From 1051fa57299a23534fc43d6af4aa350cc31aa8ee Mon Sep 17 00:00:00 2001 From: Nithish Raghunandanan Date: Wed, 15 Jan 2025 21:37:27 +0100 Subject: [PATCH] couchbase: Migrate couchbase partner package to different repo (#29239) **Description:** Migrate the couchbase partner package to [Couchbase-Ecosystem](https://github.com/Couchbase-Ecosystem/langchain-couchbase) org --- libs/packages.yml | 4 +- libs/partners/couchbase/.gitignore | 3 - libs/partners/couchbase/LICENSE | 21 - libs/partners/couchbase/Makefile | 64 - libs/partners/couchbase/README.md | 43 +- .../couchbase/langchain_couchbase/__init__.py | 10 - .../couchbase/langchain_couchbase/cache.py | 386 ---- .../chat_message_histories.py | 269 --- .../couchbase/langchain_couchbase/py.typed | 0 .../langchain_couchbase/vectorstores.py | 739 ------- libs/partners/couchbase/poetry.lock | 1790 ----------------- libs/partners/couchbase/pyproject.toml | 81 - .../couchbase/scripts/check_imports.py | 17 - .../couchbase/scripts/lint_imports.sh | 18 - libs/partners/couchbase/tests/__init__.py | 0 .../tests/integration_tests/__init__.py | 0 .../tests/integration_tests/test_cache.py | 218 -- .../test_chat_message_history.py | 294 --- .../tests/integration_tests/test_compile.py | 7 - .../integration_tests/test_vector_store.py | 396 ---- .../couchbase/tests/unit_tests/__init__.py | 0 .../tests/unit_tests/test_imports.py | 12 - .../tests/unit_tests/test_vectorstore.py | 1 - libs/partners/couchbase/tests/utils.py | 136 -- 24 files changed, 4 insertions(+), 4505 deletions(-) delete mode 100644 libs/partners/couchbase/.gitignore delete mode 100644 libs/partners/couchbase/LICENSE delete mode 100644 libs/partners/couchbase/Makefile delete mode 100644 libs/partners/couchbase/langchain_couchbase/__init__.py delete mode 100644 libs/partners/couchbase/langchain_couchbase/cache.py delete mode 100644 libs/partners/couchbase/langchain_couchbase/chat_message_histories.py delete mode 100644 libs/partners/couchbase/langchain_couchbase/py.typed delete mode 100644 libs/partners/couchbase/langchain_couchbase/vectorstores.py delete mode 100644 libs/partners/couchbase/poetry.lock delete mode 100644 libs/partners/couchbase/pyproject.toml delete mode 100644 libs/partners/couchbase/scripts/check_imports.py delete mode 100755 libs/partners/couchbase/scripts/lint_imports.sh delete mode 100644 libs/partners/couchbase/tests/__init__.py delete mode 100644 libs/partners/couchbase/tests/integration_tests/__init__.py delete mode 100644 libs/partners/couchbase/tests/integration_tests/test_cache.py delete mode 100644 libs/partners/couchbase/tests/integration_tests/test_chat_message_history.py delete mode 100644 libs/partners/couchbase/tests/integration_tests/test_compile.py delete mode 100644 libs/partners/couchbase/tests/integration_tests/test_vector_store.py delete mode 100644 libs/partners/couchbase/tests/unit_tests/__init__.py delete mode 100644 libs/partners/couchbase/tests/unit_tests/test_imports.py delete mode 100644 libs/partners/couchbase/tests/unit_tests/test_vectorstore.py delete mode 100644 libs/partners/couchbase/tests/utils.py diff --git a/libs/packages.yml b/libs/packages.yml index d8bf7ac1ccbd2..8e4502e1c51b6 100644 --- a/libs/packages.yml +++ b/libs/packages.yml @@ -259,8 +259,8 @@ packages: downloads: 35495 downloads_updated_at: '2024-12-23T20:10:11.816059+00:00' - name: langchain-couchbase - path: libs/partners/couchbase - repo: langchain-ai/langchain + path: . + repo: Couchbase-Ecosystem/langchain-couchbase downloads: 347 downloads_updated_at: '2024-12-23T20:10:11.816059+00:00' - name: langchain-ollama diff --git a/libs/partners/couchbase/.gitignore b/libs/partners/couchbase/.gitignore deleted file mode 100644 index 53fd65cf3d994..0000000000000 --- a/libs/partners/couchbase/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -__pycache__ -# mypy -.mypy_cache/ diff --git a/libs/partners/couchbase/LICENSE b/libs/partners/couchbase/LICENSE deleted file mode 100644 index fc0602feecdd6..0000000000000 --- a/libs/partners/couchbase/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2024 LangChain, Inc. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/libs/partners/couchbase/Makefile b/libs/partners/couchbase/Makefile deleted file mode 100644 index 5273eff70528e..0000000000000 --- a/libs/partners/couchbase/Makefile +++ /dev/null @@ -1,64 +0,0 @@ -.PHONY: all format lint test tests integration_tests docker_tests help extended_tests - -# Default target executed when no arguments are given to make. -all: help - -# Define a variable for the test file path. -TEST_FILE ?= tests/unit_tests/ -integration_test integration_tests: TEST_FILE = tests/integration_tests/ - - -# unit tests are run with the --disable-socket flag to prevent network calls -test tests: - poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) - -test_watch: - poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) - -# integration tests are run without the --disable-socket flag to allow network calls -integration_test integration_tests: - poetry run pytest $(TEST_FILE) - -###################### -# LINTING AND FORMATTING -###################### - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -MYPY_CACHE=.mypy_cache -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/couchbase --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') -lint_package: PYTHON_FILES=langchain_couchbase -lint_tests: PYTHON_FILES=tests -lint_tests: MYPY_CACHE=.mypy_cache_test - -lint lint_diff lint_package lint_tests: - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff - [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) - -format format_diff: - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES) - -spell_check: - poetry run codespell --toml pyproject.toml - -spell_fix: - poetry run codespell --toml pyproject.toml -w - -check_imports: $(shell find langchain_couchbase -name '*.py') - poetry run python ./scripts/check_imports.py $^ - -###################### -# HELP -###################### - -help: - @echo '----' - @echo 'check_imports - check imports' - @echo 'format - run code formatters' - @echo 'lint - run linters' - @echo 'test - run unit tests' - @echo 'tests - run unit tests' - @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/couchbase/README.md b/libs/partners/couchbase/README.md index 006de243ab237..9d5685072d44a 100644 --- a/libs/partners/couchbase/README.md +++ b/libs/partners/couchbase/README.md @@ -1,42 +1,3 @@ -# langchain-couchbase +This package has moved! -This package contains the LangChain integration with Couchbase - -## Installation - -```bash -pip install -U langchain-couchbase -``` - -## Usage - -The `CouchbaseVectorStore` class exposes the connection to the Couchbase vector store. - -```python -from langchain_couchbase.vectorstores import CouchbaseVectorStore - -from couchbase.cluster import Cluster -from couchbase.auth import PasswordAuthenticator -from couchbase.options import ClusterOptions -from datetime import timedelta - -auth = PasswordAuthenticator(username, password) -options = ClusterOptions(auth) -connect_string = "couchbases://localhost" -cluster = Cluster(connect_string, options) - -# Wait until the cluster is ready for use. -cluster.wait_until_ready(timedelta(seconds=5)) - -embeddings = OpenAIEmbeddings() - -vectorstore = CouchbaseVectorStore( - cluster=cluster, - bucket_name="", - scope_name="", - collection_name="", - embedding=embeddings, - index_name="vector-search-index", -) - -``` +https://github.com/Couchbase-Ecosystem/langchain-couchbase/tree/main/langchain_couchbase diff --git a/libs/partners/couchbase/langchain_couchbase/__init__.py b/libs/partners/couchbase/langchain_couchbase/__init__.py deleted file mode 100644 index 17d50b3c2afe2..0000000000000 --- a/libs/partners/couchbase/langchain_couchbase/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -from langchain_couchbase.cache import CouchbaseCache, CouchbaseSemanticCache -from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory -from langchain_couchbase.vectorstores import CouchbaseVectorStore - -__all__ = [ - "CouchbaseVectorStore", - "CouchbaseCache", - "CouchbaseSemanticCache", - "CouchbaseChatMessageHistory", -] diff --git a/libs/partners/couchbase/langchain_couchbase/cache.py b/libs/partners/couchbase/langchain_couchbase/cache.py deleted file mode 100644 index 52118ac610ece..0000000000000 --- a/libs/partners/couchbase/langchain_couchbase/cache.py +++ /dev/null @@ -1,386 +0,0 @@ -""" -LangChain Couchbase Caches - -Functions "_hash", "_loads_generations" and "_dumps_generations" -are duplicated in this utility from modules: - - "libs/community/langchain_community/cache.py" -""" - -import hashlib -import json -import logging -from datetime import timedelta -from typing import Any, Dict, Optional, Union - -from couchbase.cluster import Cluster -from langchain_core.caches import RETURN_VAL_TYPE, BaseCache -from langchain_core.embeddings import Embeddings -from langchain_core.load.dump import dumps -from langchain_core.load.load import loads -from langchain_core.outputs import Generation - -from langchain_couchbase.vectorstores import CouchbaseVectorStore - -logger = logging.getLogger(__file__) - - -def _hash(_input: str) -> str: - """Use a deterministic hashing approach.""" - return hashlib.md5(_input.encode()).hexdigest() - - -def _dumps_generations(generations: RETURN_VAL_TYPE) -> str: - """ - Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation` - - Args: - generations (RETURN_VAL_TYPE): A list of language model generations. - - Returns: - str: a single string representing a list of generations. - - This function (+ its counterpart `_loads_generations`) rely on - the dumps/loads pair with Reviver, so are able to deal - with all subclasses of Generation. - - Each item in the list can be `dumps`ed to a string, - then we make the whole list of strings into a json-dumped. - """ - return json.dumps([dumps(_item) for _item in generations]) - - -def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]: - """ - Deserialization of a string into a generic RETURN_VAL_TYPE - (i.e. a sequence of `Generation`). - - See `_dumps_generations`, the inverse of this function. - - Args: - generations_str (str): A string representing a list of generations. - - Compatible with the legacy cache-blob format - Does not raise exceptions for malformed entries, just logs a warning - and returns none: the caller should be prepared for such a cache miss. - - Returns: - RETURN_VAL_TYPE: A list of generations. - """ - try: - generations = [loads(_item_str) for _item_str in json.loads(generations_str)] - return generations - except (json.JSONDecodeError, TypeError): - # deferring the (soft) handling to after the legacy-format attempt - pass - - try: - gen_dicts = json.loads(generations_str) - # not relying on `_load_generations_from_json` (which could disappear): - generations = [Generation(**generation_dict) for generation_dict in gen_dicts] - logger.warning( - f"Legacy 'Generation' cached blob encountered: '{generations_str}'" - ) - return generations - except (json.JSONDecodeError, TypeError): - logger.warning( - f"Malformed/unparsable cached blob encountered: '{generations_str}'" - ) - return None - - -def _validate_ttl(ttl: Optional[timedelta]) -> None: - """Validate the time to live""" - if not isinstance(ttl, timedelta): - raise ValueError(f"ttl should be of type timedelta but was {type(ttl)}.") - if ttl <= timedelta(seconds=0): - raise ValueError( - f"ttl must be greater than 0 but was {ttl.total_seconds()} seconds." - ) - - -class CouchbaseCache(BaseCache): - """Couchbase LLM Cache - LLM Cache that uses Couchbase as the backend - """ - - PROMPT = "prompt" - LLM = "llm" - RETURN_VAL = "return_val" - - def _check_bucket_exists(self) -> bool: - """Check if the bucket exists in the linked Couchbase cluster""" - bucket_manager = self._cluster.buckets() - try: - bucket_manager.get_bucket(self._bucket_name) - return True - except Exception: - return False - - def _check_scope_and_collection_exists(self) -> bool: - """Check if the scope and collection exists in the linked Couchbase bucket - Raises a ValueError if either is not found""" - scope_collection_map: Dict[str, Any] = {} - - # Get a list of all scopes in the bucket - for scope in self._bucket.collections().get_all_scopes(): - scope_collection_map[scope.name] = [] - - # Get a list of all the collections in the scope - for collection in scope.collections: - scope_collection_map[scope.name].append(collection.name) - - # Check if the scope exists - if self._scope_name not in scope_collection_map.keys(): - raise ValueError( - f"Scope {self._scope_name} not found in Couchbase " - f"bucket {self._bucket_name}" - ) - - # Check if the collection exists in the scope - if self._collection_name not in scope_collection_map[self._scope_name]: - raise ValueError( - f"Collection {self._collection_name} not found in scope " - f"{self._scope_name} in Couchbase bucket {self._bucket_name}" - ) - - return True - - def __init__( - self, - cluster: Cluster, - bucket_name: str, - scope_name: str, - collection_name: str, - ttl: Optional[timedelta] = None, - **kwargs: Dict[str, Any], - ) -> None: - """Initialize the Couchbase LLM Cache - Args: - cluster (Cluster): couchbase cluster object with active connection. - bucket_name (str): name of the bucket to store documents in. - scope_name (str): name of the scope in bucket to store documents in. - collection_name (str): name of the collection in the scope to store - documents in. - ttl (Optional[timedelta]): TTL or time for the document to live in the cache - After this time, the document will get deleted from the cache. - """ - if not isinstance(cluster, Cluster): - raise ValueError( - f"cluster should be an instance of couchbase.Cluster, " - f"got {type(cluster)}" - ) - - self._cluster = cluster - - self._bucket_name = bucket_name - self._scope_name = scope_name - self._collection_name = collection_name - - self._ttl = None - - # Check if the bucket exists - if not self._check_bucket_exists(): - raise ValueError( - f"Bucket {self._bucket_name} does not exist. " - " Please create the bucket before searching." - ) - - try: - self._bucket = self._cluster.bucket(self._bucket_name) - self._scope = self._bucket.scope(self._scope_name) - self._collection = self._scope.collection(self._collection_name) - except Exception as e: - raise ValueError( - "Error connecting to couchbase. " - "Please check the connection and credentials." - ) from e - - # Check if the scope and collection exists. Throws ValueError if they don't - try: - self._check_scope_and_collection_exists() - except Exception as e: - raise e - - # Check if the time to live is provided and valid - if ttl is not None: - _validate_ttl(ttl) - self._ttl = ttl - - def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: - """Look up from cache based on prompt and llm_string.""" - try: - doc = self._collection.get( - self._generate_key(prompt, llm_string) - ).content_as[dict] - return _loads_generations(doc[self.RETURN_VAL]) - except Exception: - return None - - def _generate_key(self, prompt: str, llm_string: str) -> str: - """Generate the key based on prompt and llm_string.""" - return _hash(prompt + llm_string) - - def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: - """Update cache based on prompt and llm_string.""" - doc = { - self.PROMPT: prompt, - self.LLM: llm_string, - self.RETURN_VAL: _dumps_generations(return_val), - } - document_key = self._generate_key(prompt, llm_string) - try: - if self._ttl: - self._collection.upsert( - key=document_key, - value=doc, - expiry=self._ttl, - ) - else: - self._collection.upsert(key=document_key, value=doc) - except Exception: - logger.error("Error updating cache") - - def clear(self, **kwargs: Any) -> None: - """Clear the cache. - This will delete all documents in the collection. This requires an index on the - collection. - """ - try: - query = f"DELETE FROM `{self._collection_name}`" - self._scope.query(query).execute() - except Exception: - logger.error("Error clearing cache. Please check if you have an index.") - - -class CouchbaseSemanticCache(BaseCache, CouchbaseVectorStore): - """Couchbase Semantic Cache - Cache backed by a Couchbase Server with Vector Store support - """ - - LLM = "llm_string" - RETURN_VAL = "return_val" - - def __init__( - self, - cluster: Cluster, - embedding: Embeddings, - bucket_name: str, - scope_name: str, - collection_name: str, - index_name: str, - score_threshold: Optional[float] = None, - ttl: Optional[timedelta] = None, - ) -> None: - """Initialize the Couchbase LLM Cache - Args: - cluster (Cluster): couchbase cluster object with active connection. - embedding (Embeddings): embedding model to use. - bucket_name (str): name of the bucket to store documents in. - scope_name (str): name of the scope in bucket to store documents in. - collection_name (str): name of the collection in the scope to store - documents in. - index_name (str): name of the Search index to use. - score_threshold (float): score threshold to use for filtering results. - ttl (Optional[timedelta]): TTL or time for the document to live in the cache - After this time, the document will get deleted from the cache. - """ - if not isinstance(cluster, Cluster): - raise ValueError( - f"cluster should be an instance of couchbase.Cluster, " - f"got {type(cluster)}" - ) - - self._cluster = cluster - - self._bucket_name = bucket_name - self._scope_name = scope_name - self._collection_name = collection_name - self._ttl = None - - # Check if the bucket exists - if not self._check_bucket_exists(): - raise ValueError( - f"Bucket {self._bucket_name} does not exist. " - " Please create the bucket before searching." - ) - - try: - self._bucket = self._cluster.bucket(self._bucket_name) - self._scope = self._bucket.scope(self._scope_name) - self._collection = self._scope.collection(self._collection_name) - except Exception as e: - raise ValueError( - "Error connecting to couchbase. " - "Please check the connection and credentials." - ) from e - - # Check if the scope and collection exists. Throws ValueError if they don't - try: - self._check_scope_and_collection_exists() - except Exception as e: - raise e - - self.score_threshold = score_threshold - - if ttl is not None: - _validate_ttl(ttl) - self._ttl = ttl - - # Initialize the vector store - super().__init__( - cluster=cluster, - bucket_name=bucket_name, - scope_name=scope_name, - collection_name=collection_name, - embedding=embedding, - index_name=index_name, - ) - - def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: - """Look up from cache based on the semantic similarity of the prompt""" - search_results = self.similarity_search_with_score( - prompt, k=1, search_options={f"metadata.{self.LLM}": llm_string} - ) - if search_results: - selected_doc, score = search_results[0] - else: - return None - - # Check if the score is above the threshold if a threshold is provided - if self.score_threshold: - if score < self.score_threshold: - return None - - # Note that the llm_string might not match the vector search result. - # So if the llm_string does not match, do not return the result. - if selected_doc.metadata["llm_string"] != llm_string: - return None - - return _loads_generations(selected_doc.metadata[self.RETURN_VAL]) - - def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: - """Update cache based on the prompt and llm_string""" - try: - self.add_texts( - texts=[prompt], - metadatas=[ - { - self.LLM: llm_string, - self.RETURN_VAL: _dumps_generations(return_val), - } - ], - ttl=self._ttl, - ) - except Exception: - logger.error("Error updating cache") - - def clear(self, **kwargs: Any) -> None: - """Clear the cache. - This will delete all documents in the collection. - This requires an index on the collection. - """ - try: - query = f"DELETE FROM `{self._collection_name}`" - self._scope.query(query).execute() - except Exception: - logger.error("Error clearing cache. Please check if you have an index.") diff --git a/libs/partners/couchbase/langchain_couchbase/chat_message_histories.py b/libs/partners/couchbase/langchain_couchbase/chat_message_histories.py deleted file mode 100644 index 172b79b9af3f7..0000000000000 --- a/libs/partners/couchbase/langchain_couchbase/chat_message_histories.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Couchbase Chat Message History""" - -import logging -import time -import uuid -from datetime import timedelta -from typing import Any, Dict, List, Optional, Sequence - -from couchbase.cluster import Cluster -from langchain_core.chat_history import BaseChatMessageHistory -from langchain_core.messages import ( - BaseMessage, - message_to_dict, - messages_from_dict, -) - -logger = logging.getLogger(__name__) - -DEFAULT_SESSION_ID_KEY = "session_id" -DEFAULT_MESSAGE_KEY = "message" -DEFAULT_TS_KEY = "ts" -DEFAULT_INDEX_NAME = "LANGCHAIN_CHAT_HISTORY" -DEFAULT_BATCH_SIZE = 100 - - -def _validate_ttl(ttl: Optional[timedelta]) -> None: - """Validate the time to live""" - if not isinstance(ttl, timedelta): - raise ValueError(f"ttl should be of type timedelta but was {type(ttl)}.") - if ttl <= timedelta(seconds=0): - raise ValueError( - f"ttl must be greater than 0 but was {ttl.total_seconds()} seconds." - ) - - -class CouchbaseChatMessageHistory(BaseChatMessageHistory): - """Couchbase Chat Message History - Chat message history that uses Couchbase as the storage - """ - - def _check_bucket_exists(self) -> bool: - """Check if the bucket exists in the linked Couchbase cluster""" - bucket_manager = self._cluster.buckets() - try: - bucket_manager.get_bucket(self._bucket_name) - return True - except Exception: - return False - - def _check_scope_and_collection_exists(self) -> bool: - """Check if the scope and collection exists in the linked Couchbase bucket - Raises a ValueError if either is not found""" - scope_collection_map: Dict[str, Any] = {} - - # Get a list of all scopes in the bucket - for scope in self._bucket.collections().get_all_scopes(): - scope_collection_map[scope.name] = [] - - # Get a list of all the collections in the scope - for collection in scope.collections: - scope_collection_map[scope.name].append(collection.name) - - # Check if the scope exists - if self._scope_name not in scope_collection_map.keys(): - raise ValueError( - f"Scope {self._scope_name} not found in Couchbase " - f"bucket {self._bucket_name}" - ) - - # Check if the collection exists in the scope - if self._collection_name not in scope_collection_map[self._scope_name]: - raise ValueError( - f"Collection {self._collection_name} not found in scope " - f"{self._scope_name} in Couchbase bucket " - f"{self._bucket_name}" - ) - - return True - - def __init__( - self, - *, - cluster: Cluster, - bucket_name: str, - scope_name: str, - collection_name: str, - session_id: str, - session_id_key: str = DEFAULT_SESSION_ID_KEY, - message_key: str = DEFAULT_MESSAGE_KEY, - create_index: bool = True, - ttl: Optional[timedelta] = None, - ) -> None: - """Initialize the Couchbase Chat Message History - Args: - cluster (Cluster): couchbase cluster object with active connection. - bucket_name (str): name of the bucket to store documents in. - scope_name (str): name of the scope in bucket to store documents in. - collection_name (str): name of the collection in the scope to store - documents in. - session_id (str): value for the session used to associate messages from - a single chat session. It is stored as a field in the chat message. - session_id_key (str): name of the field to use for the session id. - Set to "session_id" by default. - message_key (str): name of the field to use for the messages - Set to "message" by default. - create_index (bool): create an index if True. Set to True by default. - ttl (timedelta): time to live for the documents in the collection. - When set, the documents are automatically deleted after the ttl expires. - """ - if not isinstance(cluster, Cluster): - raise ValueError( - f"cluster should be an instance of couchbase.Cluster, " - f"got {type(cluster)}" - ) - - self._cluster = cluster - - self._bucket_name = bucket_name - self._scope_name = scope_name - self._collection_name = collection_name - self._ttl = None - - # Check if the bucket exists - if not self._check_bucket_exists(): - raise ValueError( - f"Bucket {self._bucket_name} does not exist. " - " Please create the bucket before searching." - ) - - try: - self._bucket = self._cluster.bucket(self._bucket_name) - self._scope = self._bucket.scope(self._scope_name) - self._collection = self._scope.collection(self._collection_name) - except Exception as e: - raise ValueError( - "Error connecting to couchbase. " - "Please check the connection and credentials." - ) from e - - # Check if the scope and collection exists. Throws ValueError if they don't - try: - self._check_scope_and_collection_exists() - except Exception as e: - raise e - - self._session_id_key = session_id_key - self._message_key = message_key - self._create_index = create_index - self._session_id = session_id - self._ts_key = DEFAULT_TS_KEY - - if ttl is not None: - _validate_ttl(ttl) - self._ttl = ttl - - # Create an index if it does not exist if requested - if create_index: - index_fields = ( - f"({self._session_id_key}, {self._ts_key}, {self._message_key})" - ) - index_creation_query = ( - f"CREATE INDEX {DEFAULT_INDEX_NAME} IF NOT EXISTS ON " - + f"{self._collection_name}{index_fields} " - ) - - try: - self._scope.query(index_creation_query).execute() - except Exception as e: - logger.error("Error creating index: ", e) - - def add_message(self, message: BaseMessage) -> None: - """Add a message to the cache""" - # Generate a UUID for the document key - document_key = uuid.uuid4().hex - # get utc timestamp for ordering the messages - timestamp = time.time() - message_content = message_to_dict(message) - - try: - if self._ttl: - self._collection.insert( - document_key, - value={ - self._message_key: message_content, - self._session_id_key: self._session_id, - self._ts_key: timestamp, - }, - expiry=self._ttl, - ) - else: - self._collection.insert( - document_key, - value={ - self._message_key: message_content, - self._session_id_key: self._session_id, - self._ts_key: timestamp, - }, - ) - except Exception as e: - logger.error("Error adding message: ", e) - - def add_messages(self, messages: Sequence[BaseMessage]) -> None: - """Add messages to the cache in a batched manner""" - batch_size = DEFAULT_BATCH_SIZE - messages_to_insert = [] - for message in messages: - document_key = uuid.uuid4().hex - timestamp = time.time() - message_content = message_to_dict(message) - messages_to_insert.append( - { - document_key: { - self._message_key: message_content, - self._session_id_key: self._session_id, - self._ts_key: timestamp, - }, - } - ) - - # Add the messages to the cache in batches of batch_size - try: - for i in range(0, len(messages_to_insert), batch_size): - batch = messages_to_insert[i : i + batch_size] - # Convert list of dictionaries to a single dictionary to insert - insert_batch = {list(d.keys())[0]: list(d.values())[0] for d in batch} - if self._ttl: - self._collection.insert_multi(insert_batch, expiry=self._ttl) - else: - self._collection.insert_multi(insert_batch) - except Exception as e: - logger.error("Error adding messages: ", e) - - def clear(self) -> None: - """Clear the cache""" - # Delete all documents in the collection with the session_id - clear_query = ( - f"DELETE FROM `{self._collection_name}`" - + f"WHERE {self._session_id_key}=$session_id" - ) - try: - self._scope.query(clear_query, session_id=self._session_id).execute() - except Exception as e: - logger.error("Error clearing cache: ", e) - - @property - def messages(self) -> List[BaseMessage]: - """Get all messages in the cache associated with the session_id""" - fetch_query = ( - f"SELECT {self._message_key} FROM `{self._collection_name}` " - + f"where {self._session_id_key}=$session_id" - + f" ORDER BY {self._ts_key} ASC" - ) - message_items = [] - - try: - result = self._scope.query(fetch_query, session_id=self._session_id) - for document in result: - message_items.append(document[f"{self._message_key}"]) - except Exception as e: - logger.error("Error fetching messages: ", e) - - return messages_from_dict(message_items) - - @messages.setter - def messages(self, messages: List[BaseMessage]) -> None: - raise NotImplementedError( - "Direct assignment to 'messages' is not allowed." - " Use the 'add_messages' instead." - ) diff --git a/libs/partners/couchbase/langchain_couchbase/py.typed b/libs/partners/couchbase/langchain_couchbase/py.typed deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/couchbase/langchain_couchbase/vectorstores.py b/libs/partners/couchbase/langchain_couchbase/vectorstores.py deleted file mode 100644 index e76a206bbd3d7..0000000000000 --- a/libs/partners/couchbase/langchain_couchbase/vectorstores.py +++ /dev/null @@ -1,739 +0,0 @@ -"""Couchbase vector stores.""" - -from __future__ import annotations - -import uuid -from typing import ( - Any, - Dict, - Iterable, - List, - Optional, - Tuple, - Type, -) - -import couchbase.search as search -from couchbase.cluster import Cluster -from couchbase.exceptions import DocumentExistsException, DocumentNotFoundException -from couchbase.options import SearchOptions -from couchbase.vector_search import VectorQuery, VectorSearch -from langchain_core.documents import Document -from langchain_core.embeddings import Embeddings -from langchain_core.vectorstores import VectorStore - - -class CouchbaseVectorStore(VectorStore): - """__ModuleName__ vector store integration. - - Setup: - Install ``langchain-couchbase`` and head over to the Couchbase [website](https://cloud.couchbase.com) and create a new connection, with a bucket, collection, and search index. - - .. code-block:: bash - - pip install -U langchain-couchbase - - .. code-block:: python - - import getpass - - COUCHBASE_CONNECTION_STRING = getpass.getpass("Enter the connection string for the Couchbase cluster: ") - DB_USERNAME = getpass.getpass("Enter the username for the Couchbase cluster: ") - DB_PASSWORD = getpass.getpass("Enter the password for the Couchbase cluster: ") - - Key init args — indexing params: - embedding: Embeddings - Embedding function to use. - - Key init args — client params: - cluster: Cluster - Couchbase cluster object with active connection. - bucket_name: str - Name of the bucket to store documents in. - scope_name: str - Name of the scope in the bucket to store documents in. - collection_name: str - Name of the collection in the scope to store documents in. - index_name: str - Name of the Search index to use. - - Instantiate: - .. code-block:: python - - from datetime import timedelta - from langchain_openai import OpenAIEmbeddings - from couchbase.auth import PasswordAuthenticator - from couchbase.cluster import Cluster - from couchbase.options import ClusterOptions - - auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD) - options = ClusterOptions(auth) - cluster = Cluster(COUCHBASE_CONNECTION_STRING, options) - - # Wait until the cluster is ready for use. - cluster.wait_until_ready(timedelta(seconds=5)) - - BUCKET_NAME = "langchain_bucket" - SCOPE_NAME = "_default" - COLLECTION_NAME = "default" - SEARCH_INDEX_NAME = "langchain-test-index" - - vector_store = CouchbaseVectorStore( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - embedding=embeddings, - index_name=SEARCH_INDEX_NAME, - ) - - Add Documents: - .. code-block:: python - - from langchain_core.documents import Document - - document_1 = Document(page_content="foo", metadata={"baz": "bar"}) - document_2 = Document(page_content="thud", metadata={"bar": "baz"}) - document_3 = Document(page_content="i will be deleted :(") - - documents = [document_1, document_2, document_3] - ids = ["1", "2", "3"] - vector_store.add_documents(documents=documents, ids=ids) - - Delete Documents: - .. code-block:: python - - vector_store.delete(ids=["3"]) - - # TODO: Fill out with example output. - Search: - .. code-block:: python - - results = vector_store.similarity_search(query="thud",k=1) - for doc in results: - print(f"* {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - # TODO: Example output - - # TODO: Fill out with relevant variables and example output. - Search with filter: - .. code-block:: python - - # TODO: Update filter to correct format - results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"}) - for doc in results: - print(f"* {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - # TODO: Example output - - # TODO: Fill out with example output. - Search with score: - .. code-block:: python - - results = vector_store.similarity_search_with_score(query="qux",k=1) - for doc, score in results: - print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - # TODO: Example output - - # TODO: Fill out with example output. - Async: - .. code-block:: python - - # add documents - # await vector_store.aadd_documents(documents=documents, ids=ids) - - # delete documents - # await vector_store.adelete(ids=["3"]) - - # search - # results = vector_store.asimilarity_search(query="thud",k=1) - - # search with score - results = await vector_store.asimilarity_search_with_score(query="qux",k=1) - for doc,score in results: - print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") - - .. code-block:: python - - # TODO: Example output - - # TODO: Fill out with example output. - Use as Retriever: - .. code-block:: python - - retriever = vector_store.as_retriever( - search_type="mmr", - search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, - ) - retriever.invoke("thud") - - .. code-block:: python - - # TODO: Example output - - """ # noqa: E501 - - # Default batch size - DEFAULT_BATCH_SIZE = 100 - _metadata_key = "metadata" - _default_text_key = "text" - _default_embedding_key = "embedding" - - def _check_bucket_exists(self) -> bool: - """Check if the bucket exists in the linked Couchbase cluster""" - bucket_manager = self._cluster.buckets() - try: - bucket_manager.get_bucket(self._bucket_name) - return True - except Exception: - return False - - def _check_scope_and_collection_exists(self) -> bool: - """Check if the scope and collection exists in the linked Couchbase bucket - Raises a ValueError if either is not found""" - scope_collection_map: Dict[str, Any] = {} - - # Get a list of all scopes in the bucket - for scope in self._bucket.collections().get_all_scopes(): - scope_collection_map[scope.name] = [] - - # Get a list of all the collections in the scope - for collection in scope.collections: - scope_collection_map[scope.name].append(collection.name) - - # Check if the scope exists - if self._scope_name not in scope_collection_map.keys(): - raise ValueError( - f"Scope {self._scope_name} not found in Couchbase " - f"bucket {self._bucket_name}" - ) - - # Check if the collection exists in the scope - if self._collection_name not in scope_collection_map[self._scope_name]: - raise ValueError( - f"Collection {self._collection_name} not found in scope " - f"{self._scope_name} in Couchbase bucket {self._bucket_name}" - ) - - return True - - def _check_index_exists(self) -> bool: - """Check if the Search index exists in the linked Couchbase cluster - Raises a ValueError if the index does not exist""" - if self._scoped_index: - all_indexes = [ - index.name for index in self._scope.search_indexes().get_all_indexes() - ] - if self._index_name not in all_indexes: - raise ValueError( - f"Index {self._index_name} does not exist. " - " Please create the index before searching." - ) - else: - all_indexes = [ - index.name for index in self._cluster.search_indexes().get_all_indexes() - ] - if self._index_name not in all_indexes: - raise ValueError( - f"Index {self._index_name} does not exist. " - " Please create the index before searching." - ) - - return True - - def __init__( - self, - cluster: Cluster, - bucket_name: str, - scope_name: str, - collection_name: str, - embedding: Embeddings, - index_name: str, - *, - text_key: Optional[str] = _default_text_key, - embedding_key: Optional[str] = _default_embedding_key, - scoped_index: bool = True, - ) -> None: - """ - Initialize the Couchbase Vector Store. - - Args: - - cluster (Cluster): couchbase cluster object with active connection. - bucket_name (str): name of bucket to store documents in. - scope_name (str): name of scope in the bucket to store documents in. - collection_name (str): name of collection in the scope to store documents in - embedding (Embeddings): embedding function to use. - index_name (str): name of the Search index to use. - text_key (optional[str]): key in document to use as text. - Set to text by default. - embedding_key (optional[str]): key in document to use for the embeddings. - Set to embedding by default. - scoped_index (optional[bool]): specify whether the index is a scoped index. - Set to True by default. - """ - if not isinstance(cluster, Cluster): - raise ValueError( - f"cluster should be an instance of couchbase.Cluster, " - f"got {type(cluster)}" - ) - - self._cluster = cluster - - if not embedding: - raise ValueError("Embeddings instance must be provided.") - - if not bucket_name: - raise ValueError("bucket_name must be provided.") - - if not scope_name: - raise ValueError("scope_name must be provided.") - - if not collection_name: - raise ValueError("collection_name must be provided.") - - if not index_name: - raise ValueError("index_name must be provided.") - - self._bucket_name = bucket_name - self._scope_name = scope_name - self._collection_name = collection_name - self._embedding_function = embedding - self._text_key = text_key - self._embedding_key = embedding_key - self._index_name = index_name - self._scoped_index = scoped_index - - # Check if the bucket exists - if not self._check_bucket_exists(): - raise ValueError( - f"Bucket {self._bucket_name} does not exist. " - " Please create the bucket before searching." - ) - - try: - self._bucket = self._cluster.bucket(self._bucket_name) - self._scope = self._bucket.scope(self._scope_name) - self._collection = self._scope.collection(self._collection_name) - except Exception as e: - raise ValueError( - "Error connecting to couchbase. " - "Please check the connection and credentials." - ) from e - - # Check if the scope and collection exists. Throws ValueError if they don't - try: - self._check_scope_and_collection_exists() - except Exception as e: - raise e - - # Check if the index exists. Throws ValueError if it doesn't - try: - self._check_index_exists() - except Exception as e: - raise e - - def add_texts( - self, - texts: Iterable[str], - metadatas: Optional[List[dict]] = None, - ids: Optional[List[str]] = None, - batch_size: Optional[int] = None, - **kwargs: Any, - ) -> List[str]: - """Run texts through the embeddings and persist in vectorstore. - - If the document IDs are passed, the existing documents (if any) will be - overwritten with the new ones. - - Args: - texts (Iterable[str]): Iterable of strings to add to the vectorstore. - metadatas (Optional[List[Dict]]): Optional list of metadatas associated - with the texts. - ids (Optional[List[str]]): Optional list of ids associated with the texts. - IDs have to be unique strings across the collection. - If it is not specified uuids are generated and used as ids. - batch_size (Optional[int]): Optional batch size for bulk insertions. - Default is 100. - - Returns: - List[str]:List of ids from adding the texts into the vectorstore. - """ - - if not batch_size: - batch_size = self.DEFAULT_BATCH_SIZE - doc_ids: List[str] = [] - - if ids is None: - ids = [uuid.uuid4().hex for _ in texts] - - if metadatas is None: - metadatas = [{} for _ in texts] - - # Check if TTL is provided - ttl = kwargs.get("ttl", None) - - embedded_texts = self._embedding_function.embed_documents(list(texts)) - - documents_to_insert = [ - { - id: { - self._text_key: text, - self._embedding_key: vector, - self._metadata_key: metadata, - } - for id, text, vector, metadata in zip( - ids, texts, embedded_texts, metadatas - ) - } - ] - - # Insert in batches - for i in range(0, len(documents_to_insert), batch_size): - batch = documents_to_insert[i : i + batch_size] - try: - # Insert with TTL if provided - if ttl: - result = self._collection.upsert_multi(batch[0], expiry=ttl) - else: - result = self._collection.upsert_multi(batch[0]) - if result.all_ok: - doc_ids.extend(batch[0].keys()) - except DocumentExistsException as e: - raise ValueError(f"Document already exists: {e}") - - return doc_ids - - def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: - """Delete documents from the vector store by ids. - - Args: - ids (List[str]): List of IDs of the documents to delete. - batch_size (Optional[int]): Optional batch size for bulk deletions. - - Returns: - bool: True if all the documents were deleted successfully, False otherwise. - - """ - - if ids is None: - raise ValueError("No document ids provided to delete.") - - batch_size = kwargs.get("batch_size", self.DEFAULT_BATCH_SIZE) - deletion_status = True - - # Delete in batches - for i in range(0, len(ids), batch_size): - batch = ids[i : i + batch_size] - try: - result = self._collection.remove_multi(batch) - except DocumentNotFoundException as e: - deletion_status = False - raise ValueError(f"Document not found: {e}") - - deletion_status &= result.all_ok - - return deletion_status - - @property - def embeddings(self) -> Embeddings: - """Return the query embedding object.""" - return self._embedding_function - - def _format_metadata(self, row_fields: Dict[str, Any]) -> Dict[str, Any]: - """Helper method to format the metadata from the Couchbase Search API. - Args: - row_fields (Dict[str, Any]): The fields to format. - - Returns: - Dict[str, Any]: The formatted metadata. - """ - metadata = {} - for key, value in row_fields.items(): - # Couchbase Search returns the metadata key with a prefix - # `metadata.` We remove it to get the original metadata key - if key.startswith(self._metadata_key): - new_key = key.split(self._metadata_key + ".")[-1] - metadata[new_key] = value - else: - metadata[key] = value - - return metadata - - def similarity_search( - self, - query: str, - k: int = 4, - search_options: Optional[Dict[str, Any]] = {}, - **kwargs: Any, - ) -> List[Document]: - """Return documents most similar to embedding vector with their scores. - - Args: - query (str): Query to look up for similar documents - k (int): Number of Documents to return. - Defaults to 4. - search_options (Optional[Dict[str, Any]]): Optional search options that are - passed to Couchbase search. - Defaults to empty dictionary - fields (Optional[List[str]]): Optional list of fields to include in the - metadata of results. Note that these need to be stored in the index. - If nothing is specified, defaults to all the fields stored in the index. - - Returns: - List of Documents most similar to the query. - """ - query_embedding = self.embeddings.embed_query(query) - docs_with_scores = self.similarity_search_with_score_by_vector( - query_embedding, k, search_options, **kwargs - ) - return [doc for doc, _ in docs_with_scores] - - def similarity_search_with_score_by_vector( - self, - embedding: List[float], - k: int = 4, - search_options: Optional[Dict[str, Any]] = {}, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Return docs most similar to embedding vector with their scores. - - Args: - embedding (List[float]): Embedding vector to look up documents similar to. - k (int): Number of Documents to return. - Defaults to 4. - search_options (Optional[Dict[str, Any]]): Optional search options that are - passed to Couchbase search. - Defaults to empty dictionary. - fields (Optional[List[str]]): Optional list of fields to include in the - metadata of results. Note that these need to be stored in the index. - If nothing is specified, defaults to all the fields stored in the index. - - Returns: - List of (Document, score) that are the most similar to the query vector. - """ - - fields = kwargs.get("fields", ["*"]) - - # Document text field needs to be returned from the search - if fields != ["*"] and self._text_key not in fields: - fields.append(self._text_key) - - search_req = search.SearchRequest.create( - VectorSearch.from_vector_query( - VectorQuery( - self._embedding_key, - embedding, - k, - ) - ) - ) - try: - if self._scoped_index: - search_iter = self._scope.search( - self._index_name, - search_req, - SearchOptions( - limit=k, - fields=fields, - raw=search_options, - ), - ) - - else: - search_iter = self._cluster.search( - self._index_name, - search_req, - SearchOptions(limit=k, fields=fields, raw=search_options), - ) - - docs_with_score = [] - - # Parse the results - for row in search_iter.rows(): - text = row.fields.pop(self._text_key, "") - id = row.id - - # Format the metadata from Couchbase - metadata = self._format_metadata(row.fields) - - score = row.score - doc = Document(id=id, page_content=text, metadata=metadata) - docs_with_score.append((doc, score)) - - except Exception as e: - raise ValueError(f"Search failed with error: {e}") - - return docs_with_score - - def similarity_search_with_score( - self, - query: str, - k: int = 4, - search_options: Optional[Dict[str, Any]] = {}, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Return documents that are most similar to the query with their scores. - - Args: - query (str): Query to look up for similar documents - k (int): Number of Documents to return. - Defaults to 4. - search_options (Optional[Dict[str, Any]]): Optional search options that are - passed to Couchbase search. - Defaults to empty dictionary. - fields (Optional[List[str]]): Optional list of fields to include in the - metadata of results. Note that these need to be stored in the index. - If nothing is specified, defaults to text and metadata fields. - - Returns: - List of (Document, score) that are most similar to the query. - """ - query_embedding = self.embeddings.embed_query(query) - docs_with_score = self.similarity_search_with_score_by_vector( - query_embedding, k, search_options, **kwargs - ) - return docs_with_score - - def similarity_search_by_vector( - self, - embedding: List[float], - k: int = 4, - search_options: Optional[Dict[str, Any]] = {}, - **kwargs: Any, - ) -> List[Document]: - """Return documents that are most similar to the vector embedding. - - Args: - embedding (List[float]): Embedding to look up documents similar to. - k (int): Number of Documents to return. - Defaults to 4. - search_options (Optional[Dict[str, Any]]): Optional search options that are - passed to Couchbase search. - Defaults to empty dictionary. - fields (Optional[List[str]]): Optional list of fields to include in the - metadata of results. Note that these need to be stored in the index. - If nothing is specified, defaults to document text and metadata fields. - - Returns: - List of Documents most similar to the query. - """ - docs_with_score = self.similarity_search_with_score_by_vector( - embedding, k, search_options, **kwargs - ) - return [doc for doc, _ in docs_with_score] - - @classmethod - def _from_kwargs( - cls: Type[CouchbaseVectorStore], - embedding: Embeddings, - **kwargs: Any, - ) -> CouchbaseVectorStore: - """Initialize the Couchbase vector store from keyword arguments for the - vector store. - - Args: - embedding: Embedding object to use to embed text. - **kwargs: Keyword arguments to initialize the vector store with. - Accepted arguments are: - - cluster - - bucket_name - - scope_name - - collection_name - - index_name - - text_key - - embedding_key - - scoped_index - - """ - cluster = kwargs.get("cluster", None) - bucket_name = kwargs.get("bucket_name", None) - scope_name = kwargs.get("scope_name", None) - collection_name = kwargs.get("collection_name", None) - index_name = kwargs.get("index_name", None) - text_key = kwargs.get("text_key", cls._default_text_key) - embedding_key = kwargs.get("embedding_key", cls._default_embedding_key) - scoped_index = kwargs.get("scoped_index", True) - - return cls( - embedding=embedding, - cluster=cluster, - bucket_name=bucket_name, - scope_name=scope_name, - collection_name=collection_name, - index_name=index_name, - text_key=text_key, - embedding_key=embedding_key, - scoped_index=scoped_index, - ) - - @classmethod - def from_texts( - cls: Type[CouchbaseVectorStore], - texts: List[str], - embedding: Embeddings, - metadatas: Optional[List[dict]] = None, - **kwargs: Any, - ) -> CouchbaseVectorStore: - """Construct a Couchbase vector store from a list of texts. - - Example: - .. code-block:: python - - from langchain_couchbase import CouchbaseVectorStore - from langchain_openai import OpenAIEmbeddings - - from couchbase.cluster import Cluster - from couchbase.auth import PasswordAuthenticator - from couchbase.options import ClusterOptions - from datetime import timedelta - - auth = PasswordAuthenticator(username, password) - options = ClusterOptions(auth) - connect_string = "couchbases://localhost" - cluster = Cluster(connect_string, options) - - # Wait until the cluster is ready for use. - cluster.wait_until_ready(timedelta(seconds=5)) - - embeddings = OpenAIEmbeddings() - - texts = ["hello", "world"] - - vectorstore = CouchbaseVectorStore.from_texts( - texts, - embedding=embeddings, - cluster=cluster, - bucket_name="", - scope_name="", - collection_name="", - index_name="vector-index", - ) - - Args: - texts (List[str]): list of texts to add to the vector store. - embedding (Embeddings): embedding function to use. - metadatas (optional[List[Dict]): list of metadatas to add to documents. - **kwargs: Keyword arguments used to initialize the vector store with and/or - passed to `add_texts` method. Check the constructor and/or `add_texts` - for the list of accepted arguments. - - Returns: - A Couchbase vector store. - - """ - vector_store = cls._from_kwargs(embedding, **kwargs) - batch_size = kwargs.get("batch_size", vector_store.DEFAULT_BATCH_SIZE) - ids = kwargs.get("ids", None) - vector_store.add_texts( - texts, metadatas=metadatas, ids=ids, batch_size=batch_size - ) - - return vector_store diff --git a/libs/partners/couchbase/poetry.lock b/libs/partners/couchbase/poetry.lock deleted file mode 100644 index 328d7abc51d7c..0000000000000 --- a/libs/partners/couchbase/poetry.lock +++ /dev/null @@ -1,1790 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. - -[[package]] -name = "aiohappyeyeballs" -version = "2.4.3" -description = "Happy Eyeballs for asyncio" -optional = false -python-versions = ">=3.8" -files = [ - {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"}, - 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-[tool.poetry] -name = "langchain-couchbase" -version = "0.2.2" -description = "An integration package connecting Couchbase and LangChain" -authors = [] -readme = "README.md" -repository = "https://github.com/langchain-ai/langchain" -license = "MIT" - -[tool.mypy] -disallow_untyped_defs = "True" -ignore_missing_imports = "True" - -[tool.poetry.urls] -"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/couchbase" -"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-couchbase%3D%3D0%22&expanded=true" - -[tool.poetry.dependencies] -python = ">=3.9,<4.0" -langchain-core = "^0.3.15" -couchbase = "^4.3.2" - -[tool.ruff.lint] -select = [ "E", "F", "I", "T201",] - -[tool.coverage.run] -omit = [ "tests/*",] - -[tool.pytest.ini_options] -addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" -markers = [ "compile: mark placeholder test used to compile integration tests without running them",] -asyncio_mode = "auto" - -[tool.poetry.group.test] -optional = true - -[tool.poetry.group.codespell] -optional = true - -[tool.poetry.group.test_integration] -optional = true - -[tool.poetry.group.lint] -optional = true - -[tool.poetry.group.dev] -optional = true - -[tool.poetry.group.test.dependencies] -pytest = "^7.4.3" -pytest-asyncio = "^0.23.2" -pytest-socket = "^0.7.0" -syrupy = "^4.0.2" -langchain = "^0.3.0.dev" - -[tool.poetry.group.codespell.dependencies] -codespell = "^2.2.6" - -[tool.poetry.group.test_integration.dependencies] - -[tool.poetry.group.lint.dependencies] -ruff = "^0.5" - -[tool.poetry.group.typing.dependencies] -mypy = "^1.10" - -[tool.poetry.group.test.dependencies.langchain-core] -path = "../../core" -develop = true - -[tool.poetry.group.dev.dependencies.langchain-core] -path = "../../core" -develop = true - -[tool.poetry.group.typing.dependencies.langchain-core] -path = "../../core" -develop = true diff --git a/libs/partners/couchbase/scripts/check_imports.py b/libs/partners/couchbase/scripts/check_imports.py deleted file mode 100644 index 58a460c149353..0000000000000 --- a/libs/partners/couchbase/scripts/check_imports.py +++ /dev/null @@ -1,17 +0,0 @@ -import sys -import traceback -from importlib.machinery import SourceFileLoader - -if __name__ == "__main__": - files = sys.argv[1:] - has_failure = False - for file in files: - try: - SourceFileLoader("x", file).load_module() - except Exception: - has_failure = True - print(file) # noqa: T201 - traceback.print_exc() - print() # noqa: T201 - - sys.exit(1 if has_failure else 0) diff --git a/libs/partners/couchbase/scripts/lint_imports.sh b/libs/partners/couchbase/scripts/lint_imports.sh deleted file mode 100755 index 19ccec1480c01..0000000000000 --- a/libs/partners/couchbase/scripts/lint_imports.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -set -eu - -# Initialize a variable to keep track of errors -errors=0 - -# make sure not importing from langchain, langchain_experimental, or langchain_community -git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) -git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) -git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) - -# Decide on an exit status based on the errors -if [ "$errors" -gt 0 ]; then - exit 1 -else - exit 0 -fi diff --git a/libs/partners/couchbase/tests/__init__.py b/libs/partners/couchbase/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/couchbase/tests/integration_tests/__init__.py b/libs/partners/couchbase/tests/integration_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/couchbase/tests/integration_tests/test_cache.py b/libs/partners/couchbase/tests/integration_tests/test_cache.py deleted file mode 100644 index 08187c3a462b5..0000000000000 --- a/libs/partners/couchbase/tests/integration_tests/test_cache.py +++ /dev/null @@ -1,218 +0,0 @@ -"""Test Couchbase Cache functionality""" - -import os -from datetime import datetime, timedelta -from typing import Any - -import pytest -from couchbase.auth import PasswordAuthenticator -from couchbase.cluster import Cluster -from couchbase.options import ClusterOptions -from langchain_core.globals import get_llm_cache, set_llm_cache -from langchain_core.outputs import Generation - -from langchain_couchbase.cache import CouchbaseCache, CouchbaseSemanticCache -from tests.utils import ( - FakeEmbeddings, - FakeLLM, - cache_key_hash_function, - fetch_document_expiry_time, - get_document_keys, -) - -CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "") -BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "") -SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "") -CACHE_COLLECTION_NAME = os.getenv("COUCHBASE_CACHE_COLLECTION_NAME", "") -SEMANTIC_CACHE_COLLECTION_NAME = os.getenv( - "COUCHBASE_SEMANTIC_CACHE_COLLECTION_NAME", "" -) -USERNAME = os.getenv("COUCHBASE_USERNAME", "") -PASSWORD = os.getenv("COUCHBASE_PASSWORD", "") -INDEX_NAME = os.getenv("COUCHBASE_SEMANTIC_CACHE_INDEX_NAME", "") - - -def set_all_env_vars() -> bool: - """Check if all environment variables are set""" - return all( - [ - CONNECTION_STRING, - BUCKET_NAME, - SCOPE_NAME, - CACHE_COLLECTION_NAME, - USERNAME, - PASSWORD, - INDEX_NAME, - ] - ) - - -def get_cluster() -> Any: - """Get a couchbase cluster object""" - auth = PasswordAuthenticator(USERNAME, PASSWORD) - options = ClusterOptions(auth) - connect_string = CONNECTION_STRING - cluster = Cluster(connect_string, options) - - # Wait until the cluster is ready for use. - cluster.wait_until_ready(timedelta(seconds=5)) - - return cluster - - -@pytest.fixture() -def cluster() -> Any: - """Get a couchbase cluster object""" - return get_cluster() - - -@pytest.mark.skipif( - not set_all_env_vars(), reason="Missing Couchbase environment variables" -) -class TestCouchbaseCache: - def test_cache(self, cluster: Any) -> None: - """Test standard LLM cache functionality""" - set_llm_cache( - CouchbaseCache( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=CACHE_COLLECTION_NAME, - ) - ) - - llm = FakeLLM() - - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) - cache_output = get_llm_cache().lookup("foo", llm_string) - assert cache_output == [Generation(text="fizz")] - - get_llm_cache().clear() - output = get_llm_cache().lookup("bar", llm_string) - assert output != [Generation(text="fizz")] - - def test_cache_with_ttl(self, cluster: Any) -> None: - """Test standard LLM cache functionality with TTL""" - ttl = timedelta(minutes=10) - set_llm_cache( - CouchbaseCache( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=CACHE_COLLECTION_NAME, - ttl=ttl, - ) - ) - - llm = FakeLLM() - - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) - cache_output = get_llm_cache().lookup("foo", llm_string) - assert cache_output == [Generation(text="fizz")] - - # Check the document's expiry time by fetching it from the database - document_key = cache_key_hash_function("foo" + llm_string) - document_expiry_time = fetch_document_expiry_time( - cluster, BUCKET_NAME, SCOPE_NAME, CACHE_COLLECTION_NAME, document_key - ) - current_time = datetime.now() - - time_to_expiry = document_expiry_time - current_time - - assert time_to_expiry < ttl - - def test_semantic_cache(self, cluster: Any) -> None: - """Test semantic LLM cache functionality""" - set_llm_cache( - CouchbaseSemanticCache( - cluster=cluster, - embedding=FakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=SEMANTIC_CACHE_COLLECTION_NAME, - ) - ) - - llm = FakeLLM() - - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update( - "foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")] - ) - - # foo and bar will have the same embedding produced by FakeEmbeddings - cache_output = get_llm_cache().lookup("bar", llm_string) - assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")] - - # clear the cache - get_llm_cache().clear() - output = get_llm_cache().lookup("bar", llm_string) - assert output != [Generation(text="fizz"), Generation(text="Buzz")] - - def test_semantic_cache_with_ttl(self, cluster: Any) -> None: - """Test semantic LLM cache functionality with TTL""" - ttl = timedelta(minutes=10) - - set_llm_cache( - CouchbaseSemanticCache( - cluster=cluster, - embedding=FakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=SEMANTIC_CACHE_COLLECTION_NAME, - ttl=ttl, - ) - ) - - llm = FakeLLM() - - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - - # Add a document to the cache - seed_prompt = "foo" - get_llm_cache().update( - seed_prompt, llm_string, [Generation(text="fizz"), Generation(text="Buzz")] - ) - - # foo and bar will have the same embedding produced by FakeEmbeddings - cache_output = get_llm_cache().lookup("bar", llm_string) - assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")] - - # Check the document's expiry time by fetching it from the database - fetch_document_query = ( - f"SELECT meta().id, * from `{SEMANTIC_CACHE_COLLECTION_NAME}` doc " - f"WHERE doc.text = '{seed_prompt}'" - ) - - document_keys = get_document_keys( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - query=fetch_document_query, - ) - assert len(document_keys) == 1 - - document_expiry_time = fetch_document_expiry_time( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=SEMANTIC_CACHE_COLLECTION_NAME, - document_key=document_keys[0], - ) - current_time = datetime.now() - - time_to_expiry = document_expiry_time - current_time - - assert time_to_expiry < ttl diff --git a/libs/partners/couchbase/tests/integration_tests/test_chat_message_history.py b/libs/partners/couchbase/tests/integration_tests/test_chat_message_history.py deleted file mode 100644 index 67076f15cdce9..0000000000000 --- a/libs/partners/couchbase/tests/integration_tests/test_chat_message_history.py +++ /dev/null @@ -1,294 +0,0 @@ -"""Test Couchbase Chat Message History functionality""" - -import os -import time -from datetime import datetime, timedelta -from typing import Any - -import pytest -from couchbase.auth import PasswordAuthenticator -from couchbase.cluster import Cluster -from couchbase.options import ClusterOptions -from langchain.memory import ConversationBufferMemory -from langchain_core.messages import AIMessage, HumanMessage - -from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory -from tests.utils import fetch_document_expiry_time, get_document_keys - -CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "") -BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "") -SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "") -MESSAGE_HISTORY_COLLECTION_NAME = os.getenv( - "COUCHBASE_CHAT_HISTORY_COLLECTION_NAME", "" -) -USERNAME = os.getenv("COUCHBASE_USERNAME", "") -PASSWORD = os.getenv("COUCHBASE_PASSWORD", "") -SLEEP_DURATION = 0.2 - - -def set_all_env_vars() -> bool: - """Check if all environment variables are set""" - return all( - [ - CONNECTION_STRING, - BUCKET_NAME, - SCOPE_NAME, - MESSAGE_HISTORY_COLLECTION_NAME, - USERNAME, - PASSWORD, - ] - ) - - -def get_cluster() -> Any: - """Get a couchbase cluster object""" - auth = PasswordAuthenticator(USERNAME, PASSWORD) - options = ClusterOptions(auth) - connect_string = CONNECTION_STRING - cluster = Cluster(connect_string, options) - - # Wait until the cluster is ready for use. - cluster.wait_until_ready(timedelta(seconds=5)) - - return cluster - - -@pytest.fixture() -def cluster() -> Any: - """Get a couchbase cluster object""" - return get_cluster() - - -@pytest.mark.skipif( - not set_all_env_vars(), reason="Missing Couchbase environment variables" -) -class TestCouchbaseCache: - def test_memory_with_message_store(self, cluster: Any) -> None: - """Test chat message history with a message store""" - - message_history = CouchbaseChatMessageHistory( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=MESSAGE_HISTORY_COLLECTION_NAME, - session_id="test-session", - ) - - memory = ConversationBufferMemory( - memory_key="baz", chat_memory=message_history, return_messages=True - ) - - # clear the memory - memory.chat_memory.clear() - - # wait for the messages to be cleared - time.sleep(SLEEP_DURATION) - assert memory.chat_memory.messages == [] - - # add some messages - ai_message = AIMessage(content="Hello, how are you doing ?") - user_message = HumanMessage(content="I'm good, how are you?") - memory.chat_memory.add_messages([ai_message, user_message]) - - # wait until the messages can be retrieved - time.sleep(SLEEP_DURATION) - - # check that the messages are in the memory - messages = memory.chat_memory.messages - assert len(messages) == 2 - - # check that the messages are in the order of creation - assert messages == [ai_message, user_message] - - # clear the memory - memory.chat_memory.clear() - time.sleep(SLEEP_DURATION) - assert memory.chat_memory.messages == [] - - def test_memory_with_separate_sessions(self, cluster: Any) -> None: - """Test the chat message history with multiple sessions""" - - message_history_a = CouchbaseChatMessageHistory( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=MESSAGE_HISTORY_COLLECTION_NAME, - session_id="test-session-a", - ) - - message_history_b = CouchbaseChatMessageHistory( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=MESSAGE_HISTORY_COLLECTION_NAME, - session_id="test-session-b", - ) - - memory_a = ConversationBufferMemory( - memory_key="a", chat_memory=message_history_a, return_messages=True - ) - memory_b = ConversationBufferMemory( - memory_key="b", chat_memory=message_history_b, return_messages=True - ) - - # clear the memory - memory_a.chat_memory.clear() - memory_b.chat_memory.clear() - - # add some messages - ai_message = AIMessage(content="Hello, how are you doing ?") - user_message = HumanMessage(content="I'm good, how are you?") - memory_a.chat_memory.add_ai_message(ai_message) - memory_b.chat_memory.add_user_message(user_message) - - # wait until the messages can be retrieved - time.sleep(SLEEP_DURATION) - - # check that the messages are in the memory - messages_a = memory_a.chat_memory.messages - messages_b = memory_b.chat_memory.messages - assert len(messages_a) == 1 - assert len(messages_b) == 1 - assert messages_a == [ai_message] - assert messages_b == [user_message] - - # clear the memory - memory_a.chat_memory.clear() - time.sleep(SLEEP_DURATION) - # ensure that only the session that is cleared is empty - assert memory_a.chat_memory.messages == [] - assert memory_b.chat_memory.messages == [user_message] - - # clear the other session's memory - memory_b.chat_memory.clear() - time.sleep(SLEEP_DURATION) - assert memory_b.chat_memory.messages == [] - - def test_memory_message_with_ttl(self, cluster: Any) -> None: - """Test chat message history with a message being saved with a TTL""" - ttl = timedelta(minutes=5) - session_id = "test-session-ttl" - message_history = CouchbaseChatMessageHistory( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=MESSAGE_HISTORY_COLLECTION_NAME, - session_id=session_id, - ttl=ttl, - ) - - memory = ConversationBufferMemory( - memory_key="baz", chat_memory=message_history, return_messages=True - ) - - # clear the memory - memory.chat_memory.clear() - - # wait for the messages to be cleared - time.sleep(SLEEP_DURATION) - assert memory.chat_memory.messages == [] - - # add some messages - ai_message = AIMessage(content="Hello, how are you doing ?") - memory.chat_memory.add_ai_message(ai_message) - - # wait until the messages can be retrieved - time.sleep(SLEEP_DURATION) - - # check that the messages are in the memory - messages = memory.chat_memory.messages - assert len(messages) == 1 - - # check that the messages are in the order of creation - assert messages == [ai_message] - - # Check the document's expiry time by fetching it from the database - fetch_documents_query = ( - f"SELECT meta().id, * from `{MESSAGE_HISTORY_COLLECTION_NAME}` doc" - f" WHERE doc.session_id = '{session_id}'" - ) - - document_keys = get_document_keys( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - query=fetch_documents_query, - ) - assert len(document_keys) == 1 - - # Ensure that the document will expire within the TTL - - document_expiry_time = fetch_document_expiry_time( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=MESSAGE_HISTORY_COLLECTION_NAME, - document_key=document_keys[0], - ) - current_time = datetime.now() - assert document_expiry_time - current_time < ttl - - def test_memory_messages_with_ttl(self, cluster: Any) -> None: - """Test chat message history with messages being stored with a TTL""" - ttl = timedelta(minutes=5) - session_id = "test-session-ttl" - message_history = CouchbaseChatMessageHistory( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=MESSAGE_HISTORY_COLLECTION_NAME, - session_id=session_id, - ttl=ttl, - ) - - memory = ConversationBufferMemory( - memory_key="baz", chat_memory=message_history, return_messages=True - ) - - # clear the memory - memory.chat_memory.clear() - - # wait for the messages to be cleared - time.sleep(SLEEP_DURATION) - assert memory.chat_memory.messages == [] - - # add some messages - ai_message = AIMessage(content="Hello, how are you doing ?") - user_message = HumanMessage(content="I'm good, how are you?") - memory.chat_memory.add_messages([ai_message, user_message]) - - # wait until the messages can be retrieved - time.sleep(SLEEP_DURATION) - - # check that the messages are in the memory - messages = memory.chat_memory.messages - assert len(messages) == 2 - - # check that the messages are in the order of creation - assert messages == [ai_message, user_message] - - # Check the documents' expiry time by fetching the documents from the database - fetch_documents_query = ( - f"SELECT meta().id, * from `{MESSAGE_HISTORY_COLLECTION_NAME}` doc" - f" WHERE doc.session_id = '{session_id}'" - ) - - document_keys = get_document_keys( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - query=fetch_documents_query, - ) - assert len(document_keys) == 2 - - # Ensure that each document will expire within the TTL - for document_key in document_keys: - document_expiry_time = fetch_document_expiry_time( - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=MESSAGE_HISTORY_COLLECTION_NAME, - document_key=document_key, - ) - current_time = datetime.now() - assert document_expiry_time - current_time < ttl diff --git a/libs/partners/couchbase/tests/integration_tests/test_compile.py b/libs/partners/couchbase/tests/integration_tests/test_compile.py deleted file mode 100644 index 33ecccdfa0fbd..0000000000000 --- a/libs/partners/couchbase/tests/integration_tests/test_compile.py +++ /dev/null @@ -1,7 +0,0 @@ -import pytest - - -@pytest.mark.compile -def test_placeholder() -> None: - """Used for compiling integration tests without running any real tests.""" - pass diff --git a/libs/partners/couchbase/tests/integration_tests/test_vector_store.py b/libs/partners/couchbase/tests/integration_tests/test_vector_store.py deleted file mode 100644 index ebc6c93f6c873..0000000000000 --- a/libs/partners/couchbase/tests/integration_tests/test_vector_store.py +++ /dev/null @@ -1,396 +0,0 @@ -"""Test Couchbase Vector Store functionality""" - -import os -import time -from typing import Any - -import pytest -from langchain_core.documents import Document - -from langchain_couchbase import CouchbaseVectorStore -from tests.utils import ( - ConsistentFakeEmbeddings, -) - -CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "") -BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "") -SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "") -COLLECTION_NAME = os.getenv("COUCHBASE_COLLECTION_NAME", "") -USERNAME = os.getenv("COUCHBASE_USERNAME", "") -PASSWORD = os.getenv("COUCHBASE_PASSWORD", "") -INDEX_NAME = os.getenv("COUCHBASE_INDEX_NAME", "") -SLEEP_DURATION = 1 - - -def set_all_env_vars() -> bool: - return all( - [ - CONNECTION_STRING, - BUCKET_NAME, - SCOPE_NAME, - COLLECTION_NAME, - USERNAME, - PASSWORD, - INDEX_NAME, - ] - ) - - -def get_cluster() -> Any: - """Get a couchbase cluster object""" - from datetime import timedelta - - from couchbase.auth import PasswordAuthenticator - from couchbase.cluster import Cluster - from couchbase.options import ClusterOptions - - auth = PasswordAuthenticator(USERNAME, PASSWORD) - options = ClusterOptions(auth) - connect_string = CONNECTION_STRING - cluster = Cluster(connect_string, options) - - # Wait until the cluster is ready for use. - cluster.wait_until_ready(timedelta(seconds=5)) - - return cluster - - -@pytest.fixture() -def cluster() -> Any: - """Get a couchbase cluster object""" - return get_cluster() - - -def delete_documents( - cluster: Any, bucket_name: str, scope_name: str, collection_name: str -) -> None: - """Delete all the documents in the collection""" - query = f"DELETE FROM `{bucket_name}`.`{scope_name}`.`{collection_name}`" - cluster.query(query).execute() - - -@pytest.mark.skipif( - not set_all_env_vars(), reason="Missing Couchbase environment variables" -) -class TestCouchbaseVectorStore: - @classmethod - def setup_method(self) -> None: - cluster = get_cluster() - # Delete all the documents in the collection - delete_documents(cluster, BUCKET_NAME, SCOPE_NAME, COLLECTION_NAME) - - def test_from_documents(self, cluster: Any) -> None: - """Test end to end search using a list of documents.""" - - documents = [ - Document(page_content="foo", metadata={"page": 1}), - Document(page_content="bar", metadata={"page": 2}), - Document(page_content="baz", metadata={"page": 3}), - ] - - vectorstore = CouchbaseVectorStore.from_documents( - documents, - ConsistentFakeEmbeddings(), - cluster=cluster, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - index_name=INDEX_NAME, - ) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search("baz", k=1) - assert output[0].page_content == "baz" - assert output[0].metadata["page"] == 3 - - def test_from_texts(self, cluster: Any) -> None: - """Test end to end search using a list of texts.""" - - texts = [ - "foo", - "bar", - "baz", - ] - - vectorstore = CouchbaseVectorStore.from_texts( - texts, - ConsistentFakeEmbeddings(), - cluster=cluster, - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search("foo", k=1) - assert len(output) == 1 - assert output[0].page_content == "foo" - - def test_from_texts_with_metadatas(self, cluster: Any) -> None: - """Test end to end search using a list of texts and metadatas.""" - - texts = [ - "foo", - "bar", - "baz", - ] - - metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] - - vectorstore = CouchbaseVectorStore.from_texts( - texts, - ConsistentFakeEmbeddings(), - metadatas=metadatas, - cluster=cluster, - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search("baz", k=1) - assert output[0].page_content == "baz" - assert output[0].metadata["c"] == 3 - - def test_add_texts_with_ids_and_metadatas(self, cluster: Any) -> None: - """Test end to end search by adding a list of texts, ids and metadatas.""" - - texts = [ - "foo", - "bar", - "baz", - ] - - ids = ["a", "b", "c"] - - metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] - - vectorstore = CouchbaseVectorStore( - cluster=cluster, - embedding=ConsistentFakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - results = vectorstore.add_texts( - texts, - ids=ids, - metadatas=metadatas, - ) - assert results == ids - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search("foo", k=1) - assert output[0].id == "a" - assert output[0].page_content == "foo" - assert output[0].metadata["a"] == 1 - - def test_delete_texts_with_ids(self, cluster: Any) -> None: - """Test deletion of documents by ids.""" - texts = [ - "foo", - "bar", - "baz", - ] - - ids = ["a", "b", "c"] - - metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] - - vectorstore = CouchbaseVectorStore( - cluster=cluster, - embedding=ConsistentFakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - results = vectorstore.add_texts( - texts, - ids=ids, - metadatas=metadatas, - ) - assert results == ids - assert vectorstore.delete(ids) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search("foo", k=1) - assert len(output) == 0 - - def test_similarity_search_with_scores(self, cluster: Any) -> None: - """Test similarity search with scores.""" - - texts = ["foo", "bar", "baz"] - - metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] - - vectorstore = CouchbaseVectorStore( - cluster=cluster, - embedding=ConsistentFakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - vectorstore.add_texts(texts, metadatas=metadatas) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search_with_score("foo", k=2) - - assert len(output) == 2 - assert output[0][0].page_content == "foo" - - # check if the scores are sorted - assert output[0][0].metadata["a"] == 1 - assert output[0][1] > output[1][1] - - def test_similarity_search_by_vector(self, cluster: Any) -> None: - """Test similarity search by vector.""" - - texts = ["foo", "bar", "baz"] - - metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] - - vectorstore = CouchbaseVectorStore( - cluster=cluster, - embedding=ConsistentFakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - vectorstore.add_texts(texts, metadatas=metadatas) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - vector = ConsistentFakeEmbeddings().embed_query("foo") - vector_output = vectorstore.similarity_search_by_vector(vector, k=1) - - assert vector_output[0].page_content == "foo" - - similarity_output = vectorstore.similarity_search("foo", k=1) - - assert similarity_output == vector_output - - def test_output_fields(self, cluster: Any) -> None: - """Test that output fields are set correctly.""" - - texts = [ - "foo", - "bar", - "baz", - ] - - metadatas = [{"page": 1, "a": 1}, {"page": 2, "b": 2}, {"page": 3, "c": 3}] - - vectorstore = CouchbaseVectorStore( - cluster=cluster, - embedding=ConsistentFakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - ids = vectorstore.add_texts(texts, metadatas) - assert len(ids) == len(texts) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search("foo", k=1, fields=["metadata.page"]) - assert output[0].page_content == "foo" - assert output[0].metadata["page"] == 1 - assert "a" not in output[0].metadata - - def test_hybrid_search(self, cluster: Any) -> None: - """Test hybrid search.""" - - texts = [ - "foo", - "bar", - "baz", - ] - - metadatas = [ - {"section": "index"}, - {"section": "glossary"}, - {"section": "appendix"}, - ] - - vectorstore = CouchbaseVectorStore( - cluster=cluster, - embedding=ConsistentFakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - vectorstore.add_texts(texts, metadatas=metadatas) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - result, score = vectorstore.similarity_search_with_score("foo", k=1)[0] - - # Wait for the documents to be indexed for hybrid search - time.sleep(SLEEP_DURATION) - - hybrid_result, hybrid_score = vectorstore.similarity_search_with_score( - "foo", - k=1, - search_options={"query": {"match": "index", "field": "metadata.section"}}, - )[0] - - assert result == hybrid_result - assert score <= hybrid_score - - def test_id_in_results(self, cluster: Any) -> None: - """Test that the id is returned in the result documents.""" - - texts = [ - "foo", - "bar", - "baz", - ] - - metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] - - vectorstore = CouchbaseVectorStore( - cluster=cluster, - embedding=ConsistentFakeEmbeddings(), - index_name=INDEX_NAME, - bucket_name=BUCKET_NAME, - scope_name=SCOPE_NAME, - collection_name=COLLECTION_NAME, - ) - - ids = vectorstore.add_texts(texts, metadatas=metadatas) - assert len(ids) == len(texts) - - # Wait for the documents to be indexed - time.sleep(SLEEP_DURATION) - - output = vectorstore.similarity_search("foo", k=1) - assert output[0].id == ids[0] diff --git a/libs/partners/couchbase/tests/unit_tests/__init__.py b/libs/partners/couchbase/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/partners/couchbase/tests/unit_tests/test_imports.py b/libs/partners/couchbase/tests/unit_tests/test_imports.py deleted file mode 100644 index 6fab410176139..0000000000000 --- a/libs/partners/couchbase/tests/unit_tests/test_imports.py +++ /dev/null @@ -1,12 +0,0 @@ -from langchain_couchbase import __all__ - -EXPECTED_ALL = [ - "CouchbaseVectorStore", - "CouchbaseCache", - "CouchbaseSemanticCache", - "CouchbaseChatMessageHistory", -] - - -def test_all_imports() -> None: - assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/couchbase/tests/unit_tests/test_vectorstore.py b/libs/partners/couchbase/tests/unit_tests/test_vectorstore.py deleted file mode 100644 index 8b137891791fe..0000000000000 --- a/libs/partners/couchbase/tests/unit_tests/test_vectorstore.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/libs/partners/couchbase/tests/utils.py b/libs/partners/couchbase/tests/utils.py deleted file mode 100644 index 1e8a9c3f986e6..0000000000000 --- a/libs/partners/couchbase/tests/utils.py +++ /dev/null @@ -1,136 +0,0 @@ -"""Utilities for testing purposes.""" - -import hashlib -from datetime import datetime -from typing import Any, Dict, List, Mapping, Optional, cast - -from couchbase.cluster import Cluster -from couchbase.options import GetOptions -from langchain_core.callbacks import CallbackManagerForLLMRun -from langchain_core.embeddings import Embeddings -from langchain_core.language_models.llms import LLM - - -class FakeEmbeddings(Embeddings): - """Fake embeddings functionality for testing.""" - - def embed_documents(self, texts: List[str]) -> List[List[float]]: - """Return simple embeddings. - Embeddings encode each text as its index.""" - return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))] - - async def aembed_documents(self, texts: List[str]) -> List[List[float]]: - return self.embed_documents(texts) - - def embed_query(self, text: str) -> List[float]: - """Return constant query embeddings. - Embeddings are identical to embed_documents(texts)[0]. - Distance to each text will be that text's index, - as it was passed to embed_documents.""" - return [float(1.0)] * 9 + [float(0.0)] - - async def aembed_query(self, text: str) -> List[float]: - return self.embed_query(text) - - -class ConsistentFakeEmbeddings(FakeEmbeddings): - """Fake embeddings which remember all the texts seen so far to return consistent - vectors for the same texts.""" - - def __init__(self, dimensionality: int = 10) -> None: - self.known_texts: List[str] = [] - self.dimensionality = dimensionality - - def embed_documents(self, texts: List[str]) -> List[List[float]]: - """Return consistent embeddings for each text seen so far.""" - out_vectors = [] - for text in texts: - if text not in self.known_texts: - self.known_texts.append(text) - vector = [float(1.0)] * (self.dimensionality - 1) + [ - float(self.known_texts.index(text)) - ] - out_vectors.append(vector) - return out_vectors - - def embed_query(self, text: str) -> List[float]: - """Return consistent embeddings for the text, if seen before, or a constant - one if the text is unknown.""" - return self.embed_documents([text])[0] - - -class FakeLLM(LLM): - """Fake LLM wrapper for testing purposes.""" - - queries: Optional[Mapping] = None - sequential_responses: Optional[bool] = False - response_index: int = 0 - - def get_num_tokens(self, text: str) -> int: - """Return number of tokens.""" - return len(text.split()) - - @property - def _llm_type(self) -> str: - """Return type of llm.""" - return "fake" - - def _call( - self, - prompt: str, - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> str: - if self.sequential_responses: - return self._get_next_response_in_sequence - if self.queries is not None: - return self.queries[prompt] - if stop is None: - return "foo" - else: - return "bar" - - @property - def _identifying_params(self) -> Dict[str, Any]: - return {} - - @property - def _get_next_response_in_sequence(self) -> str: - queries = cast(Mapping, self.queries) - response = queries[list(queries.keys())[self.response_index]] - self.response_index = self.response_index + 1 - return response - - -def cache_key_hash_function(_input: str) -> str: - """Use a deterministic hashing approach.""" - return hashlib.md5(_input.encode()).hexdigest() - - -def fetch_document_expiry_time( - cluster: Cluster, - bucket_name: str, - scope_name: str, - collection_name: str, - document_key: str, -) -> datetime: - """Fetch the document's expiry time from the database.""" - collection = ( - cluster.bucket(bucket_name).scope(scope_name).collection(collection_name) - ) - result = collection.get(document_key, GetOptions(with_expiry=True)) - - return result.expiryTime - - -def get_document_keys( - cluster: Cluster, bucket_name: str, scope_name: str, query: str -) -> List[str]: - """Get the document key from the database based on the query using meta().id.""" - scope = cluster.bucket(bucket_name).scope(scope_name) - - result = scope.query(query).execute() - - document_keys = [row["id"] for row in result] - return document_keys