEnS7p5QpQ4WQIghBBCCHEVKY4lAKqiYzZoKDll1ld9UNO347PrevA3Yd8WTuqCKuh2FcWqQ9w5jMNfxHjfg2hJSbnOIigqDrcBrRRNctV1nek/TmXenJmcPhVFcEgIt9x6B08MGV6i04CvZUcOH+LzTz9i88YNuDU39eo14PGnnqZV67Yl3TRRBpW2JQASABBCCCGEuIoURwDAoGiYDVoOr+joxkDMx17DdHIsmMuj+NlxRQeQMr0W7kN2DC0bYv50CnpqSq71F+Xjp1NTceulJ7meEKJsK20BAFkCIIQQQgghMsl9Lb2CorlQUzeCmpHQT0s2YCiXQtDgvST/GAw9HwZVycgVkMvov68ZlMscpHe5wObMfZa/qui4ZXhLCCFyJAEAIYQQQgiRiZJbAEAxgjsO1XEAXbWA5gJAt6moSgr+AwNJb9QUPSU9l90DdFBh7O8q51LB4OVSfbeeUTbFDrc11rm3hU5Kei5BAJncKoQQuZIAgBBCCCGEyCIjaV82ignFGQv2uOxnOG1oQS3BHArO5Oyv67qnw779pMKpBDDkMAsgp/67poOqQHwahAXC/a303GcAqAq4c78zIYQoyyQAIIQQQgghMslrYF7R7ShaesYMgCw0UwS6zvnkgdkz/l9gNV380nLo8F9aXNMv7lut6ZBmB3dO6QmEEELkSwIAQgghhBDiCmh4uuiGwEyv5Jfs79LOf15FL/T3VQXsbtC1jOn/MttfCCEKRgIAQgghhBDiMmmZ/+9O8rySX+ffoGZ06CEjEJB1Sr+u5zw7wFh6dvgTQoirjgQAhBBCCCFEJnoOHXLPa4ZAMAaguJKzLQMwp6xB09Jw6ioKuc/Tj0uFs8lgymW3PqMBAnLYJUvTM44bDKA7cj5XyylqIIQQApAAgBBCCCGEyEJHRdcvduA9Gf11J7qlIoqlOrprp+d1Iy4wmfgtPpW6qdHU8K+E3WXPsW5Fh95tdVJzfhmApHT4a6eSKUmgqoDLDeX8L07/zylIoShqRg5DIYQQ2UgAQAghhBBCZOJya5gveUr0TOfXHWAIwmlthDF5E6gWjLjQFI1PUlvxflwEo06u4ulGD2N32ciaTvBCNQM6aDl23nUdFBMs2qowd6NCiN/FZQAX/l89VM9z7b/LrYGSy9QCIYQo42QVlRBCCCGEyEzNuwPtDr4NAKOSzlndyLDELkxKqUt1s8rPh37nSMoJ/Ex+uXbUk9IgMTX7V3I6uB0wZ4OC2Zg5B4BbA7MRGlfRcbtzX6Kg5NN2IYQoyyQAIIQQQgghMtF0BT3HzQAVFHc67pCbwFyeTfZg+sbfxr+2MEKVdEyqmRRnGm9s+hyX7sZiNJHTfHxFyf4FEBgIC7YqbD2u4GPOfI7TDTXKQ5VghXRnzu3OSByY1yaGQghRtkkAQAghhBBCZKHgziWHn645CLCEsSbkZR6Kack5l4UgJWNBv6a7CTIH8l/MDl747wM0dPxMfhfOzLk+HQyKgSDfQNbtMzBhsY5/lgSAqgIpNuhcT8fPqqPlll9QUXLcOUAIIUQGCQAIIYQQQohsND23x0QFmyudylUGEmoNQdFSs5znJsQSyN8n19B/+Ui2x+8j0BKIn8kPi8GMQTFgUAwYDUb8TL4EWQPQDW6m7V7CSzPdoPll2x3A6YYQP7ijiYbdkfv0f6dLv5iwUAghRDaSBFAIIYQQQmSjoeSaad/hdlDLN4gRTR7hxfUfUsEQkq1MiCWQfQlHGLjiFTpHtOL2KtfTIOQ6go0BANhcDs6kx7Lx7C5+P7acnfH7CKvZCP/Tz6PbqoIxI7CgAmdT4ckbdaqWy8gVkHMCQR1Uk+wAIIQQeVB0Pa88qkIIIYQQojSx2Wy4XC5cLhc2m41z585RoVKtIrmWARdm4yW7AGQRaAlg9KbPmH5oIRWsIehZet8KCm7dTZIjozMfZA7Az+iDoijY3A4SHUnY3A78TD5YVQuaIQXF7Yv/mWdREzuAMZ2ENI26ETBpoBvdTY6JBRVFwaWBU5OxLSFE6RJ76jChoaFYrVaMRiNGoxGr1Zr/iUVElgAIIYQQQogcuXUDLreW47R6XYdURxqjmj1O14qtibXFo1ySODAjIZ+OgkqQOYAgcwBOzUWCI5mz9njSXTZ8jVZCLcFYVBM6GorbF92QRnLlsbjCppOWbiDIqvD6PW6sKrmu/XdpbhyuonoXhBDi2iEBACGEEEIIkTNFQdMNaDlk1lMUcOsudF1jQvtRdKvaiej0s2i6luMovY6GUVUxqioW1YRRVT3HM9Xr9kVx+xIV+ClqjU/4qI+ZuuGQas997b+uqShq0Y7+p6elMW3aZJ4eMoiBA3rxxusj2blze5FeU5Ss5f8uYeiQR0u6GUXu2NHD9Ovbg6SkxCK7RlTUSYYOeZT4uLgcX7elp9Ovbw8O7t9XZG0QGSQAIIQQQgghcuXGgKaTS3I9BYfbAcD4tiMZHjmANJeNREeyp4SOlq2TnxtVUbFrTs7a4mng35IpPe6iSVUHSWk5d/4VRcGt6biLIa3VxIkT2L1zB4MGD+GNN9+lSZNmvP/umxw4sL/Iry3E1a5cSDnuuOMe/AMycoCsWbOSxx97uIRbVTbJQikhhBBCCJEnN0ZU3YWqKDnkA1BwuV24NTdPN3qYjuHN+XTXD6yN2YoBA75GH4yqipLLuJOOhkvTcGgO0lzpBJuDGB45gIH1e+KrWEhKT8t15F/TdDSM6BRt5v+0tDQ2bVzPyJffoHGTZgBUr1GLAwf28dfi36lT57kivX5J0DQNVS0bY4XX8r2Wlnvz8fXljjvvKelmCCQAIIQQQggh8qHpCg63ilF1YVBy6cjrOom2JJqVq8+UTmNZG7OVBceWsjZmG4mOJFyaGzfubOcZMGAxmqkXXJPOEa3pUf1GqvlXJtWRSqqemue2fk43aLm0pzCpqoqiKJw7dzbT8UcGDsbhsAMZ9//77/P4568/SU5OokaNWgx89HGqVqvByhVLmT59Gl98OcXTGftk/Hv4+wcw6LGnAPhz4a8s/GM+ToeTJk2bM+CRx/D3D8h0PU3TGPLkQO7ucT/dunUHYNPG//h0wvtMnPQ9vr5+bNmykRm//MiZ01GEh1ekz4P9ad68FQAbN6xn0sQJfDNluqfON0ePJLJxM+67vw8//fgdhw4dwGrxYdeubXzz7XTMZnOmNrw6agTX1a7L0aOHOXH8KGHhFRn8+FBq1aqd7/sAkJycxNQpX7N922ZMFgsdOnSi74MDMBgy9n5cu3YVc2b/TNy5s1SpWo2H+z9GnTp12fDfWqZ8+xUTJ30PQNTJE7z4wjA++vhLIipWQtM0Hh/0EE8MeZaWLVtjS0/nhx+m8N/6NaDrtG7bgf79/4fVx8dzH1WqVOPo0cNYrFbeHPMeZ8+dZdKXEzh4cB9VqlajTp16me49v7ZfStd1Zs2czvLl/+Cw2WkY2ZhHBj5OSEg5AGx2Oz9+/y3r163Cx8eXLjfczL09e3n+fqxZvZy5c2cSd+4s1avXpP8jg6hZM+M9fvmlZ+nS5QZuv+NuAHbu2Mo7497gp5/nY0tP53+P9qXbHXezbt0qru/YlT4P9s/3ehcsXrSQuXN+YeKk7z2vjXr5ORo3bkrfBwdkKjvyxeHcdPPt3HJrNwA+m/ABqsHIkKHPArBw4XzWrF7J2HEfcezoYUa9/BwTJ33P5G++YNPG/wDo17cHI154hYYNIgHYd2APkyd/SUzMGerWa8CTQ54lKDAo2/srLl/Jh4OEEEIIIUSpp2PArRnObw2Yc6dcUSDVmUqaK532Yc34oO0LzLppPBM7jua5Jo/Qt9admb6GNnyIcW2eZcaN4/mxy3s83eghwqzlSLIn4tbdkMvIvq6Dyw2aYirCO77IarVy6613MPW7Sfz043dERZ0EIDyioqdju2zpXyz8bT6Dn3ia9z74jLDwinw64UMAWrZqR3paKgcOZKxvdjqd7NixlXbtOgDw1+I/WLz4d4YMeY5XR48lIT6OqVMmZWuHqqq0aduBTRvWeY5t3bqRyMim+Pr6cfDgAcZ/9A6dOndl3Lvj6Xh9F8Z/9A5RJ094fa/79+2hafMWvDX2A0ymnN/fXbu2M3jwUD759GuqVavOp5+8j3Y+Q2Ne7wPAtO+/JTExnjfeep+hw55jzZqV/PnHrwCcOnWSLz//mHvv7cW7702gVq3afPj+WzgcDho2akxKSrLnXrZu2wTAlq0Z/z9x4hg2u42GDRoB8PnnH3HkyCFeevkNXho5msOHDvDtt19luo9t27fwQO+HeOqpZwCYNHEC6elpjBz1Jr16PcTmTRsylc+r7Vkt+/dvli37m+HPvMTrY94lOTmZyV9/4Xl90sQJnD0Xw2tvvsMTQ4azbNlfLFv6N5DRoZ/01Wfccdc9jHtnPHXq1Of9994mLS01n5/eRQcP7Ofp4S9ye7e78r3epdq2bU9qagp79+4CID4+jmNHD9OmTYdsZRtFNmXvnp1ARnBqx45tbN+22fN3Yd/ePTSObJrtvKHDnufxJ57G3z+Ab6f8TLNmLT2vLflnMQ8PGMSLI1/n9KkoFv46z+t7Ft6RGQBCCCGEEMIrGkY0NNDcqGpOywEAMo6nODKm7gca/Wgf1ozrI1rmUDaD3e3A4XbgsCeR0enPLcCQUbeGgquYH2MfHjCIqtWq8ftv8/lj4QKuq12H3n3606hRYwCaNG1Bw0ZNiIioCMDtt9/JK6OWkpKSjL9/AJGNm7Jp43/Uq9eA3bu3YzQYadAw49w/Fs6nV5+HadAwYxS074OPMPr1F3nC5cJozHyf7dpfz7i3XyM5OYmAgEC2bdnMfQ/0AWDRn7/SrHkr7ryzBwB331OF/fv2sHDhAgY/PtSr+2zQIJLbbrszzzKdO99I5SpVAeg/YBBDnhzInl07aNS4ab7vw5nTJ2nUqCmVK1ehcuUqDBn6HG6nE4Do6NOoqkrzFq3w9fWjT98BVKxUBYfDjr9/ADVrXceevbuoXKUq27ZupnWb9mzdsolu3bqzd+9uateui4+vL6dPRbFl80bGvTue6tVrAjD4iWG89srz9OrdjwoVwgC45ZbbadmyNQBnTp9i964dvD3uQ89I+z097mPOnBme+86r7VmdiTpFWIUw6tSph6IoPPbYEPbt2wNATGwM/61fw+dfTvHMCOjWrTurVy/npptv46+//qR9+07ceMOtAPTtN4Cjxw4TFRVFnTp1vfo59u77MHXr1vfqepcKDilHgwaRbNy4noYNG7N1yybKl6/AdbXrZLtG4yZNmfTVZwAc2L+X8hXCSEtL5eDB/dStW5/9+/Zw6/nZAZcym80YzweXLszIuGDAI4M8/6Y6dOjMocMHvLpf4T0JAAghhBBCCK85NRUDOgbdhcFgyCUIcDFpn1t3nw8G5FzufOks/8+pPgVN089P+y/+R1hFUbjhxtvoesOt7N2ziz/+WMC740bz0sjXiWzcjNDQ8iz8fT4rVywjLu4cmp4xCuo630Fs1+565s2dwYP9BrB54wZatW6HwWAgNTWF2NgYvpn0GZO//txzPU3TiIs7R1hYeKZ21KvXgKCgIDZv2kDNWrWIT4ijZau2ABw/dpQuXW/MXL5BQzb8t9br+8wacMiPv38AoeUrEBN7hkY0zfd9uPue+5n45SccOLCP5i1a0b5DJ0JDywPQqFFT6tatz4hnn6Jlq7a0bNWaW27p5pmK3iiyKXt37+T6jl04dOgA7743gRefH4rNZmP/3t00btwMgGPHjmKxWDydf4BatWpjsViIijrhCQAYjRdnOJw+cwqDwUCNGtd5jqlZdpbIq+1Z3XDjLaxft4oXnx9Gq1Ztad22PV1vuBmA40ePADDi2Sc95TVN80x1P30qihsv6ZgrisKoV8Z49fO4wGS42Pb8rpdVu/bX89uvc+jffxBbt26kdZv2OZar3yCStNQUTp06ydatm2jWrCW29DS2btmEn68fNls6des1LFC7/Xz8PX+2WCzYbOkFOl/kTwIAQgghhBCiQNwY0DRQFA1FuTgyn5uMYMDlJeq7sNxA03VcmoqmZF9vXdTi4+KIj4+j1nW1URSFBg0jadAwknHjRvPXX38Q2bgZixb9zj9//8kzz75Eteo1ORl1gpdfHO6po2XLNkz+5gtOnjjO5s0beOyxIZmu8eRTz1Dtkg4rQLlyodnaoqoqbdp1ZNOm9SQmJtAosoknV4DRZMKgZn5/NLeG0+EqrLciR26n07P1Y37vQ+s27WnQMJJNG/9j06b/mDP7Z4YMHUGr1m0xm8288trbHDiwly2bNzH120mEVghj1CtjMBqNNG7clC+WL2HXru3Url2PChXCqFa9Jrt2bmPfvt3cenvGdHeTyYSa5X3QdR1N03A5c34vFEXBaDTmmXMir7ZnVbFSZT4cP5Ht27eybetG3n7rVW695Q76PNjf08Zx736S6Rz1fC4Bg9GIUsiJLfO6Xlat27Rj6neTOHTwADt3bmfky6NzLGe1WKhTpx579+xi29bNPPLo49jS0/nl52lUqFCeuvUbZsshIUqe5AAQQgghhBAFpisG7G4jDhe4NHeeHafLdSGw4HC6cbiNuCn+zj/Avn17ePONkaSmpmQ6HlouFM2dMcK9a+c2WrZqS42a16GqKnabPVNZH19fmjRtzuzZP2esaY9sAoCfnz9BwSHEno0hIqKi5ysoMCjX0fgO7TqxY/tW/lu/hrZtL47OVqlS1ZNn4IID+/dSrXr1jDZYrdgdds8abQCXu+DBgQuj+gAJ8XHExccRFl4x3/fB6XTyy/RppKen06XrTTw34mU6d76Rv/5aCMCWLRtYtvRv6tSpT6/e/Rgz9kP27d3NoYMZWy3WqVOf9LQ0Fv35O82btQCgeYtWLF68kHSbjdq1M6bHV65SlfT0tEy5D44cPoTT6aRq1Wo53lPFiErY7XZios9ccvRiUCu/tme1eNFC9u/fS8uWrXn0f08y+PGh/PHHAnRdp1LlyjidTtLSUj0/77CwcM+IfOVKlTl27HCm+mb8/APHTxwDwMfHis1mu9g2V94/w/yul1VgYBCRkU356afv8PHxoXbtejmWA4iMbMraNas4d+4stWvXpUHDSKJjzrBmzSoaN86+/v+Covh9IbwjAQAhhBBCCHF5FAVNMeFyG3E43bjPdwyv5OH+wrkZif507C5wK5Yi3+ovLy1atiY8PIIP33+b7du2cPzEMZYuWczaNStp1/56ACIiKrHxv7Xs3LGVLVs2eqbzX9o5a9euIxv+W0vLVm0yde7vuqsH8+fMZM2alcTERDN/3kzefOPlXGdV1K5bj8DAII4dO0KLVu08x++8qwcbNqzjzz9/4/SpKH7/bR7bt2/htvMj45WrVEVRFH6dP5vjJ47x64I5HD50sMDvx5K/F7Fr1w5OnDzON19/QYWwcBqez1+Q1/tgMpnYunUT30/9mqiTJzh29DAHDx2gYsXKALicLr6f+jXr1q0iNjaGdetWYzAYCAuLADJGsevVb8Tu3Ttoej5xXIsWrdi1czsNGjTyZOOPiKhI6zbt+eqrCRw6eICDBw/wzddf0LJVW8LP5ybIKjyiIo0imzDt+8kkJiYQExPN4sV/eF7Pr+1ZRZ85xZTJX7Jv3x6iz5xm65bNRERUQlEUKlWqQvMWrZg08VP27t1FVNRJPvv0Q36ePhWA27p1Z83qFfy77B+iz5xm1szpLF36F+XOr9+vUaMWK1f9y4ED+9m9ewezZvyU588rv+vlpF37juzbu5s2bdrn+e+5UeNm7N69g8ZNmqGqKiaTicjIJuzetYPI80GunAT4+5OSkszOHVtJSUnOs/2icMkSACGEEEIIcUV0xYAbA7qmoSsaChqqoqCqmTsOWTu0l3YsLrym6Tq6ruLUQNPVUjFSaDabeeX1scye+RNfffUp6WmphIdX5JGBj3N9p64A3NuzF2fPxvLxx+9SPrQC3e/pya/z55AYH+9Zc96iRRvMZnO2NdW3d+uOzZbOTz9OITUlhZo1r+PJp4bnee+tWrfj+PGjmUZxq1evyfBnXmTGLz/wy/TvqVSpKiNeGOXZoi84pBwDHh3M3Fk/8+efv9G8eSvq1WtQ4PcjsnFT5s35hcOHDxIeXomnn37es04/v/fhuREvM/W7b3jt1ecxmcw0b96K3n0eBjKm2Pfp8zC/TJ9GQkI84eEVGf7Mi4SUK+e5duPGTTlz5hQVK2V0vKtVq0n50PJERjbL1MbBjw9l2tTJjBv3OgbVQJs2HXio/6N53tfgJ55m0pcTGD7sMSIiKtEwsnHGNoLn5dX2rHr17Y/L7eLjD8fhdDq4rnZdhj3zguf1J54czrTvJ/Ph+2PR0WnerCX33d8XgLp16zPosaeYM2cG3035iho1avHSy6M9Sz3u6XE/p05H8c7Y16hQIZw2bTtw7NiRPO8tr+vlpGWrtjDpc9q0zZ79/1LXXVcbX1+/TJn8mzdvzd49uzPlU8iqfoNIGkU24eOP3mHIsOdpdD6AJIqeoue1YEsIIYQQQpQqNpsNl8uFy+XCZrNx7tw5KlSqVdLNykzXURUddDcKF/MEKGR97FTQdB1FUXFr5ydcK8YSHe2/Wrz7zhu0atWWm2/JnmW9KL06agRt2nbg7nvuK9briuJ14MBexn/8Hp9/8a0nuCMuT+ypw4SGhmK1WjEajRiNRqxWa4m1R2YACCGEEEKIwqUoaCigXNJxyGvISedycwSWOTGxMWzbsokDB/YxdNiIkm6OuMY4HA6OHj3MTz9+R5euN0nn/xokAQAhhBBCCCGuErNm/MSuXdt58slnPFPChSgsZ8/GMO7t14hs3JR7ejxQ0s0RRUCWAAghhBBCXEWuiiUAQgghgNK3BEDmdAghhBBCCCGEEGWABACEEEIIIYQQQogyQAIAQgghhBBCCCFEGSABACGEEEIIIYQQogyQAIAQQgghhBBCCFEGSABACCGEEEKIq8Bfi//g+RFDeKT/A4x47ikWLpyPpmnF2obHHn2Q9evWXFEdx08co1/fHmzauL6QWiWE8JYEAIQQQgghhCjlfvttLtN/+o6bbr6dN958l3vv7cWv8+cwf97Mkm5aga1dsxJFUVizemVJN6XAnh8xhMWLF5Z0M4S4bMaSboAQQgghhBAig6ZpqGrmMTqbzca8uTPp3bc/3bp1B6BGzetwuVxM+/4but99HyaTqSSae1nWrlnJ7d26s3TJYmw2W4nuiS5EWSMBACGEEEIIIfLwyfh30TSd50a8DEBKSjJPPj6AES+8QrNmLTl9KoqpU7/hwP49+Pr5cdvtd9G9e08AUlNTGDzoIca98zHVa9QCYOaMn9i/bzevvj4WW3o6/3u0L93uuJt161Zxfceu9Hmwf6br79+3B7vNRseOnTMdb9W6LWdOnyI1JZngkHIAbNmykRm//MiZ01GEh1ekz4P9ad68leecvNoKcPZsLJMmTuDAgX2Eh1ekddv2LPlnMRO/mprjexN95jRTpkziwP49lC9fgfsfeJA2bTvk+l4eOLCfhPg47ruvD/+tW8PmTevp0LFLpjJrVi9n7tyZxJ07S/XqNen/yCBq1qwNQFJSIt9/9zXbtm3BYrXQuctNPPDAg56gSV73v3HDeiZNnMA3U6Z7rvXm6JFENm7Gfff3YfPmDXz91Wf07tOPOXNmYLfZ6dCxM48MHMzxY0cY9fJzAEyb+g0rly/l7XEf5XqfQpRWsgRACCGEEEKIPLRr14kd27dgs9sB2L59K1arD5GRTUlLS2Ps2NcIDg5mzFsf0L//IH6dP4ely/4q0DUOHtjP08Nf5PZud2V77WxcLBaLhcDAoEzH/f0D6PNgf0/n/+DBA4z/6B06de7KuHfH0/H6Loz/6B2iTp4A8KqtkyZOwGZL5+VXxvDQw4+yauW/ubbZZrMxbuzrVK9ek3HvfsJd3Xvyxecfc/LE8VzPWbd2JY2bNMPH15dWbduxZk3mZQA7d2xl0lefccdd9zDunfHUqVOf9997m7S0VAA++fhdEhMTeG30WIYOe541K//l99/neXX/3khOTmLjhv8Y8fwoHh30JEuXLGbr1o1UrVaDb6f8TETFSvR7aCCvjR7ndZ1ClCYSABBCCCGEECIPzVq0QlEUdm7fCsDWLRtp2bINRqORdWtX4nZrDHpsCFWqVqNN2w706PkAv86fU6Br9O77MHXr1vd05i/lcriwWH3yrWPRn7/SrHkr7ryzB5UqVeHue+6jSZPmLFy4ACDftp4+FcXu3TsZNHgo9eo1oHGTZtx7b69cr7d+3WrMZgsP9htARERFOne5kcaNm7F23aocy2uaxvp1q2jTpj0Abdt0YMf2raSkJHvK/PXXn7Rv34kbb7iViIqV6NtvAFWrViMqKopjx46wb98eBj/xNNWr16RBg0b06fcIZ06f9ur+vaGqKkOHjaBmzdq0b389tWrV5tiRI6iqitXHB0VRMBiNWCwWr+sUojSRAIAQQgghhBB5sFostGjRmo0b16NpGtu3baH1+Wnux44d5bpatTOtwa9XrxGxMdGeGQPeMBlyX5lrtVpJT0tF13UAVq9ewSP9H/B87dmzC4Djx45Sr179TOfWa9CQkyePedXWM9GnMRqNVKtWw/O6wWDItV3Hjh/hzJlTPPpIb8/Xtm2bORcbm2P5PXt2kZSURPMWrQGoU7c+/gEB/LdhrafM6VNRVK9Zy/O9oiiMemUMderU5dSpKHx9/QgLC/e83r799Qx+fKhX9+8Ng8GAj6+v53uL1YrNZvP6fCFKO8kBIIQQQgghRD7ate/EN19/zoH9e3G6nDRp0gwAo9GEasg8pqbrbgDcLmehXDssPByn00ns2VjCKoTRvHkrxr4zHofDxqujnkfXM7YCNJpMGNTMHXbNreF0uLxqq0FRURQFRVG8blvdug147HwH/AKfXJL6rV29ErfbzdCnHvUcc7lcrF29khtvuBUAg9GIQs7XNxqMebYtv/sXQkgAQAghhBBCiHw1bdYCt9vNjBk/0qJFa88oepUqVVm/fhUulwujMePRev++PYSWK4+fnz8ulwtFUTKNIrtcBeuQ1qlTn8CgYP5Z/AcPPvQIvr6++Pr6YktPz1SuSpWqHDiwj9svOXZg/16qVa/uVVsjKlbC6XRy/MQxqlXNOMftzr2tlStVZe3qlYSElPNMibelp2P1yb5cweVysWHDWvo9NJCmzVp6jkedPM6nEz4gIT6O4JByVK5UmWPHDmc6d8bPP9D++s5UqlSZ1NQUYmJjCKsQBsDevbs4cGAf3bv3zPf+faxW7A57pp0WXHncX05yC04IcbWQJQBCCCGEEELkw2Qy0apVW/bt3U2bNhez3Hfo2BkFhSnfTiTq5Ak2bljPgvlz6HbXPQAYjUaqVKnGH38s4MTxo6xbt4p/l/1doGsbDAb6DxjEn3/+yvy5Mzl+4hgHDuxl9uyfMRgMBAUGA3DnXT3YsGEdf/75G6dPRfH7b/PYvn0Lt91+l1dtDQuPoFHjpkz++nP279/Lju1bmTdvVq7t6tixMwaDysQvPznfpv28/tqLrF+3JlvZHTu2kJ6eRpeuN1G5chXPV+s27QkJKce68+fc1q07a1av4N9l/xB95jSzZk5n6dK/KBdSjspVqtK4STO++eozTz6Aryd9jtPu8Or+K1epiqIo/Dp/NsdPHOPXBXM4fOhggX4W/v7+7Nm9k6iokwU6T4jSQgIAQgghhBBCeKF1m3ZYLBaaNG3uOWaxWHhp5GhiY6J5ZdRzTJv6Dffcez/dunX3lBk06CnOnD7N6NEjWfL3Ilq3blfga7dvfz3PjniZzVs2MPrVF3j/3beIOnmCV159i8pVqgJQvXpNhj/zIsuWLGbkS8NZvWoFI14YRa1atb1u6+DHh2ExW3ln7OvMnPEDLVq2RlVzHvW2+vjw0sjRpKYk8/orzzP+43do3aY9rdtkv781q1cRGdkMPz//TMcVRaFN2w6sXbMCgLp16zPosaeYP38WL74wjJ07tvLSy6Px9w8A4Mknh+Pn78ebo0fyycfv0rZNB3r07OXV/QeHlGPAo4P5558/GTvmVU5FnaRevQYF+jnccWcP9uzeyddffVqg84QoLRT9QjYRIYQQQghR6tlsNlwuFy6XC5vNxrlz56hQqVb+J4ortmD+bI4fO8Kw4S+UdFOKjNPpzJQkcO7sX9i6bRNj3vqgBFslxNUr9tRhQkNDsVqtGI1GjEYj1lzyZBQHmQEghBBCCCFEHpKSEtmyZSOL/vyNrjfcUtLNKVLvvzeG33+bR0xMNJs3b2Dx4oV06NClpJslhCgkkgRQCCGEEEKIPGzctJ4fp02hW7e7aXw++/+16qGHBvLTT1OZM/tnAgOD6NatO7fedkdJN0sIUUhkCYAQQgghxFVElgAIIcTVQ5YACCGEEEIIIYQQothJAEAIIYQQQgghhCgDJAAghBBCCCGEEEKUARIAEEIIIYQQQgghygAJAAghhBBCCCGEEGWABACEEEIIIYQQQogyQAIAQgghhBBCCCFEGWAs6QYIIYS4dqQ7dE7GaZxJ1IlL0UlK13G5wa2DUQWrCQJ8VEL9FSKCFCqVUzEbSrrVQgghhBBlgwQAhBBCXLaYJJ3/DrnZccLN7ig3pxN0dN37842qQpVyCo2qGmhaVaX1dQYCfZSia7AQQgghRBkmAQAhhBAFcjZZ5++dLv7d7eJgtHZFdbk0naNndY6e1Vi4BRQFmlU3cHMjI10bGrGaCqnRQgghhBBCAgBCCCG8s+ukm1nrnaze70YrwCh/Qeg6bDnqZstRNxP/cXBncyP3tzFRzl9mBYjSpfWM+/Mts6H37GJoiRBCCOE9CQAIIYTI054oN98ud7LlqLtYr5ti15mxzsn8jU56tjHxYAcTvmYJBAghhBBCXC4JAAghhMhRfKrOxH8cLNnlBEqu4213wc9rnCze5mTYrVY6N5CsgUIIIYQQl0MCAEIIIbL5e6ebz/+ykWKDkuz8XyouFd6cZ6PrXgPPdrPgby0d7RJCCCGEuFpIAEAIIYRHml3noz/s/LuneKf7F8S/e9zsPZXOG/dZqROhlnRzhBBCCCGuGvLkJIQQAoCoeI0h39tKdef/gjOJOsN/SGfl3tLfViGEEEKI0kICAEIIIdgdpTFsqo3jZ69sW7+szAYI9lUIC1QI8VMwF+LyfbsT3pybzoJNzsKrVAghhBDiGiZLAIQQoozbekzjlZk2bM4r29uvRgWV5tVVGlY2UK28SpVyKlZT9nJJ6TqnEnQOnnGzJ0pj4xGNs8mXGXhQFHzMV9RsIYQQQogyQwIAQghRhm075mbUTDv2y+z8RwSp3NbEwC2NTVQM9i4pX6CPQqCPQv2KKnc1zzi2J8rNoh0ulux0ke7w7tqKAi/eZebWxjlEGYQQQgghRDYSABBCiDLqSKzO63Mur/NfLVTlwQ4mboo0ohZCMv4GlQ00qGxgUFczcze4mLXekWcgQDr/QgghhBAFJwEAIYQogxLTdF6ZkU6KrWCdfx8T9O9k4r42ZgxFkEUmwKowoJOJ7i2MfPmXg2V7XNnKSOdfCCGEEOLySABACCHKGE2Ht+bZiU4qWOe/fiWF1+/1ITyoEIb881HOT+HVey1cX9/Ix3/YSLVnHJfOvxBCCCHE5ZNdAIQQooyZsdbJlmMF2z7v7pYmJvT3LZbO/6W6NjDw5UAfqpRTpPMvhBBCCHGFZAaAEEKUIUdiNb5f6WWWvfMGdjbz0PUl1+muUk7lswE+7Drhpn1d+dgSQgghhLhc8iQlhBBlhK7DJ386cBZg8H9QVzN9O5T8iHugjyKdfyGEEEKIKyRPU0IIUUYs2eli50nve/89WhlLRedfCCGEEN47l+xm3f40th2xcSzWyck4JyfPOkguYOLfq0WAVaFKeTNVQ01UK2+iaU0rHev7Eewnq91zIgEAIYQoA1wafFeAqf+Nq6k8dbO5CFskhBBCiMISk+hm6tI4lu1MZd+pgi31u9ol23T2nLSz56Q90/FGVS10beTHw11DCAsylFDrSh8JAAghRBnw1w4XZxK8i/wH+ii8eo8Vg1q8Cf+EEEIIUTAxiW4mLY5j+soE7K5rc4T/cu06YWfXCTuT/4mnX+dgBt9aTgIBSABACCGueboOs9c7vS4/+EYz5QOk8y+EEEKUVtLx957dpTNlaTw/rUigX+dgnuoWSjn/srs8oOzeuRBClBFbj7s5dlbzqmyDSgZubyqxYSGEEKK0crp0Pl14lu+WxUvnvwAuBAI+/jUWZxl+3yQAIIQQ17hFW70f/R/YxYSM/QshhBClU3yKRt/xJ5i+MrGkm3LVmr4ykb7jTxCf4t3gyLVGAgBCCHENc7hg9QHvMv/XrWigZU1ZGyfEVUPXM76EEGWC060z6MuTbD5sK+mmXPU2H7bx+FdRON1l73eozPMUQohr2JajbtK9TAbco6V8JHhFc+PauxvHti24Du3FffI4WmwsWkoKuuZCMRhR/fxRK4RhqFIVY+36mJo0x1S/IailIMCiu9GTNqAlrIDkbZB2AOxRaK5EcDtBNaGYglAslcGnDkpgM5TgTiiBrUEp+fY73bD1mJvtxzX2n3ETFacTl6rhcIKiQKAPVAxRqROu0qy6SpvrTPhc7Rta2O04tm7EsWMz7gP7ST1xDMe5s7hsNuyaThwqFX5dVdKtFEIUsTd+iWHLEen8F5aNh9J545cYxvYLL+mmFCt52hNCiGvYhiMur8pZjAqd6pd85640cx3Yi23hfGwrl6InJuRYRgFwOdES49ES43Ed3If9338AUIOCsXS+CeudPTDWrlds7b5AT96MFjUFLWYOivNctteVC//RHeCIRXfEQvJW9JhZGQXM5VEr3Ida+VEIaF6cTQfgUIzG/I1Olu9xk2rPecRG1yEhDRLSNPZEafy6GawmBzc2MtKnnYnK5S5OfNxy1M3z0/N/kF4yyq/Q7qGgnLt3kL5gFo7Vy9Ft6Z7jbreWeeTfWba2/BKiLPppRSI/r5Jp/4Xt51WJNKxqpV/noJJuSrGRAIAQQlzDdpzwbn1b29oGfM2y+j8nzp3bSJ36Fc6tm66oHi0xgfTf5pD+2xxMzVrhN/AJTI2aFFIrc6cnrsV96HWIXw5w+TkeHGfRoiahRU1CCemKWutNlOD2hdXMXB07q/HNMgdrvVzKkpXNCX9sdbF4u4v725p5pJMJcyl/+nHu203q15/h3HZlf+eEENeG2CQ3b82KKelmXLPenhXDLU39y8wWgaX8I1AIIcTlsjt1jkR7t7atWY2y8aFXEHpiAslffIR92V+Fvs7auXUjCc88hvWm2/B/agRKYBGMPDjP4t4/Av3ML0Dhtl+P/xf3pq6oEX1R634EptBCrR/A5YYfVzuZvsaBuxDyNLk1mLHWwcbDLt5+wHrlFRYFh52UyV+QPm+GrO0XQnh88ttZHGU4a31Rs7t0vvrrHK8/EFbSTSkWkgRQCCGuUcfO6bi97EQ0qyYfB5dybN5A3KC+2JcuLrqOmK5j+2cRcYP64ty6sXCrjluGa11z9DM/U9id/0uugnZmOu71zdHjlxVqzbFJOsN/SOOHVYXT+b/UoWiNZ35I53Ri6XqYdp+OIm7oQNLn/iKdfyGEx/FYJzNWy9T/ovbzikRiEi9vptnVRp74hBDiGnUyzruek8mgUzVUPg4uSP9tDokjh6HFZ18nXxS0uLPEvzSM9N/nFk59Ud/g2nonOKILpb786PYzuDbfiRY1uVDqOxyjMWRqOntPFV0nODpR56OF9iKrv6Bch/aT8PT/cB8+WNJNEUKUMp/+cQ5NYoJFzu7SmfD72ZJuRrGQJz4hhLhGRSd4FwCoVM6AKsv/AUif+SMpE94DrXj3BlbcblI+eZe0WT9dUT3a8fFoe4eg6N4lfywsCi60vU+hHRt/RfUcjNZ47sd0zqWUnadd97HDJL44FC0+rqSbIoQohbZK1v9is/5Aev6FrgGSA0AIIa5RSV5+jkUEFX3v/6ZxqUV+jbzMecaXYN+879P2xwJSvv60mFqUs9SvP0UNCMR6e/cCn6udmop2YGQRtKoAbTg4EszlUCsOKPC5ZxJ1Rv5iI7kMPetqCfEkjnoWLZddJUTZlmrT+OjXs/y+KYWkNDfXRVgYdkc5bm/uX9JN83hw/EnS7G7mj6xeou2YvSaRF3+IZvnbNakaairRthSmg6cdHI4u2l0+/Cwqo/tU5L72wQT7GdgXZePdudH8+l/GsoOwICOr363LzNUJvPLjqSJpw01NAvjh2Ro8/uVxfttQcssdDkc7OHjaQe2KV/vesXmTGQBCCHGNSvVyhrOf5dof/o/KZzmEc+c2kie8d9n1q+VCMTVsjKlZK0wNG6OWu8ykeLpO8ifv4Ny1vWCnJa5F2zeUy17vb45ACWqLEtIVJagtmCMurx50tD1D0BPXFugsuwten2UjPvXy2h/oo1C/kkrz6gYaVlYpV3r6R7nTdZLfHY07+nTBz7VYMUY2xXLLHVjvuBtLhy6F3z5R4l7+MZqpyxJodZ2Vfp2DSbW7GfLNqUyjlF1fP8Kz350psTaeSXASneguM1PUi/v9/nt7SpFf44vHq/Lk7eVZuy+Vb/4+i7+PgR+frUHH+hlboPqYVUIDjFQMKbrASmiAgUAflfKBJT82XRzveUkr+XdZCCFEkXC4vXsi87l2BktyldeUcj01laR3XgN3wabNG+vUw+eOHpjaXY+hQni217XYaOxrV2H7Yz6ug/u8r9jlImnca5T7ZjqKrxd70LuTce98GLSCjRIpAc1QKg1CKX8HirVK9gL2k2ixC9FPTUZP3uZ9xboD967+GNtuBkOAV6d8vdTBoZiCLbsIC1Lo3txE5/oGqpTLPp4RnaizYq+TBZvdnI4v3iUd3kj/fR6OjesKdI6pXiN87u+Lf4u2uA0GXC4XNpsN27niyVchio/TrfPH5mQe6hzEmL4Zv1+evrMcN75+lL+3ptC2jk8JtzDDb6Oqo2nIMrIisuVI0U5JNxrg3nbBfPP3WZ6bEgXAO7Oj2T6hAXe1DmL13lSOxTqo8+QuEtOKLkHezNUJLN+VQnRC8S5fy8meE6UnP0xRkQCAEEJco7x9HisLIzeJ6bnfZOq3X6BFez+io4ZF4D/kOSwdu+ZdrkI4Pnffh8/d92FfuYyULz5CO+vdPs5a9GlSp0zEf+jz+ZZ1H3wVbMe9qhcAS1UMdT9GCbsnn3JVUKs8DlUeR4uZh7b/ObBHeXeN9GNoB19DrfdJvkV3ntBYsNHpXb2A1aTwSBcT97Y0Ycxj98rwIIUH2prp2RrmbHDw3XInjpJ/tgRAS0okbcqXXpdXA4PwG/Ic1pu6AWCz2cBVSm5GFAmjquBnUTkW68Lh0jEbFYJ8Dfz3/nUY1IzlAY2fzUgaeTzWyYL/kvjq8Urc2syfE+ecvDUrho0HbBgMCh3q+fDaAxUoH2hkztokXph2hm+HVOKGyIypMqk2jTYvHeKuVoG893D2YOaiLSmM//Usx885qV7BzNN3hHJHy4xzH//qFHaHzqwXqgIQk+Bi1PRo1u5LIzTAyNt9wxj81SmG3xXKk7eVY8PBdHp/dIKX76vAnDVJHD/rILKalfceDqdm+MVp11OWJjB1aTwxiS5qhpkZckc57mp5MaD428ZkJvx+jtMJLlpf50PH+r75vqc/rkhkypI4Tse7qF7BzJO3hXBPm0AAlu9KZeDnUcx5oRrNa2VsE/revLN8tzSevZ/VyfP9LkqxSUWbld7lhhSbRq1wCyaDgtOtk5jmpubgnZmeDQ5PiuSjBdGMmZHxWdm2ri8fPlKFBlWt7DiWzhd/xDL16erc8OoBNhxM47dXr8Pl0omKc3Jf+2BSbRpfLY7lw/k5fwa2r+fH32/Wpvvbh1i2M4UX7w1nePcwxs46zbN3h+FrMfDn5kSe/uYkafaiDejGJl37v1tlCYAQQlyjzEbvQgBpjms/ApCey+C4+/gR0hd6n33f3Lo95b7+Kd/Of1aWTjcQ8s10zK3aeX1O+q9zcJ84lmcZPXUPetQ3XtephN6Gsd2m/Dv/Wahh92Jsuxkl9Bavz3FHfQ1pec980HX44h+H1wsXKoWofDnQygNt8u78X8qgQq+2Zj4b4EM5/9IxTJk+6ye05CSvyhqq1SBk4jRP51+UDYoCg24OYeWeVG4afYRPfj/H4WgHhvNP7haTyriHLnbWxz0UTsOqVtwa9Bt/khOxLl66tzxP3BrCmr1pvPRDRserWwt/Aqwqc9cme85dtCWFdIdO745B2dpx8pyLod+comI5E6N7h1Gvkpmhk0+x+0T2ZB2aDv+beIpVe9Lo3TGIbs39eWbKmRz3r//k93MMvjWEdx4K5+BpB69Ov9gx/PafeN6eFcMNkX6M6RNG5VAjT08+zebDGddcvTeN4d+exqgqDLo5BJMRPskne/uUpQm8/nM0VcqbefzWcvhbFZ797gy/bUzO87wLcnu/i9rZxKLvjH76eww3Nw1g2ycNGHV/BLUrWvIcGKgcamLBqOuICDHyya8xHDxl56snq2Urd0uzAEIDDPQbf5R/dyXzRp+KnmUF3gjyVXn05vIM++YkHy2Ips/1ITx9V4XLucUCKeqgS2kgMwCEEOIaFeDjXWcnpQwkXdNy+TxP/eFbvN1o3nx9V4JeewcMXvY8s1ADAgl8+2OSxozEsWZF/idobtJ++paAkWNyL3L0XfAy479S/h4MTX4G5TI/+k0hGJrMx72zD3rsb/lfT3fhPvIOhkZTcy2zer+L/ae9e9iqFKzwycNWQi+zE187XOWjflaG/2AjKa3kgl56ehrpv872qqxapToh479GCQou2kaJUmnYnaHUijAz7d8EPl14jk8XnqN7qwDG9QvHz6rSp2MQXy2Oo3lNH/qc77xrOnz+WCUiggyEBWf8W1dVhXfmxuByg69FpXvrAOasSyIxzU2Qr4F5/yVRJ8JMi1rZO7RnEpxoOtzbNoB72gTyQPsgBtwQQvWw7EnSNhxMZ9dxG2P7hdH3+mAAmtSwMmxy9jwXT90WQs92GaPvu0/YmboswdPpnPR3PL06BvFmnzAA7msfRJdXDzNnbSItalmZuiyesGADc16qip8lIyLyzJTT/Loh5868psNXi+Po0siP74ZWznhv7wjl3veOMXFRHN1b5b9UyWggx/e7qBVHZ/S9udHsP2Xjidsr8PJ94Yy6P5xZaxIYOukEqTmMtj/ctRx+FpXOo/az/1TGdPlkm5vBt5bPVC4pXWPgp8ewOXXW70uld8cQOjTwZ/Ve75MCD510gnX7U1m0OYkebYPpUN8fKNotbmOLIehS0mQGgBBCXKOCvFwiesbL7QKvZqqavcOnnYvFvnKpV+cb6zUkcNRbl935v0AxGgl85W2Mdep5Vd627G+0c7mMbDlOo0d715EksBVq4x8uv/N/gWpCbfQjSkBzr4rr0bPQ7bknuZv9n3dT/33MMK735Xf+L6gWqvLqPRYuO1liIbAt/Rs9Nf8kU4qPD8FvfySd/zLuzpYBzBhRldVja/G/m0L4bWMy782LzbW8qkBskpOHJ5ykzpD9tBhxiO//jcflhqT0jM5kr47B2J0ZOQbOxLtYuy+NXrl0aJvV8KFzQz+e/e4Md407zpiZMRhUPB3vSx04nTHV6ubGF6fF39I05ynykdUuBhuC/Q043Trpdo2YBBdnk1zMXJ1IrSf3U+vJ/dQZsp9T8S7OJGS0f/8pBx3q+mVqw61Nc+/EX6jztmYXR58NKtzSxJ+9UXacXubLKQkWL2fyXal56xK57Y2D1Buym08XxnJ/+2De6lcxx7INqlg5cNru6fwD/J5D5v59UTZszoz3NtWu4XDpBPsV7DN0y5E0z5/jkl2EFPB8kTOZASCEENeoisHexXhPJ+o43WC6hj9XzTl82tn+/sOrddSKyUzgy2NQzJZCaYtisRIwcgzxTzwEznw6wG436f/8iV/vh7O9pJ/+CXQvOtCqBWOjqaAWznRVxeCD2mgq7v/agJZPsiTdiXZ6OoYaI7K9dPycxo4T3gWfHr/RTNXQwhmzaFnTwB3NTPyxtWRGeRzLFntVznfA4xiqZJ9WK8qGM/Eu/tiSwu3N/akUYqRiOSOv3F+BY2cdLNuZ+wjq2SQXQ74+zfUNfBn3cDg2h870lQkcj3V6wl5NqluoX8XC3LXJJKVpGFWFHudH47MyGmDqsMpsOWxjzb5UFm9N4ccVCUwZUpkujXKezq140Wc15JI1UD/fyCduK8ddLTMHD/x9Ln5IpTky/+7Q8wjqXagzt4YpuWTMsbtKPjheIchIsq3otgGsGGLi3nZBLPgvkahzTk7FORn1wylqhVu4vXkQz5Fz3hfdi5iJqxACK4VRR0FVCLr2u8cyA0AIIa5RVcp5N3KgaXAouminGaqKXiRfipcjuTmNVjlW/evVudYe9xd6R8xYvSa+d9/vVVnnqmU5HnfHzPPqfLXyk+Bb1+u2eUPxa4BaebB3hc/Oz/Hwv7u9+ztXs4LCnc0Ld6uKRzqbSyTgpaWm4Ni+Jd9yamgFfO95oBhaJEqrU/Eu3p4Vw+S/4zzH3BrEJrpRLunIqgqZkqIdOOPA4dJ55IYQWtbKSI7na8n+l71XhyA2HU7nvXlnubWpP6H+Of+D2H3CxjtzYqlT0cyQbqHMe6k6vhaVpTuyByFqR2QsC1iy/eJrOZXLS3iIkdAAI9EJLhpWtdKwqpVq5c3MWJ1EyvkZDHUqmtlyJD3Tfed1nfAQI+UDjfy99WIZTYclO1KoX9mC0QCB54MLUfEZQVW3Buv2Z8/An/X9LmoVAov2F1XV8ibeH1CZ4XeFeY6pCkQEG9Fy6eXvPWmjTiUL10VcXAbSrWXxLIkoDkX9npcG136IQwghyqiqoQpmAzi86GdtPeamfqWi+9D7++WiyZS8Zr+L12bnv2VPSJap43pyIs59u/O/gNGIz339Lrd5efK5/0HS5s/INweBa99utOQk1ICLI3S6Mw49aVP+Oz0oJpTqz1xxW3OiVn8W7eTEfHMQ6Ikb0Z3xKKaQTMfXH/JuBL5XO3OhbzEW6q/QtaGBv3cUb7In185tuSekuIT1jrvBlH/QQ7fZSF+yiKRf51LhqxmF0URRSrSoZaVTAz+mLktg/2kHdSLMbDqczs7jdl69/2IitGrlzazam8Y7c2K5tbk/14WZMRrgsz/OkZjmZusRGwv+yz49u0fbAMbMzEi817tjzqP/kNFRnrwkns2HbdzWwp8dR22k2jTqV8meA6B1bR/qV7HwxswYDp5xYDIqzFyd/dp5URUYdHMw7807i1vTaVTVyl9bU9hx3MbDXYIBGNA1mAGfRfHAhyfp1tyPvVF2lu1Ky7POJ24N4e3ZsTz6eRQtr7OyYnca24/Z+eR/GdPc61Q0E+hj4O3ZMRw87WDncRvHYrKPvGd9v1vWKtrtGCsEFm1X7b8DafyzLZmnupWnQVUre0/aaFfXj+a1fHjp+1M5nvPDv3E8c3cYC1+rzdSl56hW3swDHUNyLHs1Kur3vDSQGQBCCHGNMqgK9bzs1G86cnVmvT0a691ITFhA5h6kc88ur+Ywmlu0wVC+aLIOqxXCMTdrnW85XdNw7d2V+WDSfyjkf+9q6E0olkqX28S8WaqghHT1oqAbkv7LdMTm1DlwJv/2+5igS4OieRi7qVHhzirwhmu/F0EnwHL9DXm+7o46Tso3nxP3v16kfTUB17EjhdE8Ucp8Obgij9wYzMEzdn5ZnYjNqfNG7zAeveliZ+vFHqFUCTHy/b8JJKa6CQs28uVjlYk65+TFH6I5Guvg+XvKZ6s72Dfjs8HXotKxQe6Z2SOrWfl+WBWSbRrvz4tlw8F0nuseSp+OwdnKGlT49snKtK3jy08rEvh9YzJvPxiO0VCwteyP31qOl3tWYNOhdMb/dhabS2fyk5WoXTEj6NCpoR8fPRJBusPNxL/iSbXrjH4g79/Tj94Uwuu9wjgS4+CzP+JISHHz0YAI7j6fANDfR2X8o+H4WQxM/iceq0nhiVvLZasn6/td1BpULZylZ3np9/FRvvjzLPUrWxh4Uyg+FpXnpkTxxZ8555o4ec7Jfe8eJj7FzQs9wmlUzcoLU08CeNb8X82K4z0vaYque7OKQwghxNXo23/tTF+T/0irqsCMYb6lZps0b42Za2P53rwfwhQF/njBL1MegNRfvidt8hf51u8/ZAQ+9/a+0mbmKn3Oz6RMHJ9vOd9BQ/Hr09/zvXb0A7RDr+R7nlJ3PIaqQ66ojXnRTnyKtv/5/NtRexyG6hfL7Tut8dR32afXZtWhjpG3HiiahzGHC7p/lIorn2f4JaNy7hy1npH/Eo4NvTMnaUx6+xXs//6d90kWHyr8tgzULGM0bjf2datI/3U2Sf+tRdN1XLqOXdOJc7qo9+d/OdcnRA7OJrto8+JhnuseytA7QgulznSHzuq9adQMM3mmh+8+YeOuccf55qlK3NS4aGaCXcsOnnZw65ijJd2MTMKCjHRu5M+fm5I8uwQM6VaBsQ9VovpjO0lMuzoHFC746/UanmBTYYk9dZjQ0FCsVitGoxGj0YjVWvTbSObm2p/jIIQQZVjb2iavAgCaDkt3u7i/TfGPil6JnSfzH0WuHKJmSwKonc45sVFWxvqNLqdZXvO2fv1M5qmYus27EV81MP8ZBldC8bJ+Jf1opu9Pxnk3c6N+paILSJmNULOC6tVMhMLijs59R4QLjJUrZ+r8a/Fx2P5cgO33ebhjzgDkv/RDiFxsP2bn350pLN+Vio9Fpc/1hbd2W1Hg9Z+jces6D14fjKLAjNWJVKtg4voC7P8uLqpd0UytcDOHo4suEWBBhQUb+XZodbYdTWfO2ngqhpj4383lmbUm/qrv/NcKNxd65780kiUAQghxDWtQSfF6VH/eRld+y9FLlSOxGudS8p/EVjci+0ednhDv1TUMlasWuF0FYahcxatyWnxcpu8VR4xX5ym+1xW4TQWh+HhZvzPzvs3e/NwAqpQr2mRMlUKK9zFIT0zIt4wakNEhc+7cRtK41zj3YHdSp0z0dP6FuBLJ6W4mLo4jLsXNxMcqUr4Q1ztbTQo/PlOFyKo+fP1PPF//HU/9ylamDauCxSRhq8vVuZFvSTchk53HbNz33mEUYHTvivS+PoRpy84xfPLJkm7aFctt28prjcwAEEKIa5hBVehS38i8jflvF3cmQePf3W5uirw6MuCu2uddErkGlbN38rS03BNGXaBDpsR7RUEN9G70TUvPnOFadyXne46OAqbsa1gLlcm7qcNZ25tm9y4AEORbtJ2GgGKeganb8l/24D4dRfzgfrgOHyiGFomypmN9X/Z8WqfI6q8VbubbIUWUd6SMGnhDCNNXJOJwlZ5V2/9sS+afbfl/Dl1NzEaF/9187SQzzIvMABBCiGvcHc28j/VOWWHHfhUk8dF1+HundwGAFjWyBzQUb9LfKIp3G1pfCUX17hpalvYqXrRfVyj6yeKqd9fQM08tyXo7uTEU8VOK0VDMo5Je3Lc75ox0/oUQHlXLmxh4Y9nomJakgTeGUD7g6hgAuVISABBCiGtcrTCVJlW9+3V/JkHnl7X5zxYoaRuPuImKy783FR4ENSrkcO/m/Nf4Kbru1YjtldDT07zajUCxZEmEp+Y/dK0oGrgLtgd3gbmT8apXa8i8VZbJy4zg6Y6iDUYVdf1ZKV78vRNCiKyevD2UAKssoygq/laFp7oVTjLMq4EEAIQQogzo1c775H7T1zjYf7p0JwP4YZV3CZG61M959oPi7dT7Il537T6Tf1I4ADUoOPMBo5dT723HC9iigtFtx7wqp5gyb0MW7OPdg2xMUtF20M8mF3MAIOvP8YorVDG3bkfAsyMLt14hRKkS6KMwpm9ESTfjmjWuX0SZCrBIAEAIIcqA9nWMNKjk3dQ2l6bw9nwbaaUn6XAmS3e72OVF9n+AWxrnHPgwhId7db7r4H6v23U5nIe8q18Ny/LgZ/EyOWHKtgK2qGD0ZO/q17O0NyzIuwetQzFFG4g6UsT1Z2XI+nO8TEpIMD69HiLkm58IeHE0pshmhVKvEKL0uqdNAI/fWsR5Xcqgx28tx12tAkq6GcVKAgBCCFFGPHaj97MAouJ13phjy3eP9OJ2LkXni7+9i0w0qmKgVljOH3OGarW8qsO+ZYPXbbscLi/rN1avmel7xb+BV+dp55YVuE0Focd7V7+apb01c1qWkYOtR4vuL2BUvE5cavHOADBUq3FF55simxEw6i1Cv5+HX//HMFTwLpAlhLg2vNCjPK1r++RfUHilcyNfXuhRPv+C1xgJAAghRBnRtJqBrg28T3Cz6Yib9xfavU7YVtRsTnhtto0ELzttvdrmHvAw1W/kVR2OVcvBWTRTIXS7Hfvq5V6VNWZprxLY2rtrnF0Amr3AbfOKOx099lfvymZpb1igQqgX21MeP6dxKLpoRulX7C3+XBfGeg0LfI7i44v1rp6EfDOd4E++xnrjbSgm74N5Qohrh6rAF4MrUTFENnK7UhEhRj79X2XUsjPz30MCAEIIUYYMvdVCoJfrrwGW7HTx9jw7zhKeCZDugFdm2th3yrvO4HXhKh3r5R7sMFSuihqW/+ipnpyI7e8/vW5nQdgW/4aempJvOTU8AkPFypmOKb61vVsG4IxDO/3T5TYxT9qpaeBKzL+gtTqKT/YZFy1qeheMmuvFFpYFpenw57bi/0ttbtIcVO8evQxVq+E/9AXK/fI7Ac+MxFizdhG3TghxNSgfYODfMTV5sJN3uWxEdr06BrJ8TM0CPQ9dSyQAIIQQZUiIn8Lw2y35F7zE8r0uXpqeTlxKyUwFOJus88wP6Ww95n2HbfAN5nw3p7N06OJVXak/fFPouwHoaamk/jTFq7LmDl1zPK5UuNur87Ujbxf6bgC6Kxnt6DivyioVuud4/Pp63o1g/b3DxbHYwp0FsGSXi6i44k90qQQEYmrczKuyxpq18enxAKqfv1fl3W7vtsUUQlz9TEaFtx8M562+4WVyBPtyqQq881AE7z4U4fVuNFeqNP5ulgCAEEKUMV0bGLirecGmEG87ofH4lHQ2HS7eUdNNR9w8OSWdgwWYBt61gYFWtfIfXbbccodX9WmxMaRO+tTr63sjZeIn6OfOelXW55ZuOR5XKz7o3cXsJ9EOFm6WeP3A8+DwcgeDiv1yPN72OgNBXixldWvw/kIb7kLqryel60xaUnIZLi033u5VOfuKpTjWrc63nKqqKIqC01FKs3YKIYpMv85B/DW6Jr06BmI0SCQgN0YD3N8+kL9G16R3x8BivbbT4UBRFFQvZ38Vh9LTEiGEEMVm6K1mGlQq2MNCXIrOS7/YGLvARnwRJ09LtulMWOTgpZ9tBUrUVs5f4enbvJvhYKrXEGM973IBpP82B9vC+V63I8+6FszC9ucCr8qaGkRirJtzwj8lsDVKYCuv6tFOTkI79a3Xbcy7rq/QTn3nVVklqC1KQMscXzMZ4K7m3s0C2HtK59PFV57LQNNh7AJ7kf/9zYv1pttQ/b3LOJ380VtoMdE5vqYoF//9qqqKs4hyVQghSrda4SbefSiCFW/V5KHOwZiLaWT7auBjVnn0xhBWvFWL9/tHUCu8+POnOJ2OTJ3/S393lxQJAAghRBlkMsDbvXyoFFywDyIdWLrLzUNfpjHxHwfnCnlZQKpd5+c1DvpPTOfXzU4KUrtRVRh1j4UgX+/vyffBR7wum/zJO6T9OrsALcoubd4MUj7/0OvyPg8OzPN1tYb3I/vuPUNwn5jkdfmcaCe+QNv/jNfl1eov5fn6fW3M+Ji9q+v3LS4++8tx2UkpXW4Yt8DOxmKexZKVYvXB2qOXV2W1+DgSRg5DyzJb5MID5IX/q6paKqeZCiGKT0SIkTF9w9j7WR1mv1CNgTeE0KKWlesiTJQPMFzTgQGzUaF8gIFa4SZa1LIy6JZg5r1UjV0TavPqAxWIKMGkiW63yxMAyPq7u6Qouq6XkvzOQgghitvpBI3nfrQTk3R586uNBmh7nZFbIo20vs6A9TKC6y43bDvuZtluF//ucZF+mQOZw283c3eLgjcg/pnBuHZu9bq89ba78H/yWRQvR3EB9OQkUr74CNs/3icUNDVpQfDHX+Vbzr3pBvSE/KeKX6BWfBi17kdgDPb6HFzxuPc9i35mutenKMGdMLRckm+5n1Y7mbLc+x96i+oqz3e3Eh7o/QNUdKLOOwts7DhZ8L/nS0b55Xi89Yz78z13Q++cA0Z6Wipx/XuiJcR71QZDWASBo97CGNkUALvdjsvlwuVy4XQ6SUhIwOFWKR9W0av6hBBCFI+zMacxGzSCg4MxmUwYjUaMRiMWS8HyMRUmCQAIIUQZdypB54XpNs4kXNkia6OqUL+SSqMqKtVCVaqEqgT5KPhaMmYcuDRIs+vEp+qcSdQ5flZj/2mNnSc1bM4r+yh6tIuZfh0vb2qf6+gh4p/oDy7vs82rgcH43P8g1jvuRg0ul2s5Lf4ctj9/JW32dPQkLzLmX2AyUW7Sjxiq1cy3qJ6yC/eGtqAVIHJiCkWt9gxqpYFgDsu9bkc0+qnv0I5/As447+tXzBjbbgC/nJcvXMrphsenpBco0Z/VpHB3SyM9WpoID8o9EHA2WWfBJidzNzixXeZmAkURAACw/f0Hye+94X1DFAXLTbfje38/tGo1PAEAh8NBcnIySclpVKyafbcFIYQQJef0icMEBvgSEBCA2Wz2BADMZi+nvxUBCQAIIYTgbLLOy7+kczj26vtIeKSzmYevv7J1fWlzppM68ZOCn6iqmBpEYqrfCDWiIorFBz09HXf0KZx7d+Pat5PLyV7n/9Rz+PTs43V57fgEtAMvFPg6KMaMXAJBrcFaHVQ/0FLQ04+hJ22ApI2gF3xquVrnI9Rqw7wufzBaY9j36TgKfCmduhWNNKysUjFIwccCDiecTtTZe0pjd5SbK33KKaoAAEDSGy9iX/VvgdvkjqiM2iASLbwimr8/qTY7MbFnqT1wGIrBu+0VhRBCFC1d1zl+eDfhYWH4+/t7ZgAYDAYJAAghhCh5aXadd36zs2Z/ya6R9pai6Dx1i4WerQonqU/y2FexLfurUOq6Etabbifg5TEFPk/b+RBa9MwiaFHBqBF9UBtNK/B5i7Y5+WBh6UtkV5QBAD01hfihA3GfOFagNjk0DbcObl3HpevYNI04p4uwNz8htE3HAtUlhBCiaCTGn8OWGk/58uWxWq2e0f+SDgBIEkAhhBAA+FoUxtxnZfCNZoylfBDRzwJvP2AttM4/gP+Lr2Nu0brQ6rscphZtCHj+tcs6V234LUrIjYXcooJRyt2E2nDyZZ17e1MTAzoVf4bmkqT4+RM09hPU0PIFOy/Ll0FRsKgq8Wv+LfxGCiGEuCwpyQlYLBYMBgOKomT6KkkSABBCCOGhKNC7nYnPB1i5Lrx0fkTUi1CY9D9f2tUu3Ky+islM0FsfY27ToVDr9Za5TUeC3voITJfZCVYtGJrNQwn1bp/5wqaEdsPQdC4olz+q0b+Tmf5lLAhgqFSZ4I8mooaFe32OgoJyvvevKBkPc0ZFIWXdcq54zYMQQohC4bCnYzKZUFVVAgBCCCFKtzoRBiYO9OGJm8z4W0vH1kEWEzx2g5nPHvGhYgG3L/T+IhYC3/oQ3wKsv79iioLvfX0JevtDlCvNCqz6YGg6F7Wq9+vvr5yCWm14Rudf9bni2gZ0MjPijqKdhWJUde5pWXLbQmVlqFKd4E+nYKzXyOtzLoz+qyioioJZUTAnxHPk+/x3jhBCCFG0Tp04gtVixmw2o6pqpiBASZMAgBBCiBwZVHigrYlpT/jQu60Jq6lkPrQUBW6ONDL1cV/6tDdhUIu2HYrBiN9TzxH45vuoIbln+C8MakgogW9+gN+Tz4JaSD1exYha9yPUJrPyzPBfKMzhGJrMRq3zASiF12O/o5mJz/r7ULVc4T+mWEzw5v0+dKpXegIAAIbyFQj+5Gt8ej8Mat73rSgXO/8Z/8/YhcPPoHJ27nQccWeLo8lCCCFy4HDYSU1J8CT+u9D5Ly1BAAkACCGEyFOQr8Lgm8z8PNSHgV3MVCjA/utXwmJUuLO5ke8G+/Dy3RbCium6nut37Eq572ZlZOO/3Gn5uVBMZnzvf5CQ72Zi6dC5UOu+QK1wD8b2OzNmA1zBtPycK7dkjPq324FSoXvh1n1e3YoqXw/yoX8nE9ZCevurl1f5bIAP7Wp7F6wo7kc0xWTC/7FhhHz5PaamLXIvx8VlAIoCqqJgUBTMqoqfw8bRn78rvkYLIYTI5Myp4/j7+XnW/2edAVDSAQDZBUAIIUSBaDpsPuJi2W4Xaw+4SUwvvLqNqkKzGiqd6xno2tCE3xXOiC8s2tkY0ubOwP7Xb2gJCZddjxIcgvX27vje2xs1tELhNTA/9ii0E5+jnZoGztjLrkY3VcBQaQBq1aFgqVSIDcxbfKrO7PVO/tjqJMlW8PODfaFvezP3tDJhOt/333LUzfPT867MaIDFLxXdLgD5cW7bTPrcn7GtW4Xivrg7h6braDpo6Jl2A3DqOqkuNzFujevem0i5pq2u6PpCCCEKJikxnuioI0RERBAQEODZ+u9C9v8LwQA1n5leRUkCAEIIIS6bpsOBMxrbj7vZE+XmcIxGVIKOpnlztk5YkEqN8ip1I1QaVlFpUtWAj7nk18flRne5cG7ZgGP9Gpw7NuM8ejhTxyxbeYMBU43rMDVujrldR8zNW0NJ7tOuOdHil8G5P9ESVqEn70LBlWtxHSOKfyOUkOtRQu9ALXcDKCU3dd7phvWH3Kzd72LbcY0zCRq5PcQE+ii0qGGgU30jHeqomI2Z/179d8jNyzPyDgAE+ijMe9a3kFp/+bSEeBxrluPY9B+u3TtwxZxBIyMQ4NbBTUZAwKnr2N0aSVZfkq6rR+SocVgDg0u6+UIIUSbYbOkc3reDChXKExwcjNVqxWQyYTAYPF+XzgYoKRIAEEIIUajcGsQm68SlaCSn6zjcGceMKpiNEOCjEuyrEOpPtk7ZVcfpxH06CndsDHpqMrrLhWI0ovgFYKgQhqFi5UJfPlCodAekH0a3RYErAV1zoqgmMAajWCuDtRaoJbdXcX5SbHAyzk18qo7dCQaDQoAVKgarhAfl/Xfrn50u3vnVnmeZquVUpj5x5YkNC5s7KRH36Shccedwp6XidrtxG4zo/gHooRVIM5mJi4sjMSmFBo1blvh0UyGEuNa53W727dpCUKA/5cr9n737Do+iatg4/Gw2vYdO6L0XQZCONOmCYH9t6GdBREEUrIBiAXkVBQRFxAqIAiK9I733Jr0H0nvf8v2RN2tCOgQCzO++rlyQnZkzZ2ZnN3OeOXOmmLy8vDJd+c/uVoCicmuNgAMAuO2ZnaQyfiaV8SvCK903i4uLzBUry1yxclHX5NqYXCXP2jJ51k77tYirU1De7lLtwGs7zoKj8u6mUtz7moq+4Zx8fOXk4yuzzSbb/36sVqssFossFos8LBb5+fkpNTVVZ0/9oyrV6xR1lQHgjnb6+GF5ebrLz89PHh4embr630r3/0sMAggAAAzoTFjeHSADb8BTCApD+glkxhPK9JPM9CtNnp6e8vf3V2pygk4dPyxb/u7LAQAUgM1m06ljh2WSVf7+/vL09MzS5f/qxn9RhwC35l82AABgaHa7dPxKzuMrXG/ZBy7k3SCuXKLor9TkJmPDP2Pj32w2y8XFRd7e3ipZsqSsqUk6cmCXUlNTirrKAHDHSE5K1OEDO2WzJqt48eKObv9Xd/m/Fe77z4hbAAAAwC0jNsmu5fstWrTXoqBIm354wUMVihfu9Yqjl2wKj807AKh1jbcX3Awmk0npwzhl7AVgt9tl/t9Ak66urvLx8ZHJZFJERISO7N+pqjXryYeBAQHgusTFRuvksUPy8/VRQECAvL295erqmmW0/6u7/d8KIQABAAAAKHInrtj01+5UrT1sVbLl3+7536xO1sePFO5AfH/uTs1zHncXk2qVuXUDACnziWT6I6Uyju1st9sdIYDZbJazs7NOHz8kb99iqlilulxcbt0BHgHgVpSamqILZ08pNjpcxYsVc3T7z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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2', 'julep_session_id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a', 'messages': [{'content': [{'image_url': {'url': 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jVr5FlWterV5OXlqfEf/U9dOndSs6ZN9cVnn15y3QAAhUcAAADIUeYu7KvXrM3SJfrdt95U/Qbp90E/8tBDrterVK6iBvUbaOfOXapTp06e0+vXq6fDhw9ryCOPuKZXqlwp1/rk1fi7OABwc3PTyBHP6O7+/bRs2Ur9Nnu2vvnuOz0/8ll17NBB23bsUKnSpbRr19+uZUoGBenQ4UPq3q2rHn7wQYWGhmrxkqWKjonRseMnFBsbl6X8jMa/lN6lu3379jKbzZKkoMBAfTvtawUElNC+ffvl5uamunXqSJJMJpPKlC2tuDwawFfbwoULWyQmJu7+/PMvrHXr1at1NdaZ11Ml2rdvq/vuvTfb64mJSUpLTdO27f/dStLiluYq4eevuIQEmc1m7fr77yzLNGrU6HIqqYTEBHl7exd4EL+D//yjU6dOa8PGTfr77z2SpLj4BC1dtlwP3DdIycnJstsd8s9jIM0unTvrhx9/VmxsrNat26CePbpLkhKTki9tG4voAR5xCQny9/PLcs++h4eHfH19FJeQ//FcpnRpTZ08SQsWLta33/2gc6HnNOjee/XAfYOKpoIAgHwRAAAAsgkKDlRCQoIsFot8fHzUplVLTZ82ValWqx597HHZ7XYlJibK4XAoMCBrQyYgIEBxCfH5Ti9IQyizvBp/ualcqbIeG/KoBj/6sH6aMVPjP/xITRs3VmJSotJSU7OUVa5cOVWumB5A/Dlvvn6aMUPdu3VT2TJl5e2dd0+IhIRElfDPWo9y5crmvUHF8FjFvKxZs6bpY48NOfjFxC8P3HTTTQ2Kuz45qVC+vMqVK6tnn3k627R/Dh2W3W7XU8OGqmRwcJGts1y58kpOTlZ0TEyByl2ybLkaNqivLp3+u0ressUt+uLLSdq9Z6+aNmksHx8fnT13TrVr1cyxjPLlyqlhg/r6c94CHTt+XB06tJeUvv1FuY2e7h6y2WwFnr9iSAXFxsUpJiZWwcFBkqTw8HAlJiapUkglef4bkths9hyXP33mtFJSrBoy+BENGfyI9u7bp5EvvKQ2rVqpRo3ql709AID8MQggACCbxg0by2w2a82atZIkb29vlS1TRqUyde0uGRwsHx+fLIO5OZ1OHT95QpVCKuY7vUSJEvL19dXZc/8NypeXCuXLq379enr91Vey/Nx8803Z5h3+zLNasHCR63c3Nzf1uqOHUlPTdPZcqCqULy8/X79sZd3Vr68k6acZMzTsySf1f48N0Z139lajhg3zrFv58uV1/sKFLK9t2rz5ij5nvqjUrlPn9C233PKPJG3fvr3+Y0MGe2/YsP7vYq5Wjnr06Kaly5br7793S0ofrHLsm28rKjpatWvXUs2aNfTFxElKSUmR0+nUvHkL9Mtvv13WOmvVrKGqVarom+nfymq1Kj4+XkuWLs1xXqvVqr/+WqP+d/VTzzt6uH569+qpli1aaOmy9MEAO3e6Tb/8+quioqOVlpamP+bOy1ZW1y5dNOOXX9S6ZQv5/TsexuVuo6+3j+LiYmX/d/DMSpUqymazuQbn3LXrbx0+cjTX5WvXqqX69epq8ldfyWq1KiUlRZOmTFWjhg1Uo0Z1eXl5qWyZMtq0ebMk6fz581q3fqNr+QMH/tGrr4/R2XNnJcnVY8bTs/gfkQgARkEAAADIJjg4SHf376+vpn2jTZs3y2azyW63a8Om9BN7bx9vSVKfXr30488zFBkZJYfDod9nz1Z8XLw6dbqtQNM7dfqvIWSz2fTHH3NzrVNejb+LNWvaVD/PnKl9+9MHM7RarZo1e478/PxUrVpVderYURfCwvTr77/L4XAoNTVVEz7/wlW2p6enDhw4KLvdrlOnT2n+woWyO3K+qilJt3frolWrVuv0mdOSpK1bt+ntd9+Xw577MteK6tWqRU79eppb167ddknSgQMHajz+f/9XatmypdtUZJ3Hi8at7TtoyOBHNObNt9RvwN0a/H+Pq3q1aipVsqTMbm4a9/rrio2NVb8B96jfgHu0YPEitSnA4HR5MZlMevGF57Vz1y7deVd/PTLk/xRQIsA1XkBm6zakP/2hTevW2aZ169ZZa9etV3JysoYMflQ+3t66974HdNfdA2XP4Ti59dYOMpvd1OXfgQclXfY2tm3bRseOn9AdvfooKjpaISEhun/QIL36xhjd0ftOTZv+rSpVqpjnvnhl9CidOxeqO+8aoL7971ZUVKReHvWSa56nhj2pP+fN1x2979Rzz7+ozGNL3t6tq2679VY99vhQ9Rtwt0a/8qqefOJxVaqU+60/AICiZXLmdRMeAOC6Y01NK5JynE6nfp45U7Nmz5HFkn61sXTp0nr4wQfUo/vt6euyWvXxJxP019p1cnd3V2BggF4cOVLNm99coOmxcXEaO+5N7T9wUF5eXrq1Q3stX7FS334zVZUrVdaYcW8puGSwRo54RpI0a84c/fDjz3JzM8lud2jAXXfp0UceylZ3h8Ohn2bM0Nw/5/97q4Fd1atV1dPDh6tJ4/T7pXfu3KWPP/tMcbFxcjqdanHLLXrphZHy8/PT1q3b9MFH/1N8fLyCgoLUvn1bLVu2XH/Oma2//96t18aM1bLFC7Psqylffa15CxbIw8ND3t5eGvHM02rftq22bduebf6XXn5FdWvX1v89NuSy3yevQl49zTg+goMCUy0Wi2e3bt12zfljboOwsLDIF194/uzcuXNbSVJISEjY//738bG7+vdvbTKZrqkLBk6nU1FR0QoIKCFPT89s05OTk2W1prq6qV8uu90umUyK/ne0+t9mzdLades16YvPL6vcqKgolQgIcHWdL4xL3UZLSoqsKSkKCvpvOYvForS0NNcgmQURHRMjk0w5rt9msykuPj7Xkf1tNptiYmNVMjjY1QugKBT2swAARkQAAAA3mKIKADLY7XZFRUXJy9tbASVK5HjlMykpSUnJySpTunShptsdDrn927AqERCgY8eO6ZlnR2r+3Dmukccvll/jLzOHw6Go6Gj5+frK19c3x3liY2Pl4eHh6maduW5RUVEqGRwsd/eCDZmTkpKihMRElS5VKsf9cCUURQAwa/acel5eXj7h4eHhr77y8pEZM2a0cTgcbqVKlY5+7/339j/44ENtzGazYccNevm111W/bj21b99WF8IuaMKnX2jQvffo7v79i7tqyIQAAADyRwAAADeYog4ArqSfZ/6iY8eOa0D/u+R0OvXV1KkKCgrW22+OLe6qXTeKMgCQpNjY2Ji33hy3d+rUqW1tNpt7QEBA4pgxY3c88eSTrT09PfMeDfEGderUKf3w0886ePAflQgooW5du6h/v35yc7umOkYYHgEAAOSPAAAAbjDXUwCQkJCgH376WVu3bpfJZFLzW27Sow8/nG1EfeSuKAKA2XP+qO/p6emdMU9SUmLi+A/G7/jss09bW61WL19fX8tLo0Ztee65kS19fHxy7koBFDMCAADIHwEAANxgrqcAAJfvSgQAkpSSkmKZ+MUXWz744P0WiYmJfp6enqnDn35606uvvnZTiRIlCn6zOHCVEAAAQP7ouwYAALLx9vb2efa559q++977O4KCguJTU1M9P//ss/Yvvzx6d3R0Do9eAAAA1zwCAAAAkCMPDw/Pxx9/vN0nEybsKVOmbJTdbjdP/+ab9iOfe+7QhQsXwoq7fgAAoHAIAAAAQK7c3NzM9913f9tJkyYdqVy58nmn02n69ddf2gx/atjpU6dOnSnu+gEAgIIjAAAAAHkymUxufe68s/VXU6eG1q5d+5QkLVy4sMUTTzwefeiff44Wd/0AAEDBEAAAAIAC6dy5S/Np30yPb9Kk6RFJWvPXX00fe2xI2q5duw4Ud90AAED+CAAAADAwk8lUqKcBtWrVqvE307+xt27der8kbd++vf7//d9jXhs3bNh9ZWoIAACKCgEAAAAolMaNm9T7eto3Pl26dN0lSfv37av5f//3WMlly5Ztk8TjhQEAuEYRAADADcbsxle7Ubi5mQq9jLvZLKeccjqdhV84k9q1a9f4aurUsn379t0qScePH6889MknqsyZM3uT0+l0XE7ZQGGYZLqkzwIAGBFniQBwgzGb+Wo3CnezudDLFOXxUalSpYpfTPyy2oMPPrjBzc3NERoaWu6Zp5+p98MPP2yw2+22IlsRkAuTTHLIIQ939+KuCgBcFzhLBIAbjMlkkqeHO1fEbmBubunvscl0ae+xt6dnkdWlbNmyZf/38ScNnnxy6Hp3d3dbVFRkyVEvvdhsyuTJG1JTU61FtiLgYibJ5Fa0xzMA3OiISwHgBmQymbgihgJxc3OTl6eHPD09LrmMcmXLBH/44fibg4ICN0yYMKF1XFxciTfeeL1lcnLSphdeeKGlj4+PbxFWGQAAXCJ6AAAAgMvm7+/vP2bMmJZjx47d5O/vn5ScnOzz9ttvtx0zZsy2+Pj4+OKuHwAAIAAAAABFxNvb2+fFF19s9+GHH+4IDg6OS01N9ZwwYUL7UaNG7Y6Ojo4u7voBAGB0BAAAAOCyOJ1Oh81mS0tJSbFYLJaUe+65p8Ho0aN3+fj4pNjtdvPUqVPbP/PMM4cuXLgQVtx1BQDAyLhBFAAAFNjevXsPrVu3LiwxMdERFxdnSkhIMMXFxZkTEhI8EhISPBMSEjyTk5M9k5KSaqWlpblLktPpNM2YMaNNQkLCts8//zy1WrVqlYt7OwAAMCICAAAAUGChoaHxo0aNuiUpKanQA/vNnz+/RVJS0u7PPvvM0qhRozpXon4AACB3BAAAACBHcXFxcZs3bz582223Nfby8vKWpLZt29Zr167doWXLlt0kSd7e3lZ/f/8kb2/vNB8fH6uvr6/Vz8/P6u/vn1aiRInUgIAAW0BAgD0gIMAREBBgKlGihCklJcVcvFsGAIAxEQAAAGBgJpMpx9fj4+Pjx4wZs3vOnDl158+ff6JZs2b1JalEiRIl7rvvvsSVK1fa7Xa7+bbbbts/fvx4X39/fx8fHx8vLy8vf09PT08PDw93d3d3d7PZbJaU80oAAMBVxSCAAAAYkNPpzLVRHhMTEzN69OjdX375ZbuzZ8+W+/3338MzT+/Ro0edxo0bH5Ok/fv3h7i7u7vXqFGjaoUKFcqXLFmypL+/v7+Xl5e32Wx2F41/AACuGQQAAADAJSoqKuqFF144MHXq1HZ2u90sSXPmzKl6+vTpsxnzlC9fvtw999wTKklnzpwp//vvv5/NrTwAAHDtIAAAAACSpPDw8PBnn3320HfffdfW4XC4ubu72yTp0KFDVefPn38s87z9+/evXKVKlfOSNHv27Mrnzp0LLY46AwCAgiMAAADAwJxOp0wmk9v58+cvDB8+/MTPP//c1ul0mmrWrHlm3Lhx68uUKRPtdDpNv/zyS8nY2NiYjOXq1KlTrXfv3kclaf/+/dUXL158LPe1AACAawEBAAAABubm5uY8ffp06NChQ8/MmjWrlSTVqlXrzJQpU8JfeumlNp07dz4sSdu3b6+zevXqQ5mWM993330BQUFB8Q6Hw23GjBkB8fHx8cW1HQAAIH8EAAAAGNjZs2cDhw4dGjtv3rwWklSvXr2T33zzTVTXrl2be3p6et1///0mHx+flJSUFK8ZM2aYrFZrSsayt9xyS51OnTr9I0lbtmyps27dun+KazsAAED+CAAAADCwPXv21FqxYkUzSWrUqNGx6dOnx996663NMqZ37NixbosWLQ5L0sqVK+vt3LnzcMY0b29vn/vvv9/u5eWVmpyc7DNjxgx7Wlpa6tXeBgAAUDAEAAAAQDfddNPh7777LqVNmzZNMr8eGBgYdP/998eaTCZnTExM4C+//BLrdDodGdM7d+5c9+abbz4iScuWLau7e/fuI1e77gAAoGAIAAAAMLiWLVse/O677xzNmzdvmNP0Xr161apfv/4JSZo3b16tI0eOnMyYVrJkyZKDBg2KlKTIyMiSv/76a6Qk59WoNwAAKBwCAAAADKx9+/Z7v/32W48mTZrUy22eSpUqhQwYMOC0JJ08eTLkjz/+OJ15et++fWvUrVv3pCTNnTu3xvHjx0/nUAwAAChmBAAAABiQ0+k0derUaff06dP9GjRoUCu/+e++++4KISEh4ZL022+/VQgPDw/PmBYQEODXoEGDcEk6evRo5Xnz5p28YhUHAACXjAAAAAAD6t+//7avv/46sHbt2jUKMn/Dhg1r3nHHHYclac+ePTWXLFly+Pz58xcmTZq0tk+fPqGLFi1qmjHv77//Xjo2NjbmStUdAABcGpPT6eQ+PQAADCYmJiY6ODi4ZGGWWbly5Y677rqrXkJCgl+tWrXOeHh42A4dOlTV4XC4SZK3t7e1RYsWhx999NHYQYMG3ezr6+t3ZWoPAAAuBQEAAAAokKSkpKSBAwf+s2jRouaZXw8ODo7r0qXLPw8++KCzY8eO9YKCgoKKqYoAACAP3AIA4IZxPtSijesisv2Eh6Vccpl7dsVo8KANRVjL/K1dFaYXhm8vsvIO7o/Tn7POaN1f4UpKtBVZuXl5/cVdWjz/XKGXczikbZujNG/OWe3eeW30IB/+2BZt3RSZ47TQcxYd2BdXqPIyH6dbNkbq5PGkLNOL4phbuypMI4dtK1RdMv/ERKfmOP/OrUl+9957X4q7u7vNZDI5q1WrFjpixIi1ixYtOl29wotNfTxatC6qxn9EuFWPP7hJ7W9acs0cCxeb9OkhffvV0WKtwysjd2rZotDLKuNSP695Kcz7V9TfeQCA3LkXdwUAoKgsmX9O743bq4aNg7K8PuLF+up8e/lLKjMuNk3bNkcVQe1yF3YhRd9+dVQvj20kKf3Eec+uy2/w2O1OPfP4Vq1ZeUHNW5ZSdFSqQs8m67tf26nJTcGXXX5e9u2JVc3aJQq1TEx0qgb2XiO73akatUpo354YVavur+9+ay9vbzdt2xylo4fjdd/D1a9QrdPNmnlKFSr6qt2tZSRJu7ZHq89dlXOcd+HcszqwL1YTJrcocPmZj1OnUzp+NEFVqvnp5zm3KijYo0iOuYhwq/7ekf8xlNtn5rW3mqhF61JZXktNdeilEdv10cSWNW677bZ9zZo1i3v00Ucr1KtXr63ZbHZ/59VVatT00sO2i3349j4lJdn03a/tVKmyb5GVW5SOHUmQn3/Rnkpd/H2Qn727Y1W/UdBlrfNSPq/5yev9u1LfeQCA/BEAALih1KxdQnOXdSruahRKRHiKvpzwT4FP+Avq9xmntG1TpFZu7q7yFbwlSR+9s1/PPrlNq7feXqTrKgpfTvhHpUp76Zd5t0qS0tKc6tNlpb7+8rCeeaGe/t4RrRVLzl/xAGDhn+fU9OZgVwCQl8eH176kdWQ+TlNTHep120r9OP2Ynnkh1yfxXTEF/cx4erpp896eklRhecflFa50vQ7/E6977q+qWnWKtmF6rbtS3wdXW17v342yjQBwPeIWAACGsGjeOd3RcaVSLHZJ0mcfHdSj926Q0ykNuOMvffzeAd3afKnqV/5TI4dtU0qKI8dy1q8JV/f2K1QnZK76dFmVpWvrgDv+0puv7lbzugs0bdIRSdLUiUd0c50Falx9nkaN2CGrNWu5v/x4Ug8NWC+HQ2rZcJHrym9amkNvjPpbjarNU4sGC7N08T19Mkn39lmrWuX/UJfWy7RpfUSOdf3lxxN6+P9quhr/kvR/T9XWgEFVXPvhzKlkPdB/nepVmqtbmy/RrJmnspQxbdIRtW26WA2r/qn/e2CTIiOsWereqtEiNa4+Tx+8uU+dWy3L1p1dkizJdr349A7VrThXrRot0jeTc+4yHROdquCSnq7fPTxMevvDm9S4abBeemaHJow/oG2bI9Wy4SLZ7U5Zku16+bmdalpzvprXXaB33tijtLT0YW2WLAjVoDvX6vEHN6lJjfkF3m9dWi/T2lUX9NUXh/XEQ5tcr+/YGqXb2y1X7Qp/aMh9G13Hx5TPD+uVkTtd83364UE1qzVf9Sv/qSce2qTYmLQctzUzT083Va7qp+goa47Tf/jmuFo3XqS6Fefq7p5rsuzjvX/HqudtK1Wz7Bx1vGWpViw5n215q9WhwYM26t0xe/OtS2bvjtmrF4ZvV/f2K/TY/RtltzvVsuEiHTuSKCn9VoXu7VeobsW5+r8HNikh/r9tTU116LUXdqlpzflqUOVPPftk+mfq4P44tWmyOMt+6dVppZYtylrvAXf8pd07o/XBm/s04I6/JKnQ73dmeS27YO5ZPXrvf7dcnDyepJYNF8lqdSg8LEUtGy7Su2P2qkGVP7Vvd2y2ss+cStKAO/5SnZC56t/jL509neyatmZlmDq1XKa6FedqUN+1Cj1ncU3L6VjJ7fsgs7z2e16ftdCzFj0ycIMaVPlTN9dZoMmfHc5WtiQlJdo0/LEtql/5TzWtOV/j39qX43x57dOc3r8MV+o774Xh2/XJBwdcv//x22kN7L0m3+U3b4hUj1vTv9Nva7FUf60Ik6QCvfcAcD0iAABwQ0lKtGnd6nDXz67t0ZKkO/pUlL+/u774+B8dO5KoLz7+Ry+80lAmU/p90Lt2ROvX+bdqwarO2rY5SpMm/JOt7FMnkvTIwA16bnR97T1xp+5/pLoG9V2r6Kj0+6XPh1q0eUOkJn3bWgMGVdWsmaf07dSjmrOkkzb8fYeOHUnQ5M8OZSnznvuravrMtnJzk9bu6KHmLdO7XR85lKD6DQO1/WAvPTSkpquRabM59eCAdWrToYwOnO6rUW800mP3b1RiQvZ7+08eT1Td+oFZXgsu6amnn68nbx+zbDanHui/To2aBGvX4T76ZFILjRn9t9atTn+8+6yZpzTl88OaPrOdtu7vpaBgT/3fAxslSQf2xenl53bo7Q+baev+XvLzd9ehg/Gy2bIHJ6+/tEuRESnafrC3Zvxxqz776GCO99Q/+ngtLV9yXk8N2aINayPkdEotWpfSbV3L6b1PbtYzL9RX85altHZHD5nNJr3+0i6dPJ6o1Vu7a8HqLlq/JlyfvL//v+Pgr3A1b1lKsxZ1LPB+W7Smqzp0Kqf/G1ZbE79p5Xp9/Zpwffdbe63Z1kN7d8dozq/pQUlcbKorFFm7KkzffnVUi9Z01faDvZSUZNNH7+TceIqPS9OSBaFaNO+cxr+1T7u2R+vBwdmfxrdlY6Q+fHufZvxxq/af6quq1f30xqhdktJ7SPzfgxvVq28lHQq9S29/2EzDH9ui0LP/NTJtNqeGPrJZaWkOjXoj56utdptT0VGprp+MfRIdZdXi+ef03Oj6evvDm+R0SufOJCstzaHUVIeG3LdR3e6ooK37e+me+6vqzKn/gokvJxzSgX1xWrezhzbu7qn9e2I1fcoR1W8YKJNJWrIg/X7zfbtjdXBfnNp2yNrbYuaft6phkyCNeqORZv6Z3iOkMO/3xfJb9sL5zPvMoXNnkuV0pu+/c2eSFRGeol/ndczxavaOrdF6c3z656ByVT89NzR97IXjRxP1xMOb9OYHTbXn+J1q3DRYI/+dltuxktv3QYb89nten7Xnhm5VjVr+2nviTs3881b97919Od4m8sXH/ygq0qqdh3pr4eou+u3nk1ow92yh9mlO71+GK/Wd16Z9Gf3y40nX7/PmnFHzlqXyXN6SbNfD96zXsy/V16Fz/TR0RF09+cgm2e3OAr33AHA9IgAAcEMJu5CiiZ/84/r5fUZ6Q81kkv438RZ99/VRPf1/W/T4U7XVuFmQa7kHB9dQhRAf1axdQsOerZvjCe8vP57QrZ3K6Y4+FeXl5ab7Hq6u2nVLaO7vp13zjHixnlq1La3gkp766dvjenBwDQWX9JTTKQ16qLoWz8s60JbZbJKHZ/pXsbe3m9z+/VZu0ChQ9z9SXd4+ZvW8s6IunE9RSopDm9ZFKDLCqkcfryVLsl2t25VRmbLe2rguPFt94+PSXPcnnz2drBeGb3f9hIelaNO6CEVHWfXS6w3l42vWLa1K6aEhNfX9tGOSpJ++Pa4nn6mjeg0C5OfvrnHvN9XunTE6uD9OC/44q45dy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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01SF7tWSGgctWmwTWqET1zbV'}], 'workflow_label': 'check_goal_status'}\n", "--------------------------------------------------\n", "Transition type: init_branch\n", - "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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u7du9WiRQutXr1a0dHR6ty5s6pVq6a///5bR44cUcWKFdWtWzc5O6c1G+x2u9atW6fjx4+rePHi6tatW6a/2UVhy5Ytuv/++1W7dm1JUp06dfTSSy/pm2++cQQAOe2XRYsW6dSpU4qJidG8efNUt25dxy1ja9euVUpKiho1apTpeMvPud+xY8e0bt06lShRQl26dNHq1avVpUsX+fr6Ssr93Aa4kbgFADfVggUL1KtXL/Xu3VtLlizJ0jXrlVde0eTJkxUSEqLJkydr/fr1kiRvb2998cUX2rhxo2PeWbNmafny5ZKkEydOqG/fvtq9e7ciIyP1wgsvaNGiRY5pI0eO1AsvvKDt27crLCxMx44dU3JysoKDg3X27FlJ0uzZsx3LxMfH65FHHtHSpUuVmJioiRMnavDgwY51f/rpp44u3qtXr9b777+v4cOHKzw8XPPmzdN//vMfx5XeqVOnaujQobp06ZJWr16tZ555RuPHj78Be/f6xMTE6OTJk6pYsaIk6Z9//tHHH3+caZ7p06dr7dq1jul9+/bVkSNHFBYWpldeeUUzZsyQlNaYGj16tOLi4mSxWDR69GgNGDDAEQAMGjRI8+fPd5Q7ZswYffDBB4qPj9eiRYv0xBNPOPbf3LlzNXDgQEVEROjIkSPq37+/9uzZk2cdbgfvv/++Ro0apejoaG3atEl9+vTRqVOnJEnLli3TH3/84Zj3008/zXTczJ07N9vbY6pUqSKbzaY5c+Zkuldx7ty5jsb/4sWL9eSTTyo0NFSHDx/Wgw8+qNOnT0tKO54ff/xxXbhwQSEhIRowYIDjc5H+Xj777LNavny5Ll++rKioKD388MNaunSpEhISNGHCBL3++uuZ6jR69Ogc3/t0R48ezbR9v/32m4YPH664uDhJ0u7du/Xll1/K09Mzz323evVqvfPOOxoxYoT27t2bpTfG3LlzNXz4cJUqVUrS7X8cAXcqX19fpaamymazZfu5zvh3e8WKFfrll18cy27ZskVffPGFvLy8cv1ek6SffvpJL7/8siIiInTo0CH1799f+/fvV3R0tN58802FhYVJSgtB3377bZ0/f/7m7ogCSEhI0NNPP63Zs2crPj5eEyZM0OjRoyVl/x1+/PhxjRo1Sq+++qrOnDmjrVu36pFHHtGIESM0a9YsRUVFafz48Xr33Xcd63jrrbf0xRdfOILZ/v37Kyoqqmg2+H+qVaum1atXKzg42PFaz549He9zbvvl+PHjioyMVFxcnI4dO6bw8HCdPHlSknT27FmFhIRIynyemNe53/bt2/XII4/owIEDOnLkiAYMGKDRo0c7jqXczm2AG40eALhpDh8+rDNnzqhbt27y9fWVh4eH1qxZ47iCuXnzZu3YsUOLFy92pNnPPfecpLQubV27dtXKlSvVpUsXSWkDBL766quSpE8++UTdunXTO++8I0mqVauWvv76a/Xu3VtS2h/tL7/8UjVr1pQktWvXTjt27FCXLl0ctwBktGfPHl26dEnz58+Xs7OzHn74YQ0fPlxhYWGOq4wZ2e12ff/99/Lz81NISIg6deqkkydPKjAwUDNmzNDo0aMd3eonTZqkefPmFdp+vR4LFizQrl27ZLFYdPToUd1zzz3Z7o/sLFu2TA0aNNAnn3wiSbr77ru1ZMmSHOfv16+fnn76aUmSi4uL1q5dq379+mnHjh3666+/tHjxYpUpU0YWi0W9evXSsmXL1KdPH82fP19PPvmkXnrpJUlp3fx27typJk2aFLgON9PEiRMdKX+6kydPqlatWpKkXbt2afHixVq4cKHjitWIESM0duxYfffdd2rbtq3mzp0rKa0Xy8WLF5WSkuI4Bnfu3KmHH344y3qrVaumkSNHavz48frll1/Us2dP9erVy3FlIT4+XmPGjNFHH33k+CyNGDFCM2bM0EcffaTjx4/r/fffd3wufXx89Mcffzg+S5I0YMAAPfbYY5LSjmcvLy9Nnz5dzs7OevbZZ9WlSxft37/fcSXm0Ucfzfa9z6ht27b6/PPPFR0dLT8/P61bt07FihXT5s2b1bVrV23fvl2tW7eWk5NTnvtOktzd3TVv3jx5eXllWs+SJUs0duxYTZ48WQ0bNpRU8GMZwI1lt9t17tw5TZ8+XS1atHD0vsvpcy1JvXr10vjx4/X222/LZDJp1apV6tKli9zc3HL9XouJidGUKVP04YcfqmvXrpKk//73v5oyZYqmTJmiYsWKadWqVXrssce0b98+xcfHO3oO3Yp+/PFHWSwWzZw5U2azWQ8//LDuv/9+Pf/8847QM+N3+Pr162WxWDRp0iQVL15cNptNPXr0UGRkpKZNmyZJatGihYYNG6ZPPvlEqampWrp0qX788Uc1bdpUVqtVgwcP1sGDB9W2bdsi2+4RI0bojTfeUI8ePdS2bVv16tVL7dq1c9wSkdt+GTZsmJKTk3XlyhXHeWS9evU0f/58Pfvss45bAK6W07lf3bp1NXHiRPXp00cffPCBJGnr1q164YUXHMvmdm4D3GgEALhpFixYoLZt28rf319SWjK7YMECxx/k/fv3q1GjRpm6sqU3CCWpd+/eeumll5SUlKRTp04pKipKHTt2VHJysnbt2qV69eppwYIFktLGGrh06ZIiIyMlSU5OTo7Gf37UrFlT7u7ueu+999SzZ081a9ZMs2bNynH+SpUqyc/PT5JUqlQpOTk5KSIiQnFxcUpJScnUfbBKlSr5rseNVq9ePbVv3152u11NmjTRr7/+qvXr1+fr5KZJkyaaOXOmvvrqK3Xo0EGdOnVyNCiz06BBA8f/y5YtqxMnTkhKu0pTqlQpbdu2zTG9WLFiOnz4sPr06aOmTZtq7ty58vb2Vtu2bR2hz7XU4WaqV6+eypUrl+m148ePO/6/detWNW3aNFPX+379+umll15Samqq2rZtq48//liRkZFat26d2rdvr/j4eP3999+67777dPToUbVp0ybbdT/44IPq1KmTlixZoqVLl2ratGkaMGCAhgwZor179yohIUHR0dGOz4uzs7MOHTokSXr55Zd1/vx5zZ8/X2FhYfrnn38cn6N06SGGlNZ1v3Pnzo6uoQEBAVq4cKHj8yDl/N5nVLVqVZUrV047d+5U5cqVZbVaNWDAAK1bt05du3bVjh07HCFCXvtOksqXL5+lkbBnzx598803GjlypFq0aOF4/VY+jgAjeeqppySlNaxcXV3Vvn37TPf/Z/e5TtexY0eNGTNGe/fuVcOGDbV+/Xp99dVXknL/Xjt8+LCSk5Mz/d0bOHCgoqOjZTab1aNHD61cuVKPPfaY1q1bp1atWmX6frvVbN68WaVKlcrUw8HLy0tHjx51BAAZv8OltL8BxYsXl5R2vlS2bNks39sWi0WxsbHy9/dX/fr19cUXX+ixxx5T69atNWXKlJuwZbkrUaKEfvzxR+3cuVOLFy/WqFGjFBAQoAkTJqhGjRq57peMf0sKIqdzP4vFosOHD2c6X7n63C+3cxvgRiMAwE2RnJyspUuXqlSpUo4vufDwcB08eFBBQUEKDAxUdHR0liumGd11110qVqyYNm3apMOHD6tjx47y8PBwdKc6efJkpq5f3bp1u+ZH0ZUuXVpz587VH3/8oUmTJuncuXN67rnnMqW3ebHb7YqJiZGnp+ctOyhPjRo1Mt1DbrVaNXny5HwFAO3bt9eMGTP0559/6s0335TVatXIkSPVrl27fK07vXt6bGyskpOTtXnzZse0cuXKOa5YDx06VPXr19fKlSs1depUVaxYUWPHjlWlSpWuuw43UqdOnRz366dLv2VFkqKiorKcRPr5+clisSg+Pl6BgYGqWrWqtm/frrVr1+rRRx9VYmKili5dquLFi6tmzZqOk7nsBAQE6IknntATTzyhLVu26OWXX9Zdd92lhIQEOTs7a/v27ZnmT7/qMHv2bH377bfq06ePypQp4+hyn5OYmJgsn9vAwEBJynH05Zweo9S2bVtt27ZNp0+f1r333qtOnTppypQpioiI0IkTJxxjB+S173KydetWVapUSStWrFDfvn0d4xTcyscRYCSTJ09WnTp15OTkpICAgExjieTF3d1dnTt31ooVK5SYmChvb2/HmCK5fa/FxMTIy8vL0ctASmsYpgcNvXr10i+//KKwsDCtW7dOgwYNKqStvTFiY2OVmpqa6W9q69atC+Ue/fTv7unTp+vPP//UggULNHr0aHXs2FEffvihY4ymotSsWTM1a9ZMw4cP15tvvqn33ntPc+bMuaH7JSO73a74+HhZrdZcz2lzO7cBbjQCANwUa9askclk0hNPPJHp9bi4OP31118aOHCgAgMDtXXr1lzL6dWrl1atWqUjR47orbfekiQVL15cHh4e6tu3r7p165ZlmWPHjhW4vqdPn1ZiYqJeffVVvfrqq9qzZ4+efvpptW/fPktynpty5copLi4ux1sHbjW+vr6ORpuLi4uj90V29u3bpxIlSmjUqFGSpG+//VZvv/12pj+u+VG+fHl5e3tnOy6CxWLR1q1b1bJlS3Xt2lUpKSkaMmSIJkyYoC+//LLQ6lAUKlasmOW59CdOnJCvr6+jl0zbtm21evVqHT58WK1atVJqaqo++ugjlSxZMseulp9++qni4uIyjd/QqlUrlS9fXseOHVPLli1lsVg0YsSIbI/Jb7/9Vm+++aZjkEq73a5//vknx+0oV65cpkcnSdLff/+t2rVrZxoMKT/atm2rcePGydvbW6+//rrKlCmjihUr6ptvvlG9evUc+yU/+y47TzzxhJ555hk9+OCDmj59up5//nlJhXcsA7g+vr6+1/W3slevXnrnnXeUlJSkHj16OAKE3L7XypUrp5iYmExh5sWLF3XmzBm1bdtWderUUdWqVfXNN98oNDRU99577/Vt5A1Wvnx5BQYG6r333ssyrTAeERgeHq5jx445AuawsDA9/PDDjnvgi8KlS5f01FNPacqUKY5zNC8vL/Xs2VPvv/++7HZ7rvulsPn5+cnb21tnz57N9vaBvM5tgBuNQQBxUyxYsEDdu3dX//79s/wsXLhQdrtdXbp00cWLFzV37lxZrVadOHFCW7ZsyVROegAQFxfn6MJrMpnUt29fff3117p06ZKktNF833rrrRyvNEqSp6enQkNDs51n//79evnll3Xu3DlJclwZcHNzK9B2165dW1WrVtVXX32lpKQkRUVFZTsAWlFJSkpSZGSkwsPDtX37dv36669q3769JKly5cpKTU11DLy4fft2HT161LHs77//rpEjRyomJkZS2j4q6P6R0npqBAUF6YcffpDNZlNycrI+/PBDbd++Xc7Ozvroo4/03XffOcIIs9nsuMpQWHUoCj179lRwcLBmz54tm82moKAgffvtt3rkkUcc87Rt21YrVqzQ3XffLQ8PD/n6+qpx48b666+/cgwA2rRpo2XLlumvv/5SSkqKbDabli9frgsXLqhx48aqW7euateurY8//liJiYmy2+2aPXu2vv/+e0lpx/j+/ftlsVh0+vRp/f7777JarTluR58+fbRkyRLHIIIbNmzQG2+8kWt4lJNmzZrpypUrunTpkuPKXadOnfT7779nuhqfn32XHT8/P5UqVUoff/yxpkyZ4hhw6XY+jgD8q3nz5nJyctLChQszPc42t++1evXqqXr16po6daqsVqvi4uI0ZswYx4C3Utq5x5w5c9ShQ4c8e0UVtQceeEALFixw9PIKCQnRa6+95ugteb3i4uI0cOBArVu3TlLaOVhRf2cGBgaqTJkyGj9+vC5cuCBJCgsL059//qnGjRvLZDLluV88PDwUERHhOC48PDwkpT1a91p0795dM2bMUFhYmFJTU/Xrr786puV1bgPcaAQAuOEuX76sbdu2Zfts+R49eujy5cvasWOHI5kdN26cmjVrpmHDhmVJTitVqqQ6deqoe/fumbrrDRkyRHfddZd69+6tNm3a6PXXX1fnzp1z7T7Ys2dP/fDDD3rggQeyTOvTp4+6du2qvn37qnXr1nrhhRf0xhtvqHLlygXadpPJpDFjxmjr1q1q0aKFevbsKT8/vwJ1a7yRvv32W7Vr104dOnTQe++9p65du2ro0KGS0q6yPv/88xo0aJCaNm2qL7/8MtP2Dxs2TM7Ozmrfvr1atGihOXPmZHlqQH6ULVtWEydO1Jw5c9SiRQu1adNGkZGRjkdEjh8/XuvWrVPLli3VqlUrhYeHO24jKaw6FIUSJUpo/Pjxmj59upo3b67u3burfv36evHFFx3zNGnSRD4+PurYsaPjtc6dO8vLy8sxgN3V2rZtq7Fjx2ratGlq1qyZmjZtqrFjx+q9995T8+bNZTab9cUXXygiIkKtW7dW69atNW/ePHXo0EGS9O6772rp0qW66667NGDAADVt2lQhISGOkY2vdt9996lXr1566KGH1LJlS40aNUrjxo3L8liq/HB3d1fz5s3Vrl07x+e7S5cuslqtmQKP/Oy73LRt21ZPP/20hg8frsjIyNv6OALwL5PJpJ49e6pGjRqqVq2a4/XcvtdMJtP/t3f/vuz8ARzHX1fK0KTEldXCaKGJqERSPwYDiclAQmKR+BGDmAwsJDqKNLFY/Q8GA4PV5keEyaDnStNq61Q/U5to8P3w1eon93zM7d373ne59+X1/qWNjQ0dHx+ru7tbfX198vl8Wl5eLv6/MHJgeHi44tf0VUNDQ1pcXNTS0pJ6e3s1Ojqq9vb2HxuF2NraqrW1Na2urqqnp0eDg4MKBoNvtgyuNMMwtLOzo0AgoLGxMQWDQYXDYdXV1Wlzc1PSf9dLf3+/Li4u1NnZKcuyilNK5ubmvtUeLCwsqL6+XuFwWKFQSLZtF8sqff5tA5SbkUgkPu4iBX5BJpNRMpn8VmP1/PyseDyuQCDwJiD4yMPDg2pra4v7ipdyHEe2bcs0zeIiZ1/18vIiwzBkWZaam5u1t7eng4MD7e/vf+t4lZZKpeQ4zodDq1OplNLptEzT/N/Bhm3b8nq9787Ji8fjqqmpeXdO3U+WodJeX191d3cnn8/343MR7+/vZRhGcc/oUslkUtlstrj4U0Eul1MsFpNpmvJ6vX91rnQ6rUQioZaWlordg5+uu3/5OQLwub95rxXeJ6ULDV5eXmp6elqHh4dVu6ZPqXw+r1gspoaGhrL0zheO7/f7q6rnOpvNyrZtNTU1vXvdn9XL09OTMpnMmzaz8Nuv3vdcLiePxyPLsuT3+3V+fq6JiQmdnJy8eb4++7YByoUAACiz2dlZdXR0aGBgQLe3t1pfX9fMzExxtWMAAFB9HMfRzc2NIpGI2tratLKy8ttFwj9id3dXZ2dnmpqaUj6f19bWlkzT1Pb29m8XDSAAAMrt6upK0WhUp6enamxs1MjIiCYnJ+XxMAMHAIBqdX19rfHxcXV1dSkSiXw4WhAo9fj4qGg0qqOjIxmGoVAopPn5eXr6URUIAAAAET7w7QAAAI5JREFUAAAAcAG6IAEAAAAAcAECAAAAAAAAXIAAAAAAAAAAFyAAAAAAAADABQgAAAAAAABwAQIAAAAAAABcgAAAAAAAAAAXIAAAAAAAAMAFCAAAAAAAAHABAgAAAAAAAFyAAAAAAAAAABcgAAAAAAAAwAUIAAAAAAAAcAECAAAAAAAAXIAAAAAAAAAAF/gDDfpPV1CXVBUAAAAASUVORK5CYII='}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2', 'julep_session_id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01SF7tWSGgctWmwTWqET1zbV'}], 'workflow_label': 'check_goal_status'}\n", "--------------------------------------------------\n", "Transition type: init_branch\n", - "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'check_goal_status'}\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2', 'julep_session_id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01SF7tWSGgctWmwTWqET1zbV'}], 'workflow_label': 'check_goal_status'}\n", "--------------------------------------------------\n", "Transition type: step\n", - "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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LhxjB07lk2bNvHMM88QHh7umZVQHIY89RRfT56M2WzmfwMfvezjRS082ERSeuFuXbf7hI2NB9Pof0M5Tp5zsvFAKlXKm7m/Qwgnzjo5Fuug/iWd/XSHzu8bE+nRNpgh3dI4HG3nxXsjCrVNxSU82LucBIWh2JYAbNq0iSNHjvDiiy/Sp08fz9eIESMwm80sXLjQU/bnn3/mxIkTOJ1OvvjiC1RVpVOnTkRGRtKsWTPeeustbDYb6enpjBkzhpYtW9KgQQOsVisVK1bk77//BuDEiROehB8X+Pn5ERMTUyQRssqVK1OpUiXeeustjh8/DmT8wpkxYwYtW7ZEURRPkCMmJgYg33vKSffu3Tl58iSTJk1C0zTsdjujRo1izZo1hX5PQgghhBCi9Onfvz8Oh4Onn37aM/IPGevjf/vtN8LCwgpUX1JSEgAhISEArFy5kj179pTKLP1JSUmeUX673c60adNwOp04nU6aN2/Ovn37OHLkCJCxZeGFeytXrhzNmjXj559/9tQ1bdo05s2bh8Ph4NFHHyU6OhpFUWjVqhX16tXLFkwoah3ad2Dqt1P4euJXtG3T5rKPF7Xw4KIZR+757mFmrU7gxXvD+e3V65j4RFX2nLTR893D5NR9e2FqFHtO2nhvQCV+fLYGZ+IzptJfbbkbi+r9zEmxXWnmzJl07Ngx25Z/JpOJ7t27M3PmTB544AEgI6LZp08foqOjsVqtfPDBB/j7+wMZGT+HDRvmWY8TGRnJZ5995qlv9OjRjBgxgm+//ZagoCAiIyM9/+ghIyP/m2++ydKlSz2BgsKiKApTpkzhzTff5JZbbgEyfindcMMNvPPOO0DGkofbb7+dgQMH8vDDD/PWW2/le09ZVapUicmTJzNy5Eg++eQTdF2na9euNG7cuFDvRwghhBBClE5BQUHMnj2bt956iw4dOhASEkJycjLly5fn4YcfZsCAAQWqr3r16txzzz3ccccdVKhQgaZNm/Liiy/y/vvvU6dOnSK6i8vTv39/Bg0axI4dO3A6nbz++uvs3LmT+++/nwULFvD000/Tq1cvypcvT9euXWnYsKEnx8CHH37IM888w++//46mafj5+fHFF19gNpvp3Lkzd999N5UrVyYhIYFKlSqV2lkQJa2oRqzjUtwM/vI4QyadICzYyLkkFzbnxZ7/os1J+Pe5uONDbJKLW984gL/VwNkkF0/fVYFbmgVw/GxGEvbqj+3MVP+E32OZ8Htsjtd+5cdTvPLjKc/3l14H4P73j1zx/eWmOGcAKHpxLhYpoJiYGIKCgrBYLNlei42NRVEUz7T5SzmdzmyJUS4VHx+PyWTyBBWKgt1u59y5c4SGhubY/piYGIKDgz3LFiDve8rNuXPnMJvNmdZyCSGEEEKIssPhcBAfH4+fn98VP98mJiZiMBg89dhsNsxmc467DZQkl8tFfHw8oaGhqKqKpmk4HA6sVitnz571zBBQVZUOHTrw4Ycf0qFDB8/5F0b2sz5Du1wu4uLi8Pf3z3F5clFbs3YNk7+dgqLA4McGe0b1C3q8qH20IIbRP58ulmvlZWy/StzZKpAfl8fhY1YZckcFjkQ76DxqP053qe3mZvNm34qMuKdgs3YuV6kOAAghhBBCCCGEt2w2G506daJ3797ceOONLF26lDlz5rB06VLP1oilWZ9+D3LuXMZe9hEREfwwNWOLwgf69PYkCPfmeFHbF2Wn5Yi9xXKtvIQHGxl1fwSdG/njcuus3pPKuNlnPNv+XS02fVSfepWzDxoXhWJNAiiEEEIIIYQQRcVqtTJ79my+//57vvnmG6pUqcKcOXOuis4/XD27ANSrbKFORQsHThduIsCCik5weXYMuFrVqWgpts4/SABACCGEEEIIcQ2pXr06r7/+ekk347JcLbsAANzSLKDEAwDXgrtaBxXr9WQJgBBCCCGEEEKIAjkaY6fliH3YndKdvFwWk8LezxtQIega3AZQCCGEEEIIIcS1oUaYhae65Zx0XXjnqW4VirXzDxIAEEIIIYQQQghxGZ7vEUagj3QpL0eAj8oLPYon8/+l5KclhBBCCCGEEKLAgnwNfPK/KiXdjKvS549VJdDXUOzXlQCAEEIIIYQQQojL0uv6EJ69u/hHsq9mz94dxn0dgkvk2hIAEEIIIYQQQghx2d7sE0HH+n4l3Yyrwi1NA3izT0SJXV8CAEIIIYQQQgghLpuqKvz4bHWqhBZvQrurTeVQE1OHV0dVlRJrgwQAhBBCCCGEEEJckQpBJrZPqM//bg4t6aaUSgNuKMeOCfUJKoF1/5dSdF2XjRuFEEIIIYQQQhSKyX+f5bkpUWjS00RV4NPHqvDIjaUjMCIBACGEEEIIIYQQhWr/KTsTfo9h+vJ4nO6y1+U0GqBvp3I8e3cYdStZSro5HhIAEEIIIYQQQghRJKLOOfloQQzfLzuH3Xntdz19LSqP3hTKsDsrULkU5kSQAIAQQgghhBBCiCK3fn8ac9cmsPFQKgmpbuJT3CSmua/KwIDFpBDkayDYz0CIv4F29fy4t20wrWr7lnTT8iQBACGEEEIIIYQQogyQXQCEEEIIIYQQQogyQAIAQgghhBBCCCFEGSABACGEEEIIIYQQogyQAIAQQgghhBBCCFEGSABACCGEEEIIIYQoAyQAIIQQQgghhBBClAESABBCCCGEEEIIIcoACQAIIYQQQgghhBBlgAQAhBBCCCGEEEKIMkACAEIIIYQQQgghRBkgAQAhhBBCCCGEEKIMkACAEEIIIYQQQghRBkgAQAghhBBCCCGEKAMkACCEEEIIIYQQQpQBEgAQQgghhBBCCCHKAAkACCGEEEIIIYQQZYAEAIQQQgghhBBCiDJAAgBCCCGEEEIIIUQZIAEAIYQQQgghhBCiDJAAgBBCCCGEEEIIUQZIAEAIIYQQQgghhCgDJAAghBBCCCGEEEKUARIAEEIIIYQQQgghygAJAAghhBBCCCGEEGWABACEEEIIIYQQQogywFjSDRBCCCGEEEVL13XP/y98Zf0+p+NZzxdCiGuNoiiZ/nzh+wt/vvQr6/Gs518NJAAghBBCCHGNyqujf+mXpmk5Hrv0XCGEuBZd2plXVTVbpz+nY1mDABfquRpIAEAIIYQQ4hqUdUT/0k7+hT9rmoamabjdbjRNw+UGTQcUFVBQVAOKoqKosmpUCHFt0jUNze0GXQM00DVUBYyGjICAwWBAVVVPIODSgICa5Xfj1RAEUHQJ6QohhBBCXDNyG/W/0Nm/0Pl3uVw4nS5cmgKKAdVovioeXoUQojjouo7mcoDuxmjQMRmNGI1GT8f/0qDA1bQsQAIAQgghhBDXiKyd/6wj/ZqmeTr+Tk3FYLKW6gdVIYQoDXRdx+1Mx2wAo9GA0WjMFgS4NBgApTcIIAEAIYQQQohrQE7T/bN2/B1OJ04XGEw+Mq1fCCEKSNc03M50TEYwm0x5BgKgdAYBJAAghBBCCHGVy9r5z/rldDqxO5zoqhWD0VTCrRVCiKub2+UEdzpWixmTyeQJABgMhlIfBJAkgEIIIYQQV7G8Ov9utxun00mazYHJEpAtYZUQQoiCMxhNaKpKWnoKPrqOyXQxsHrh9+yFIICu66UqCCABACGEEEKIq1R+nX+Hw0G63YXZJ6iEWyqEENcWVTWgWANJsyXjo+uYzeYcypS+IIAEAIQQQgghrmI5Zfp3u93Y7XZsDjdmn8CSbqIQQlyTFEXB7BNIenoSuq5jsVgyvXYhSFtaOv8gAQAhhBBCiKtSblv8XRj5T0u3Y/ELKelmCiHENc9kDSAtNT7TMqsLnf6cjpUkWQgmhBBCCHGVuTSH86VBAM+a/7R0TD6BpeJhUwghrnWKomDyCSQ1NQ2Xy+UJyF74/XxBaci/LzMAhBBCCCGuQjmN/LvdbtJtNlSzHwaDPOYJIURxMRiM6BZ/0tPTPKP+lwZhL90ZoCTJDAAhhBBCiKvIpYn/si4BsNvtuNwKJrO1hFsphBBlj9FkwelWsNvt2WYBXPq7uyRJAEAIIYQQ4iqTU+ff5XJhszuw+AaUdPOEEKLMsvgGkJ5uy3EpQEl3/kECAEIIIYQQV52ckv/Z7XZ0xYSqGkq6eUIIUWapqgFdNeFwOCQAIIQQQgghrsylD5IXggBOpxO7w4mPn2z5J4QQJc3qG4jN7sDpdJa6IIAEAIQQQgghrkKXPkw6nU7cWunYYkoIIco6VVXRdAWn01lqOv4XSABACCGEEOIqknX03+1243A4MJok8Z8QQpQWBqNFZgAIIYQQQogrc2nnX9d13G43TpcLq69/STdNCCHEeVZf/4ydWVyuTL+zJQAghBBCCCEKJOsMADDI9H8hhChFFEUBxYjb7S41nX+QAIAQQgghxFUlaxJAp9OJrsgjnRBClDY6imcGQGlZAmAs0asLIYQQQogCyZoDwOVyUVof6RR0VDQMKiiKTsbjcAb9fAlNB01X0HUFDQWQmQxCiGuDohpwuVylKgdA6fy0EEIIIYQQucoaBDCYTCXdJA9V0TEoOuhuDAYVdO2S5QkXO/cXj2SUvxAQcGug6SqaTFQVQlzlDEYTbqej1HT+QZYACCGEEEJcVS59gLyQA8BoLPkAgIqGWXVjVl0YVQ2jQUFB9zo3QcbYv45R1TGpbiwGF4ruLBUPzN5yOp088tAD9LmvO2lpaUV+vb8W/0HX61sxf+7MIr/WteaDd9+iTfMGnDh+rKSbIq5hRqPJkwPggpL+nSYzAIQQQgghrjKXjiS53W6MJTgDQEHHpGqoyoWR/iufwp8RM9CxGMGtuXDpKjqGK673Si1etJCff5zKoYMHMBpNNIxszBNPPU3jJs0ASEtLZd/ePbjdbhIS4vH19S3S9uzeuYO01FR27thOj569rqiun3/8HqPJxAO9Hyyk1sH6tav56Yep7Nq5HafLScWIStxw8630efBhgoNDCu06QpRWRpPpfKJWSs0MAAkACCGEEEJcpS48UBoMJfNIZ1I1DIq70Dr+OTGooOoabl3HpRuK7Dr5WTBvNmPHvIbFYqFBw0jS09PZsH4t27Zs4pvvfqJBw0iCgoL5avI0HA4HlSpVLvI2Pfb4EOrUrUenLjdccV2/TJ+Gj49voQUApkz+iq++mADAdbXrYrGYOXb0KFO+mchfixYy8evvCY+IKJRrCVFaGQzGUtPxv0ACAEIIIYQQV5ELD5IluqZU1zEZ3BhVKI4OuaKAUdFRNBdujGh68QcBfpn+A/7+Afzwy1wqV64CwJK/F/Pyi8/w4/dTGPvexwA0bdai2Nrk5+/Pnd17FNv1vLVh/Vq++mIClSpX4YOPP6NO3foA2O12Pv/0I2ZM/4FvJn3Oq6PfLuGWClH0sv6uLulggAQAhBBCCCGuMpc+QBb3w6SqgMngRvWyD67r5/MAGFRQjVyaEkDXNBSXC12/sD4270ozZgO4cGJA04s3lZXL5cTqYyUsLNxz7KZbbmPC519TtVp1z7E+93UnPT2NBX8sATLu/8dpU5j1y0/Ex8cR2bgp/R8ZxPChg+n/yCCGDh9BfHwct93YkYf6P4rZYmH+3Fk4HQ7ate/ICy+/lut0+QsBiNfeGEv3e3p6vn9r3AesXPEvK1csI8A/gDvvvpfBTwzFYMi+jGLrlk0MfvQhz/dtmjegRcvWfDV5GgAHD+5n4uefsHXzJjRNy7bsISdTJn8FwDvvj/d0/gEsFgvPjhhJ+fIVaNiocaZzVvy7lO+mTOLQgf34+PrRrn1Hhgx7jrDwi++3pmlM/3Eq8+fOIvrMaUJDy3PbHd15dNATWCwWT7kjhw8x/sN32bplI/4BAdx3fx9Onz7Fr/PnsHLd1kxlL7V/3x6++HQ827dtQUenXr0GPDroCdq275jrvQqRn5L8fZ0TSQIohBBCCHGVKu6HyYz1/q58O/+edll9UAMDUfz8wWWDmNNop056vkhJOl8mCMXH7/xSgrzvSVHArLpRFS3PcoWtc5cbORsby7An/8f6tas963rbd+xElarVcj1v4hcT+OyTD7E7HHTqfAPnzp1l5IvP5Fj21/lzmDH9B5o0bUZAYCB///Un748bU+C2fvDu2+zcvpU2bduTnp7Od5O/Yt6cnBMFVggLp/eDD3u+7/3gw9x4821ARkf6fwP6snrlcuo1aEiz5i3ZunkjTwzqz9Ytm3KsLy0tja1bNtGwUSQNGkZme11VVQYMfIzWbdp5jv3x+wKef3YIRw4fok27DlSpUpU/F/7Ko/17k5iY4Cn3zluj+XT8B9htNjpc3xmjycR3k79i5PPDPWXOxsYw+NGHWLd2FQ0bNaZBw0imTP6Kv//6M8/37MjhQwwa2I9dO7dz+x13cedd9xB18gTDhw5mx/ateZ4rRH5KQ8f/ApkBIIQQQgghvKBj9nLkX/UPAEA7sBf32pVou7ahn4lCj48Htws0DVQVAgNRK0Sg1K2Poe31KI2bofoGoCcnn58VkPvFTKobh1tBL6acAI8/9TQ2WzpzZ89g2FODCAoK5tbb7+DBhwd6lgRklZycxE/TplCpchW+/2kWQUHBuFwuXnh2KKtXLc9W3m63MXnqdOrVb4jNZqN3zztZsXwpmqahqt6P2wWHhDD1h5n4BwRw+NBB+tzfnX+X/cP9vfpmK1u5chVGvDCKFcuW4OPjy4gXRnle+/zTj0hPS+PjTydyfaeuAOzetZPHBj7IhI/f47sfsgcVYmOicbvd1Kh5XabjX37+CYkJ8ZmOPTn0Gfz8/Bn/4bsEB4fw/fTZVKxYCYAZP//IR++PZdp3kxn2zPPs27ubBfNn07xFKz79cjIWiwW3282rI0ew5J/FrF61nI7Xd+Hnn6aRmJjAsyNG0vehAQBs27qZx//3MHn5bMKH2NLT+Xzit54ZHT0f6EO/Xj2Y+ctPec54EOJqIjMAhBBCCCFEvsyqlmfnX9d1FLMZxc8fbe0KHC8NxfH0o7imfIm2ZQP62VjQz3f8DYaMofyEBLRd23HN+AHHC0OwP/EQrjnTwaCi+PiR12wABTAb3FBMI2tms5kXRr7G74uW8fxLr1Knbj1mzZhO3wfuZsP6tTmes2P7VpxOJz16PkBQUDAARqORhx/5X47lGzdtTr36DQGwWq00imyCw+EgPj6uQG2948678Q/ICMLUuq42IeVCiY2JLlAduq7z37o1NGrcxNP5B2jYKJLrO3dl184dJCUl5ngekG37x8V//Ma8OTMzfaWmprJ3zy4SExO4u8d9ns4/wAO9HyQkpBzr1q4GYM3qlQAMePQxzxR+g8HA/x5/CsBTbvPmDfj4+vJAn36eupo2a0GLlq3zvN9VK/4FYNAjD3LbjR257caO9L3/bjRN41TUyXzfLyGuFjIDQAghhBBC5OnCNn+5jcjruo7qH4AWewbXxPFoy5dkdPJ9/cDHN6OTfqGjrmkXq1EUMBozlghobjhxHOd7Y3D98Svm519FbRCJlpSUa7sUMpIROrXie6QNLV+BXn360atPP3bv2smTgwfw9puvMn/hP9k6vcnn235p3gCA8PCcs99nPW4wnr+vAgY5KmS5ntFoyLQPuTdSU1Kw2+1UjKiU7bWI88cS4uMJDAzK9FpYWDiqqnL06OFMxy/kRAB4ZujjrFm9AoD4uIzgRkTFzNdRVZWw8AjOnYv1XAugYsXMuytEhFfM9HpSYiIhIeUwGjP/ncj6nmS914z7qphjYsKAwMBczxXiaiMzAIQQQgghRK5URb9kq7/sdF1HDQxE27oBx9OPZnT+g4MhICCjg5+18+85MYc/+/iglC+Pvm83jqcG4JozHcWcc8K2CwyKjqGI8wEcOXyIIU88ypeff5LpeMNGkTRr1pLTp0+RnJw9UHGh4xiTZfT9zJnTRdbWwuLn74/FYsmxrWfOnAIgpFy5bK/5+vnRKLIJu3ZsZ9vWzdlej4mOZvOm/zzfh4Rk1BGd5TqaphETfYaQkNBM1zpz+lTmtkRnnBcckpEoMTAoiPj4OJxOZ5brnsn1Xn39/DCbzdSsVZs27Tp4vlq3bU/lKlVzzGUgxNVKAgBCCCGEECJXRkXLv/O/ejmOl4dDUlJG51/n4uz9S0evVTXjC0BRM74yVwi6jhIUhJ6YgLbsb9Bzv/7FNrrJL3nglShfoQI7tm3hl5++Z/u2LZ7jhw8dZM/unfj7B+Dn55/tvMZNmmEymZg/d5Znurzb7eanaVOKrK2Xy2yxkJiUkGkKf5t2Hdi5Y5tntB5g755drF65nEaRjQkIyHlk/KH+jwIwZvQojh65OBMgNiaGV18egc1m8xyr37ARQUHB/LpgbqZgw5xZvxAfH0e78xn423e4HoBpUydjt9uBjCDBd99k7DhwoVyLFq1JT0tjzqxfPHVt37aFLZs35nrviqLQuk17Nvy3lr17dnmOL160kHu738ofvy/I9VwhrjayBEAIIYQQQuRI0d0Y1Jw71p7O/5aNOMaMBKMRzOZLC5yvRAG3G2zp4HCgXzILQDGZMs65dLq2oqDHxmK46XZMYz4EXcs3g7aigAE37iJ6tA0ICOTJoc8y/sN3eGxgP2rXqYeiKBw5fBCn08nQ4SNy3GIvICCQBx96hO+/+4be93WnRYtWHDywn4RLMtuXFg0aRrLoj9945KEHuK52XV5/cxxDhj3Hxg3rGTH8KVq2bovJaGLDf2vRNI2nn30x17puuOkWevXpx8xffqLP/d2pU7c+uq5z5PBBzGYLt3W7i8V//g6AyWTimREv8ebrL9P3/rtp1aYt8XFxbN+2hQphYZ58CfXqN+TuHvfx6/w59Lr3Dho0iuTwoYMcPXKYDh070/H6LgD07defBfNm8/EH41jx7xJ8/fxYv3Y1QcEhxMedy7XNTw4dzsYN6xj86EN0uL4zbreblcuXUblKVTp3ubEQ32khSpYEAIQQQgghRI5M2fu0HqrFghZ9Gse7r2WM6l/o/F/aV9d1SE4CgwG1fiOUuvVRKmSsxdbj49CPHELbuRU9IR7FPwCMRvTYWNSOXTM6/4DucpHXbgAXGFUdt6Z7VfZy9O3Xn/DwCKb/OJX9+/diMppoFNmEvv0GcMNNt+R63lPDnsXX15fZs35mxfKlNGnWghdHvc6Tjw1AzSFoUFKGPj2Cs2dj2bFti2e9fK3rajP5u5/48rNP2LZ1M5qu0aRpcwY/OYxmzVvmWd/zL71K46bNmfnzjxw4sA+jwUjbdh0YMnwES/5enKnsnd174Ovrx7Sp37B+7Wp8fHy59fY7GTb8eYKDQzzlRr02hipVq/Hr/DmsWvEvoaHlGTDwMQY9PsRTpnyFML76dhoff/AOO7ZtITAoiCeHPMOhQwf4bcHcXHdTqFuvAV9N/oEvPx/PurWrMZvM3HDjLQx79gVPQkUhrgWKXpo2JRRCCCGEEHmy2Wy4XC5cLhc2m41z585RoVKtQr+OquiYVVeu0+8VP38cY166uOYfMnf+nQ5IS0PtchPG3v1Ra9UBS5b1/JqOfvIYrt/n4p77C3rcOdQut2AeNx4UFd1hoyAdeqdmwK2XvhWuaWlp+Pr6er7fsX0r/xvQl2eee4kHH36k5Bp2jbLb7RiNxkyzMoY9NYid27exbNWGEmyZKItiTx0mNDQUq9WK0WjEaDRitVpLrD2l7zekEEIIIYQocQZFz7Hzr+t6xlZ/G9bgXrwczm9vl6nzb7eBy4Xphdcxv/E+Su366A4HWlJS5q+UZAiriOmp5zC/+QGGO+7F9Ob7l9X5v9Dm0sTlcnHfPbfzUJ97SUtNBTLev1kzpgPQrEWrkmzeNWnenBnc1LkN8+fO8hw7fOggmzasz3fWghBlgSwBEEIIIYQQ2elucuqAK4qC7nZjNb6NeutZ0laXQ/W1ZSwD0HVwOUHTML/+LmrHLujJyWREB5RMAYULk1B1hx3dYUNp0xFz+87otrTL6vwDKGigqxlJAUoBo9FI1xtu5ofvv6XvA3fTrEUrjhw+yN49u7m+UxcaNpLs8oWtQ8cufGEdz/vvjGHVin+xWq2sW7saXdd59LEnS7p5QpQ4WQIghBBCCHEVKY4lAKqiYzZoKDll1ld9UNO347PrevA3Yd8WTuqCKuh2FcWqQ9w5jMNfxHjfg2hJSbnOIigqDrcBrRRNctV1nek/TmXenJmcPhVFcEgIt9x6B08MGV6i04CvZUcOH+LzTz9i88YNuDU39eo14PGnnqZV67Yl3TRRBpW2JQASABBCCCGEuIoURwDAoGiYDVoOr+joxkDMx17DdHIsmMuj+NlxRQeQMr0W7kN2DC0bYv50CnpqSq71F+Xjp1NTceulJ7meEKJsK20BAFkCIIQQQgghMsl9Lb2CorlQUzeCmpHQT0s2YCiXQtDgvST/GAw9HwZVycgVkMvov68ZlMscpHe5wObMfZa/qui4ZXhLCCFyJAEAIYQQQgiRiZJbAEAxgjsO1XEAXbWA5gJAt6moSgr+AwNJb9QUPSU9l90DdFBh7O8q51LB4OVSfbeeUTbFDrc11rm3hU5Kei5BAJncKoQQuZIAgBBCCCGEyCIjaV82ignFGQv2uOxnOG1oQS3BHArO5Oyv67qnw779pMKpBDDkMAsgp/67poOqQHwahAXC/a303GcAqAq4c78zIYQoyyQAIIQQQgghMslrYF7R7ShaesYMgCw0UwS6zvnkgdkz/l9gNV380nLo8F9aXNMv7lut6ZBmB3dO6QmEEELkSwIAQgghhBDiCmh4uuiGwEyv5Jfs79LOf15FL/T3VQXsbtC1jOn/MttfCCEKRgIAQgghhBDiMmmZ/+9O8rySX+ffoGZ06CEjEJB1Sr+u5zw7wFh6dvgTQoirjgQAhBBCCCFEJnoOHXLPa4ZAMAaguJKzLQMwp6xB09Jw6ioKuc/Tj0uFs8lgymW3PqMBAnLYJUvTM44bDKA7cj5XyylqIIQQApAAgBBCCCGEyEJHRdcvduA9Gf11J7qlIoqlOrprp+d1Iy4wmfgtPpW6qdHU8K+E3WXPsW5Fh95tdVJzfhmApHT4a6eSKUmgqoDLDeX8L07/zylIoShqRg5DIYQQ2UgAQAghhBBCZOJya5gveUr0TOfXHWAIwmlthDF5E6gWjLjQFI1PUlvxflwEo06u4ulGD2N32ciaTvBCNQM6aDl23nUdFBMs2qowd6NCiN/FZQAX/l89VM9z7b/LrYGSy9QCIYQo42QVlRBCCCGEyEzNuwPtDr4NAKOSzlndyLDELkxKqUt1s8rPh37nSMoJ/Ex+uXbUk9IgMTX7V3I6uB0wZ4OC2Zg5B4BbA7MRGlfRcbtzX6Kg5NN2IYQoyyQAIIQQQgghMtF0BT3HzQAVFHc67pCbwFyeTfZg+sbfxr+2MEKVdEyqmRRnGm9s+hyX7sZiNJHTfHxFyf4FEBgIC7YqbD2u4GPOfI7TDTXKQ5VghXRnzu3OSByY1yaGQghRtkkAQAghhBBCZKHgziWHn645CLCEsSbkZR6Kack5l4UgJWNBv6a7CTIH8l/MDl747wM0dPxMfhfOzLk+HQyKgSDfQNbtMzBhsY5/lgSAqgIpNuhcT8fPqqPlll9QUXLcOUAIIUQGCQAIIYQQQohsND23x0QFmyudylUGEmoNQdFSs5znJsQSyN8n19B/+Ui2x+8j0BKIn8kPi8GMQTFgUAwYDUb8TL4EWQPQDW6m7V7CSzPdoPll2x3A6YYQP7ijiYbdkfv0f6dLv5iwUAghRDaSBFAIIYQQQmSjoeSaad/hdlDLN4gRTR7hxfUfUsEQkq1MiCWQfQlHGLjiFTpHtOL2KtfTIOQ6go0BANhcDs6kx7Lx7C5+P7acnfH7CKvZCP/Tz6PbqoIxI7CgAmdT4ckbdaqWy8gVkHMCQR1Uk+wAIIQQeVB0Pa88qkIIIYQQojSx2Wy4XC5cLhc2m41z585RoVKtIrmWARdm4yW7AGQRaAlg9KbPmH5oIRWsIehZet8KCm7dTZIjozMfZA7Az+iDoijY3A4SHUnY3A78TD5YVQuaIQXF7Yv/mWdREzuAMZ2ENI26ETBpoBvdTY6JBRVFwaWBU5OxLSFE6RJ76jChoaFYrVaMRiNGoxGr1Zr/iUVElgAIIYQQQogcuXUDLreW47R6XYdURxqjmj1O14qtibXFo1ySODAjIZ+OgkqQOYAgcwBOzUWCI5mz9njSXTZ8jVZCLcFYVBM6GorbF92QRnLlsbjCppOWbiDIqvD6PW6sKrmu/XdpbhyuonoXhBDi2iEBACGEEEIIkTNFQdMNaDlk1lMUcOsudF1jQvtRdKvaiej0s2i6luMovY6GUVUxqioW1YRRVT3HM9Xr9kVx+xIV+ClqjU/4qI+ZuuGQas997b+uqShq0Y7+p6elMW3aZJ4eMoiBA3rxxusj2blze5FeU5Ss5f8uYeiQR0u6GUXu2NHD9Ovbg6SkxCK7RlTUSYYOeZT4uLgcX7elp9Ovbw8O7t9XZG0QGSQAIIQQQgghcuXGgKaTS3I9BYfbAcD4tiMZHjmANJeNREeyp4SOlq2TnxtVUbFrTs7a4mng35IpPe6iSVUHSWk5d/4VRcGt6biLIa3VxIkT2L1zB4MGD+GNN9+lSZNmvP/umxw4sL/Iry3E1a5cSDnuuOMe/AMycoCsWbOSxx97uIRbVTbJQikhhBBCCJEnN0ZU3YWqKDnkA1BwuV24NTdPN3qYjuHN+XTXD6yN2YoBA75GH4yqipLLuJOOhkvTcGgO0lzpBJuDGB45gIH1e+KrWEhKT8t15F/TdDSM6BRt5v+0tDQ2bVzPyJffoHGTZgBUr1GLAwf28dfi36lT57kivX5J0DQNVS0bY4XX8r2Wlnvz8fXljjvvKelmCCQAIIQQQggh8qHpCg63ilF1YVBy6cjrOom2JJqVq8+UTmNZG7OVBceWsjZmG4mOJFyaGzfubOcZMGAxmqkXXJPOEa3pUf1GqvlXJtWRSqqemue2fk43aLm0pzCpqoqiKJw7dzbT8UcGDsbhsAMZ9//77/P4568/SU5OokaNWgx89HGqVqvByhVLmT59Gl98OcXTGftk/Hv4+wcw6LGnAPhz4a8s/GM+ToeTJk2bM+CRx/D3D8h0PU3TGPLkQO7ucT/dunUHYNPG//h0wvtMnPQ9vr5+bNmykRm//MiZ01GEh1ekz4P9ad68FQAbN6xn0sQJfDNluqfON0ePJLJxM+67vw8//fgdhw4dwGrxYdeubXzz7XTMZnOmNrw6agTX1a7L0aOHOXH8KGHhFRn8+FBq1aqd7/sAkJycxNQpX7N922ZMFgsdOnSi74MDMBgy9n5cu3YVc2b/TNy5s1SpWo2H+z9GnTp12fDfWqZ8+xUTJ30PQNTJE7z4wjA++vhLIipWQtM0Hh/0EE8MeZaWLVtjS0/nhx+m8N/6NaDrtG7bgf79/4fVx8dzH1WqVOPo0cNYrFbeHPMeZ8+dZdKXEzh4cB9VqlajTp16me49v7ZfStd1Zs2czvLl/+Cw2WkY2ZhHBj5OSEg5AGx2Oz9+/y3r163Cx8eXLjfczL09e3n+fqxZvZy5c2cSd+4s1avXpP8jg6hZM+M9fvmlZ+nS5QZuv+NuAHbu2Mo7497gp5/nY0tP53+P9qXbHXezbt0qru/YlT4P9s/3ehcsXrSQuXN+YeKk7z2vjXr5ORo3bkrfBwdkKjvyxeHcdPPt3HJrNwA+m/ABqsHIkKHPArBw4XzWrF7J2HEfcezoYUa9/BwTJ33P5G++YNPG/wDo17cHI154hYYNIgHYd2APkyd/SUzMGerWa8CTQ54lKDAo2/srLl/Jh4OEEEIIIUSpp2PArRnObw2Yc6dcUSDVmUqaK532Yc34oO0LzLppPBM7jua5Jo/Qt9admb6GNnyIcW2eZcaN4/mxy3s83eghwqzlSLIn4tbdkMvIvq6Dyw2aYirCO77IarVy6613MPW7Sfz043dERZ0EIDyioqdju2zpXyz8bT6Dn3ia9z74jLDwinw64UMAWrZqR3paKgcOZKxvdjqd7NixlXbtOgDw1+I/WLz4d4YMeY5XR48lIT6OqVMmZWuHqqq0aduBTRvWeY5t3bqRyMim+Pr6cfDgAcZ/9A6dOndl3Lvj6Xh9F8Z/9A5RJ094fa/79+2hafMWvDX2A0ymnN/fXbu2M3jwUD759GuqVavOp5+8j3Y+Q2Ne7wPAtO+/JTExnjfeep+hw55jzZqV/PnHrwCcOnWSLz//mHvv7cW7702gVq3afPj+WzgcDho2akxKSrLnXrZu2wTAlq0Z/z9x4hg2u42GDRoB8PnnH3HkyCFeevkNXho5msOHDvDtt19luo9t27fwQO+HeOqpZwCYNHEC6elpjBz1Jr16PcTmTRsylc+r7Vkt+/dvli37m+HPvMTrY94lOTmZyV9/4Xl90sQJnD0Xw2tvvsMTQ4azbNlfLFv6N5DRoZ/01Wfccdc9jHtnPHXq1Of9994mLS01n5/eRQcP7Ofp4S9ye7e78r3epdq2bU9qagp79+4CID4+jmNHD9OmTYdsZRtFNmXvnp1ARnBqx45tbN+22fN3Yd/ePTSObJrtvKHDnufxJ57G3z+Ab6f8TLNmLT2vLflnMQ8PGMSLI1/n9KkoFv46z+t7Ft6RGQBCCCGEEMIrGkY0NNDcqGpOywEAMo6nODKm7gca/Wgf1ozrI1rmUDaD3e3A4XbgsCeR0enPLcCQUbeGgquYH2MfHjCIqtWq8ftv8/lj4QKuq12H3n3606hRYwCaNG1Bw0ZNiIioCMDtt9/JK6OWkpKSjL9/AJGNm7Jp43/Uq9eA3bu3YzQYadAw49w/Fs6nV5+HadAwYxS074OPMPr1F3nC5cJozHyf7dpfz7i3XyM5OYmAgEC2bdnMfQ/0AWDRn7/SrHkr7ryzBwB331OF/fv2sHDhAgY/PtSr+2zQIJLbbrszzzKdO99I5SpVAeg/YBBDnhzInl07aNS4ab7vw5nTJ2nUqCmVK1ehcuUqDBn6HG6nE4Do6NOoqkrzFq3w9fWjT98BVKxUBYfDjr9/ADVrXceevbuoXKUq27ZupnWb9mzdsolu3bqzd+9uateui4+vL6dPRbFl80bGvTue6tVrAjD4iWG89srz9OrdjwoVwgC45ZbbadmyNQBnTp9i964dvD3uQ89I+z097mPOnBme+86r7VmdiTpFWIUw6tSph6IoPPbYEPbt2wNATGwM/61fw+dfTvHMCOjWrTurVy/npptv46+//qR9+07ceMOtAPTtN4Cjxw4TFRVFnTp1vfo59u77MHXr1vfqepcKDilHgwaRbNy4noYNG7N1yybKl6/AdbXrZLtG4yZNmfTVZwAc2L+X8hXCSEtL5eDB/dStW5/9+/Zw6/nZAZcym80YzweXLszIuGDAI4M8/6Y6dOjMocMHvLpf4T0JAAghhBBCCK85NRUDOgbdhcFgyCUIcDFpn1t3nw8G5FzufOks/8+pPgVN089P+y/+R1hFUbjhxtvoesOt7N2ziz/+WMC740bz0sjXiWzcjNDQ8iz8fT4rVywjLu4cmp4xCuo630Fs1+565s2dwYP9BrB54wZatW6HwWAgNTWF2NgYvpn0GZO//txzPU3TiIs7R1hYeKZ21KvXgKCgIDZv2kDNWrWIT4ijZau2ABw/dpQuXW/MXL5BQzb8t9br+8wacMiPv38AoeUrEBN7hkY0zfd9uPue+5n45SccOLCP5i1a0b5DJ0JDywPQqFFT6tatz4hnn6Jlq7a0bNWaW27p5pmK3iiyKXt37+T6jl04dOgA7743gRefH4rNZmP/3t00btwMgGPHjmKxWDydf4BatWpjsViIijrhCQAYjRdnOJw+cwqDwUCNGtd5jqlZdpbIq+1Z3XDjLaxft4oXnx9Gq1Ztad22PV1vuBmA40ePADDi2Sc95TVN80x1P30qihsv6ZgrisKoV8Z49fO4wGS42Pb8rpdVu/bX89uvc+jffxBbt26kdZv2OZar3yCStNQUTp06ydatm2jWrCW29DS2btmEn68fNls6des1LFC7/Xz8PX+2WCzYbOkFOl/kTwIAQgghhBCiQNwY0DRQFA1FuTgyn5uMYMDlJeq7sNxA03VcmoqmZF9vXdTi4+KIj4+j1nW1URSFBg0jadAwknHjRvPXX38Q2bgZixb9zj9//8kzz75Eteo1ORl1gpdfHO6po2XLNkz+5gtOnjjO5s0beOyxIZmu8eRTz1Dtkg4rQLlyodnaoqoqbdp1ZNOm9SQmJtAosoknV4DRZMKgZn5/NLeG0+EqrLciR26n07P1Y37vQ+s27WnQMJJNG/9j06b/mDP7Z4YMHUGr1m0xm8288trbHDiwly2bNzH120mEVghj1CtjMBqNNG7clC+WL2HXru3Url2PChXCqFa9Jrt2bmPfvt3cenvGdHeTyYSa5X3QdR1N03A5c34vFEXBaDTmmXMir7ZnVbFSZT4cP5Ht27eybetG3n7rVW695Q76PNjf08Zx736S6Rz1fC4Bg9GIUsiJLfO6Xlat27Rj6neTOHTwADt3bmfky6NzLGe1WKhTpx579+xi29bNPPLo49jS0/nl52lUqFCeuvUbZsshIUqe5AAQQgghhBAFpisG7G4jDhe4NHeeHafLdSGw4HC6cbiNuCn+zj/Avn17ePONkaSmpmQ6HlouFM2dMcK9a+c2WrZqS42a16GqKnabPVNZH19fmjRtzuzZP2esaY9sAoCfnz9BwSHEno0hIqKi5ysoMCjX0fgO7TqxY/tW/lu/hrZtL47OVqlS1ZNn4IID+/dSrXr1jDZYrdgdds8abQCXu+DBgQuj+gAJ8XHExccRFl4x3/fB6XTyy/RppKen06XrTTw34mU6d76Rv/5aCMCWLRtYtvRv6tSpT6/e/Rgz9kP27d3NoYMZWy3WqVOf9LQ0Fv35O82btQCgeYtWLF68kHSbjdq1M6bHV65SlfT0tEy5D44cPoTT6aRq1Wo53lPFiErY7XZios9ccvRiUCu/tme1eNFC9u/fS8uWrXn0f08y+PGh/PHHAnRdp1LlyjidTtLSUj0/77CwcM+IfOVKlTl27HCm+mb8/APHTxwDwMfHis1mu9g2V94/w/yul1VgYBCRkU356afv8PHxoXbtejmWA4iMbMraNas4d+4stWvXpUHDSKJjzrBmzSoaN86+/v+Covh9IbwjAQAhhBBCCHF5FAVNMeFyG3E43bjPdwyv5OH+wrkZif507C5wK5Yi3+ovLy1atiY8PIIP33+b7du2cPzEMZYuWczaNStp1/56ACIiKrHxv7Xs3LGVLVs2eqbzX9o5a9euIxv+W0vLVm0yde7vuqsH8+fMZM2alcTERDN/3kzefOPlXGdV1K5bj8DAII4dO0KLVu08x++8qwcbNqzjzz9/4/SpKH7/bR7bt2/htvMj45WrVEVRFH6dP5vjJ47x64I5HD50sMDvx5K/F7Fr1w5OnDzON19/QYWwcBqez1+Q1/tgMpnYunUT30/9mqiTJzh29DAHDx2gYsXKALicLr6f+jXr1q0iNjaGdetWYzAYCAuLADJGsevVb8Tu3Ttoej5xXIsWrdi1czsNGjTyZOOPiKhI6zbt+eqrCRw6eICDBw/wzddf0LJVW8LP5ybIKjyiIo0imzDt+8kkJiYQExPN4sV/eF7Pr+1ZRZ85xZTJX7Jv3x6iz5xm65bNRERUQlEUKlWqQvMWrZg08VP27t1FVNRJPvv0Q36ePhWA27p1Z83qFfy77B+iz5xm1szpLF36F+XOr9+vUaMWK1f9y4ED+9m9ewezZvyU588rv+vlpF37juzbu5s2bdrn+e+5UeNm7N69g8ZNmqGqKiaTicjIJuzetYPI80GunAT4+5OSkszOHVtJSUnOs/2icMkSACGEEEIIcUV0xYAbA7qmoSsaChqqoqCqmTsOWTu0l3YsLrym6Tq6ruLUQNPVUjFSaDabeeX1scye+RNfffUp6WmphIdX5JGBj3N9p64A3NuzF2fPxvLxx+9SPrQC3e/pya/z55AYH+9Zc96iRRvMZnO2NdW3d+uOzZbOTz9OITUlhZo1r+PJp4bnee+tWrfj+PGjmUZxq1evyfBnXmTGLz/wy/TvqVSpKiNeGOXZoi84pBwDHh3M3Fk/8+efv9G8eSvq1WtQ4PcjsnFT5s35hcOHDxIeXomnn37es04/v/fhuREvM/W7b3jt1ecxmcw0b96K3n0eBjKm2Pfp8zC/TJ9GQkI84eEVGf7Mi4SUK+e5duPGTTlz5hQVK2V0vKtVq0n50PJERjbL1MbBjw9l2tTJjBv3OgbVQJs2HXio/6N53tfgJ55m0pcTGD7sMSIiKtEwsnHGNoLn5dX2rHr17Y/L7eLjD8fhdDq4rnZdhj3zguf1J54czrTvJ/Ph+2PR0WnerCX33d8XgLp16zPosaeYM2cG3035iho1avHSy6M9Sz3u6XE/p05H8c7Y16hQIZw2bTtw7NiRPO8tr+vlpGWrtjDpc9q0zZ79/1LXXVcbX1+/TJn8mzdvzd49uzPlU8iqfoNIGkU24eOP3mHIsOdpdD6AJIqeoue1YEsIIYQQQpQqNpsNl8uFy+XCZrNx7tw5KlSqVdLNykzXURUddDcKF/MEKGR97FTQdB1FUXFr5ydcK8YSHe2/Wrz7zhu0atWWm2/JnmW9KL06agRt2nbg7nvuK9briuJ14MBexn/8Hp9/8a0nuCMuT+ypw4SGhmK1WjEajRiNRqxWa4m1R2YACCGEEEKIwqUoaCigXNJxyGvISedycwSWOTGxMWzbsokDB/YxdNiIkm6OuMY4HA6OHj3MTz9+R5euN0nn/xokAQAhhBBCCCGuErNm/MSuXdt58slnPFPChSgsZ8/GMO7t14hs3JR7ejxQ0s0RRUCWAAghhBBCXEWuiiUAQgghgNK3BEDmdAghhBBCCCGEEGWABACEEEIIIYQQQogyQAIAQgghhBBCCCFEGSABACGEEEIIIYQQogyQAIAQQgghhBBCCFEGSABACCGEEEKIq8Bfi//g+RFDeKT/A4x47ikWLpyPpmnF2obHHn2Q9evWXFEdx08co1/fHmzauL6QWiWE8JYEAIQQQgghhCjlfvttLtN/+o6bbr6dN958l3vv7cWv8+cwf97Mkm5aga1dsxJFUVizemVJN6XAnh8xhMWLF5Z0M4S4bMaSboAQQgghhBAig6ZpqGrmMTqbzca8uTPp3bc/3bp1B6BGzetwuVxM+/4but99HyaTqSSae1nWrlnJ7d26s3TJYmw2W4nuiS5EWSMBACGEEEIIIfLwyfh30TSd50a8DEBKSjJPPj6AES+8QrNmLTl9KoqpU7/hwP49+Pr5cdvtd9G9e08AUlNTGDzoIca98zHVa9QCYOaMn9i/bzevvj4WW3o6/3u0L93uuJt161Zxfceu9Hmwf6br79+3B7vNRseOnTMdb9W6LWdOnyI1JZngkHIAbNmykRm//MiZ01GEh1ekz4P9ad68leecvNoKcPZsLJMmTuDAgX2Eh1ekddv2LPlnMRO/mprjexN95jRTpkziwP49lC9fgfsfeJA2bTvk+l4eOLCfhPg47ruvD/+tW8PmTevp0LFLpjJrVi9n7tyZxJ07S/XqNen/yCBq1qwNQFJSIt9/9zXbtm3BYrXQuctNPPDAg56gSV73v3HDeiZNnMA3U6Z7rvXm6JFENm7Gfff3YfPmDXz91Wf07tOPOXNmYLfZ6dCxM48MHMzxY0cY9fJzAEyb+g0rly/l7XEf5XqfQpRWsgRACCGEEEKIPLRr14kd27dgs9sB2L59K1arD5GRTUlLS2Ps2NcIDg5mzFsf0L//IH6dP4ely/4q0DUOHtjP08Nf5PZud2V77WxcLBaLhcDAoEzH/f0D6PNgf0/n/+DBA4z/6B06de7KuHfH0/H6Loz/6B2iTp4A8KqtkyZOwGZL5+VXxvDQw4+yauW/ubbZZrMxbuzrVK9ek3HvfsJd3Xvyxecfc/LE8VzPWbd2JY2bNMPH15dWbduxZk3mZQA7d2xl0lefccdd9zDunfHUqVOf9997m7S0VAA++fhdEhMTeG30WIYOe541K//l99/neXX/3khOTmLjhv8Y8fwoHh30JEuXLGbr1o1UrVaDb6f8TETFSvR7aCCvjR7ndZ1ClCYSABBCCCGEECIPzVq0QlEUdm7fCsDWLRtp2bINRqORdWtX4nZrDHpsCFWqVqNN2w706PkAv86fU6Br9O77MHXr1vd05i/lcriwWH3yrWPRn7/SrHkr7ryzB5UqVeHue+6jSZPmLFy4ACDftp4+FcXu3TsZNHgo9eo1oHGTZtx7b69cr7d+3WrMZgsP9htARERFOne5kcaNm7F23aocy2uaxvp1q2jTpj0Abdt0YMf2raSkJHvK/PXXn7Rv34kbb7iViIqV6NtvAFWrViMqKopjx46wb98eBj/xNNWr16RBg0b06fcIZ06f9ur+vaGqKkOHjaBmzdq0b389tWrV5tiRI6iqitXHB0VRMBiNWCwWr+sUojSRAIAQQgghhBB5sFostGjRmo0b16NpGtu3baH1+Wnux44d5bpatTOtwa9XrxGxMdGeGQPeMBlyX5lrtVpJT0tF13UAVq9ewSP9H/B87dmzC4Djx45Sr179TOfWa9CQkyePedXWM9GnMRqNVKtWw/O6wWDItV3Hjh/hzJlTPPpIb8/Xtm2bORcbm2P5PXt2kZSURPMWrQGoU7c+/gEB/LdhrafM6VNRVK9Zy/O9oiiMemUMderU5dSpKHx9/QgLC/e83r799Qx+fKhX9+8Ng8GAj6+v53uL1YrNZvP6fCFKO8kBIIQQQgghRD7ate/EN19/zoH9e3G6nDRp0gwAo9GEasg8pqbrbgDcLmehXDssPByn00ns2VjCKoTRvHkrxr4zHofDxqujnkfXM7YCNJpMGNTMHXbNreF0uLxqq0FRURQFRVG8blvdug147HwH/AKfXJL6rV29ErfbzdCnHvUcc7lcrF29khtvuBUAg9GIQs7XNxqMebYtv/sXQkgAQAghhBBCiHw1bdYCt9vNjBk/0qJFa88oepUqVVm/fhUulwujMePRev++PYSWK4+fnz8ulwtFUTKNIrtcBeuQ1qlTn8CgYP5Z/AcPPvQIvr6++Pr6YktPz1SuSpWqHDiwj9svOXZg/16qVa/uVVsjKlbC6XRy/MQxqlXNOMftzr2tlStVZe3qlYSElPNMibelp2P1yb5cweVysWHDWvo9NJCmzVp6jkedPM6nEz4gIT6O4JByVK5UmWPHDmc6d8bPP9D++s5UqlSZ1NQUYmJjCKsQBsDevbs4cGAf3bv3zPf+faxW7A57pp0WXHncX05yC04IcbWQJQBCCCGEEELkw2Qy0apVW/bt3U2bNhez3Hfo2BkFhSnfTiTq5Ak2bljPgvlz6HbXPQAYjUaqVKnGH38s4MTxo6xbt4p/l/1doGsbDAb6DxjEn3/+yvy5Mzl+4hgHDuxl9uyfMRgMBAUGA3DnXT3YsGEdf/75G6dPRfH7b/PYvn0Lt91+l1dtDQuPoFHjpkz++nP279/Lju1bmTdvVq7t6tixMwaDysQvPznfpv28/tqLrF+3JlvZHTu2kJ6eRpeuN1G5chXPV+s27QkJKce68+fc1q07a1av4N9l/xB95jSzZk5n6dK/KBdSjspVqtK4STO++eozTz6Aryd9jtPu8Or+K1epiqIo/Dp/NsdPHOPXBXM4fOhggX4W/v7+7Nm9k6iokwU6T4jSQgIAQgghhBBCeKF1m3ZYLBaaNG3uOWaxWHhp5GhiY6J5ZdRzTJv6Dffcez/dunX3lBk06CnOnD7N6NEjWfL3Ilq3blfga7dvfz3PjniZzVs2MPrVF3j/3beIOnmCV159i8pVqgJQvXpNhj/zIsuWLGbkS8NZvWoFI14YRa1atb1u6+DHh2ExW3ln7OvMnPEDLVq2RlVzHvW2+vjw0sjRpKYk8/orzzP+43do3aY9rdtkv781q1cRGdkMPz//TMcVRaFN2w6sXbMCgLp16zPosaeYP38WL74wjJ07tvLSy6Px9w8A4Mknh+Pn78ebo0fyycfv0rZNB3r07OXV/QeHlGPAo4P5558/GTvmVU5FnaRevQYF+jnccWcP9uzeyddffVqg84QoLRT9QjYRIYQQQghR6tlsNlwuFy6XC5vNxrlz56hQqVb+J4ortmD+bI4fO8Kw4S+UdFOKjNPpzJQkcO7sX9i6bRNj3vqgBFslxNUr9tRhQkNDsVqtGI1GjEYj1lzyZBQHmQEghBBCCCFEHpKSEtmyZSOL/vyNrjfcUtLNKVLvvzeG33+bR0xMNJs3b2Dx4oV06NClpJslhCgkkgRQCCGEEEKIPGzctJ4fp02hW7e7aXw++/+16qGHBvLTT1OZM/tnAgOD6NatO7fedkdJN0sIUUhkCYAQQgghxFVElgAIIcTVQ5YACCGEEEIIIYQQothJAEAIIYQQQgghhCgDJAAghBBCCCGEEEKUARIAEEIIIYQQQgghygAJAAghhBBCCCGEEGWABACEEEIIIYQQQogyQAIAQgghhBBCCCFEGWAs6QYIIYS4dqQ7dE7GaZxJ1IlL0UlK13G5wa2DUQWrCQJ8VEL9FSKCFCqVUzEbSrrVQgghhBBlgwQAhBBCXLaYJJ3/DrnZccLN7ig3pxN0dN37842qQpVyCo2qGmhaVaX1dQYCfZSia7AQQgghRBkmAQAhhBAFcjZZ5++dLv7d7eJgtHZFdbk0naNndY6e1Vi4BRQFmlU3cHMjI10bGrGaCqnRQgghhBBCAgBCCCG8s+ukm1nrnaze70YrwCh/Qeg6bDnqZstRNxP/cXBncyP3tzFRzl9mBYjSpfWM+/Mts6H37GJoiRBCCOE9CQAIIYTI054oN98ud7LlqLtYr5ti15mxzsn8jU56tjHxYAcTvmYJBAghhBBCXC4JAAghhMhRfKrOxH8cLNnlBEqu4213wc9rnCze5mTYrVY6N5CsgUIIIYQQl0MCAEIIIbL5e6ebz/+ykWKDkuz8XyouFd6cZ6PrXgPPdrPgby0d7RJCCCGEuFpIAEAIIYRHml3noz/s/LuneKf7F8S/e9zsPZXOG/dZqROhlnRzhBBCCCGuGvLkJIQQAoCoeI0h39tKdef/gjOJOsN/SGfl3tLfViGEEEKI0kICAEIIIdgdpTFsqo3jZ69sW7+szAYI9lUIC1QI8VMwF+LyfbsT3pybzoJNzsKrVAghhBDiGiZLAIQQoozbekzjlZk2bM4r29uvRgWV5tVVGlY2UK28SpVyKlZT9nJJ6TqnEnQOnnGzJ0pj4xGNs8mXGXhQFHzMV9RsIYQQQogyQwIAQghRhm075mbUTDv2y+z8RwSp3NbEwC2NTVQM9i4pX6CPQqCPQv2KKnc1zzi2J8rNoh0ulux0ke7w7tqKAi/eZebWxjlEGYQQQgghRDYSABBCiDLqSKzO63Mur/NfLVTlwQ4mboo0ohZCMv4GlQ00qGxgUFczcze4mLXekWcgQDr/QgghhBAFJwEAIYQogxLTdF6ZkU6KrWCdfx8T9O9k4r42ZgxFkEUmwKowoJOJ7i2MfPmXg2V7XNnKSOdfCCGEEOLySABACCHKGE2Ht+bZiU4qWOe/fiWF1+/1ITyoEIb881HOT+HVey1cX9/Ix3/YSLVnHJfOvxBCCCHE5ZNdAIQQooyZsdbJlmMF2z7v7pYmJvT3LZbO/6W6NjDw5UAfqpRTpPMvhBBCCHGFZAaAEEKUIUdiNb5f6WWWvfMGdjbz0PUl1+muUk7lswE+7Drhpn1d+dgSQgghhLhc8iQlhBBlhK7DJ386cBZg8H9QVzN9O5T8iHugjyKdfyGEEEKIKyRPU0IIUUYs2eli50nve/89WhlLRedfCCGEEN47l+xm3f40th2xcSzWyck4JyfPOkguYOLfq0WAVaFKeTNVQ01UK2+iaU0rHev7Eewnq91zIgEAIYQoA1wafFeAqf+Nq6k8dbO5CFskhBBCiMISk+hm6tI4lu1MZd+pgi31u9ol23T2nLSz56Q90/FGVS10beTHw11DCAsylFDrSh8JAAghRBnw1w4XZxK8i/wH+ii8eo8Vg1q8Cf+EEEIIUTAxiW4mLY5j+soE7K5rc4T/cu06YWfXCTuT/4mnX+dgBt9aTgIBSABACCGueboOs9c7vS4/+EYz5QOk8y+EEEKUVtLx957dpTNlaTw/rUigX+dgnuoWSjn/srs8oOzeuRBClBFbj7s5dlbzqmyDSgZubyqxYSGEEKK0crp0Pl14lu+WxUvnvwAuBAI+/jUWZxl+3yQAIIQQ17hFW70f/R/YxYSM/QshhBClU3yKRt/xJ5i+MrGkm3LVmr4ykb7jTxCf4t3gyLVGAgBCCHENc7hg9QHvMv/XrWigZU1ZGyfEVUPXM76EEGWC060z6MuTbD5sK+mmXPU2H7bx+FdRON1l73eozPMUQohr2JajbtK9TAbco6V8JHhFc+PauxvHti24Du3FffI4WmwsWkoKuuZCMRhR/fxRK4RhqFIVY+36mJo0x1S/IailIMCiu9GTNqAlrIDkbZB2AOxRaK5EcDtBNaGYglAslcGnDkpgM5TgTiiBrUEp+fY73bD1mJvtxzX2n3ETFacTl6rhcIKiQKAPVAxRqROu0qy6SpvrTPhc7Rta2O04tm7EsWMz7gP7ST1xDMe5s7hsNuyaThwqFX5dVdKtFEIUsTd+iWHLEen8F5aNh9J545cYxvYLL+mmFCt52hNCiGvYhiMur8pZjAqd6pd85640cx3Yi23hfGwrl6InJuRYRgFwOdES49ES43Ed3If9338AUIOCsXS+CeudPTDWrlds7b5AT96MFjUFLWYOivNctteVC//RHeCIRXfEQvJW9JhZGQXM5VEr3Ida+VEIaF6cTQfgUIzG/I1Olu9xk2rPecRG1yEhDRLSNPZEafy6GawmBzc2MtKnnYnK5S5OfNxy1M3z0/N/kF4yyq/Q7qGgnLt3kL5gFo7Vy9Ft6Z7jbreWeeTfWba2/BKiLPppRSI/r5Jp/4Xt51WJNKxqpV/noJJuSrGRAIAQQlzDdpzwbn1b29oGfM2y+j8nzp3bSJ36Fc6tm66oHi0xgfTf5pD+2xxMzVrhN/AJTI2aFFIrc6cnrsV96HWIXw5w+TkeHGfRoiahRU1CCemKWutNlOD2hdXMXB07q/HNMgdrvVzKkpXNCX9sdbF4u4v725p5pJMJcyl/+nHu203q15/h3HZlf+eEENeG2CQ3b82KKelmXLPenhXDLU39y8wWgaX8I1AIIcTlsjt1jkR7t7atWY2y8aFXEHpiAslffIR92V+Fvs7auXUjCc88hvWm2/B/agRKYBGMPDjP4t4/Av3ML0Dhtl+P/xf3pq6oEX1R634EptBCrR/A5YYfVzuZvsaBuxDyNLk1mLHWwcbDLt5+wHrlFRYFh52UyV+QPm+GrO0XQnh88ttZHGU4a31Rs7t0vvrrHK8/EFbSTSkWkgRQCCGuUcfO6bi97EQ0qyYfB5dybN5A3KC+2JcuLrqOmK5j+2cRcYP64ty6sXCrjluGa11z9DM/U9id/0uugnZmOu71zdHjlxVqzbFJOsN/SOOHVYXT+b/UoWiNZ35I53Ri6XqYdp+OIm7oQNLn/iKdfyGEx/FYJzNWy9T/ovbzikRiEi9vptnVRp74hBDiGnUyzruek8mgUzVUPg4uSP9tDokjh6HFZ18nXxS0uLPEvzSM9N/nFk59Ud/g2nonOKILpb786PYzuDbfiRY1uVDqOxyjMWRqOntPFV0nODpR56OF9iKrv6Bch/aT8PT/cB8+WNJNEUKUMp/+cQ5NYoJFzu7SmfD72ZJuRrGQJz4hhLhGRSd4FwCoVM6AKsv/AUif+SMpE94DrXj3BlbcblI+eZe0WT9dUT3a8fFoe4eg6N4lfywsCi60vU+hHRt/RfUcjNZ47sd0zqWUnadd97HDJL44FC0+rqSbIoQohbZK1v9is/5Aev6FrgGSA0AIIa5RSV5+jkUEFX3v/6ZxqUV+jbzMecaXYN+879P2xwJSvv60mFqUs9SvP0UNCMR6e/cCn6udmop2YGQRtKoAbTg4EszlUCsOKPC5ZxJ1Rv5iI7kMPetqCfEkjnoWLZddJUTZlmrT+OjXs/y+KYWkNDfXRVgYdkc5bm/uX9JN83hw/EnS7G7mj6xeou2YvSaRF3+IZvnbNakaairRthSmg6cdHI4u2l0+/Cwqo/tU5L72wQT7GdgXZePdudH8+l/GsoOwICOr363LzNUJvPLjqSJpw01NAvjh2Ro8/uVxfttQcssdDkc7OHjaQe2KV/vesXmTGQBCCHGNSvVyhrOf5dof/o/KZzmEc+c2kie8d9n1q+VCMTVsjKlZK0wNG6OWu8ykeLpO8ifv4Ny1vWCnJa5F2zeUy17vb45ACWqLEtIVJagtmCMurx50tD1D0BPXFugsuwten2UjPvXy2h/oo1C/kkrz6gYaVlYpV3r6R7nTdZLfHY07+nTBz7VYMUY2xXLLHVjvuBtLhy6F3z5R4l7+MZqpyxJodZ2Vfp2DSbW7GfLNqUyjlF1fP8Kz350psTaeSXASneguM1PUi/v9/nt7SpFf44vHq/Lk7eVZuy+Vb/4+i7+PgR+frUHH+hlboPqYVUIDjFQMKbrASmiAgUAflfKBJT82XRzveUkr+XdZCCFEkXC4vXsi87l2BktyldeUcj01laR3XgN3wabNG+vUw+eOHpjaXY+hQni217XYaOxrV2H7Yz6ug/u8r9jlImnca5T7ZjqKrxd70LuTce98GLSCjRIpAc1QKg1CKX8HirVK9gL2k2ixC9FPTUZP3uZ9xboD967+GNtuBkOAV6d8vdTBoZiCLbsIC1Lo3txE5/oGqpTLPp4RnaizYq+TBZvdnI4v3iUd3kj/fR6OjesKdI6pXiN87u+Lf4u2uA0GXC4XNpsN27niyVchio/TrfPH5mQe6hzEmL4Zv1+evrMcN75+lL+3ptC2jk8JtzDDb6Oqo2nIMrIisuVI0U5JNxrg3nbBfPP3WZ6bEgXAO7Oj2T6hAXe1DmL13lSOxTqo8+QuEtOKLkHezNUJLN+VQnRC8S5fy8meE6UnP0xRkQCAEEJco7x9HisLIzeJ6bnfZOq3X6BFez+io4ZF4D/kOSwdu+ZdrkI4Pnffh8/d92FfuYyULz5CO+vdPs5a9GlSp0zEf+jz+ZZ1H3wVbMe9qhcAS1UMdT9GCbsnn3JVUKs8DlUeR4uZh7b/ObBHeXeN9GNoB19DrfdJvkV3ntBYsNHpXb2A1aTwSBcT97Y0Ycxj98rwIIUH2prp2RrmbHDw3XInjpJ/tgRAS0okbcqXXpdXA4PwG/Ic1pu6AWCz2cBVSm5GFAmjquBnUTkW68Lh0jEbFYJ8Dfz3/nUY1IzlAY2fzUgaeTzWyYL/kvjq8Urc2syfE+ecvDUrho0HbBgMCh3q+fDaAxUoH2hkztokXph2hm+HVOKGyIypMqk2jTYvHeKuVoG893D2YOaiLSmM//Usx885qV7BzNN3hHJHy4xzH//qFHaHzqwXqgIQk+Bi1PRo1u5LIzTAyNt9wxj81SmG3xXKk7eVY8PBdHp/dIKX76vAnDVJHD/rILKalfceDqdm+MVp11OWJjB1aTwxiS5qhpkZckc57mp5MaD428ZkJvx+jtMJLlpf50PH+r75vqc/rkhkypI4Tse7qF7BzJO3hXBPm0AAlu9KZeDnUcx5oRrNa2VsE/revLN8tzSevZ/VyfP9LkqxSUWbld7lhhSbRq1wCyaDgtOtk5jmpubgnZmeDQ5PiuSjBdGMmZHxWdm2ri8fPlKFBlWt7DiWzhd/xDL16erc8OoBNhxM47dXr8Pl0omKc3Jf+2BSbRpfLY7lw/k5fwa2r+fH32/Wpvvbh1i2M4UX7w1nePcwxs46zbN3h+FrMfDn5kSe/uYkafaiDejGJl37v1tlCYAQQlyjzEbvQgBpjms/ApCey+C4+/gR0hd6n33f3Lo95b7+Kd/Of1aWTjcQ8s10zK3aeX1O+q9zcJ84lmcZPXUPetQ3XtephN6Gsd2m/Dv/Wahh92Jsuxkl9Bavz3FHfQ1pec980HX44h+H1wsXKoWofDnQygNt8u78X8qgQq+2Zj4b4EM5/9IxTJk+6ye05CSvyhqq1SBk4jRP51+UDYoCg24OYeWeVG4afYRPfj/H4WgHhvNP7haTyriHLnbWxz0UTsOqVtwa9Bt/khOxLl66tzxP3BrCmr1pvPRDRserWwt/Aqwqc9cme85dtCWFdIdO745B2dpx8pyLod+comI5E6N7h1Gvkpmhk0+x+0T2ZB2aDv+beIpVe9Lo3TGIbs39eWbKmRz3r//k93MMvjWEdx4K5+BpB69Ov9gx/PafeN6eFcMNkX6M6RNG5VAjT08+zebDGddcvTeN4d+exqgqDLo5BJMRPskne/uUpQm8/nM0VcqbefzWcvhbFZ797gy/bUzO87wLcnu/i9rZxKLvjH76eww3Nw1g2ycNGHV/BLUrWvIcGKgcamLBqOuICDHyya8xHDxl56snq2Urd0uzAEIDDPQbf5R/dyXzRp+KnmUF3gjyVXn05vIM++YkHy2Ips/1ITx9V4XLucUCKeqgS2kgMwCEEOIaFeDjXWcnpQwkXdNy+TxP/eFbvN1o3nx9V4JeewcMXvY8s1ADAgl8+2OSxozEsWZF/idobtJ++paAkWNyL3L0XfAy479S/h4MTX4G5TI/+k0hGJrMx72zD3rsb/lfT3fhPvIOhkZTcy2zer+L/ae9e9iqFKzwycNWQi+zE187XOWjflaG/2AjKa3kgl56ehrpv872qqxapToh479GCQou2kaJUmnYnaHUijAz7d8EPl14jk8XnqN7qwDG9QvHz6rSp2MQXy2Oo3lNH/qc77xrOnz+WCUiggyEBWf8W1dVhXfmxuByg69FpXvrAOasSyIxzU2Qr4F5/yVRJ8JMi1rZO7RnEpxoOtzbNoB72gTyQPsgBtwQQvWw7EnSNhxMZ9dxG2P7hdH3+mAAmtSwMmxy9jwXT90WQs92GaPvu0/YmboswdPpnPR3PL06BvFmnzAA7msfRJdXDzNnbSItalmZuiyesGADc16qip8lIyLyzJTT/Loh5868psNXi+Po0siP74ZWznhv7wjl3veOMXFRHN1b5b9UyWggx/e7qBVHZ/S9udHsP2Xjidsr8PJ94Yy6P5xZaxIYOukEqTmMtj/ctRx+FpXOo/az/1TGdPlkm5vBt5bPVC4pXWPgp8ewOXXW70uld8cQOjTwZ/Ve75MCD510gnX7U1m0OYkebYPpUN8fKNotbmOLIehS0mQGgBBCXKOCvFwiesbL7QKvZqqavcOnnYvFvnKpV+cb6zUkcNRbl935v0AxGgl85W2Mdep5Vd627G+0c7mMbDlOo0d715EksBVq4x8uv/N/gWpCbfQjSkBzr4rr0bPQ7bknuZv9n3dT/33MMK735Xf+L6gWqvLqPRYuO1liIbAt/Rs9Nf8kU4qPD8FvfySd/zLuzpYBzBhRldVja/G/m0L4bWMy782LzbW8qkBskpOHJ5ykzpD9tBhxiO//jcflhqT0jM5kr47B2J0ZOQbOxLtYuy+NXrl0aJvV8KFzQz+e/e4Md407zpiZMRhUPB3vSx04nTHV6ubGF6fF39I05ynykdUuBhuC/Q043Trpdo2YBBdnk1zMXJ1IrSf3U+vJ/dQZsp9T8S7OJGS0f/8pBx3q+mVqw61Nc+/EX6jztmYXR58NKtzSxJ+9UXacXubLKQkWL2fyXal56xK57Y2D1Buym08XxnJ/+2De6lcxx7INqlg5cNru6fwD/J5D5v59UTZszoz3NtWu4XDpBPsV7DN0y5E0z5/jkl2EFPB8kTOZASCEENeoisHexXhPJ+o43WC6hj9XzTl82tn+/sOrddSKyUzgy2NQzJZCaYtisRIwcgzxTzwEznw6wG436f/8iV/vh7O9pJ/+CXQvOtCqBWOjqaAWznRVxeCD2mgq7v/agJZPsiTdiXZ6OoYaI7K9dPycxo4T3gWfHr/RTNXQwhmzaFnTwB3NTPyxtWRGeRzLFntVznfA4xiqZJ9WK8qGM/Eu/tiSwu3N/akUYqRiOSOv3F+BY2cdLNuZ+wjq2SQXQ74+zfUNfBn3cDg2h870lQkcj3V6wl5NqluoX8XC3LXJJKVpGFWFHudH47MyGmDqsMpsOWxjzb5UFm9N4ccVCUwZUpkujXKezq140Wc15JI1UD/fyCduK8ddLTMHD/x9Ln5IpTky/+7Q8wjqXagzt4YpuWTMsbtKPjheIchIsq3otgGsGGLi3nZBLPgvkahzTk7FORn1wylqhVu4vXkQz5Fz3hfdi5iJqxACK4VRR0FVCLr2u8cyA0AIIa5RVcp5N3KgaXAouminGaqKXiRfipcjuTmNVjlW/evVudYe9xd6R8xYvSa+d9/vVVnnqmU5HnfHzPPqfLXyk+Bb1+u2eUPxa4BaebB3hc/Oz/Hwv7u9+ztXs4LCnc0Ld6uKRzqbSyTgpaWm4Ni+Jd9yamgFfO95oBhaJEqrU/Eu3p4Vw+S/4zzH3BrEJrpRLunIqgqZkqIdOOPA4dJ55IYQWtbKSI7na8n+l71XhyA2HU7nvXlnubWpP6H+Of+D2H3CxjtzYqlT0cyQbqHMe6k6vhaVpTuyByFqR2QsC1iy/eJrOZXLS3iIkdAAI9EJLhpWtdKwqpVq5c3MWJ1EyvkZDHUqmtlyJD3Tfed1nfAQI+UDjfy99WIZTYclO1KoX9mC0QCB54MLUfEZQVW3Buv2Z8/An/X9LmoVAov2F1XV8ibeH1CZ4XeFeY6pCkQEG9Fy6eXvPWmjTiUL10VcXAbSrWXxLIkoDkX9npcG136IQwghyqiqoQpmAzi86GdtPeamfqWi+9D7++WiyZS8Zr+L12bnv2VPSJap43pyIs59u/O/gNGIz339Lrd5efK5/0HS5s/INweBa99utOQk1ICLI3S6Mw49aVP+Oz0oJpTqz1xxW3OiVn8W7eTEfHMQ6Ikb0Z3xKKaQTMfXH/JuBL5XO3OhbzEW6q/QtaGBv3cUb7In185tuSekuIT1jrvBlH/QQ7fZSF+yiKRf51LhqxmF0URRSrSoZaVTAz+mLktg/2kHdSLMbDqczs7jdl69/2IitGrlzazam8Y7c2K5tbk/14WZMRrgsz/OkZjmZusRGwv+yz49u0fbAMbMzEi817tjzqP/kNFRnrwkns2HbdzWwp8dR22k2jTqV8meA6B1bR/qV7HwxswYDp5xYDIqzFyd/dp5URUYdHMw7807i1vTaVTVyl9bU9hx3MbDXYIBGNA1mAGfRfHAhyfp1tyPvVF2lu1Ky7POJ24N4e3ZsTz6eRQtr7OyYnca24/Z+eR/GdPc61Q0E+hj4O3ZMRw87WDncRvHYrKPvGd9v1vWKtrtGCsEFm1X7b8DafyzLZmnupWnQVUre0/aaFfXj+a1fHjp+1M5nvPDv3E8c3cYC1+rzdSl56hW3swDHUNyLHs1Kur3vDSQGQBCCHGNMqgK9bzs1G86cnVmvT0a691ITFhA5h6kc88ur+Ywmlu0wVC+aLIOqxXCMTdrnW85XdNw7d2V+WDSfyjkf+9q6E0olkqX28S8WaqghHT1oqAbkv7LdMTm1DlwJv/2+5igS4OieRi7qVHhzirwhmu/F0EnwHL9DXm+7o46Tso3nxP3v16kfTUB17EjhdE8Ucp8Obgij9wYzMEzdn5ZnYjNqfNG7zAeveliZ+vFHqFUCTHy/b8JJKa6CQs28uVjlYk65+TFH6I5Guvg+XvKZ6s72Dfjs8HXotKxQe6Z2SOrWfl+WBWSbRrvz4tlw8F0nuseSp+OwdnKGlT49snKtK3jy08rEvh9YzJvPxiO0VCwteyP31qOl3tWYNOhdMb/dhabS2fyk5WoXTEj6NCpoR8fPRJBusPNxL/iSbXrjH4g79/Tj94Uwuu9wjgS4+CzP+JISHHz0YAI7j6fANDfR2X8o+H4WQxM/iceq0nhiVvLZasn6/td1BpULZylZ3np9/FRvvjzLPUrWxh4Uyg+FpXnpkTxxZ8555o4ec7Jfe8eJj7FzQs9wmlUzcoLU08CeNb8X82K4z0vaYque7OKQwghxNXo23/tTF+T/0irqsCMYb6lZps0b42Za2P53rwfwhQF/njBL1MegNRfvidt8hf51u8/ZAQ+9/a+0mbmKn3Oz6RMHJ9vOd9BQ/Hr09/zvXb0A7RDr+R7nlJ3PIaqQ66ojXnRTnyKtv/5/NtRexyG6hfL7Tut8dR32afXZtWhjpG3HiiahzGHC7p/lIorn2f4JaNy7hy1npH/Eo4NvTMnaUx6+xXs//6d90kWHyr8tgzULGM0bjf2datI/3U2Sf+tRdN1XLqOXdOJc7qo9+d/OdcnRA7OJrto8+JhnuseytA7QgulznSHzuq9adQMM3mmh+8+YeOuccf55qlK3NS4aGaCXcsOnnZw65ijJd2MTMKCjHRu5M+fm5I8uwQM6VaBsQ9VovpjO0lMuzoHFC746/UanmBTYYk9dZjQ0FCsVitGoxGj0YjVWvTbSObm2p/jIIQQZVjb2iavAgCaDkt3u7i/TfGPil6JnSfzH0WuHKJmSwKonc45sVFWxvqNLqdZXvO2fv1M5qmYus27EV81MP8ZBldC8bJ+Jf1opu9Pxnk3c6N+paILSJmNULOC6tVMhMLijs59R4QLjJUrZ+r8a/Fx2P5cgO33ebhjzgDkv/RDiFxsP2bn350pLN+Vio9Fpc/1hbd2W1Hg9Z+jces6D14fjKLAjNWJVKtg4voC7P8uLqpd0UytcDOHo4suEWBBhQUb+XZodbYdTWfO2ngqhpj4383lmbUm/qrv/NcKNxd65780kiUAQghxDWtQSfF6VH/eRld+y9FLlSOxGudS8p/EVjci+0ednhDv1TUMlasWuF0FYahcxatyWnxcpu8VR4xX5ym+1xW4TQWh+HhZvzPzvs3e/NwAqpQr2mRMlUKK9zFIT0zIt4wakNEhc+7cRtK41zj3YHdSp0z0dP6FuBLJ6W4mLo4jLsXNxMcqUr4Q1ztbTQo/PlOFyKo+fP1PPF//HU/9ylamDauCxSRhq8vVuZFvSTchk53HbNz33mEUYHTvivS+PoRpy84xfPLJkm7aFctt28prjcwAEEKIa5hBVehS38i8jflvF3cmQePf3W5uirw6MuCu2uddErkGlbN38rS03BNGXaBDpsR7RUEN9G70TUvPnOFadyXne46OAqbsa1gLlcm7qcNZ25tm9y4AEORbtJ2GgGKeganb8l/24D4dRfzgfrgOHyiGFomypmN9X/Z8WqfI6q8VbubbIUWUd6SMGnhDCNNXJOJwlZ5V2/9sS+afbfl/Dl1NzEaF/9187SQzzIvMABBCiGvcHc28j/VOWWHHfhUk8dF1+HundwGAFjWyBzQUb9LfKIp3G1pfCUX17hpalvYqXrRfVyj6yeKqd9fQM08tyXo7uTEU8VOK0VDMo5Je3Lc75ox0/oUQHlXLmxh4Y9nomJakgTeGUD7g6hgAuVISABBCiGtcrTCVJlW9+3V/JkHnl7X5zxYoaRuPuImKy783FR4ENSrkcO/m/Nf4Kbru1YjtldDT07zajUCxZEmEp+Y/dK0oGrgLtgd3gbmT8apXa8i8VZbJy4zg6Y6iDUYVdf1ZKV78vRNCiKyevD2UAKssoygq/laFp7oVTjLMq4EEAIQQogzo1c775H7T1zjYf7p0JwP4YZV3CZG61M959oPi7dT7Il537T6Tf1I4ADUoOPMBo5dT723HC9iigtFtx7wqp5gyb0MW7OPdg2xMUtF20M8mF3MAIOvP8YorVDG3bkfAsyMLt14hRKkS6KMwpm9ESTfjmjWuX0SZCrBIAEAIIcqA9nWMNKjk3dQ2l6bw9nwbaaUn6XAmS3e72OVF9n+AWxrnHPgwhId7db7r4H6v23U5nIe8q18Ny/LgZ/EyOWHKtgK2qGD0ZO/q17O0NyzIuwetQzFFG4g6UsT1Z2XI+nO8TEpIMD69HiLkm58IeHE0pshmhVKvEKL0uqdNAI/fWsR5Xcqgx28tx12tAkq6GcVKAgBCCFFGPHaj97MAouJ13phjy3eP9OJ2LkXni7+9i0w0qmKgVljOH3OGarW8qsO+ZYPXbbscLi/rN1avmel7xb+BV+dp55YVuE0Focd7V7+apb01c1qWkYOtR4vuL2BUvE5cavHOADBUq3FF55simxEw6i1Cv5+HX//HMFTwLpAlhLg2vNCjPK1r++RfUHilcyNfXuhRPv+C1xgJAAghRBnRtJqBrg28T3Cz6Yib9xfavU7YVtRsTnhtto0ELzttvdrmHvAw1W/kVR2OVcvBWTRTIXS7Hfvq5V6VNWZprxLY2rtrnF0Amr3AbfOKOx099lfvymZpb1igQqgX21MeP6dxKLpoRulX7C3+XBfGeg0LfI7i44v1rp6EfDOd4E++xnrjbSgm74N5Qohrh6rAF4MrUTFENnK7UhEhRj79X2XUsjPz30MCAEIIUYYMvdVCoJfrrwGW7HTx9jw7zhKeCZDugFdm2th3yrvO4HXhKh3r5R7sMFSuihqW/+ipnpyI7e8/vW5nQdgW/4aempJvOTU8AkPFypmOKb61vVsG4IxDO/3T5TYxT9qpaeBKzL+gtTqKT/YZFy1qeheMmuvFFpYFpenw57bi/0ttbtIcVO8evQxVq+E/9AXK/fI7Ac+MxFizdhG3TghxNSgfYODfMTV5sJN3uWxEdr06BrJ8TM0CPQ9dSyQAIIQQZUiIn8Lw2y35F7zE8r0uXpqeTlxKyUwFOJus88wP6Ww95n2HbfAN5nw3p7N06OJVXak/fFPouwHoaamk/jTFq7LmDl1zPK5UuNur87Ujbxf6bgC6Kxnt6DivyioVuud4/Pp63o1g/b3DxbHYwp0FsGSXi6i44k90qQQEYmrczKuyxpq18enxAKqfv1fl3W7vtsUUQlz9TEaFtx8M562+4WVyBPtyqQq881AE7z4U4fVuNFeqNP5ulgCAEEKUMV0bGLirecGmEG87ofH4lHQ2HS7eUdNNR9w8OSWdgwWYBt61gYFWtfIfXbbccodX9WmxMaRO+tTr63sjZeIn6OfOelXW55ZuOR5XKz7o3cXsJ9EOFm6WeP3A8+DwcgeDiv1yPN72OgNBXixldWvw/kIb7kLqryel60xaUnIZLi033u5VOfuKpTjWrc63nKqqKIqC01FKs3YKIYpMv85B/DW6Jr06BmI0SCQgN0YD3N8+kL9G16R3x8BivbbT4UBRFFQvZ38Vh9LTEiGEEMVm6K1mGlQq2MNCXIrOS7/YGLvARnwRJ09LtulMWOTgpZ9tBUrUVs5f4enbvJvhYKrXEGM973IBpP82B9vC+V63I8+6FszC9ucCr8qaGkRirJtzwj8lsDVKYCuv6tFOTkI79a3Xbcy7rq/QTn3nVVklqC1KQMscXzMZ4K7m3s0C2HtK59PFV57LQNNh7AJ7kf/9zYv1pttQ/b3LOJ380VtoMdE5vqYoF//9qqqKs4hyVQghSrda4SbefSiCFW/V5KHOwZiLaWT7auBjVnn0xhBWvFWL9/tHUCu8+POnOJ2OTJ3/S393lxQJAAghRBlkMsDbvXyoFFywDyIdWLrLzUNfpjHxHwfnCnlZQKpd5+c1DvpPTOfXzU4KUrtRVRh1j4UgX+/vyffBR7wum/zJO6T9OrsALcoubd4MUj7/0OvyPg8OzPN1tYb3I/vuPUNwn5jkdfmcaCe+QNv/jNfl1eov5fn6fW3M+Ji9q+v3LS4++8tx2UkpXW4Yt8DOxmKexZKVYvXB2qOXV2W1+DgSRg5DyzJb5MID5IX/q6paKqeZCiGKT0SIkTF9w9j7WR1mv1CNgTeE0KKWlesiTJQPMFzTgQGzUaF8gIFa4SZa1LIy6JZg5r1UjV0TavPqAxWIKMGkiW63yxMAyPq7u6Qouq6XkvzOQgghitvpBI3nfrQTk3R586uNBmh7nZFbIo20vs6A9TKC6y43bDvuZtluF//ucZF+mQOZw283c3eLgjcg/pnBuHZu9bq89ba78H/yWRQvR3EB9OQkUr74CNs/3icUNDVpQfDHX+Vbzr3pBvSE/KeKX6BWfBi17kdgDPb6HFzxuPc9i35mutenKMGdMLRckm+5n1Y7mbLc+x96i+oqz3e3Eh7o/QNUdKLOOwts7DhZ8L/nS0b55Xi89Yz78z13Q++cA0Z6Wipx/XuiJcR71QZDWASBo97CGNkUALvdjsvlwuVy4XQ6SUhIwOFWKR9W0av6hBBCFI+zMacxGzSCg4MxmUwYjUaMRiMWS8HyMRUmCQAIIUQZdypB54XpNs4kXNkia6OqUL+SSqMqKtVCVaqEqgT5KPhaMmYcuDRIs+vEp+qcSdQ5flZj/2mNnSc1bM4r+yh6tIuZfh0vb2qf6+gh4p/oDy7vs82rgcH43P8g1jvuRg0ul2s5Lf4ctj9/JW32dPQkLzLmX2AyUW7Sjxiq1cy3qJ6yC/eGtqAVIHJiCkWt9gxqpYFgDsu9bkc0+qnv0I5/As447+tXzBjbbgC/nJcvXMrphsenpBco0Z/VpHB3SyM9WpoID8o9EHA2WWfBJidzNzixXeZmAkURAACw/f0Hye+94X1DFAXLTbfje38/tGo1PAEAh8NBcnIySclpVKyafbcFIYQQJef0icMEBvgSEBCA2Wz2BADMZi+nvxUBCQAIIYTgbLLOy7+kczj26vtIeKSzmYevv7J1fWlzppM68ZOCn6iqmBpEYqrfCDWiIorFBz09HXf0KZx7d+Pat5PLyV7n/9Rz+PTs43V57fgEtAMvFPg6KMaMXAJBrcFaHVQ/0FLQ04+hJ22ApI2gF3xquVrnI9Rqw7wufzBaY9j36TgKfCmduhWNNKysUjFIwccCDiecTtTZe0pjd5SbK33KKaoAAEDSGy9iX/VvgdvkjqiM2iASLbwimr8/qTY7MbFnqT1wGIrBu+0VhRBCFC1d1zl+eDfhYWH4+/t7ZgAYDAYJAAghhCh5aXadd36zs2Z/ya6R9pai6Dx1i4WerQonqU/y2FexLfurUOq6Etabbifg5TEFPk/b+RBa9MwiaFHBqBF9UBtNK/B5i7Y5+WBh6UtkV5QBAD01hfihA3GfOFagNjk0DbcObl3HpevYNI04p4uwNz8htE3HAtUlhBCiaCTGn8OWGk/58uWxWq2e0f+SDgBIEkAhhBAA+FoUxtxnZfCNZoylfBDRzwJvP2AttM4/gP+Lr2Nu0brQ6rscphZtCHj+tcs6V234LUrIjYXcooJRyt2E2nDyZZ17e1MTAzoVf4bmkqT4+RM09hPU0PIFOy/Ll0FRsKgq8Wv+LfxGCiGEuCwpyQlYLBYMBgOKomT6KkkSABBCCOGhKNC7nYnPB1i5Lrx0fkTUi1CY9D9f2tUu3Ky+islM0FsfY27ToVDr9Za5TUeC3voITJfZCVYtGJrNQwn1bp/5wqaEdsPQdC4olz+q0b+Tmf5lLAhgqFSZ4I8mooaFe32OgoJyvvevKBkPc0ZFIWXdcq54zYMQQohC4bCnYzKZUFVVAgBCCCFKtzoRBiYO9OGJm8z4W0vH1kEWEzx2g5nPHvGhYgG3L/T+IhYC3/oQ3wKsv79iioLvfX0JevtDlCvNCqz6YGg6F7Wq9+vvr5yCWm14Rudf9bni2gZ0MjPijqKdhWJUde5pWXLbQmVlqFKd4E+nYKzXyOtzLoz+qyioioJZUTAnxHPk+/x3jhBCCFG0Tp04gtVixmw2o6pqpiBASZMAgBBCiBwZVHigrYlpT/jQu60Jq6lkPrQUBW6ONDL1cV/6tDdhUIu2HYrBiN9TzxH45vuoIbln+C8MakgogW9+gN+Tz4JaSD1exYha9yPUJrPyzPBfKMzhGJrMRq3zASiF12O/o5mJz/r7ULVc4T+mWEzw5v0+dKpXegIAAIbyFQj+5Gt8ej8Mat73rSgXO/8Z/8/YhcPPoHJ27nQccWeLo8lCCCFy4HDYSU1J8CT+u9D5Ly1BAAkACCGEyFOQr8Lgm8z8PNSHgV3MVCjA/utXwmJUuLO5ke8G+/Dy3RbCium6nut37Eq572ZlZOO/3Gn5uVBMZnzvf5CQ72Zi6dC5UOu+QK1wD8b2OzNmA1zBtPycK7dkjPq324FSoXvh1n1e3YoqXw/yoX8nE9ZCevurl1f5bIAP7Wp7F6wo7kc0xWTC/7FhhHz5PaamLXIvx8VlAIoCqqJgUBTMqoqfw8bRn78rvkYLIYTI5Myp4/j7+XnW/2edAVDSAQDZBUAIIUSBaDpsPuJi2W4Xaw+4SUwvvLqNqkKzGiqd6xno2tCE3xXOiC8s2tkY0ubOwP7Xb2gJCZddjxIcgvX27vje2xs1tELhNTA/9ii0E5+jnZoGztjLrkY3VcBQaQBq1aFgqVSIDcxbfKrO7PVO/tjqJMlW8PODfaFvezP3tDJhOt/333LUzfPT867MaIDFLxXdLgD5cW7bTPrcn7GtW4Xivrg7h6braDpo6Jl2A3DqOqkuNzFujevem0i5pq2u6PpCCCEKJikxnuioI0RERBAQEODZ+u9C9v8LwQA1n5leRUkCAEIIIS6bpsOBMxrbj7vZE+XmcIxGVIKOpnlztk5YkEqN8ip1I1QaVlFpUtWAj7nk18flRne5cG7ZgGP9Gpw7NuM8ejhTxyxbeYMBU43rMDVujrldR8zNW0NJ7tOuOdHil8G5P9ESVqEn70LBlWtxHSOKfyOUkOtRQu9ALXcDKCU3dd7phvWH3Kzd72LbcY0zCRq5PcQE+ii0qGGgU30jHeqomI2Z/179d8jNyzPyDgAE+ijMe9a3kFp/+bSEeBxrluPY9B+u3TtwxZxBIyMQ4NbBTUZAwKnr2N0aSVZfkq6rR+SocVgDg0u6+UIIUSbYbOkc3reDChXKExwcjNVqxWQyYTAYPF+XzgYoKRIAEEIIUajcGsQm68SlaCSn6zjcGceMKpiNEOCjEuyrEOpPtk7ZVcfpxH06CndsDHpqMrrLhWI0ovgFYKgQhqFi5UJfPlCodAekH0a3RYErAV1zoqgmMAajWCuDtRaoJbdXcX5SbHAyzk18qo7dCQaDQoAVKgarhAfl/Xfrn50u3vnVnmeZquVUpj5x5YkNC5s7KRH36Shccedwp6XidrtxG4zo/gHooRVIM5mJi4sjMSmFBo1blvh0UyGEuNa53W727dpCUKA/5cr9n737Do+iatg4/Gw2vYdO6L0XQZCONOmCYH9t6GdBREEUrIBiAXkVBQRFxAqIAiK9I733Jr0H0nvf8v2RN2tCOgQCzO++rlyQnZkzZ2ZnN3OeOXOmmLy8vDJd+c/uVoCicmuNgAMAuO2ZnaQyfiaV8SvCK903i4uLzBUry1yxclHX5NqYXCXP2jJ51k77tYirU1De7lLtwGs7zoKj8u6mUtz7moq+4Zx8fOXk4yuzzSbb/36sVqssFossFos8LBb5+fkpNTVVZ0/9oyrV6xR1lQHgjnb6+GF5ebrLz89PHh4embr630r3/0sMAggAAAzoTFjeHSADb8BTCApD+glkxhPK9JPM9CtNnp6e8vf3V2pygk4dPyxb/u7LAQAUgM1m06ljh2WSVf7+/vL09MzS5f/qxn9RhwC35l82AABgaHa7dPxKzuMrXG/ZBy7k3SCuXKLor9TkJmPDP2Pj32w2y8XFRd7e3ipZsqSsqUk6cmCXUlNTirrKAHDHSE5K1OEDO2WzJqt48eKObv9Xd/m/Fe77z4hbAAAAwC0jNsmu5fstWrTXoqBIm354wUMVihfu9Yqjl2wKj807AKh1jbcX3Awmk0npwzhl7AVgt9tl/t9Ak66urvLx8ZHJZFJERISO7N+pqjXryYeBAQHgusTFRuvksUPy8/VRQECAvL295erqmmW0/6u7/d8KIQABAAAAKHInrtj01+5UrT1sVbLl3+7536xO1sePFO5AfH/uTs1zHncXk2qVuXUDACnziWT6I6Uyju1st9sdIYDZbJazs7NOHz8kb99iqlilulxcbt0BHgHgVpSamqILZ08pNjpcxYsVc3T7z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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'run_browser'}\n", - "--------------------------------------------------\n", - "Transition type: init_branch\n", - "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=a959cfd1-1b96-4c15-b025-863c31ffcea5', 'julep_session_id': 'e2086073-f24c-4a1e-8551-f49b978dc63d', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01KVBWUuhNPpZ2UB7FFNr5DJ'}], 'workflow_label': 'run_browser'}\n", - "--------------------------------------------------\n", - "Transition type: step\n", - "Transition output: {'choices': [{'finish_reason': 'tool_calls', 'index': 0, 'logprobs': None, 'message': {'content': \"2. Since we need to go to a specific URL (https://github.com/julep-ai/julep), I'll type the URL:\", 'created_at': None, 'id': None, 'name': None, 'role': 'assistant', 'tool_call_id': None, 'tool_calls': [{'api_call': None, 'bash_20241022': None, 'computer_20241022': None, 'function': {'arguments': '{\"action\": \"type\", \"text\": \"https://github.com/julep-ai/julep\"}', 'name': 'computer'}, 'id': 'toolu_01T4kCjSbcj3bug9PepZfw6J', 'integration': None, 'system': None, 'text_editor_20241022': None, 'type': 'function'}]}, 'tool_calls': None}], 'created_at': '2024-11-22T07:05:25.267991Z', 'docs': [], 'id': 'e2117108-a359-450e-ab88-20ffc3e23e6b', 'jobs': [], 'usage': {'completion_tokens': 114, 'prompt_tokens': 2489, 'total_tokens': 2603}}\n", + "Transition output: {'cdp_url': 'wss://connect.browserbase.com/?apiKey=bb_live_fvKLOUF6VHrh78JFPRolwpPlQug&sessionId=4cfd1fca-7939-4d75-b563-81f1c19458f2', 'julep_session_id': '6d257b7c-d3f5-4720-b160-9ec7f012ef9a', 'messages': [{'content': [{'image_url': {'url': 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'}, 'type': 'image_url'}], 'name': 'computer', 'role': 'tool', 'tool_call_id': 'toolu_01SF7tWSGgctWmwTWqET1zbV'}], 'workflow_label': 'run_browser'}\n", "--------------------------------------------------\n" ] } diff --git a/llm-proxy/litellm-config.yaml b/llm-proxy/litellm-config.yaml index 8423b3bc2..afa244de8 100644 --- a/llm-proxy/litellm-config.yaml +++ b/llm-proxy/litellm-config.yaml @@ -19,18 +19,6 @@ model_list: api_key: os.environ/GEMINI_API_KEY tags: ["paid"] -- model_name: gemini-1.5-pro - litellm_params: - model: vertex_ai_beta/gemini-1.5-pro - tags: ["paid"] - vertex_credentials: os.environ/GOOGLE_APPLICATION_CREDENTIALS - -- model_name: claude-3.5-sonnet-20240620 - litellm_params: - model: vertex_ai/claude-3-5-sonnet@20240620 - tags: ["paid"] - vertex_credentials: os.environ/GOOGLE_APPLICATION_CREDENTIALS - # OpenAI models - model_name: "gpt-4-turbo" litellm_params: From 5b6388689aeeaf63dd7167523aa67580c03110d8 Mon Sep 17 00:00:00 2001 From: Hamada Salhab Date: Fri, 22 Nov 2024 12:00:35 +0300 Subject: [PATCH 23/23] fix(agents-api): Fix embedding model litellm error (#870) > [!IMPORTANT] > Commented out a line in `aembedding()` in `litellm.py` to prevent errors with `voyage/voyage-3` model. > > - **Behavior**: > - Commented out line in `aembedding()` in `litellm.py` to prevent errors with `voyage/voyage-3` model. > - The line `model = f"openai/{model}"` is temporarily disabled. > - **Misc**: > - No other changes or fixes are included in this PR. > > This description was created by [Ellipsis](https://www.ellipsis.dev?ref=julep-ai%2Fjulep&utm_source=github&utm_medium=referral) for 3d429beb3b5270b8fd944985bf482c3d138b691a. It will automatically update as commits are pushed. --------- Co-authored-by: HamadaSalhab --- agents-api/agents_api/clients/litellm.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/agents-api/agents_api/clients/litellm.py b/agents-api/agents_api/clients/litellm.py index 7cca97ae8..657c82b0c 100644 --- a/agents-api/agents_api/clients/litellm.py +++ b/agents-api/agents_api/clients/litellm.py @@ -58,8 +58,9 @@ async def aembedding( custom_api_key: None | str = None, **settings, ) -> list[list[float]]: - if not custom_api_key: - model = f"openai/{model}" # FIXME: This is for litellm + # Temporarily commented out (causes errors when using voyage/voyage-3) + # if not custom_api_key: + # model = f"openai/{model}" # FIXME: This is for litellm if isinstance(inputs, str): input = [inputs]