diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py
index a85f05dc613f8..374a5dc239d60 100644
--- a/.github/scripts/check_diff.py
+++ b/.github/scripts/check_diff.py
@@ -30,6 +30,9 @@
# specifically in huggingface jobs
# https://github.com/langchain-ai/langchain/issues/25558
"huggingface",
+ # prompty exhibiting issues with numpy for Python 3.13
+ # https://github.com/langchain-ai/langchain/actions/runs/12651104685/job/35251034969?pr=29065
+ "prompty",
]
PY_312_MAX_PACKAGES = [
diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml
index 4316fc407e520..5471ed76cbd87 100644
--- a/.github/workflows/_test.yml
+++ b/.github/workflows/_test.yml
@@ -59,7 +59,8 @@ jobs:
env:
MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
run: |
- poetry run pip install $MIN_VERSIONS
+ poetry run pip install uv
+ poetry run uv pip install $MIN_VERSIONS
make tests
working-directory: ${{ inputs.working-directory }}
diff --git a/cookbook/README.md b/cookbook/README.md
index cfb9ea8e73473..2b90e07c7919a 100644
--- a/cookbook/README.md
+++ b/cookbook/README.md
@@ -50,7 +50,7 @@ Notebook | Description
[press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
[program_aided_language_model.i...](https://github.com/langchain-ai/langchain/tree/master/cookbook/program_aided_language_model.ipynb) | Implement program-aided language models as described in the provided research paper.
[qa_citations.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/qa_citations.ipynb) | Different ways to get a model to cite its sources.
-[rag_upstage_layout_analysis_groundedness_check.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb) | End-to-end RAG example using Upstage Layout Analysis and Groundedness Check.
+[rag_upstage_document_parse_groundedness_check.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_upstage_document_parse_groundedness_check.ipynb) | End-to-end RAG example using Upstage Document Parse and Groundedness Check.
[retrieval_in_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/retrieval_in_sql.ipynb) | Perform retrieval-augmented-generation (rag) on a PostgreSQL database using pgvector.
[sales_agent_with_context.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/sales_agent_with_context.ipynb) | Implement a context-aware ai sales agent, salesgpt, that can have natural sales conversations, interact with other systems, and use a product knowledge base to discuss a company's offerings.
[self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset.
diff --git a/cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb b/cookbook/rag_upstage_document_parse_groundedness_check.ipynb
similarity index 89%
rename from cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb
rename to cookbook/rag_upstage_document_parse_groundedness_check.ipynb
index fafb1dfbbad0c..6fba9623ca3b4 100644
--- a/cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb
+++ b/cookbook/rag_upstage_document_parse_groundedness_check.ipynb
@@ -4,8 +4,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# RAG using Upstage Layout Analysis and Groundedness Check\n",
- "This example illustrates RAG using [Upstage](https://python.langchain.com/docs/integrations/providers/upstage/) Layout Analysis and Groundedness Check."
+ "# RAG using Upstage Document Parse and Groundedness Check\n",
+ "This example illustrates RAG using [Upstage](https://python.langchain.com/docs/integrations/providers/upstage/) Document Parse and Groundedness Check."
]
},
{
@@ -23,16 +23,16 @@
"from langchain_core.runnables.base import RunnableSerializable\n",
"from langchain_upstage import (\n",
" ChatUpstage,\n",
+ " UpstageDocumentParseLoader,\n",
" UpstageEmbeddings,\n",
" UpstageGroundednessCheck,\n",
- " UpstageLayoutAnalysisLoader,\n",
")\n",
"\n",
"model = ChatUpstage()\n",
"\n",
"files = [\"/PATH/TO/YOUR/FILE.pdf\", \"/PATH/TO/YOUR/FILE2.pdf\"]\n",
"\n",
- "loader = UpstageLayoutAnalysisLoader(file_path=files, split=\"element\")\n",
+ "loader = UpstageDocumentParseLoader(file_path=files, split=\"element\")\n",
"\n",
"docs = loader.load()\n",
"\n",
diff --git a/docs/docs/concepts/lcel.mdx b/docs/docs/concepts/lcel.mdx
index da45da268b301..d7701c0ad51ad 100644
--- a/docs/docs/concepts/lcel.mdx
+++ b/docs/docs/concepts/lcel.mdx
@@ -140,7 +140,7 @@ is Equivalent to:
chain = RunnableSequence([runnable1, runnable2])
```
-### The `.pipe` method`
+### The `.pipe` method
If you have moral qualms with operator overloading, you can use the `.pipe` method instead. This is equivalent to the `|` operator.
diff --git a/docs/docs/concepts/streaming.mdx b/docs/docs/concepts/streaming.mdx
index 34dbd891ca521..1f613c07dab31 100644
--- a/docs/docs/concepts/streaming.mdx
+++ b/docs/docs/concepts/streaming.mdx
@@ -39,7 +39,7 @@ In some cases, you may need to stream **custom data** that goes beyond the infor
## Streaming APIs
-LangChain two main APIs for streaming output in real-time. These APIs are supported by any component that implements the [Runnable Interface](/docs/concepts/runnables), including [LLMs](/docs/concepts/chat_models), [compiled LangGraph graphs](https://langchain-ai.github.io/langgraph/concepts/low_level/), and any Runnable generated with [LCEL](/docs/concepts/lcel).
+LangChain has two main APIs for streaming output in real-time. These APIs are supported by any component that implements the [Runnable Interface](/docs/concepts/runnables), including [LLMs](/docs/concepts/chat_models), [compiled LangGraph graphs](https://langchain-ai.github.io/langgraph/concepts/low_level/), and any Runnable generated with [LCEL](/docs/concepts/lcel).
1. sync [stream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) and async [astream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream): Use to stream outputs from individual Runnables (e.g., a chat model) as they are generated or stream any workflow created with LangGraph.
2. The async only [astream_events](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events): Use this API to get access to custom events and intermediate outputs from LLM applications built entirely with [LCEL](/docs/concepts/lcel). Note that this API is available, but not needed when working with LangGraph.
diff --git a/docs/docs/how_to/callbacks_custom_events.ipynb b/docs/docs/how_to/callbacks_custom_events.ipynb
index 7282cd8de7695..8fd6b0d0662db 100644
--- a/docs/docs/how_to/callbacks_custom_events.ipynb
+++ b/docs/docs/how_to/callbacks_custom_events.ipynb
@@ -15,7 +15,7 @@
"- [Astream Events API](/docs/concepts/streaming/#astream_events) the `astream_events` method will surface custom callback events.\n",
":::\n",
"\n",
- "In some situations, you may want to dipsatch a custom callback event from within a [Runnable](/docs/concepts/runnables) so it can be surfaced\n",
+ "In some situations, you may want to dispatch a custom callback event from within a [Runnable](/docs/concepts/runnables) so it can be surfaced\n",
"in a custom callback handler or via the [Astream Events API](/docs/concepts/streaming/#astream_events).\n",
"\n",
"For example, if you have a long running tool with multiple steps, you can dispatch custom events between the steps and use these custom events to monitor progress.\n",
diff --git a/docs/docs/how_to/custom_tools.ipynb b/docs/docs/how_to/custom_tools.ipynb
index 8becb03c7c91c..4225569ab6175 100644
--- a/docs/docs/how_to/custom_tools.ipynb
+++ b/docs/docs/how_to/custom_tools.ipynb
@@ -169,7 +169,7 @@
" return a * max(b)\n",
"\n",
"\n",
- "multiply_by_max.args_schema.schema()"
+ "print(multiply_by_max.args_schema.model_json_schema())"
]
},
{
@@ -285,7 +285,7 @@
" return bar\n",
"\n",
"\n",
- "foo.args_schema.schema()"
+ "print(foo.args_schema.model_json_schema())"
]
},
{
diff --git a/docs/docs/how_to/indexing.ipynb b/docs/docs/how_to/indexing.ipynb
index 4e6fed96abdcd..891af935f6e64 100644
--- a/docs/docs/how_to/indexing.ipynb
+++ b/docs/docs/how_to/indexing.ipynb
@@ -61,7 +61,7 @@
" * document addition by id (`add_documents` method with `ids` argument)\n",
" * delete by id (`delete` method with `ids` argument)\n",
"\n",
- "Compatible Vectorstores: `Aerospike`, `AnalyticDB`, `AstraDB`, `AwaDB`, `AzureCosmosDBNoSqlVectorSearch`, `AzureCosmosDBVectorSearch`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MongoDBAtlasVectorSearch`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SingleStoreDB`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `Yellowbrick`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
+ "Compatible Vectorstores: `Aerospike`, `AnalyticDB`, `AstraDB`, `AwaDB`, `AzureCosmosDBNoSqlVectorSearch`, `AzureCosmosDBVectorSearch`, `AzureSearch`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MongoDBAtlasVectorSearch`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SingleStoreDB`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `Yellowbrick`, `ZepVectorStore`, `TencentVectorDB`, `OpenSearchVectorSearch`.\n",
" \n",
"## Caution\n",
"\n",
diff --git a/docs/docs/how_to/installation.mdx b/docs/docs/how_to/installation.mdx
index 081fe32fd8546..871ce87bd71ce 100644
--- a/docs/docs/how_to/installation.mdx
+++ b/docs/docs/how_to/installation.mdx
@@ -31,7 +31,7 @@ By default, the dependencies needed to do that are NOT installed. You will need
## Ecosystem packages
With the exception of the `langsmith` SDK, all packages in the LangChain ecosystem depend on `langchain-core`, which contains base
-classes and abstractions that other packages use. The dependency graph below shows how the difference packages are related.
+classes and abstractions that other packages use. The dependency graph below shows how the different packages are related.
A directed arrow indicates that the source package depends on the target package:
@@ -115,4 +115,4 @@ If you want to install a package from source, you can do so by cloning the [main
pip install -e .
```
-LangGraph, LangSmith SDK, and certain integration packages live outside the main LangChain repo. You can see [all repos here](https://github.com/langchain-ai).
\ No newline at end of file
+LangGraph, LangSmith SDK, and certain integration packages live outside the main LangChain repo. You can see [all repos here](https://github.com/langchain-ai).
diff --git a/docs/docs/how_to/multi_vector.ipynb b/docs/docs/how_to/multi_vector.ipynb
index a68086b14fa2d..e4c1b1020fdc3 100644
--- a/docs/docs/how_to/multi_vector.ipynb
+++ b/docs/docs/how_to/multi_vector.ipynb
@@ -292,7 +292,7 @@
"id": "3faa9fde-1b09-4849-a815-8b2e89c30a02",
"metadata": {},
"source": [
- "Note that we can [batch](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) the chain accross documents:"
+ "Note that we can [batch](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) the chain across documents:"
]
},
{
diff --git a/docs/docs/how_to/structured_output.ipynb b/docs/docs/how_to/structured_output.ipynb
index c00755650c1c9..66d961491d265 100644
--- a/docs/docs/how_to/structured_output.ipynb
+++ b/docs/docs/how_to/structured_output.ipynb
@@ -165,6 +165,8 @@
}
],
"source": [
+ "from typing import Optional\n",
+ "\n",
"from typing_extensions import Annotated, TypedDict\n",
"\n",
"\n",
@@ -206,10 +208,10 @@
{
"data": {
"text/plain": [
- "{'setup': 'Why was the cat sitting on the computer?',\n",
- " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
- " 'rating': 7}"
- ]
+ "{'setup': 'Why was the cat sitting on the computer?',\n",
+ " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
+ " 'rating': 7}"
+ ]
},
"execution_count": 4,
"metadata": {},
diff --git a/docs/docs/integrations/chat/friendli.ipynb b/docs/docs/integrations/chat/friendli.ipynb
index dab9e0bdd1f74..019f62a31bfa6 100644
--- a/docs/docs/integrations/chat/friendli.ipynb
+++ b/docs/docs/integrations/chat/friendli.ipynb
@@ -2,10 +2,14 @@
"cells": [
{
"cell_type": "raw",
- "metadata": {},
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
"source": [
"---\n",
- "sidebar_label: Friendli\n",
+ "sidebar_label: ChatFriendli\n",
"---"
]
},
@@ -37,7 +41,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -57,13 +61,13 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.chat_models.friendli import ChatFriendli\n",
"\n",
- "chat = ChatFriendli(model=\"llama-2-13b-chat\", max_tokens=100, temperature=0)"
+ "chat = ChatFriendli(model=\"meta-llama-3.1-8b-instruct\", max_tokens=100, temperature=0)"
]
},
{
@@ -84,16 +88,16 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
+ "AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-d47c1056-54e8-4ea9-ad63-07cf74b834b7-0')"
]
},
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -111,17 +115,17 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
- " AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
+ "[AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-36775b84-2a7a-48f0-8c68-df23ffffe4b2-0'),\n",
+ " AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-b204be41-bc06-4d3a-9f74-e66ab1e60e4f-0')]"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -132,16 +136,16 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('a0c2d733-6971-4ae7-beea-653856f4e57c')), RunInfo(run_id=UUID('f3d35e44-ac9a-459a-9e4b-b8e3a73a91e1'))])"
+ "LLMResult(generations=[[ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-2e4cb949-8c51-40d5-92a0-cd0ac577db83-0'))], [ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-afcdd1be-463c-4e50-9731-7a9f5958e396-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('2e4cb949-8c51-40d5-92a0-cd0ac577db83')), RunInfo(run_id=UUID('afcdd1be-463c-4e50-9731-7a9f5958e396'))], type='LLMResult')"
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -152,18 +156,14 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- " Knock, knock!\n",
- "Who's there?\n",
- "Cows go.\n",
- "Cows go who?\n",
- "MOO!"
+ "Why don't eggs tell jokes? They'd crack each other up."
]
}
],
@@ -181,16 +181,16 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
+ "AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-ba8062fb-68af-47b8-bd7b-d1e01b914744-0')"
]
},
- "execution_count": 7,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -201,17 +201,17 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
- " AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
+ "[AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-5d2c77ab-2637-45da-8bbe-1b1f18a22369-0'),\n",
+ " AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-f1338470-8b52-4d6e-9428-a694a08ae484-0')]"
]
},
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -222,16 +222,16 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('f2255321-2d8e-41cc-adbd-3f4facec7573')), RunInfo(run_id=UUID('fcc297d0-6ca9-48cb-9d86-e6f78cade8ee'))])"
+ "LLMResult(generations=[[ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-d4e44569-39cc-40cc-93fc-de53e599fd51-0'))], [ChatGeneration(text=\"Why don't eggs tell jokes? They'd crack each other up.\", message=AIMessage(content=\"Why don't eggs tell jokes? They'd crack each other up.\", additional_kwargs={}, response_metadata={}, id='run-54647cc2-bee3-4154-ad00-2e547993e6d7-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('d4e44569-39cc-40cc-93fc-de53e599fd51')), RunInfo(run_id=UUID('54647cc2-bee3-4154-ad00-2e547993e6d7'))], type='LLMResult')"
]
},
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -242,18 +242,14 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- " Knock, knock!\n",
- "Who's there?\n",
- "Cows go.\n",
- "Cows go who?\n",
- "MOO!"
+ "Why don't eggs tell jokes? They'd crack each other up."
]
}
],
@@ -265,7 +261,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "langchain",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -279,7 +275,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.7"
+ "version": "3.12.2"
}
},
"nbformat": 4,
diff --git a/docs/docs/integrations/chat/modelscope_chat_endpoint.ipynb b/docs/docs/integrations/chat/modelscope_chat_endpoint.ipynb
new file mode 100644
index 0000000000000..4cbf9e959d699
--- /dev/null
+++ b/docs/docs/integrations/chat/modelscope_chat_endpoint.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "afaf8039",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "sidebar_label: ModelScope\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e49f1e0d",
+ "metadata": {},
+ "source": [
+ "# ModelScopeChatEndpoint\n",
+ "\n",
+ "\n",
+ "ModelScope ([Home](https://www.modelscope.cn/) | [GitHub](https://github.com/modelscope/modelscope)) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. \n",
+ "\n",
+ "This will help you getting started with ModelScope Chat Endpoint.\n",
+ "\n",
+ "\n",
+ "## Overview\n",
+ "### Integration details\n",
+ "\n",
+ "|Provider| Class | Package | Local | Serializable | Package downloads | Package latest |\n",
+ "|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
+ "|[ModelScope](/docs/integrations/providers/modelscope/)| ModelScopeChatEndpoint | [langchain-modelscope-integration](https://pypi.org/project/langchain-modelscope-integration/) | ❌ | ❌ |  |  |\n",
+ "\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "To access ModelScope chat endpoint you'll need to create a ModelScope account, get an SDK token, and install the `langchain-modelscope-integration` integration package.\n",
+ "\n",
+ "### Credentials\n",
+ "\n",
+ "Head to [ModelScope](https://modelscope.cn/) to sign up to ModelScope and generate an [SDK token](https://modelscope.cn/my/myaccesstoken). Once you've done this set the `MODELSCOPE_SDK_TOKEN` environment variable:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "if not os.getenv(\"MODELSCOPE_SDK_TOKEN\"):\n",
+ " os.environ[\"MODELSCOPE_SDK_TOKEN\"] = getpass.getpass(\n",
+ " \"Enter your ModelScope SDK token: \"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+ "metadata": {},
+ "source": [
+ "### Installation\n",
+ "\n",
+ "The LangChain ModelScope integration lives in the `langchain-modelscope-integration` package:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install -qU langchain-modelscope-integration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+ "metadata": {},
+ "source": [
+ "## Instantiation\n",
+ "\n",
+ "Now we can instantiate our model object and generate chat completions:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_modelscope import ModelScopeChatEndpoint\n",
+ "\n",
+ "llm = ModelScopeChatEndpoint(\n",
+ " model=\"Qwen/Qwen2.5-Coder-32B-Instruct\",\n",
+ " temperature=0,\n",
+ " max_tokens=1024,\n",
+ " timeout=60,\n",
+ " max_retries=2,\n",
+ " # other params...\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b4f3e15",
+ "metadata": {},
+ "source": [
+ "## Invocation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "62e0dbc3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "AIMessage(content='我喜欢编程。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 33, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'qwen2.5-coder-32b-instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-60bb3461-60ae-4c0b-8997-ab55ef77fcd6-0', usage_metadata={'input_tokens': 33, 'output_tokens': 3, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "messages = [\n",
+ " (\n",
+ " \"system\",\n",
+ " \"You are a helpful assistant that translates English to Chinese. Translate the user sentence.\",\n",
+ " ),\n",
+ " (\"human\", \"I love programming.\"),\n",
+ "]\n",
+ "ai_msg = llm.invoke(messages)\n",
+ "ai_msg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "我喜欢编程。\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(ai_msg.content)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+ "metadata": {},
+ "source": [
+ "## Chaining\n",
+ "\n",
+ "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "AIMessage(content='我喜欢编程。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 28, 'total_tokens': 31, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'qwen2.5-coder-32b-instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9f011a3a-9a11-4759-8d16-5b1843a78862-0', usage_metadata={'input_tokens': 28, 'output_tokens': 3, 'total_tokens': 31, 'input_token_details': {}, 'output_token_details': {}})"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from langchain_core.prompts import ChatPromptTemplate\n",
+ "\n",
+ "prompt = ChatPromptTemplate(\n",
+ " [\n",
+ " (\n",
+ " \"system\",\n",
+ " \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+ " ),\n",
+ " (\"human\", \"{input}\"),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "chain = prompt | llm\n",
+ "chain.invoke(\n",
+ " {\n",
+ " \"input_language\": \"English\",\n",
+ " \"output_language\": \"Chinese\",\n",
+ " \"input\": \"I love programming.\",\n",
+ " }\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+ "metadata": {},
+ "source": [
+ "## API reference\n",
+ "\n",
+ "For detailed documentation of all ModelScopeChatEndpoint features and configurations head to the reference: https://modelscope.cn/docs/model-service/API-Inference/intro\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/document_loaders/index.mdx b/docs/docs/integrations/document_loaders/index.mdx
index 986ef80e97744..971d40cd10cdf 100644
--- a/docs/docs/integrations/document_loaders/index.mdx
+++ b/docs/docs/integrations/document_loaders/index.mdx
@@ -45,7 +45,7 @@ The below document loaders allow you to load documents from your favorite cloud
## Social Platforms
-The below document loaders allow you to load documents from differnt social media platforms.
+The below document loaders allow you to load documents from different social media platforms.
diff --git a/docs/docs/integrations/document_loaders/pull_md.ipynb b/docs/docs/integrations/document_loaders/pull_md.ipynb
new file mode 100644
index 0000000000000..af81b738c4a05
--- /dev/null
+++ b/docs/docs/integrations/document_loaders/pull_md.ipynb
@@ -0,0 +1,140 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "sidebar_label: PullMdLoader\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# PullMdLoader\n",
+ "\n",
+ "Loader for converting URLs into Markdown using the pull.md service.\n",
+ "\n",
+ "This package implements a [document loader](/docs/concepts/document_loaders/) for web content. Unlike traditional web scrapers, PullMdLoader can handle web pages built with dynamic JavaScript frameworks like React, Angular, or Vue.js, converting them into Markdown without local rendering.\n",
+ "\n",
+ "## Overview\n",
+ "### Integration details\n",
+ "\n",
+ "| Class | Package | Local | Serializable | JS Support |\n",
+ "| :--- | :--- | :---: | :---: | :---: |\n",
+ "| PullMdLoader | langchain-pull-md | ✅ | ✅ | ❌ |\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "### Installation\n",
+ "\n",
+ "```bash\n",
+ "pip install langchain-pull-md\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Initialization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_pull_md.markdown_loader import PullMdLoader\n",
+ "\n",
+ "# Instantiate the loader with a URL\n",
+ "loader = PullMdLoader(url=\"https://example.com\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = loader.load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'source': 'https://example.com',\n",
+ " 'page_content': '# Example Domain\\nThis domain is used for illustrative examples in documents. You may use this domain in literature without prior coordination or asking for permission.'}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "documents[0].metadata"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Lazy Load\n",
+ "\n",
+ "No lazy loading is implemented. `PullMdLoader` performs a real-time conversion of the provided URL into Markdown format whenever the `load` method is called."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## API reference:\n",
+ "\n",
+ "- [GitHub](https://github.com/chigwell/langchain-pull-md)\n",
+ "- [PyPi](https://pypi.org/project/langchain-pull-md/)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/document_loaders/recursive_url.ipynb b/docs/docs/integrations/document_loaders/recursive_url.ipynb
index 98c87977435e3..0820fb2935616 100644
--- a/docs/docs/integrations/document_loaders/recursive_url.ipynb
+++ b/docs/docs/integrations/document_loaders/recursive_url.ipynb
@@ -44,7 +44,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%pip install -qU langchain-community beautifulsoup4"
+ "%pip install -qU langchain-community beautifulsoup4 lxml"
]
},
{
diff --git a/docs/docs/integrations/document_loaders/slack.ipynb b/docs/docs/integrations/document_loaders/slack.ipynb
index 648ecda4e86fd..98a8243511bd5 100644
--- a/docs/docs/integrations/document_loaders/slack.ipynb
+++ b/docs/docs/integrations/document_loaders/slack.ipynb
@@ -41,7 +41,7 @@
"source": [
"# Optionally set your Slack URL. This will give you proper URLs in the docs sources.\n",
"SLACK_WORKSPACE_URL = \"https://xxx.slack.com\"\n",
- "LOCAL_ZIPFILE = \"\" # Paste the local paty to your Slack zip file here.\n",
+ "LOCAL_ZIPFILE = \"\" # Paste the local path to your Slack zip file here.\n",
"\n",
"loader = SlackDirectoryLoader(LOCAL_ZIPFILE, SLACK_WORKSPACE_URL)"
]
diff --git a/docs/docs/integrations/graphs/kuzu_db.ipynb b/docs/docs/integrations/graphs/kuzu_db.ipynb
index dde4dddc6c6f4..a030a205380c1 100644
--- a/docs/docs/integrations/graphs/kuzu_db.ipynb
+++ b/docs/docs/integrations/graphs/kuzu_db.ipynb
@@ -7,12 +7,17 @@
"source": [
"# Kuzu\n",
"\n",
- ">[Kùzu](https://kuzudb.com) is an embeddable property graph database management system built for query speed and scalability.\n",
- "> \n",
- "> Kùzu has a permissive (MIT) open source license and implements [Cypher](https://en.wikipedia.org/wiki/Cypher_(query_language)), a declarative graph query language that allows for expressive and efficient data querying in a property graph.\n",
- "> It uses columnar storage and its query processor contains novel join algorithms that allow it to scale to very large graphs without sacrificing query performance.\n",
- "> \n",
- "> This notebook shows how to use LLMs to provide a natural language interface to [Kùzu](https://kuzudb.com) database with Cypher."
+ "> [Kùzu](https://kuzudb.com/) is an embeddable, scalable, extremely fast graph database.\n",
+ "> It is permissively licensed with an MIT license, and you can see its source code [here](https://github.com/kuzudb/kuzu).\n",
+ "\n",
+ "> Key characteristics of Kùzu:\n",
+ ">- Performance and scalability: Implements modern, state-of-the-art join algorithms for graphs.\n",
+ ">- Usability: Very easy to set up and get started with, as there are no servers (embedded architecture).\n",
+ ">- Interoperability: Can conveniently scan and copy data from external columnar formats, CSV, JSON and relational databases.\n",
+ ">- Structured property graph model: Implements the property graph model, with added structure.\n",
+ ">- Cypher support: Allows convenient querying of the graph in Cypher, a declarative query language.\n",
+ "\n",
+ "> Get started with Kùzu by visiting their [documentation](https://docs.kuzudb.com/)."
]
},
{
@@ -22,19 +27,28 @@
"source": [
"## Setting up\n",
"\n",
- "Kùzu is an embedded database (it runs in-process), so there are no servers to manage.\n",
- "Simply install it via its Python package:\n",
+ "Kùzu is an embedded database (it runs in-process), so there are no servers to manage. Install the\n",
+ "following dependencies to get started:\n",
"\n",
"```bash\n",
- "pip install kuzu\n",
+ "pip install -U langchain-kuzu langchain-openai langchain-experimental\n",
"```\n",
"\n",
- "Create a database on the local machine and connect to it:"
+ "This installs Kùzu along with the LangChain integration for it, as well as the OpenAI Python package\n",
+ "so that we can use OpenAI's LLMs. If you want to use other LLM providers, you can install their\n",
+ "respective Python packages that come with LangChain."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's how you would first create a Kùzu database on your local machine and connect to it:"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -45,102 +59,75 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "First, we create the schema for a simple movie database:"
+ "## Create `KuzuGraph`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Kùzu's integration with LangChain makes it convenient to create and update graphs from unstructured text, and also to query graphs via a Text2Cypher pipeline that utilizes the\n",
+ "power of LangChain's LLM chains. To begin, we create a `KuzuGraph` object that uses the database object we created above in combination with the `KuzuGraph` constructor."
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "conn.execute(\"CREATE NODE TABLE Movie (name STRING, PRIMARY KEY(name))\")\n",
- "conn.execute(\n",
- " \"CREATE NODE TABLE Person (name STRING, birthDate STRING, PRIMARY KEY(name))\"\n",
- ")\n",
- "conn.execute(\"CREATE REL TABLE ActedIn (FROM Person TO Movie)\")"
+ "from langchain_kuzu.graphs.kuzu_graph import KuzuGraph\n",
+ "\n",
+ "graph = KuzuGraph(db, allow_dangerous_requests=True)"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Then we can insert some data."
+ "Say we want to transform the following text into a graph:"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "conn.execute(\"CREATE (:Person {name: 'Al Pacino', birthDate: '1940-04-25'})\")\n",
- "conn.execute(\"CREATE (:Person {name: 'Robert De Niro', birthDate: '1943-08-17'})\")\n",
- "conn.execute(\"CREATE (:Movie {name: 'The Godfather'})\")\n",
- "conn.execute(\"CREATE (:Movie {name: 'The Godfather: Part II'})\")\n",
- "conn.execute(\n",
- " \"CREATE (:Movie {name: 'The Godfather Coda: The Death of Michael Corleone'})\"\n",
- ")\n",
- "conn.execute(\n",
- " \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather' CREATE (p)-[:ActedIn]->(m)\"\n",
- ")\n",
- "conn.execute(\n",
- " \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather: Part II' CREATE (p)-[:ActedIn]->(m)\"\n",
- ")\n",
- "conn.execute(\n",
- " \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Al Pacino' AND m.name = 'The Godfather Coda: The Death of Michael Corleone' CREATE (p)-[:ActedIn]->(m)\"\n",
- ")\n",
- "conn.execute(\n",
- " \"MATCH (p:Person), (m:Movie) WHERE p.name = 'Robert De Niro' AND m.name = 'The Godfather: Part II' CREATE (p)-[:ActedIn]->(m)\"\n",
- ")"
+ "text = \"Tim Cook is the CEO of Apple. Apple has its headquarters in California.\""
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Creating `KuzuQAChain`\n",
- "\n",
- "We can now create the `KuzuGraph` and `KuzuQAChain`. To create the `KuzuGraph` we simply need to pass the database object to the `KuzuGraph` constructor."
+ "We will make use of `LLMGraphTransformer` to use an LLM to extract nodes and relationships from the text.\n",
+ "To make the graph more useful, we will define the following schema, such that the LLM will only\n",
+ "extract nodes and relationships that match the schema."
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "from langchain.chains import KuzuQAChain\n",
- "from langchain_community.graphs import KuzuGraph\n",
- "from langchain_openai import ChatOpenAI"
+ "# Define schema\n",
+ "allowed_nodes = [\"Person\", \"Company\", \"Location\"]\n",
+ "allowed_relationships = [\n",
+ " (\"Person\", \"IS_CEO_OF\", \"Company\"),\n",
+ " (\"Company\", \"HAS_HEADQUARTERS_IN\", \"Location\"),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `LLMGraphTransformer` class provides a convenient way to convert the text into a list of graph documents."
]
},
{
@@ -149,7 +136,47 @@
"metadata": {},
"outputs": [],
"source": [
- "graph = KuzuGraph(db)"
+ "from langchain_core.documents import Document\n",
+ "from langchain_experimental.graph_transformers import LLMGraphTransformer\n",
+ "from langchain_openai import ChatOpenAI\n",
+ "\n",
+ "# Define the LLMGraphTransformer\n",
+ "llm_transformer = LLMGraphTransformer(\n",
+ " llm=ChatOpenAI(model=\"gpt-4o-mini\", temperature=0, api_key=OPENAI_API_KEY), # noqa: F821\n",
+ " allowed_nodes=allowed_nodes,\n",
+ " allowed_relationships=allowed_relationships,\n",
+ ")\n",
+ "\n",
+ "documents = [Document(page_content=text)]\n",
+ "graph_documents = llm_transformer.convert_to_graph_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[GraphDocument(nodes=[Node(id='Tim Cook', type='Person', properties={}), Node(id='Apple', type='Company', properties={}), Node(id='California', type='Location', properties={})], relationships=[Relationship(source=Node(id='Tim Cook', type='Person', properties={}), target=Node(id='Apple', type='Company', properties={}), type='IS_CEO_OF', properties={}), Relationship(source=Node(id='Apple', type='Company', properties={}), target=Node(id='California', type='Location', properties={}), type='HAS_HEADQUARTERS_IN', properties={})], source=Document(metadata={}, page_content='Tim Cook is the CEO of Apple. Apple has its headquarters in California.'))]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "graph_documents[:2]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can then call the above defined `KuzuGraph` object's `add_graph_documents` method to ingest the graph documents into the Kùzu database.\n",
+ "The `include_source` argument is set to `True` so that we also create relationships between each entity node and the source document that it came from."
]
},
{
@@ -158,10 +185,10 @@
"metadata": {},
"outputs": [],
"source": [
- "chain = KuzuQAChain.from_llm(\n",
- " llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
- " graph=graph,\n",
- " verbose=True,\n",
+ "# Add the graph document to the graph\n",
+ "graph.add_graph_documents(\n",
+ " graph_documents,\n",
+ " include_source=True,\n",
")"
]
},
@@ -170,10 +197,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Refresh graph schema information\n",
+ "## Creating `KuzuQAChain`\n",
"\n",
- "If the schema of database changes, you can refresh the schema information needed to generate Cypher statements.\n",
- "You can also display the schema of the Kùzu graph as demonstrated below."
+ "To query the graph via a Text2Cypher pipeline, we can define a `KuzuQAChain` object. Then, we can invoke the chain with a query by connecting to the existing database that's stored in the `test_db` directory defined above."
]
},
{
@@ -182,27 +208,22 @@
"metadata": {},
"outputs": [],
"source": [
- "# graph.refresh_schema()"
+ "from langchain_kuzu.chains.graph_qa.kuzu import KuzuQAChain\n",
+ "\n",
+ "# Create the KuzuQAChain with verbosity enabled to see the generated Cypher queries\n",
+ "chain = KuzuQAChain.from_llm(\n",
+ " llm=ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.3, api_key=OPENAI_API_KEY), # noqa: F821\n",
+ " graph=graph,\n",
+ " verbose=True,\n",
+ " allow_dangerous_requests=True,\n",
+ ")"
]
},
{
- "cell_type": "code",
- "execution_count": 10,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Node properties: [{'properties': [('name', 'STRING')], 'label': 'Movie'}, {'properties': [('name', 'STRING'), ('birthDate', 'STRING')], 'label': 'Person'}]\n",
- "Relationships properties: [{'properties': [], 'label': 'ActedIn'}]\n",
- "Relationships: ['(:Person)-[:ActedIn]->(:Movie)']\n",
- "\n"
- ]
- }
- ],
"source": [
- "print(graph.get_schema)"
+ "Note that we set a temperature that's slightly higher than zero to avoid the LLM being overly concise in its response."
]
},
{
@@ -210,14 +231,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Querying the graph\n",
- "\n",
- "We can now use the `KuzuQAChain` to ask questions of the graph."
+ "Let's ask some questions using the QA chain."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -228,11 +247,9 @@
"\n",
"\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
- "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie)\n",
- "WHERE m.name = 'The Godfather: Part II'\n",
- "RETURN p.name\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:IS_CEO_OF]->(c:Company {id: 'Apple'}) RETURN p\u001b[0m\n",
"Full Context:\n",
- "\u001b[32;1m\u001b[1;3m[{'p.name': 'Al Pacino'}, {'p.name': 'Robert De Niro'}]\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3m[{'p': {'_id': {'offset': 0, 'table': 1}, '_label': 'Person', 'id': 'Tim Cook', 'type': 'entity'}}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@@ -240,22 +257,22 @@
{
"data": {
"text/plain": [
- "{'query': 'Who acted in The Godfather: Part II?',\n",
- " 'result': 'Al Pacino, Robert De Niro acted in The Godfather: Part II.'}"
+ "{'query': 'Who is the CEO of Apple?',\n",
+ " 'result': 'Tim Cook is the CEO of Apple.'}"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "chain.invoke(\"Who acted in The Godfather: Part II?\")"
+ "chain.invoke(\"Who is the CEO of Apple?\")"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -266,11 +283,9 @@
"\n",
"\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
- "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie)\n",
- "WHERE p.name = 'Robert De Niro'\n",
- "RETURN m.name\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3mMATCH (c:Company {id: 'Apple'})-[:HAS_HEADQUARTERS_IN]->(l:Location) RETURN l\u001b[0m\n",
"Full Context:\n",
- "\u001b[32;1m\u001b[1;3m[{'m.name': 'The Godfather: Part II'}]\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3m[{'l': {'_id': {'offset': 0, 'table': 2}, '_label': 'Location', 'id': 'California', 'type': 'entity'}}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@@ -278,93 +293,50 @@
{
"data": {
"text/plain": [
- "{'query': 'Robert De Niro played in which movies?',\n",
- " 'result': 'Robert De Niro played in The Godfather: Part II.'}"
+ "{'query': 'Where is Apple headquartered?',\n",
+ " 'result': 'Apple is headquartered in California.'}"
]
},
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "chain.invoke(\"Robert De Niro played in which movies?\")"
+ "chain.invoke(\"Where is Apple headquartered?\")"
]
},
{
- "cell_type": "code",
- "execution_count": 13,
+ "attachments": {},
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
- "Generated Cypher:\n",
- "\u001b[32;1m\u001b[1;3mMATCH (:Person)-[:ActedIn]->(:Movie {name: 'Godfather: Part II'})\n",
- "RETURN count(*)\u001b[0m\n",
- "Full Context:\n",
- "\u001b[32;1m\u001b[1;3m[{'COUNT_STAR()': 0}]\u001b[0m\n",
- "\n",
- "\u001b[1m> Finished chain.\u001b[0m\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "{'query': 'How many actors played in the Godfather: Part II?',\n",
- " 'result': \"I don't know the answer.\"}"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
- "chain.invoke(\"How many actors played in the Godfather: Part II?\")"
+ "## Refresh graph schema\n",
+ "\n",
+ "If you mutate or update the graph, you can inspect the refreshed schema information that's used by the Text2Cypher chain to generate Cypher statements.\n",
+ "You don't need to manually call `refresh_schema()` each time as it's called automatically when you invoke the chain."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\n",
- "\n",
- "\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
- "Generated Cypher:\n",
- "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie {name: 'The Godfather: Part II'})\n",
- "RETURN p.name\n",
- "ORDER BY p.birthDate ASC\n",
- "LIMIT 1\u001b[0m\n",
- "Full Context:\n",
- "\u001b[32;1m\u001b[1;3m[{'p.name': 'Al Pacino'}]\u001b[0m\n",
- "\n",
- "\u001b[1m> Finished chain.\u001b[0m\n"
+ "Node properties: [{'properties': [('id', 'STRING'), ('type', 'STRING')], 'label': 'Person'}, {'properties': [('id', 'STRING'), ('type', 'STRING')], 'label': 'Location'}, {'properties': [('id', 'STRING'), ('text', 'STRING'), ('type', 'STRING')], 'label': 'Chunk'}, {'properties': [('id', 'STRING'), ('type', 'STRING')], 'label': 'Company'}]\n",
+ "Relationships properties: [{'properties': [], 'label': 'HAS_HEADQUARTERS_IN'}, {'properties': [('label', 'STRING'), ('triplet_source_id', 'STRING')], 'label': 'MENTIONS_Chunk_Person'}, {'properties': [('label', 'STRING'), ('triplet_source_id', 'STRING')], 'label': 'MENTIONS_Chunk_Location'}, {'properties': [], 'label': 'IS_CEO_OF'}, {'properties': [('label', 'STRING'), ('triplet_source_id', 'STRING')], 'label': 'MENTIONS_Chunk_Company'}]\n",
+ "Relationships: ['(:Company)-[:HAS_HEADQUARTERS_IN]->(:Location)', '(:Chunk)-[:MENTIONS_Chunk_Person]->(:Person)', '(:Chunk)-[:MENTIONS_Chunk_Location]->(:Location)', '(:Person)-[:IS_CEO_OF]->(:Company)', '(:Chunk)-[:MENTIONS_Chunk_Company]->(:Company)']\n",
+ "\n"
]
- },
- {
- "data": {
- "text/plain": [
- "{'query': 'Who is the oldest actor who played in The Godfather: Part II?',\n",
- " 'result': 'Al Pacino is the oldest actor who played in The Godfather: Part II.'}"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
}
],
"source": [
- "chain.invoke(\"Who is the oldest actor who played in The Godfather: Part II?\")"
+ "graph.refresh_schema()\n",
+ "\n",
+ "print(graph.get_schema)"
]
},
{
@@ -378,30 +350,22 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 13,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/prrao/code/langchain/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 0.3.0. Use RunnableSequence, e.g., `prompt | llm` instead.\n",
- " warn_deprecated(\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"chain = KuzuQAChain.from_llm(\n",
- " cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
+ " cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-4o-mini\"),\n",
" qa_llm=ChatOpenAI(temperature=0, model=\"gpt-4\"),\n",
" graph=graph,\n",
" verbose=True,\n",
+ " allow_dangerous_requests=True,\n",
")"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -410,40 +374,11 @@
"text": [
"\n",
"\n",
- "\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/prrao/code/langchain/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
- " warn_deprecated(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
- "\u001b[32;1m\u001b[1;3mMATCH (:Person)-[:ActedIn]->(:Movie {name: 'The Godfather: Part II'})\n",
- "RETURN count(*)\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:IS_CEO_OF]->(c:Company {id: 'Apple'}) RETURN p.id, p.type\u001b[0m\n",
"Full Context:\n",
- "\u001b[32;1m\u001b[1;3m[{'COUNT_STAR()': 2}]\u001b[0m\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/prrao/code/langchain/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
- " warn_deprecated(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "\u001b[32;1m\u001b[1;3m[{'p.id': 'Tim Cook', 'p.type': 'entity'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@@ -451,17 +386,17 @@
{
"data": {
"text/plain": [
- "{'query': 'How many actors played in The Godfather: Part II?',\n",
- " 'result': 'Two actors played in The Godfather: Part II.'}"
+ "{'query': 'Who is the CEO of Apple?',\n",
+ " 'result': 'Tim Cook is the CEO of Apple.'}"
]
},
- "execution_count": 16,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "chain.invoke(\"How many actors played in The Godfather: Part II?\")"
+ "chain.invoke(\"Who is the CEO of Apple?\")"
]
}
],
@@ -481,7 +416,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.7"
+ "version": "3.13.1"
}
},
"nbformat": 4,
diff --git a/docs/docs/integrations/llms/friendli.ipynb b/docs/docs/integrations/llms/friendli.ipynb
index 529b6aec7a0ba..74978a39aa5f7 100644
--- a/docs/docs/integrations/llms/friendli.ipynb
+++ b/docs/docs/integrations/llms/friendli.ipynb
@@ -29,7 +29,7 @@
"Ensure the `langchain_community` and `friendli-client` are installed.\n",
"\n",
"```sh\n",
- "pip install -U langchain-community friendli-client.\n",
+ "pip install -U langchain-community friendli-client\n",
"```\n",
"\n",
"Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."
@@ -40,13 +40,20 @@
"execution_count": 1,
"metadata": {},
"outputs": [],
- "source": ["import getpass\nimport os\n\nif \"FRIENDLI_TOKEN\" not in os.environ:\n os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"]
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "if \"FRIENDLI_TOKEN\" not in os.environ:\n",
+ " os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
+ ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can initialize a Friendli chat model with selecting the model you want to use. The default model is `mixtral-8x7b-instruct-v0-1`. You can check the available models at [docs.friendli.ai](https://docs.periflow.ai/guides/serverless_endpoints/pricing#text-generation-models)."
+ "You can initialize a Friendli chat model with selecting the model you want to use. \n",
+ "The default model is `meta-llama-3.1-8b-instruct`. You can check the available models at [friendli.ai/docs](https://friendli.ai/docs/guides/serverless_endpoints/pricing#text-generation-models)."
]
},
{
@@ -54,7 +61,11 @@
"execution_count": 2,
"metadata": {},
"outputs": [],
- "source": ["from langchain_community.llms.friendli import Friendli\n\nllm = Friendli(model=\"mixtral-8x7b-instruct-v0-1\", max_tokens=100, temperature=0)"]
+ "source": [
+ "from langchain_community.llms.friendli import Friendli\n",
+ "\n",
+ "llm = Friendli(model=\"meta-llama-3.1-8b-instruct\", max_tokens=100, temperature=0)"
+ ]
},
{
"cell_type": "markdown",
@@ -80,7 +91,7 @@
{
"data": {
"text/plain": [
- "'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"'"
+ "\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\""
]
},
"execution_count": 3,
@@ -88,7 +99,9 @@
"output_type": "execute_result"
}
],
- "source": ["llm.invoke(\"Tell me a joke.\")"]
+ "source": [
+ "llm.invoke(\"Tell me a joke.\")"
+ ]
},
{
"cell_type": "code",
@@ -98,8 +111,8 @@
{
"data": {
"text/plain": [
- "['Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"',\n",
- " 'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"']"
+ "[\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\",\n",
+ " \" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\"]"
]
},
"execution_count": 4,
@@ -107,7 +120,9 @@
"output_type": "execute_result"
}
],
- "source": ["llm.batch([\"Tell me a joke.\", \"Tell me a joke.\"])"]
+ "source": [
+ "llm.batch([\"Tell me a joke.\", \"Tell me a joke.\"])"
+ ]
},
{
"cell_type": "code",
@@ -117,7 +132,7 @@
{
"data": {
"text/plain": [
- "LLMResult(generations=[[Generation(text='Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"')], [Generation(text='Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"')]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('a2009600-baae-4f5a-9f69-23b2bc916e4c')), RunInfo(run_id=UUID('acaf0838-242c-4255-85aa-8a62b675d046'))])"
+ "LLMResult(generations=[[Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")], [Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")]], llm_output={'model': 'meta-llama-3.1-8b-instruct'}, run=[RunInfo(run_id=UUID('ee97984b-6eab-4d40-a56f-51d6114953de')), RunInfo(run_id=UUID('cbe501ea-a20f-4420-9301-86cdfcf898c0'))], type='LLMResult')"
]
},
"execution_count": 5,
@@ -125,25 +140,19 @@
"output_type": "execute_result"
}
],
- "source": ["llm.generate([\"Tell me a joke.\", \"Tell me a joke.\"])"]
+ "source": [
+ "llm.generate([\"Tell me a joke.\", \"Tell me a joke.\"])"
+ ]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Username checks out.\n",
- "User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.\n",
- "User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.\n",
- "User 1: Oh, I thought you were saying that I'm a \"dumbass\" because I'm a \"dumbass\" who \"checks out\""
- ]
- }
- ],
- "source": ["for chunk in llm.stream(\"Tell me a joke.\"):\n print(chunk, end=\"\", flush=True)"]
+ "outputs": [],
+ "source": [
+ "for chunk in llm.stream(\"Tell me a joke.\"):\n",
+ " print(chunk, end=\"\", flush=True)"
+ ]
},
{
"cell_type": "markdown",
@@ -154,81 +163,79 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"'"
+ "\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\""
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": ["await llm.ainvoke(\"Tell me a joke.\")"]
+ "source": [
+ "await llm.ainvoke(\"Tell me a joke.\")"
+ ]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "['Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"',\n",
- " 'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"']"
+ "[\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\",\n",
+ " \" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\"]"
]
},
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": ["await llm.abatch([\"Tell me a joke.\", \"Tell me a joke.\"])"]
+ "source": [
+ "await llm.abatch([\"Tell me a joke.\", \"Tell me a joke.\"])"
+ ]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "LLMResult(generations=[[Generation(text=\"Username checks out.\\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\\nUser 0: I'm being serious. I'm not sure\")], [Generation(text=\"Username checks out.\\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\\nUser 0: I'm being serious. I'm not sure\")]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('46144905-7350-4531-a4db-22e6a827c6e3')), RunInfo(run_id=UUID('e2b06c30-ffff-48cf-b792-be91f2144aa6'))])"
+ "LLMResult(generations=[[Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")], [Generation(text=\" I need a laugh.\\nHere's one: Why couldn't the bicycle stand up by itself?\\nBecause it was two-tired!\\nI hope that made you laugh! Do you want to hear another one? I have a million of 'em! (Okay, maybe not a million, but I have a few more where that came from!) What kind of joke are you in the mood for? A pun, a play on words, or something else? Let me know and I'll try to come\")]], llm_output={'model': 'meta-llama-3.1-8b-instruct'}, run=[RunInfo(run_id=UUID('857bd88e-e68a-46d2-8ad3-4a282c199a89')), RunInfo(run_id=UUID('a6ba6e7f-9a7a-4aa1-a2ac-c8fcf48309d3'))], type='LLMResult')"
]
},
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": ["await llm.agenerate([\"Tell me a joke.\", \"Tell me a joke.\"])"]
+ "source": [
+ "await llm.agenerate([\"Tell me a joke.\", \"Tell me a joke.\"])"
+ ]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Username checks out.\n",
- "User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.\n",
- "User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.\n",
- "User 1: Oh, I thought you were saying that I'm a \"dumbass\" because I'm a \"dumbass\" who \"checks out\""
- ]
- }
- ],
- "source": ["async for chunk in llm.astream(\"Tell me a joke.\"):\n print(chunk, end=\"\", flush=True)"]
+ "outputs": [],
+ "source": [
+ "async for chunk in llm.astream(\"Tell me a joke.\"):\n",
+ " print(chunk, end=\"\", flush=True)"
+ ]
}
],
"metadata": {
"kernelspec": {
- "display_name": "langchain",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -242,7 +249,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.7"
+ "version": "3.12.2"
}
},
"nbformat": 4,
diff --git a/docs/docs/integrations/llms/modelscope_endpoint.ipynb b/docs/docs/integrations/llms/modelscope_endpoint.ipynb
new file mode 100644
index 0000000000000..e4bd19a368dd1
--- /dev/null
+++ b/docs/docs/integrations/llms/modelscope_endpoint.ipynb
@@ -0,0 +1,294 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "67db2992",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "sidebar_label: ModelScope\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9597802c",
+ "metadata": {},
+ "source": [
+ "# ModelScopeEndpoint\n",
+ "\n",
+ "ModelScope ([Home](https://www.modelscope.cn/) | [GitHub](https://github.com/modelscope/modelscope)) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. This will help you get started with ModelScope completion models (LLMs) using LangChain.\n",
+ "\n",
+ "## Overview\n",
+ "### Integration details\n",
+ "\n",
+ "| Provider | Class | Package | Local | Serializable | Package downloads | Package latest |\n",
+ "| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
+ "| [ModelScope](/docs/integrations/providers/modelscope/) | ModelScopeEndpoint | [langchain-modelscope-integration](https://pypi.org/project/langchain-modelscope-integration/) | ❌ | ❌ |  |  |\n",
+ "\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "To access ModelScope models you'll need to create a ModelScope account, get an SDK token, and install the `langchain-modelscope-integration` integration package.\n",
+ "\n",
+ "### Credentials\n",
+ "\n",
+ "\n",
+ "Head to [ModelScope](https://modelscope.cn/) to sign up to ModelScope and generate an [SDK token](https://modelscope.cn/my/myaccesstoken). Once you've done this set the `MODELSCOPE_SDK_TOKEN` environment variable:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bc51e756",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "if not os.getenv(\"MODELSCOPE_SDK_TOKEN\"):\n",
+ " os.environ[\"MODELSCOPE_SDK_TOKEN\"] = getpass.getpass(\n",
+ " \"Enter your ModelScope SDK token: \"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "809c6577",
+ "metadata": {},
+ "source": [
+ "### Installation\n",
+ "\n",
+ "The LangChain ModelScope integration lives in the `langchain-modelscope-integration` package:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "59c710c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install -qU langchain-modelscope-integration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a760037",
+ "metadata": {},
+ "source": [
+ "## Instantiation\n",
+ "\n",
+ "Now we can instantiate our model object and generate chat completions:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a0562a13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_modelscope import ModelScopeEndpoint\n",
+ "\n",
+ "llm = ModelScopeEndpoint(\n",
+ " model=\"Qwen/Qwen2.5-Coder-32B-Instruct\",\n",
+ " temperature=0,\n",
+ " max_tokens=1024,\n",
+ " timeout=60,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ee90032",
+ "metadata": {},
+ "source": [
+ "## Invocation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "035dea0f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Certainly! Quick sort is a popular and efficient sorting algorithm that uses a divide-and-conquer approach to sort elements. Below is a simple implementation of the Quick Sort algorithm in Python:\\n\\n```python\\ndef quick_sort(arr):\\n # Base case: if the array is empty or has one element, it\\'s already sorted\\n if len(arr) <= 1:\\n return arr\\n else:\\n # Choose a pivot element from the array\\n pivot = arr[len(arr) // 2]\\n \\n # Partition the array into three parts:\\n # - elements less than the pivot\\n # - elements equal to the pivot\\n # - elements greater than the pivot\\n less_than_pivot = [x for x in arr if x < pivot]\\n equal_to_pivot = [x for x in arr if x == pivot]\\n greater_than_pivot = [x for x in arr if x > pivot]\\n \\n # Recursively apply quick_sort to the less_than_pivot and greater_than_pivot subarrays\\n return quick_sort(less_than_pivot) + equal_to_pivot + quick_sort(greater_than_pivot)\\n\\n# Example usage:\\narr = [3, 6, 8, 10, 1, 2, 1]\\nsorted_arr = quick_sort(arr)\\nprint(\"Sorted array:\", sorted_arr)\\n```\\n\\n### Explanation:\\n1. **Base Case**: If the array has one or zero elements, it is already sorted, so we return it as is.\\n2. **Pivot Selection**: We choose the middle element of the array as the pivot. This is a simple strategy, but there are other strategies for choosing a pivot.\\n3. **Partitioning**: We partition the array into three lists:\\n - `less_than_pivot`: Elements less than the pivot.\\n - `equal_to_pivot`: Elements equal to the pivot.\\n - `greater_than_pivot`: Elements greater than the pivot.\\n4. **Recursive Sorting**: We recursively sort the `less_than_pivot` and `greater_than_pivot` lists and concatenate them with the `equal_to_pivot` list to get the final sorted array.\\n\\nThis implementation is straightforward and easy to understand, but it may not be the most efficient in terms of space complexity due to the use of additional lists. For an in-place version of Quick Sort, you can modify the algorithm to sort the array within its own memory space.'"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "input_text = \"Write a quick sort algorithm in python\"\n",
+ "\n",
+ "completion = llm.invoke(input_text)\n",
+ "completion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d5431620",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Certainly! Sorting an array is a common task in programming, and Python provides several ways to do it. Below is a simple example using Python's built-in sorting functions. We'll use the `sorted()` function and the `sort()` method of a list.\n",
+ "\n",
+ "### Using `sorted()` Function\n",
+ "\n",
+ "The `sorted()` function returns a new sorted list from the elements of any iterable.\n",
+ "\n",
+ "```python\n",
+ "def sort_array(arr):\n",
+ " return sorted(arr)\n",
+ "\n",
+ "# Example usage\n",
+ "array = [5, 2, 9, 1, 5, 6]\n",
+ "sorted_array = sort_array(array)\n",
+ "print(\"Original array:\", array)\n",
+ "print(\"Sorted array:\", sorted_array)\n",
+ "```\n",
+ "\n",
+ "### Using `sort()` Method\n",
+ "\n",
+ "The `sort()` method sorts the list in place and returns `None`.\n",
+ "\n",
+ "```python\n",
+ "def sort_array_in_place(arr):\n",
+ " arr.sort()\n",
+ "\n",
+ "# Example usage\n",
+ "array = [5, 2, 9, 1, 5, 6]\n",
+ "sort_array_in_place(array)\n",
+ "print(\"Sorted array:\", array)\n",
+ "```\n",
+ "\n",
+ "### Custom Sorting\n",
+ "\n",
+ "If you need to sort the array based on a custom key or in descending order, you can use the `key` and `reverse` parameters.\n",
+ "\n",
+ "```python\n",
+ "def custom_sort_array(arr):\n",
+ " # Sort in descending order\n",
+ " return sorted(arr, reverse=True)\n",
+ "\n",
+ "# Example usage\n",
+ "array = [5, 2, 9, 1, 5, 6]\n",
+ "sorted_array_desc = custom_sort_array(array)\n",
+ "print(\"Sorted array in descending order:\", sorted_array_desc)\n",
+ "```\n",
+ "\n",
+ "### Sorting with a Custom Key\n",
+ "\n",
+ "Suppose you have a list of tuples and you want to sort them based on the second element of each tuple:\n",
+ "\n",
+ "```python\n",
+ "def sort_tuples_by_second_element(arr):\n",
+ " return sorted(arr, key=lambda x: x[1])\n",
+ "\n",
+ "# Example usage\n",
+ "tuples = [(1, 3), (4, 1), (5, 2), (2, 4)]\n",
+ "sorted_tuples = sort_tuples_by_second_element(tuples)\n",
+ "print(\"Sorted tuples by second element:\", sorted_tuples)\n",
+ "```\n",
+ "\n",
+ "These examples demonstrate how to sort arrays in Python using different methods and options. Choose the one that best fits your needs!"
+ ]
+ }
+ ],
+ "source": [
+ "for chunk in llm.stream(\"write a python program to sort an array\"):\n",
+ " print(chunk, end=\"\", flush=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "add38532",
+ "metadata": {},
+ "source": [
+ "## Chaining\n",
+ "\n",
+ "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "078e9db2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'In Chinese, you can say \"我喜欢编程\" (Wǒ xǐ huān biān chéng) to express \"I love programming.\" Here\\'s a breakdown of the sentence:\\n\\n- 我 (Wǒ) means \"I\"\\n- 喜欢 (xǐ huān) means \"love\" or \"like\"\\n- 编程 (biān chéng) means \"programming\"\\n\\nSo, when you put it all together, it translates to \"I love programming.\"'"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from langchain_core.prompts import PromptTemplate\n",
+ "\n",
+ "prompt = PromptTemplate(template=\"How to say {input} in {output_language}:\\n\")\n",
+ "\n",
+ "chain = prompt | llm\n",
+ "chain.invoke(\n",
+ " {\n",
+ " \"output_language\": \"Chinese\",\n",
+ " \"input\": \"I love programming.\",\n",
+ " }\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9bdfcef",
+ "metadata": {},
+ "source": [
+ "## API reference\n",
+ "\n",
+ "Refer to https://modelscope.cn/docs/model-service/API-Inference/intro for more details."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.11.1 64-bit",
+ "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": "3.10.16"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/memory/falkordb_chat_message_history.ipynb b/docs/docs/integrations/memory/falkordb_chat_message_history.ipynb
new file mode 100644
index 0000000000000..759841778bc50
--- /dev/null
+++ b/docs/docs/integrations/memory/falkordb_chat_message_history.ipynb
@@ -0,0 +1,73 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# FalkorDB\n",
+ "\n",
+ "FalkorDB is an open-source graph database management system, renowned for its efficient management of highly connected data. Unlike traditional databases that store data in tables, FalkorDB uses a graph structure with nodes, edges, and properties to represent and store data. This design allows for high-performance queries on complex data relationships.\n",
+ "\n",
+ "This notebook goes over how to use `FalkorDB` to store chat message history\n",
+ "\n",
+ "**NOTE**: You can use FalkorDB locally or use FalkorDB Cloud. See installation instructions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# For this example notebook we will be using FalkorDB locally\n",
+ "host = \"localhost\"\n",
+ "port = 6379"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_falkordb.message_history import (\n",
+ " FalkorDBChatMessageHistory,\n",
+ ")\n",
+ "\n",
+ "history = FalkorDBChatMessageHistory(host=host, port=port, session_id=\"session_id_1\")\n",
+ "\n",
+ "history.add_user_message(\"hi!\")\n",
+ "\n",
+ "history.add_ai_message(\"whats up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[HumanMessage(content='hi!', additional_kwargs={}, response_metadata={}),\n",
+ " AIMessage(content='whats up?', additional_kwargs={}, response_metadata={})]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "history.messages"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/integrations/providers/cratedb.mdx b/docs/docs/integrations/providers/cratedb.mdx
index 24e47930407c0..d367eb3e94542 100644
--- a/docs/docs/integrations/providers/cratedb.mdx
+++ b/docs/docs/integrations/providers/cratedb.mdx
@@ -27,7 +27,7 @@ docker run --name=cratedb --rm \
[free trial][CrateDB Cloud Console].
### Install Client
-Install the most recent version of the `langchain-cratedb` package
+Install the most recent version of the [langchain-cratedb] package
and a few others that are needed for this tutorial.
```bash
pip install --upgrade langchain-cratedb langchain-openai unstructured
@@ -120,13 +120,78 @@ message_history = CrateDBChatMessageHistory(
message_history.add_user_message("hi!")
```
+### Full Cache
+The standard / full cache avoids invoking the LLM when the supplied
+prompt is exactly the same as one encountered already.
+See also [CrateDBCache Example].
+
+To use the full cache in your applications:
+```python
+import sqlalchemy as sa
+from langchain.globals import set_llm_cache
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_cratedb import CrateDBCache
+
+# Configure cache.
+engine = sa.create_engine("crate://crate@localhost:4200/?schema=testdrive")
+set_llm_cache(CrateDBCache(engine))
+
+# Invoke LLM conversation.
+llm = ChatOpenAI(
+ model_name="chatgpt-4o-latest",
+ temperature=0.7,
+)
+print()
+print("Asking with full cache:")
+answer = llm.invoke("What is the answer to everything?")
+print(answer.content)
+```
+
+### Semantic Cache
+
+The semantic cache allows users to retrieve cached prompts based on semantic
+similarity between the user input and previously cached inputs. It also avoids
+invoking the LLM when not needed.
+See also [CrateDBSemanticCache Example].
+
+To use the semantic cache in your applications:
+```python
+import sqlalchemy as sa
+from langchain.globals import set_llm_cache
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_cratedb import CrateDBSemanticCache
+
+# Configure embeddings.
+embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+# Configure cache.
+engine = sa.create_engine("crate://crate@localhost:4200/?schema=testdrive")
+set_llm_cache(
+ CrateDBSemanticCache(
+ embedding=embeddings,
+ connection=engine,
+ search_threshold=1.0,
+ )
+)
+
+# Invoke LLM conversation.
+llm = ChatOpenAI(model_name="chatgpt-4o-latest")
+print()
+print("Asking with semantic cache:")
+answer = llm.invoke("What is the answer to everything?")
+print(answer.content)
+```
+
[all features of CrateDB]: https://cratedb.com/docs/guide/feature/
[CrateDB]: https://cratedb.com/database
[CrateDB Cloud]: https://cratedb.com/database/cloud
[CrateDB Cloud Console]: https://console.cratedb.cloud/?utm_source=langchain&utm_content=documentation
[CrateDB installation options]: https://cratedb.com/docs/guide/install/
+[CrateDBCache Example]: https://github.com/crate/langchain-cratedb/blob/main/examples/basic/cache.py
+[CrateDBSemanticCache Example]: https://github.com/crate/langchain-cratedb/blob/main/examples/basic/cache.py
[CrateDBChatMessageHistory Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/conversational_memory.ipynb
[CrateDBLoader Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/document_loader.ipynb
[CrateDBVectorStore Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb
+[langchain-cratedb]: https://pypi.org/project/langchain-cratedb/
[using LangChain with CrateDB]: https://cratedb.com/docs/guide/integrate/langchain/
diff --git a/docs/docs/integrations/providers/dappier.mdx b/docs/docs/integrations/providers/dappier.mdx
new file mode 100644
index 0000000000000..7e15d7b7289e9
--- /dev/null
+++ b/docs/docs/integrations/providers/dappier.mdx
@@ -0,0 +1,48 @@
+# Dappier
+
+[Dappier](https://dappier.com) connects any LLM or your Agentic AI to
+real-time, rights-cleared, proprietary data from trusted sources,
+making your AI an expert in anything. Our specialized models include
+Real-Time Web Search, News, Sports, Financial Stock Market Data,
+Crypto Data, and exclusive content from premium publishers. Explore a
+wide range of data models in our marketplace at
+[marketplace.dappier.com](https://marketplace.dappier.com).
+
+[Dappier](https://dappier.com) delivers enriched, prompt-ready, and
+contextually relevant data strings, optimized for seamless integration
+with LangChain. Whether you're building conversational AI, recommendation
+engines, or intelligent search, Dappier's LLM-agnostic RAG models ensure
+your AI has access to verified, up-to-date data—without the complexity of
+building and managing your own retrieval pipeline.
+
+## Installation and Setup
+
+Install ``langchain-dappier`` and set environment variable
+``DAPPIER_API_KEY``.
+
+```bash
+pip install -U langchain-dappier
+export DAPPIER_API_KEY="your-api-key"
+```
+
+We also need to set our Dappier API credentials, which can be generated at
+the [Dappier site.](https://platform.dappier.com/profile/api-keys).
+
+We can find the supported data models by heading over to the
+[Dappier marketplace.](https://platform.dappier.com/marketplace)
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/dappier).
+
+```python
+from langchain_community.chat_models import ChatDappierAI
+```
+
+## Retriever
+
+See a [usage example](/docs/integrations/retrievers/dappier).
+
+```python
+from langchain_dappier import DappierRetriever
+```
diff --git a/docs/docs/integrations/providers/dappierai.mdx b/docs/docs/integrations/providers/dappierai.mdx
deleted file mode 100644
index d142ae00af454..0000000000000
--- a/docs/docs/integrations/providers/dappierai.mdx
+++ /dev/null
@@ -1,18 +0,0 @@
-# Dappier AI
-
-> [Dappier](https://platform.dappier.com/) is a platform enabling access to diverse,
-> real-time data models. Enhance your AI applications with `Dappier’s` pre-trained,
-> LLM-ready data models and ensure accurate, current responses with reduced inaccuracies.
-
-## Installation and Setup
-
-To use one of the `Dappier AI` Data Models, you will need an API key. Visit
-[Dappier Platform](https://platform.dappier.com/) to log in and create an API key in your profile.
-
-## Chat models
-
-See a [usage example](/docs/integrations/chat/dappier).
-
-```python
-from langchain_community.chat_models import ChatDappierAI
-```
diff --git a/docs/docs/integrations/providers/falkordb.ipynb b/docs/docs/integrations/providers/falkordb.ipynb
new file mode 100644
index 0000000000000..8ac41a19cfddc
--- /dev/null
+++ b/docs/docs/integrations/providers/falkordb.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# FalkorDB\n",
+ "\n",
+ ">What is `FalkorDB`?\n",
+ "\n",
+ ">- FalkorDB is an `open-source database management system` that specializes in graph database technology.\n",
+ ">- FalkorDB allows you to represent and store data in nodes and edges, making it ideal for handling connected data and relationships.\n",
+ ">- FalkorDB Supports OpenCypher query language with proprietary extensions, making it easy to interact with and query your graph data.\n",
+ ">- With FalkorDB, you can achieve high-performance `graph traversals and queries`, suitable for production-level systems.\n",
+ "\n",
+ ">Get started with FalkorDB by visiting [their website](https://docs.falkordb.com/)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installation and Setup\n",
+ "\n",
+ "- Install the Python SDK with `pip install falkordb langchain-falkordb`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## VectorStore\n",
+ "\n",
+ "The FalkorDB vector index is used as a vectorstore,\n",
+ "whether for semantic search or example selection.\n",
+ "\n",
+ "```python\n",
+ "from langchain_community.vectorstores.falkordb_vector import FalkorDBVector\n",
+ "```\n",
+ "or \n",
+ "\n",
+ "```python\n",
+ "from langchain_falkordb.vectorstore import FalkorDBVector\n",
+ "```\n",
+ "\n",
+ "See a [usage example](/docs/integrations/vectorstores/falkordbvector.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Memory\n",
+ "\n",
+ "See a [usage example](/docs/integrations/memory/falkordb_chat_message_history.ipynb).\n",
+ "\n",
+ "```python\n",
+ "from langchain_falkordb.message_history import (\n",
+ " FalkorDBChatMessageHistory,\n",
+ ")\n",
+ "```"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/integrations/providers/friendli.md b/docs/docs/integrations/providers/friendli.mdx
similarity index 56%
rename from docs/docs/integrations/providers/friendli.md
rename to docs/docs/integrations/providers/friendli.mdx
index e0f3a49b68b3d..2e8fda9ade2f6 100644
--- a/docs/docs/integrations/providers/friendli.md
+++ b/docs/docs/integrations/providers/friendli.mdx
@@ -1,6 +1,6 @@
# Friendli AI
->[FriendliAI](https://friendli.ai/) enhances AI application performance and optimizes
+> [FriendliAI](https://friendli.ai/) enhances AI application performance and optimizes
> cost savings with scalable, efficient deployment options, tailored for high-demand AI workloads.
## Installation and setup
@@ -8,10 +8,11 @@
Install the `friendli-client` python package.
```bash
-pip install friendli-client
+pip install -U langchain_community friendli-client
```
+
Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token,
-and set it as the `FRIENDLI_TOKEN` environment variable.
+and set it as the `FRIENDLI_TOKEN` environment variabzle.
## Chat models
@@ -20,6 +21,11 @@ See a [usage example](/docs/integrations/chat/friendli).
```python
from langchain_community.chat_models.friendli import ChatFriendli
+
+chat = ChatFriendli(model='meta-llama-3.1-8b-instruct')
+
+for m in chat.stream("Tell me fun things to do in NYC"):
+ print(m.content, end="", flush=True)
```
## LLMs
@@ -28,4 +34,8 @@ See a [usage example](/docs/integrations/llms/friendli).
```python
from langchain_community.llms.friendli import Friendli
+
+llm = Friendli(model='meta-llama-3.1-8b-instruct')
+
+print(llm.invoke("def bubble_sort(): "))
```
diff --git a/docs/docs/integrations/providers/friendly.md b/docs/docs/integrations/providers/friendly.md
deleted file mode 100644
index 834a1ebfe802b..0000000000000
--- a/docs/docs/integrations/providers/friendly.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# Friendli AI
-
->[Friendli AI](https://friendli.ai/) is a company that fine-tunes, deploys LLMs,
-> and serves a wide range of Generative AI use cases.
-
-
-## Installation and setup
-
-- Install the integration package:
-
- ```
- pip install friendli-client
- ```
-
-- Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token,
-and set it as the `FRIENDLI_TOKEN` environment.
-
-## Chat models
-
-See a [usage example](/docs/integrations/chat/friendli).
-
-```python
-from langchain_community.chat_models.friendli import ChatFriendli
-```
-
-## LLMs
-
-See a [usage example](/docs/integrations/llms/friendli).
-
-```python
-from langchain_community.llms.friendli import Friendli
-```
diff --git a/docs/docs/integrations/providers/kuzu.mdx b/docs/docs/integrations/providers/kuzu.mdx
index 286b38e0d81cc..aff0a0f668933 100644
--- a/docs/docs/integrations/providers/kuzu.mdx
+++ b/docs/docs/integrations/providers/kuzu.mdx
@@ -1,32 +1,40 @@
# Kùzu
->[Kùzu](https://kuzudb.com/) is a company based in Waterloo, Ontario, Canada.
-> It provides a highly scalable, extremely fast, easy-to-use [embeddable graph database](https://github.com/kuzudb/kuzu).
+> [Kùzu](https://kuzudb.com/) is an embeddable, scalable, extremely fast graph database.
+> It is permissively licensed with an MIT license, and you can see its source code [here](https://github.com/kuzudb/kuzu).
+> Key characteristics of Kùzu:
+>- Performance and scalability: Implements modern, state-of-the-art join algorithms for graphs.
+>- Usability: Very easy to set up and get started with, as there are no servers (embedded architecture).
+>- Interoperability: Can conveniently scan and copy data from external columnar formats, CSV, JSON and relational databases.
+>- Structured property graph model: Implements the property graph model, with added structure.
+>- Cypher support: Allows convenient querying of the graph in Cypher, a declarative query language.
+
+> Get started with Kùzu by visiting their [documentation](https://docs.kuzudb.com/).
## Installation and Setup
-You need to install the `kuzu` python package.
+Install the Python SDK as follows:
```bash
-pip install kuzu
+pip install -U langchain-kuzu
```
-## Graph database
+## Usage
+
+## Graphs
See a [usage example](/docs/integrations/graphs/kuzu_db).
```python
-from langchain_community.graphs import KuzuGraph
+from langchain_kuzu.graphs.kuzu_graph import KuzuGraph
```
-## Chain
+## Chains
See a [usage example](/docs/integrations/graphs/kuzu_db/#creating-kuzuqachain).
```python
-from langchain.chains import KuzuQAChain
+from langchain_kuzu.chains.graph_qa.kuzu import KuzuQAChain
```
-
-
diff --git a/docs/docs/integrations/providers/modelscope.mdx b/docs/docs/integrations/providers/modelscope.mdx
index 34c421ea707e8..30c50e33bd58a 100644
--- a/docs/docs/integrations/providers/modelscope.mdx
+++ b/docs/docs/integrations/providers/modelscope.mdx
@@ -5,20 +5,46 @@
This page covers how to use the modelscope ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.
-## Installation and Setup
+## Installation
-Install the `modelscope` package.
-
```bash
-pip install modelscope
+pip install -U langchain-modelscope-integration
```
+Head to [ModelScope](https://modelscope.cn/) to sign up to ModelScope and generate an [SDK token](https://modelscope.cn/my/myaccesstoken). Once you've done this set the `MODELSCOPE_SDK_TOKEN` environment variable:
-## Text Embedding Models
+```bash
+export MODELSCOPE_SDK_TOKEN=
+```
+
+## Chat Models
+
+`ModelScopeChatEndpoint` class exposes chat models from ModelScope. See available models [here](https://www.modelscope.cn/docs/model-service/API-Inference/intro).
+
+```python
+from langchain_modelscope import ModelScopeChatEndpoint
+
+llm = ModelScopeChatEndpoint(model="Qwen/Qwen2.5-Coder-32B-Instruct")
+llm.invoke("Sing a ballad of LangChain.")
+```
+
+## Embeddings
+`ModelScopeEmbeddings` class exposes embeddings from ModelScope.
```python
-from langchain_community.embeddings import ModelScopeEmbeddings
+from langchain_modelscope import ModelScopeEmbeddings
+
+embeddings = ModelScopeEmbeddings(model_id="damo/nlp_corom_sentence-embedding_english-base")
+embeddings.embed_query("What is the meaning of life?")
```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/modelscope_hub)
+## LLMs
+`ModelScopeLLM` class exposes LLMs from ModelScope.
+
+```python
+from langchain_modelscope import ModelScopeLLM
+
+llm = ModelScopeLLM(model="Qwen/Qwen2.5-Coder-32B-Instruct")
+llm.invoke("The meaning of life is")
+```
diff --git a/docs/docs/integrations/providers/pull-md.mdx b/docs/docs/integrations/providers/pull-md.mdx
new file mode 100644
index 0000000000000..b7384a3eda477
--- /dev/null
+++ b/docs/docs/integrations/providers/pull-md.mdx
@@ -0,0 +1,42 @@
+# PullMd Loader
+
+>[PullMd](https://pull.md/) is a service that converts web pages into Markdown format. The `langchain-pull-md` package utilizes this service to convert URLs, especially those rendered with JavaScript frameworks like React, Angular, or Vue.js, into Markdown without the need for local rendering.
+
+## Installation and Setup
+
+To get started with `langchain-pull-md`, you need to install the package via pip:
+
+```bash
+pip install langchain-pull-md
+```
+
+See the [usage example](/docs/integrations/document_loaders/pull_md) for detailed integration and usage instructions.
+
+## Document Loader
+
+The `PullMdLoader` class in `langchain-pull-md` provides an easy way to convert URLs to Markdown. It's particularly useful for loading content from modern web applications for use within LangChain's processing capabilities.
+
+```python
+from langchain_pull_md import PullMdLoader
+
+# Initialize the loader with a URL of a JavaScript-rendered webpage
+loader = PullMdLoader(url='https://example.com')
+
+# Load the content as a Document
+documents = loader.load()
+
+# Access the Markdown content
+for document in documents:
+ print(document.page_content)
+```
+
+This loader supports any URL and is particularly adept at handling sites built with dynamic JavaScript, making it a versatile tool for markdown extraction in data processing workflows.
+
+## API Reference
+
+For a comprehensive guide to all available functions and their parameters, visit the [API reference](https://github.com/chigwell/langchain-pull-md).
+
+## Additional Resources
+
+- [GitHub Repository](https://github.com/chigwell/langchain-pull-md)
+- [PyPi Package](https://pypi.org/project/langchain-pull-md/)
diff --git a/docs/docs/integrations/retrievers/dappier.ipynb b/docs/docs/integrations/retrievers/dappier.ipynb
new file mode 100644
index 0000000000000..5ceeb25d0ab12
--- /dev/null
+++ b/docs/docs/integrations/retrievers/dappier.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e6e1e5d5",
+ "metadata": {
+ "id": "e6e1e5d5"
+ },
+ "source": [
+ "# Dappier\n",
+ "\n",
+ "[Dappier](https://dappier.com) connects any LLM or your Agentic AI to real-time, rights-cleared, proprietary data from trusted sources, making your AI an expert in anything. Our specialized models include Real-Time Web Search, News, Sports, Financial Stock Market Data, Crypto Data, and exclusive content from premium publishers. Explore a wide range of data models in our marketplace at [marketplace.dappier.com](https://marketplace.dappier.com).\n",
+ "\n",
+ "[Dappier](https://dappier.com) delivers enriched, prompt-ready, and contextually relevant data strings, optimized for seamless integration with LangChain. Whether you're building conversational AI, recommendation engines, or intelligent search, Dappier's LLM-agnostic RAG models ensure your AI has access to verified, up-to-date data—without the complexity of building and managing your own retrieval pipeline."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e49f1e0d",
+ "metadata": {
+ "id": "e49f1e0d"
+ },
+ "source": [
+ "# DappierRetriever\n",
+ "\n",
+ "This will help you getting started with the Dappier [retriever](https://python.langchain.com/docs/concepts/retrievers/). For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/retrievers/langchain_dappier.retrievers.Dappier.DappierRetriever.html).\n",
+ "\n",
+ "### Integration details\n",
+ "\n",
+ "Bring-your-own data (i.e., index and search a custom corpus of documents):\n",
+ "\n",
+ "| Retriever | Self-host | Cloud offering | Package |\n",
+ "| :--- | :--- | :---: | :---: |\n",
+ "[DappierRetriever](https://python.langchain.com/en/latest/retrievers/langchain_dappier.retrievers.Dappier.DappierRetriever.html) | ❌ | ❌ | langchain-dappier |\n",
+ "\n",
+ "### Setup\n",
+ "\n",
+ "Install ``langchain-dappier`` and set environment variable ``DAPPIER_API_KEY``.\n",
+ "\n",
+ "```bash\n",
+ "pip install -U langchain-dappier\n",
+ "export DAPPIER_API_KEY=\"your-api-key\"\n",
+ "```\n",
+ "\n",
+ "We also need to set our Dappier API credentials, which can be generated at the [Dappier site.](https://platform.dappier.com/profile/api-keys).\n",
+ "\n",
+ "We can find the supported data models by heading over to the [Dappier marketplace.](https://platform.dappier.com/marketplace)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+ "metadata": {
+ "id": "72ee0c4b-9764-423a-9dbf-95129e185210"
+ },
+ "source": [
+ "If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+ "metadata": {
+ "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de"
+ },
+ "outputs": [],
+ "source": [
+ "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+ "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+ "metadata": {
+ "id": "0730d6a1-c893-4840-9817-5e5251676d5d"
+ },
+ "source": [
+ "### Installation\n",
+ "\n",
+ "This retriever lives in the `langchain-dappier` package:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+ "outputId": "d1bb87dd-860d-4255-d5a8-b3f42da1d76e"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install -qU langchain-dappier"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+ "metadata": {
+ "id": "a38cde65-254d-4219-a441-068766c0d4b5"
+ },
+ "source": [
+ "## Instantiation\n",
+ "\n",
+ "- data_model_id: str\n",
+ " Data model ID, starting with dm_.\n",
+ " You can find the available data model IDs at:\n",
+ " [Dappier marketplace.](https://platform.dappier.com/marketplace)\n",
+ "- k: int\n",
+ " Number of documents to return.\n",
+ "- ref: Optional[str]\n",
+ " Site domain where AI recommendations are displayed.\n",
+ "- num_articles_ref: int\n",
+ " Minimum number of articles from the ref domain specified.\n",
+ " The rest will come from other sites within the RAG model.\n",
+ "- search_algorithm: Literal[\n",
+ " \"most_recent\",\n",
+ " \"most_recent_semantic\",\n",
+ " \"semantic\",\n",
+ " \"trending\"\n",
+ "]\n",
+ " Search algorithm for retrieving articles.\n",
+ "- api_key: Optional[str]\n",
+ " The API key used to interact with the Dappier APIs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "70cc8e65-2a02-408a-bbc6-8ef649057d82",
+ "metadata": {
+ "id": "70cc8e65-2a02-408a-bbc6-8ef649057d82"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_dappier import DappierRetriever\n",
+ "\n",
+ "retriever = DappierRetriever(data_model_id=\"dm_01jagy9nqaeer9hxx8z1sk1jx6\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+ "metadata": {
+ "id": "5c5f2839-4020-424e-9fc9-07777eede442"
+ },
+ "source": [
+ "## Usage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+ "outputId": "f85bcf8e-4b51-4f82-8e48-582a9643fa0a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Document(metadata={'title': 'Man shot and killed on Wells Street near downtown Fort Wayne', 'author': 'Gregg Montgomery', 'source_url': 'https://www.wishtv.com/news/indiana-news/man-shot-dies-fort-wayne-december-25-2024/', 'image_url': 'https://images.dappier.com/dm_01jagy9nqaeer9hxx8z1sk1jx6/fort-wayne-police-department-vehicle-via-Flickr_.jpg?width=428&height=321', 'pubdata': 'Thu, 26 Dec 2024 01:00:33 +0000'}, page_content='A man was shot and killed on December 25, 2024, in Fort Wayne, Indiana, near West Fourth and Wells streets. Police arrived shortly after 6:30 p.m. following reports of gunfire and found the victim in the 1600 block of Wells Street, where he was pronounced dead. The area features a mix of businesses, including a daycare and restaurants.\\n\\nAs of the latest updates, police have not provided details on the safety of the area, potential suspects, or the motive for the shooting. Authorities are encouraging anyone with information to reach out to the Fort Wayne Police Department or Crime Stoppers.'),\n",
+ " Document(metadata={'title': 'House cat dies from bird flu in pet food, prompting recall', 'author': 'Associated Press', 'source_url': 'https://www.wishtv.com/news/business/house-cat-bird-flu-pet-food-recall/', 'image_url': 'https://images.dappier.com/dm_01jagy9nqaeer9hxx8z1sk1jx6/BACKGROUND-Northwest-Naturals-cat-food_.jpg?width=428&height=321', 'pubdata': 'Wed, 25 Dec 2024 23:12:41 +0000'}, page_content='An Oregon house cat has died after eating pet food contaminated with the H5N1 bird flu virus, prompting a nationwide recall of Northwest Naturals\\' 2-pound Feline Turkey Recipe raw frozen pet food. The Oregon Department of Agriculture confirmed that the strictly indoor cat contracted the virus solely from the food, which has \"best if used by\" dates of May 21, 2026, and June 23, 2026. \\n\\nThe affected product was distributed across several states, including Arizona, California, and Florida, as well as British Columbia, Canada. Consumers are urged to dispose of the recalled food and seek refunds. This incident raises concerns about the spread of bird flu and its potential impact on domestic animals, particularly as California has declared a state of emergency due to the outbreak affecting various bird species.'),\n",
+ " Document(metadata={'title': '20 big cats die from bird flu at Washington sanctuary', 'author': 'Nic F. Anderson, CNN', 'source_url': 'https://www.wishtv.com/news/national/bird-flu-outbreak-wild-felid-center-2024/', 'image_url': 'https://images.dappier.com/dm_01jagy9nqaeer9hxx8z1sk1jx6/BACKGROUND-Amur-Bengal-tiger-at-Wild-Felid-Advocacy-Center-of-Washington-FB-post_.jpg?width=428&height=321', 'pubdata': 'Wed, 25 Dec 2024 23:04:34 +0000'}, page_content='The Wild Felid Advocacy Center in Washington state has experienced a devastating bird flu outbreak, resulting in the deaths of 20 big cats, over half of its population. The first death was reported around Thanksgiving, affecting various species, including cougars and a tiger mix. The sanctuary is currently under quarantine, closed to the public, and working with animal health officials to disinfect enclosures and implement prevention strategies.\\n\\nAs the situation unfolds, the Washington Department of Fish and Wildlife has noted an increase in bird flu cases statewide, including infections in cougars. While human infections from bird flu through contact with mammals are rare, the CDC acknowledges the potential risk. The sanctuary hopes to reopen in the new year, focusing on the recovery of the remaining animals and taking measures to prevent further outbreaks, marking an unprecedented challenge in its 20-year history.')]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "query = \"latest tech news\"\n",
+ "\n",
+ "retriever.invoke(query)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
+ "metadata": {
+ "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e"
+ },
+ "source": [
+ "## Use within a chain\n",
+ "\n",
+ "Like other retrievers, DappierRetriever can be incorporated into LLM applications via [chains](/docs/how_to/sequence/).\n",
+ "\n",
+ "We will need a LLM or chat model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "25b647a3-f8f2-4541-a289-7a241e43f9df",
+ "metadata": {
+ "id": "25b647a3-f8f2-4541-a289-7a241e43f9df"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_openai import ChatOpenAI\n",
+ "\n",
+ "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae",
+ "metadata": {
+ "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae"
+ },
+ "outputs": [],
+ "source": [
+ "from langchain_core.output_parsers import StrOutputParser\n",
+ "from langchain_core.prompts import ChatPromptTemplate\n",
+ "from langchain_core.runnables import RunnablePassthrough\n",
+ "\n",
+ "prompt = ChatPromptTemplate.from_template(\n",
+ " \"\"\"Answer the question based only on the context provided.\n",
+ "\n",
+ "Context: {context}\n",
+ "\n",
+ "Question: {question}\"\"\"\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def format_docs(docs):\n",
+ " return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+ "\n",
+ "\n",
+ "chain = (\n",
+ " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+ " | prompt\n",
+ " | llm\n",
+ " | StrOutputParser()\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "d47c37dd-5c11-416c-a3b6-bec413cd70e8",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 143
+ },
+ "id": "d47c37dd-5c11-416c-a3b6-bec413cd70e8",
+ "outputId": "f23f18a9-d138-4684-cb5b-b92e0895b5f2"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "\"The key highlights and outcomes from the latest events covered in the article include:\\n\\n1. An Israeli airstrike in Gaza killed five journalists from Al-Quds Today Television, leading to condemnation from their outlet and raising concerns about violence against media professionals in the region.\\n2. The Committee to Protect Journalists reported that since October 7, 2023, at least 141 journalists have been killed in the region, marking the deadliest period for journalists since 1992, with the majority being Palestinians in Gaza.\\n3. A man was shot and killed in Fort Wayne, Indiana, with police not providing details on suspects, motive, or the safety of the area.\\n4. An Oregon house cat died after eating pet food contaminated with the H5N1 bird flu virus, leading to a nationwide recall of Northwest Naturals' Feline Turkey Recipe raw frozen pet food and raising concerns about the spread of bird flu among domestic animals.\""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "chain.invoke(\n",
+ " \"What are the key highlights and outcomes from the latest events covered in the article?\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+ "metadata": {
+ "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3"
+ },
+ "source": [
+ "## API reference\n",
+ "\n",
+ "For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/retrievers/langchain_dappier.retrievers.Dappier.DappierRetriever.html)."
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.12.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/text_embedding/modelscope_embedding.ipynb b/docs/docs/integrations/text_embedding/modelscope_embedding.ipynb
new file mode 100644
index 0000000000000..b5db8dbab9a19
--- /dev/null
+++ b/docs/docs/integrations/text_embedding/modelscope_embedding.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "afaf8039",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "sidebar_label: ModelScope\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a3d6f34",
+ "metadata": {},
+ "source": [
+ "# ModelScopeEmbeddings\n",
+ "\n",
+ "ModelScope ([Home](https://www.modelscope.cn/) | [GitHub](https://github.com/modelscope/modelscope)) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. \n",
+ "\n",
+ "This will help you get started with ModelScope embedding models using LangChain.\n",
+ "\n",
+ "## Overview\n",
+ "### Integration details\n",
+ "\n",
+ "| Provider | Package |\n",
+ "|:--------:|:-------:|\n",
+ "| [ModelScope](/docs/integrations/providers/modelscope/) | [langchain-modelscope-integration](https://pypi.org/project/langchain-modelscope-integration/) |\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "To access ModelScope embedding models you'll need to create a/an ModelScope account, get an API key, and install the `langchain-modelscope-integration` integration package.\n",
+ "\n",
+ "### Credentials\n",
+ "\n",
+ "Head to [ModelScope](https://modelscope.cn/) to sign up to ModelScope."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "36521c2a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "if not os.getenv(\"MODELSCOPE_SDK_TOKEN\"):\n",
+ " os.environ[\"MODELSCOPE_SDK_TOKEN\"] = getpass.getpass(\n",
+ " \"Enter your ModelScope SDK token: \"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d9664366",
+ "metadata": {},
+ "source": [
+ "### Installation\n",
+ "\n",
+ "The LangChain ModelScope integration lives in the `langchain-modelscope-integration` package:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64853226",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install -qU langchain-modelscope-integration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45dd1724",
+ "metadata": {},
+ "source": [
+ "## Instantiation\n",
+ "\n",
+ "Now we can instantiate our model object:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9ea7a09b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading Model to directory: /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-12-27 16:15:11,175 - modelscope - WARNING - Model revision not specified, use revision: v1.0.0\n",
+ "2024-12-27 16:15:11,443 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base\n",
+ "2024-12-27 16:15:11,444 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base.\n",
+ "2024-12-27 16:15:11,445 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base\n",
+ "2024-12-27 16:15:12,115 - modelscope - WARNING - No preprocessor field found in cfg.\n",
+ "2024-12-27 16:15:12,116 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\n",
+ "2024-12-27 16:15:12,116 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base'}. trying to build by task and model information.\n",
+ "2024-12-27 16:15:12,318 - modelscope - WARNING - No preprocessor field found in cfg.\n",
+ "2024-12-27 16:15:12,319 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\n",
+ "2024-12-27 16:15:12,319 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base', 'sequence_length': 128}. trying to build by task and model information.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from langchain_modelscope import ModelScopeEmbeddings\n",
+ "\n",
+ "embeddings = ModelScopeEmbeddings(\n",
+ " model_id=\"damo/nlp_corom_sentence-embedding_english-base\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77d271b6",
+ "metadata": {},
+ "source": [
+ "## Indexing and Retrieval\n",
+ "\n",
+ "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+ "\n",
+ "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "d817716b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/root/miniconda3/envs/langchain/lib/python3.10/site-packages/transformers/modeling_utils.py:1113: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
+ " warnings.warn(\n",
+ "/root/miniconda3/envs/langchain/lib/python3.10/site-packages/transformers/modeling_utils.py:1113: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'LangChain is the framework for building context-aware reasoning applications'"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a vector store with a sample text\n",
+ "from langchain_core.vectorstores import InMemoryVectorStore\n",
+ "\n",
+ "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+ "\n",
+ "vectorstore = InMemoryVectorStore.from_texts(\n",
+ " [text],\n",
+ " embedding=embeddings,\n",
+ ")\n",
+ "\n",
+ "# Use the vectorstore as a retriever\n",
+ "retriever = vectorstore.as_retriever()\n",
+ "\n",
+ "# Retrieve the most similar text\n",
+ "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+ "\n",
+ "# show the retrieved document's content\n",
+ "retrieved_documents[0].page_content"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e02b9855",
+ "metadata": {},
+ "source": [
+ "## Direct Usage\n",
+ "\n",
+ "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+ "\n",
+ "You can directly call these methods to get embeddings for your own use cases.\n",
+ "\n",
+ "### Embed single texts\n",
+ "\n",
+ "You can embed single texts or documents with `embed_query`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0d2befcd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-0.6046376824378967, -0.3595953583717346, 0.11333226412534714, -0.030444221571087837, 0.23397332429\n"
+ ]
+ }
+ ],
+ "source": [
+ "single_vector = embeddings.embed_query(text)\n",
+ "print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1b5a7d03",
+ "metadata": {},
+ "source": [
+ "### Embed multiple texts\n",
+ "\n",
+ "You can embed multiple texts with `embed_documents`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "2f4d6e97",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-0.6046381592750549, -0.3595949709415436, 0.11333223432302475, -0.030444379895925522, 0.23397321999\n",
+ "[-0.36103254556655884, -0.7602502107620239, 0.6505364775657654, 0.000658963865134865, 1.185304522514\n"
+ ]
+ }
+ ],
+ "source": [
+ "text2 = (\n",
+ " \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+ ")\n",
+ "two_vectors = embeddings.embed_documents([text, text2])\n",
+ "for vector in two_vectors:\n",
+ " print(str(vector)[:100]) # Show the first 100 characters of the vector"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "98785c12",
+ "metadata": {},
+ "source": [
+ "## API Reference\n",
+ "\n",
+ "For detailed documentation on `ModelScopeEmbeddings` features and configuration options, please refer to the [API reference](https://www.modelscope.cn/docs/sdk/pipelines).\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/text_embedding/modelscope_hub.ipynb b/docs/docs/integrations/text_embedding/modelscope_hub.ipynb
deleted file mode 100644
index b7d404e7beb4b..0000000000000
--- a/docs/docs/integrations/text_embedding/modelscope_hub.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# ModelScope\n",
- "\n",
- ">[ModelScope](https://www.modelscope.cn/home) is big repository of the models and datasets.\n",
- "\n",
- "Let's load the ModelScope Embedding class."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain_community.embeddings import ModelScopeEmbeddings"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model_id = \"damo/nlp_corom_sentence-embedding_english-base\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "embeddings = ModelScopeEmbeddings(model_id=model_id)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "text = \"This is a test document.\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "query_result = embeddings.embed_query(text)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "doc_results = embeddings.embed_documents([\"foo\"])"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/docs/integrations/tools/stripe.ipynb b/docs/docs/integrations/tools/stripe.ipynb
new file mode 100644
index 0000000000000..0edc4cdd6d6b8
--- /dev/null
+++ b/docs/docs/integrations/tools/stripe.ipynb
@@ -0,0 +1,196 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "10238e62-3465-4973-9279-606cbb7ccf16",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "sidebar_label: Stripe\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6f91f20",
+ "metadata": {},
+ "source": [
+ "# StripeAgentToolkit\n",
+ "\n",
+ "This notebook provides a quick overview for getting started with Stripe's agent toolkit.\n",
+ "\n",
+ "You can read more about `StripeAgentToolkit` in [Stripe's launch blog](https://stripe.dev/blog/adding-payments-to-your-agentic-workflows) or on the project's [PyPi page](https://pypi.org/project/stripe-agent-toolkit/).\n",
+ "\n",
+ "## Overview\n",
+ "\n",
+ "### Integration details\n",
+ "\n",
+ "| Class | Package | Serializable | [JS Support](https://github.com/stripe/agent-toolkit?tab=readme-ov-file#typescript) | Package latest |\n",
+ "| :--- | :--- | :---: | :---: | :---: |\n",
+ "| StripeAgentToolkit | [stripe-agent-toolkit](https://pypi.org/project/stripe-agent-toolkit) | ❌ | ✅ |  |\n",
+ "\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "This externally-managed package is hosted out of the `stripe-agent-toolkit` project, which is managed by Stripe's team.\n",
+ "\n",
+ "You can install it, along with langgraph for the following examples, with `pip`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f85b4089",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install --quiet -U langgraph stripe-agent-toolkit"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b15e9266",
+ "metadata": {},
+ "source": [
+ "### Credentials\n",
+ "\n",
+ "In addition to installing the package, you will need to configure the integration with your Stripe account's secret key, which is available in your [Stripe Dashboard](https://dashboard.stripe.com/account/apikeys)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0b178a2-8816-40ca-b57c-ccdd86dde9c9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "if not os.environ.get(\"STRIPE_SECRET_KEY\"):\n",
+ " os.environ[\"STRIPE_SECRET_KEY\"] = getpass.getpass(\"STRIPE API key:\\n\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc5ab717-fd27-4c59-b912-bdd099541478",
+ "metadata": {},
+ "source": [
+ "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a6c2f136-6367-4f1f-825d-ae741e1bf281",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+ "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c97218f-f366-479d-8bf7-fe9f2f6df73f",
+ "metadata": {},
+ "source": [
+ "## Instantiation\n",
+ "\n",
+ "Here we show how to create an instance of the Stripe Toolkit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b3ddfe9-ca79-494c-a7ab-1f56d9407a64",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from stripe_agent_toolkit.crewai.toolkit import StripeAgentToolkit\n",
+ "\n",
+ "stripe_agent_toolkit = StripeAgentToolkit(\n",
+ " secret_key=os.getenv(\"STRIPE_SECRET_KEY\"),\n",
+ " configuration={\n",
+ " \"actions\": {\n",
+ " \"payment_links\": {\n",
+ " \"create\": True,\n",
+ " },\n",
+ " }\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f53188e",
+ "metadata": {},
+ "source": [
+ "## Agent\n",
+ "\n",
+ "Here's how to use the toolkit to create a basic agent in langgraph:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4975924e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_anthropic import ChatAnthropic\n",
+ "from langgraph.prebuilt import create_react_agent\n",
+ "\n",
+ "llm = ChatAnthropic(\n",
+ " model=\"claude-3-5-sonnet-20240620\",\n",
+ ")\n",
+ "\n",
+ "langgraph_agent_executor = create_react_agent(llm, stripe_agent_toolkit.get_tools())\n",
+ "\n",
+ "input_state = {\n",
+ " \"messages\": \"\"\"\n",
+ " Create a payment link for a new product called 'test' with a price\n",
+ " of $100. Come up with a funny description about buy bots,\n",
+ " maybe a haiku.\n",
+ " \"\"\",\n",
+ "}\n",
+ "\n",
+ "output_state = langgraph_agent_executor.invoke(input_state)\n",
+ "\n",
+ "print(output_state[\"messages\"][-1].content)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/integrations/vectorstores/faiss.ipynb b/docs/docs/integrations/vectorstores/faiss.ipynb
index 0f957599065d0..26520baa6ac0d 100644
--- a/docs/docs/integrations/vectorstores/faiss.ipynb
+++ b/docs/docs/integrations/vectorstores/faiss.ipynb
@@ -286,6 +286,52 @@
" print(f\"* {res.page_content} [{res.metadata}]\")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "39cb1496",
+ "metadata": {},
+ "source": [
+ "Some [MongoDB query and projection operators](https://www.mongodb.com/docs/manual/reference/operator/query/) are supported for more advanced metadata filtering. The current list of supported operators are as follows:\n",
+ "- `$eq` (equals)\n",
+ "- `$neq` (not equals)\n",
+ "- `$gt` (greater than)\n",
+ "- `$lt` (less than)\n",
+ "- `$gte` (greater than or equal)\n",
+ "- `$lte` (less than or equal)\n",
+ "- `$in` (membership in list)\n",
+ "- `$nin` (not in list)\n",
+ "- `$and` (all conditions must match)\n",
+ "- `$or` (any condition must match)\n",
+ "- `$not` (negation of condition)\n",
+ "\n",
+ "Performing the same above similarity search with advanced metadata filtering can be done as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b3dd99d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
+ "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
+ ]
+ }
+ ],
+ "source": [
+ "results = vector_store.similarity_search(\n",
+ " \"LangChain provides abstractions to make working with LLMs easy\",\n",
+ " k=2,\n",
+ " filter={\"source\": {\"$eq\": \"tweet\"}},\n",
+ ")\n",
+ "for res in results:\n",
+ " print(f\"* {res.page_content} [{res.metadata}]\")"
+ ]
+ },
{
"cell_type": "markdown",
"id": "5ae35069",
diff --git a/docs/docs/integrations/vectorstores/faiss_async.ipynb b/docs/docs/integrations/vectorstores/faiss_async.ipynb
index e3bd1c90300ab..ec16f9e4d10ad 100644
--- a/docs/docs/integrations/vectorstores/faiss_async.ipynb
+++ b/docs/docs/integrations/vectorstores/faiss_async.ipynb
@@ -397,6 +397,49 @@
" print(f\"Content: {doc.page_content}, Metadata: {doc.metadata}\")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "8dead085",
+ "metadata": {},
+ "source": [
+ "Some [MongoDB query and projection operators](https://www.mongodb.com/docs/manual/reference/operator/query/) are supported for more advanced metadata filtering. The current list of supported operators are as follows:\n",
+ "- `$eq` (equals)\n",
+ "- `$neq` (not equals)\n",
+ "- `$gt` (greater than)\n",
+ "- `$lt` (less than)\n",
+ "- `$gte` (greater than or equal)\n",
+ "- `$lte` (less than or equal)\n",
+ "- `$in` (membership in list)\n",
+ "- `$nin` (not in list)\n",
+ "- `$and` (all conditions must match)\n",
+ "- `$or` (any condition must match)\n",
+ "- `$not` (negation of condition)\n",
+ "\n",
+ "Performing the same above similarity search with advanced metadata filtering can be done as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "af47c6f9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Content: foo, Metadata: {'page': 1}\n"
+ ]
+ }
+ ],
+ "source": [
+ "results = await db.asimilarity_search(\n",
+ " \"foo\", filter={\"page\": {\"$eq\": 1}}, k=1, fetch_k=4\n",
+ ")\n",
+ "for doc in results:\n",
+ " print(f\"Content: {doc.page_content}, Metadata: {doc.metadata}\")"
+ ]
+ },
{
"cell_type": "markdown",
"id": "1becca53",
diff --git a/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb b/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb
index 6f29773acd4e5..2e6e91dc4510d 100644
--- a/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb
+++ b/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb
@@ -70,7 +70,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "dfa92a08",
"metadata": {},
"outputs": [],
@@ -91,12 +91,12 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "c795913e",
"metadata": {},
"outputs": [],
"source": [
- "embedding_model = VertexAIEmbeddings(model_name=\"textembedding-gecko@003\")"
+ "embedding_model = VertexAIEmbeddings(model_name=\"text-embedding-005\")"
]
},
{
@@ -722,7 +722,139 @@
"cell_type": "markdown",
"id": "31222b03",
"metadata": {},
- "source": []
+ "source": [
+ "## Hybrid Search"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8a308f2",
+ "metadata": {},
+ "source": [
+ "Vector Search supports hybrid search, a popular architecture pattern in information retrieval (IR) that combines both semantic search and keyword search (also called token-based search). With hybrid search, developers can take advantage of the best of the two approaches, effectively providing higher search quality.\n",
+ "Click [here](https://cloud.google.com/vertex-ai/docs/vector-search/about-hybrid-search) to learn more.\n",
+ "\n",
+ "In order to use hybrid search, we need to fit a sparse embedding vectorizer and handle the embeddings outside of the Vector Search integration.\n",
+ "An example of sparse embedding vectorizer is sklearn TfidfVectorizer but other techniques can be used, for instance BM25."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "e319402d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define some sample data\n",
+ "texts = [\n",
+ " \"The cat sat on\",\n",
+ " \"the mat.\",\n",
+ " \"I like to\",\n",
+ " \"eat pizza for\",\n",
+ " \"dinner.\",\n",
+ " \"The sun sets\",\n",
+ " \"in the west.\",\n",
+ "]\n",
+ "\n",
+ "# optional IDs\n",
+ "ids = [\"i_\" + str(i + 1) for i in range(len(texts))]\n",
+ "\n",
+ "# optional metadata\n",
+ "metadatas = [{\"my_metadata\": i} for i in range(len(texts))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14efefc1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+ "\n",
+ "# Fit the TFIDF Vectorizer (This is usually done on a very large corpus of data to make sure that word statistics generalize well on new data)\n",
+ "vectorizer = TfidfVectorizer()\n",
+ "vectorizer.fit(texts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c7206c2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Utility function to transform text into a TF-IDF Sparse Vector\n",
+ "def get_sparse_embedding(tfidf_vectorizer, text):\n",
+ " tfidf_vector = tfidf_vectorizer.transform([text])\n",
+ " values = []\n",
+ " dims = []\n",
+ " for i, tfidf_value in enumerate(tfidf_vector.data):\n",
+ " values.append(float(tfidf_value))\n",
+ " dims.append(int(tfidf_vector.indices[i]))\n",
+ " return {\"values\": values, \"dimensions\": dims}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "0dc5b782",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# semantic (dense) embeddings\n",
+ "embeddings = embedding_model.embed_documents(texts)\n",
+ "# tfidf (sparse) embeddings\n",
+ "sparse_embeddings = [get_sparse_embedding(vectorizer, x) for x in texts]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3a353679",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sparse_embeddings[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2623cad9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add the dense and sparse embeddings in Vector Search\n",
+ "\n",
+ "vector_store.add_texts_with_embeddings(\n",
+ " texts=texts,\n",
+ " embeddings=embeddings,\n",
+ " sparse_embeddings=sparse_embeddings,\n",
+ " ids=ids,\n",
+ " metadatas=metadatas,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29885e38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run hybrid search\n",
+ "query = \"the cat\"\n",
+ "embedding = embedding_model.embed_query(query)\n",
+ "sparse_embedding = get_sparse_embedding(vectorizer, query)\n",
+ "\n",
+ "vector_store.similarity_search_by_vector_with_score(\n",
+ " embedding=embedding,\n",
+ " sparse_embedding=sparse_embedding,\n",
+ " k=5,\n",
+ " rrf_ranking_alpha=0.7, # 0.7 weight to dense and 0.3 weight to sparse\n",
+ ")"
+ ]
}
],
"metadata": {
@@ -733,7 +865,7 @@
"uri": "gcr.io/deeplearning-platform-release/base-cpu:m107"
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "langchain-google-community-3Os9yvMd-py3.10",
"language": "python",
"name": "python3"
},
@@ -747,7 +879,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.6"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/docs/docs/tutorials/agents.ipynb b/docs/docs/tutorials/agents.ipynb
index 3eca90dc8d043..9998cc7237076 100644
--- a/docs/docs/tutorials/agents.ipynb
+++ b/docs/docs/tutorials/agents.ipynb
@@ -211,7 +211,7 @@
"source": [
"## Using Language Models\n",
"\n",
- "Next, let's learn how to use a language model by to call tools. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
+ "Next, let's learn how to use a language model to call tools. LangChain supports many different language models that you can use interchangably - select the one you want to use below!\n",
"\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
diff --git a/docs/scripts/partner_pkg_table.py b/docs/scripts/partner_pkg_table.py
index 04a605235ff78..a7bdc34be7fd3 100644
--- a/docs/scripts/partner_pkg_table.py
+++ b/docs/scripts/partner_pkg_table.py
@@ -98,7 +98,7 @@ def package_row(p: dict) -> str:
def table() -> str:
- header = """| Provider | Package | Downloads | Latest | [JS](https://js.langchain.com/docs/integrations/providers/) |
+ header = """| Provider | Package | Downloads | Latest | [JS](https://js.langchain.com/docs/integrations/platforms/) |
| :--- | :---: | :---: | :---: | :---: |
"""
return header + "\n".join(package_row(p) for p in packages_sorted)
diff --git a/libs/cli/poetry.lock b/libs/cli/poetry.lock
index 72cd18d604530..5e84b31ac85b5 100644
--- a/libs/cli/poetry.lock
+++ b/libs/cli/poetry.lock
@@ -601,13 +601,13 @@ test = ["objgraph", "psutil"]
[[package]]
name = "gritql"
-version = "0.1.5"
+version = "0.2.0"
description = "Python bindings for GritQL"
optional = false
python-versions = "*"
files = [
- {file = "gritql-0.1.5-py2.py3-none-any.whl", hash = "sha256:b17b314d995a11b8e06839280b079ffc8a30bdfb0d2beebcb4332186a0b2cdf0"},
- {file = "gritql-0.1.5.tar.gz", hash = "sha256:7568ee2d7c7457000374c91289bacb05e92524c77a5d5f63fe777b29622bff4c"},
+ {file = "gritql-0.2.0-py2.py3-none-any.whl", hash = "sha256:6a37f4a6388c09801c25de8753546ca11d4b8a3ad527742821eb032ad069cd13"},
+ {file = "gritql-0.2.0.tar.gz", hash = "sha256:09e26e3d3152d3ec2e4fa80c0af4f2fe1436c82a2c6343cec6ab74ae61474bae"},
]
[[package]]
@@ -2068,4 +2068,4 @@ serve = []
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "c666eaa9945394483db2cf56ec2c147869b2fcefb767184c83c4f0e2f211ea2b"
+content-hash = "58adcda49e89173e501324ed1090cb1765057e46e94230ab90b734b6cec11ff9"
diff --git a/libs/cli/pyproject.toml b/libs/cli/pyproject.toml
index 2e3ee70fea7ab..467777bd24939 100644
--- a/libs/cli/pyproject.toml
+++ b/libs/cli/pyproject.toml
@@ -18,7 +18,7 @@ gitpython = "^3"
langserve = { extras = ["all"], version = ">=0.0.51" }
uvicorn = ">=0.23,<1.0"
tomlkit = ">=0.12"
-gritql = "^0.1.1"
+gritql = "^0.2.0"
[tool.poetry.scripts]
langchain = "langchain_cli.cli:app"
diff --git a/libs/community/Makefile b/libs/community/Makefile
index 55b63f009b519..ff8c2cdbe5ec6 100644
--- a/libs/community/Makefile
+++ b/libs/community/Makefile
@@ -16,7 +16,7 @@ coverage:
$(TEST_FILE)
test tests:
- poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+ poetry run pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE)
integration_tests:
poetry run pytest $(TEST_FILE)
diff --git a/libs/community/langchain_community/agent_toolkits/slack/toolkit.py b/libs/community/langchain_community/agent_toolkits/slack/toolkit.py
index fd61311326d70..a8ca7f564192b 100644
--- a/libs/community/langchain_community/agent_toolkits/slack/toolkit.py
+++ b/libs/community/langchain_community/agent_toolkits/slack/toolkit.py
@@ -13,7 +13,14 @@
from langchain_community.tools.slack.utils import login
if TYPE_CHECKING:
+ # This is for linting and IDE typehints
from slack_sdk import WebClient
+else:
+ try:
+ # We do this so pydantic can resolve the types when instantiating
+ from slack_sdk import WebClient
+ except ImportError:
+ pass
class SlackToolkit(BaseToolkit):
diff --git a/libs/community/langchain_community/callbacks/openai_info.py b/libs/community/langchain_community/callbacks/openai_info.py
index 12f42a566ee8d..5796065f1ee2c 100644
--- a/libs/community/langchain_community/callbacks/openai_info.py
+++ b/libs/community/langchain_community/callbacks/openai_info.py
@@ -204,8 +204,10 @@ def standardize_model_name(
or ("finetuned" in model_name and "legacy" not in model_name)
):
return model_name + "-completion"
- if token_type == TokenType.PROMPT_CACHED and (
- model_name.startswith("gpt-4o") or model_name.startswith("o1")
+ if (
+ token_type == TokenType.PROMPT_CACHED
+ and (model_name.startswith("gpt-4o") or model_name.startswith("o1"))
+ and not (model_name.startswith("gpt-4o-2024-05-13"))
):
return model_name + "-cached"
else:
diff --git a/libs/community/langchain_community/chat_models/friendli.py b/libs/community/langchain_community/chat_models/friendli.py
index a860ebf98bc59..76bf70c9af189 100644
--- a/libs/community/langchain_community/chat_models/friendli.py
+++ b/libs/community/langchain_community/chat_models/friendli.py
@@ -75,12 +75,12 @@ class ChatFriendli(BaseChatModel, BaseFriendli):
from langchain_community.chat_models import FriendliChat
chat = Friendli(
- model="llama-2-13b-chat", friendli_token="YOUR FRIENDLI TOKEN"
+ model="meta-llama-3.1-8b-instruct", friendli_token="YOUR FRIENDLI TOKEN"
)
chat.invoke("What is generative AI?")
"""
- model: str = "llama-2-13b-chat"
+ model: str = "meta-llama-3.1-8b-instruct"
@property
def lc_secrets(self) -> Dict[str, str]:
diff --git a/libs/community/langchain_community/chat_models/solar.py b/libs/community/langchain_community/chat_models/solar.py
index 1f2da3244c8c1..4bcdc0952aa82 100644
--- a/libs/community/langchain_community/chat_models/solar.py
+++ b/libs/community/langchain_community/chat_models/solar.py
@@ -24,7 +24,7 @@ class SolarChat(SolarCommon, ChatOpenAI): # type: ignore[override, override]
from langchain_community.chat_models.solar import SolarChat
- solar = SolarChat(model="solar-1-mini-chat")
+ solar = SolarChat(model="solar-mini")
"""
max_tokens: int = Field(default=1024)
diff --git a/libs/community/langchain_community/document_loaders/__init__.py b/libs/community/langchain_community/document_loaders/__init__.py
index 8a56f918ab6ac..c91345daa6b03 100644
--- a/libs/community/langchain_community/document_loaders/__init__.py
+++ b/libs/community/langchain_community/document_loaders/__init__.py
@@ -87,6 +87,7 @@
from langchain_community.document_loaders.blob_loaders import (
Blob,
BlobLoader,
+ CloudBlobLoader,
FileSystemBlobLoader,
YoutubeAudioLoader,
)
@@ -574,6 +575,7 @@
"CSVLoader": "langchain_community.document_loaders.csv_loader",
"CassandraLoader": "langchain_community.document_loaders.cassandra",
"ChatGPTLoader": "langchain_community.document_loaders.chatgpt",
+ "CloudBlobLoader": "langchain_community.document_loaders.blob_loaders",
"CoNLLULoader": "langchain_community.document_loaders.conllu",
"CollegeConfidentialLoader": "langchain_community.document_loaders.college_confidential", # noqa: E501
"ConcurrentLoader": "langchain_community.document_loaders.concurrent",
@@ -781,6 +783,7 @@ def __getattr__(name: str) -> Any:
"CSVLoader",
"CassandraLoader",
"ChatGPTLoader",
+ "CloudBlobLoader",
"CoNLLULoader",
"CollegeConfidentialLoader",
"ConcurrentLoader",
diff --git a/libs/community/langchain_community/document_loaders/confluence.py b/libs/community/langchain_community/document_loaders/confluence.py
index 8af05fbe93fde..225488c9ceb55 100644
--- a/libs/community/langchain_community/document_loaders/confluence.py
+++ b/libs/community/langchain_community/document_loaders/confluence.py
@@ -408,7 +408,7 @@ def _lazy_load(self, **kwargs: Any) -> Iterator[Document]:
include_restricted_content,
include_attachments,
include_comments,
- False, # labels are not included in the search results
+ include_labels,
content_format,
ocr_languages,
keep_markdown_format,
diff --git a/libs/community/langchain_community/document_loaders/html.py b/libs/community/langchain_community/document_loaders/html.py
index 857142bce96ae..9ea781a7e91cb 100644
--- a/libs/community/langchain_community/document_loaders/html.py
+++ b/libs/community/langchain_community/document_loaders/html.py
@@ -1,4 +1,5 @@
-from typing import List
+from pathlib import Path
+from typing import Any, List, Union
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
@@ -27,6 +28,23 @@ class UnstructuredHTMLLoader(UnstructuredFileLoader):
https://unstructured-io.github.io/unstructured/bricks.html#partition-html
"""
+ def __init__(
+ self,
+ file_path: Union[str, Path],
+ mode: str = "single",
+ **unstructured_kwargs: Any,
+ ):
+ """
+
+ Args:
+ file_path: The path to the HTML file to load.
+ mode: The mode to use when loading the file. Can be one of "single",
+ "multi", or "all". Default is "single".
+ **unstructured_kwargs: Any kwargs to pass to the unstructured.
+ """
+ file_path = str(file_path)
+ super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
+
def _get_elements(self) -> List:
from unstructured.partition.html import partition_html
diff --git a/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py b/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py
index 107e569339fb5..78ff022359555 100644
--- a/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py
+++ b/libs/community/langchain_community/document_loaders/parsers/doc_intelligence.py
@@ -79,7 +79,7 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]:
with blob.as_bytes_io() as file_obj:
poller = self.client.begin_analyze_document(
self.api_model,
- file_obj,
+ body=file_obj,
content_type="application/octet-stream",
output_content_format="markdown" if self.mode == "markdown" else "text",
)
@@ -97,8 +97,7 @@ def parse_url(self, url: str) -> Iterator[Document]:
poller = self.client.begin_analyze_document(
self.api_model,
- AnalyzeDocumentRequest(url_source=url),
- # content_type="application/octet-stream",
+ body=AnalyzeDocumentRequest(url_source=url),
output_content_format="markdown" if self.mode == "markdown" else "text",
)
result = poller.result()
@@ -115,8 +114,7 @@ def parse_bytes(self, bytes_source: bytes) -> Iterator[Document]:
poller = self.client.begin_analyze_document(
self.api_model,
- analyze_request=AnalyzeDocumentRequest(bytes_source=bytes_source),
- # content_type="application/octet-stream",
+ body=AnalyzeDocumentRequest(bytes_source=bytes_source),
output_content_format="markdown" if self.mode == "markdown" else "text",
)
result = poller.result()
diff --git a/libs/community/langchain_community/document_loaders/parsers/pdf.py b/libs/community/langchain_community/document_loaders/parsers/pdf.py
index 702d5998dd7c2..00b4510ee660d 100644
--- a/libs/community/langchain_community/document_loaders/parsers/pdf.py
+++ b/libs/community/langchain_community/document_loaders/parsers/pdf.py
@@ -6,7 +6,6 @@
from typing import (
TYPE_CHECKING,
Any,
- Dict,
Iterable,
Iterator,
Mapping,
@@ -23,15 +22,13 @@
from langchain_community.document_loaders.blob_loaders import Blob
if TYPE_CHECKING:
- import fitz.fitz
- import pdfminer.layout
- import pdfplumber.page
- import pypdf._page
- import pypdfium2._helpers.page
- from pypdf import PageObject
+ import fitz
+ import pdfminer
+ import pdfplumber
+ import pypdf
+ import pypdfium2
from textractor.data.text_linearization_config import TextLinearizationConfig
-
_PDF_FILTER_WITH_LOSS = ["DCTDecode", "DCT", "JPXDecode"]
_PDF_FILTER_WITHOUT_LOSS = [
"LZWDecode",
@@ -90,7 +87,7 @@ def __init__(
extract_images: bool = False,
*,
extraction_mode: str = "plain",
- extraction_kwargs: Optional[Dict[str, Any]] = None,
+ extraction_kwargs: Optional[dict[str, Any]] = None,
):
self.password = password
self.extract_images = extract_images
@@ -107,7 +104,7 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-ty
"`pip install pypdf`"
)
- def _extract_text_from_page(page: "PageObject") -> str:
+ def _extract_text_from_page(page: pypdf.PageObject) -> str:
"""
Extract text from image given the version of pypdf.
"""
@@ -126,12 +123,13 @@ def _extract_text_from_page(page: "PageObject") -> str:
Document(
page_content=_extract_text_from_page(page=page)
+ self._extract_images_from_page(page),
- metadata={"source": blob.source, "page": page_number}, # type: ignore[attr-defined]
+ metadata={"source": blob.source, "page": page_number},
+ # type: ignore[attr-defined]
)
for page_number, page in enumerate(pdf_reader.pages)
]
- def _extract_images_from_page(self, page: pypdf._page.PageObject) -> str:
+ def _extract_images_from_page(self, page: pypdf.PageObject) -> str:
"""Extract images from page and get the text with RapidOCR."""
if not self.extract_images or "/XObject" not in page["/Resources"].keys(): # type: ignore[attr-defined]
return ""
@@ -307,9 +305,7 @@ def lazy_parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[valid-ty
for page in doc
]
- def _get_page_content(
- self, doc: fitz.fitz.Document, page: fitz.fitz.Page, blob: Blob
- ) -> str:
+ def _get_page_content(self, doc: fitz.Document, page: fitz.Page, blob: Blob) -> str:
"""
Get the text of the page using PyMuPDF and RapidOCR and issue a warning
if it is empty.
@@ -327,7 +323,7 @@ def _get_page_content(
return content
def _extract_metadata(
- self, doc: fitz.fitz.Document, page: fitz.fitz.Page, blob: Blob
+ self, doc: fitz.Document, page: fitz.Page, blob: Blob
) -> dict:
"""Extract metadata from the document and page."""
return dict(
@@ -344,9 +340,7 @@ def _extract_metadata(
},
)
- def _extract_images_from_page(
- self, doc: fitz.fitz.Document, page: fitz.fitz.Page
- ) -> str:
+ def _extract_images_from_page(self, doc: fitz.Document, page: fitz.Page) -> str:
"""Extract images from page and get the text with RapidOCR."""
if not self.extract_images:
return ""
@@ -558,7 +552,7 @@ def __init__(
textract_features: Optional[Sequence[int]] = None,
client: Optional[Any] = None,
*,
- linearization_config: Optional["TextLinearizationConfig"] = None,
+ linearization_config: Optional[TextLinearizationConfig] = None,
) -> None:
"""Initializes the parser.
diff --git a/libs/community/langchain_community/document_loaders/pdf.py b/libs/community/langchain_community/document_loaders/pdf.py
index 8e7d0152d3dc0..c8ee848a733a9 100644
--- a/libs/community/langchain_community/document_loaders/pdf.py
+++ b/libs/community/langchain_community/document_loaders/pdf.py
@@ -6,17 +6,17 @@
import time
from abc import ABC
from io import StringIO
-from pathlib import Path
+from pathlib import Path, PurePath
from typing import (
TYPE_CHECKING,
Any,
- Dict,
+ BinaryIO,
Iterator,
- List,
Mapping,
Optional,
Sequence,
Union,
+ cast,
)
from urllib.parse import urlparse
@@ -68,7 +68,7 @@ class UnstructuredPDFLoader(UnstructuredFileLoader):
https://unstructured-io.github.io/unstructured/bricks.html#partition-pdf
"""
- def _get_elements(self) -> List:
+ def _get_elements(self) -> list:
from unstructured.partition.pdf import partition_pdf
return partition_pdf(filename=self.file_path, **self.unstructured_kwargs) # type: ignore[arg-type]
@@ -81,7 +81,9 @@ class BasePDFLoader(BaseLoader, ABC):
clean up the temporary file after completion.
"""
- def __init__(self, file_path: Union[str, Path], *, headers: Optional[Dict] = None):
+ def __init__(
+ self, file_path: Union[str, PurePath], *, headers: Optional[dict] = None
+ ):
"""Initialize with a file path.
Args:
@@ -154,7 +156,7 @@ def source(self) -> str:
class OnlinePDFLoader(BasePDFLoader):
"""Load online `PDF`."""
- def load(self) -> List[Document]:
+ def load(self) -> list[Document]:
"""Load documents."""
loader = UnstructuredPDFLoader(str(self.file_path))
return loader.load()
@@ -223,13 +225,13 @@ class PyPDFLoader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
password: Optional[Union[str, bytes]] = None,
- headers: Optional[Dict] = None,
+ headers: Optional[dict] = None,
extract_images: bool = False,
*,
extraction_mode: str = "plain",
- extraction_kwargs: Optional[Dict] = None,
+ extraction_kwargs: Optional[dict] = None,
) -> None:
"""Initialize with a file path."""
try:
@@ -262,9 +264,9 @@ class PyPDFium2Loader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
*,
- headers: Optional[Dict] = None,
+ headers: Optional[dict] = None,
extract_images: bool = False,
):
"""Initialize with a file path."""
@@ -290,7 +292,7 @@ class PyPDFDirectoryLoader(BaseLoader):
def __init__(
self,
- path: Union[str, Path],
+ path: Union[str, PurePath],
glob: str = "**/[!.]*.pdf",
silent_errors: bool = False,
load_hidden: bool = False,
@@ -308,7 +310,7 @@ def __init__(
def _is_visible(path: Path) -> bool:
return not any(part.startswith(".") for part in path.parts)
- def load(self) -> List[Document]:
+ def load(self) -> list[Document]:
p = Path(self.path)
docs = []
items = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
@@ -334,9 +336,9 @@ class PDFMinerLoader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
*,
- headers: Optional[Dict] = None,
+ headers: Optional[dict] = None,
extract_images: bool = False,
concatenate_pages: bool = True,
) -> None:
@@ -374,7 +376,9 @@ def lazy_load(
class PDFMinerPDFasHTMLLoader(BasePDFLoader):
"""Load `PDF` files as HTML content using `PDFMiner`."""
- def __init__(self, file_path: str, *, headers: Optional[Dict] = None):
+ def __init__(
+ self, file_path: Union[str, PurePath], *, headers: Optional[dict] = None
+ ):
"""Initialize with a file path."""
try:
from pdfminer.high_level import extract_text_to_fp # noqa:F401
@@ -395,14 +399,14 @@ def lazy_load(self) -> Iterator[Document]:
output_string = StringIO()
with open_filename(self.file_path, "rb") as fp:
extract_text_to_fp(
- fp,
+ cast(BinaryIO, fp),
output_string,
codec="",
laparams=LAParams(),
output_type="html",
)
metadata = {
- "source": self.file_path if self.web_path is None else self.web_path
+ "source": str(self.file_path) if self.web_path is None else self.web_path
}
yield Document(page_content=output_string.getvalue(), metadata=metadata)
@@ -412,9 +416,9 @@ class PyMuPDFLoader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
*,
- headers: Optional[Dict] = None,
+ headers: Optional[dict] = None,
extract_images: bool = False,
**kwargs: Any,
) -> None:
@@ -447,7 +451,7 @@ def _lazy_load(self, **kwargs: Any) -> Iterator[Document]:
blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
yield from parser.lazy_parse(blob)
- def load(self, **kwargs: Any) -> List[Document]:
+ def load(self, **kwargs: Any) -> list[Document]:
return list(self._lazy_load(**kwargs))
def lazy_load(self) -> Iterator[Document]:
@@ -461,11 +465,11 @@ class MathpixPDFLoader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
processed_file_format: str = "md",
max_wait_time_seconds: int = 500,
should_clean_pdf: bool = False,
- extra_request_data: Optional[Dict[str, Any]] = None,
+ extra_request_data: Optional[dict[str, Any]] = None,
**kwargs: Any,
) -> None:
"""Initialize with a file path.
@@ -499,7 +503,7 @@ def __init__(
self.should_clean_pdf = should_clean_pdf
@property
- def _mathpix_headers(self) -> Dict[str, str]:
+ def _mathpix_headers(self) -> dict[str, str]:
return {"app_id": self.mathpix_api_id, "app_key": self.mathpix_api_key}
@property
@@ -515,7 +519,7 @@ def data(self) -> dict:
return {"options_json": json.dumps(options)}
def send_pdf(self) -> str:
- with open(self.file_path, "rb") as f:
+ with open(str(self.file_path), "rb") as f:
files = {"file": f}
response = requests.post(
self.url, headers=self._mathpix_headers, files=files, data=self.data
@@ -562,7 +566,7 @@ def wait_for_processing(self, pdf_id: str) -> None:
# This indicates an error with the PDF processing
raise ValueError("Unable to retrieve PDF from Mathpix")
else:
- print(f"Status: {status}, waiting for processing to complete") # noqa: T201
+ logger.info("Status: %s, waiting for processing to complete", status)
time.sleep(5)
raise TimeoutError
@@ -572,8 +576,7 @@ def get_processed_pdf(self, pdf_id: str) -> str:
response = requests.get(url, headers=self._mathpix_headers)
return response.content.decode("utf-8")
- @staticmethod
- def clean_pdf(contents: str) -> str:
+ def clean_pdf(self, contents: str) -> str:
"""Clean the PDF file.
Args:
@@ -596,7 +599,7 @@ def clean_pdf(contents: str) -> str:
)
return contents
- def load(self) -> List[Document]:
+ def load(self) -> list[Document]:
pdf_id = self.send_pdf()
contents = self.get_processed_pdf(pdf_id)
if self.should_clean_pdf:
@@ -610,10 +613,10 @@ class PDFPlumberLoader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
text_kwargs: Optional[Mapping[str, Any]] = None,
dedupe: bool = False,
- headers: Optional[Dict] = None,
+ headers: Optional[dict] = None,
extract_images: bool = False,
) -> None:
"""Initialize with a file path."""
@@ -630,7 +633,7 @@ def __init__(
self.dedupe = dedupe
self.extract_images = extract_images
- def load(self) -> List[Document]:
+ def load(self) -> list[Document]:
"""Load file."""
parser = PDFPlumberParser(
@@ -669,13 +672,13 @@ class AmazonTextractPDFLoader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
textract_features: Optional[Sequence[str]] = None,
client: Optional[Any] = None,
credentials_profile_name: Optional[str] = None,
region_name: Optional[str] = None,
endpoint_url: Optional[str] = None,
- headers: Optional[Dict] = None,
+ headers: Optional[dict] = None,
*,
linearization_config: Optional["TextLinearizationConfig"] = None,
) -> None:
@@ -743,7 +746,7 @@ def __init__(
linearization_config=linearization_config,
)
- def load(self) -> List[Document]:
+ def load(self) -> list[Document]:
"""Load given path as pages."""
return list(self.lazy_load())
@@ -758,7 +761,7 @@ def lazy_load(
if self.web_path and self._is_s3_url(self.web_path):
blob = Blob(path=self.web_path) # type: ignore[call-arg] # type: ignore[misc]
else:
- blob = Blob.from_path(self.file_path) # type: ignore[attr-defined]
+ blob = Blob.from_path(self.file_path)
if AmazonTextractPDFLoader._get_number_of_pages(blob) > 1:
raise ValueError(
f"the file {blob.path} is a multi-page document, \
@@ -792,7 +795,9 @@ def _get_number_of_pages(blob: Blob) -> int: # type: ignore[valid-type]
elif blob.mimetype in ["image/png", "image/jpeg"]: # type: ignore[attr-defined]
return 1
else:
- raise ValueError(f"unsupported mime type: {blob.mimetype}") # type: ignore[attr-defined]
+ raise ValueError( # type: ignore[attr-defined]
+ f"unsupported mime type: {blob.mimetype}"
+ )
class DedocPDFLoader(DedocBaseLoader):
@@ -887,7 +892,7 @@ def _make_config(self) -> dict:
from dedoc.utils.langchain import make_manager_pdf_config
return make_manager_pdf_config(
- file_path=self.file_path,
+ file_path=str(self.file_path),
parsing_params=self.parsing_parameters,
split=self.split,
)
@@ -898,10 +903,10 @@ class DocumentIntelligenceLoader(BasePDFLoader):
def __init__(
self,
- file_path: str,
+ file_path: Union[str, PurePath],
client: Any,
model: str = "prebuilt-document",
- headers: Optional[Dict] = None,
+ headers: Optional[dict] = None,
) -> None:
"""
Initialize the object for file processing with Azure Document Intelligence
@@ -930,10 +935,10 @@ def __init__(
... )
"""
- self.parser = DocumentIntelligenceParser(client=client, model=model)
super().__init__(file_path, headers=headers)
+ self.parser = DocumentIntelligenceParser(client=client, model=model)
- def load(self) -> List[Document]:
+ def load(self) -> list[Document]:
"""Load given path as pages."""
return list(self.lazy_load())
@@ -964,7 +969,7 @@ class ZeroxPDFLoader(BasePDFLoader):
def __init__(
self,
- file_path: Union[str, Path],
+ file_path: Union[str, PurePath],
model: str = "gpt-4o-mini",
**zerox_kwargs: Any,
) -> None:
@@ -1005,7 +1010,7 @@ def lazy_load(self) -> Iterator[Document]:
# Directly call asyncio.run to execute zerox synchronously
zerox_output = asyncio.run(
- zerox(file_path=self.file_path, model=self.model, **self.zerox_kwargs)
+ zerox(file_path=str(self.file_path), model=self.model, **self.zerox_kwargs)
)
# Convert zerox output to Document instances and yield them
diff --git a/libs/community/langchain_community/embeddings/solar.py b/libs/community/langchain_community/embeddings/solar.py
index e730d7e6e9625..13d0c02648925 100644
--- a/libs/community/langchain_community/embeddings/solar.py
+++ b/libs/community/langchain_community/embeddings/solar.py
@@ -70,7 +70,7 @@ class SolarEmbeddings(BaseModel, Embeddings):
endpoint_url: str = "https://api.upstage.ai/v1/solar/embeddings"
"""Endpoint URL to use."""
- model: str = "solar-1-mini-embedding-query"
+ model: str = "embedding-query"
"""Embeddings model name to use."""
solar_api_key: Optional[SecretStr] = None
"""API Key for Solar API."""
diff --git a/libs/community/langchain_community/llms/azureml_endpoint.py b/libs/community/langchain_community/llms/azureml_endpoint.py
index 5c34ca1547743..c09ea2edcf482 100644
--- a/libs/community/langchain_community/llms/azureml_endpoint.py
+++ b/libs/community/langchain_community/llms/azureml_endpoint.py
@@ -434,7 +434,8 @@ def validate_endpoint_url(cls, field_value: Any) -> str:
raise ValueError(
"`endpoint_url` should contain the full invocation URL including "
"`/score` for `endpoint_api_type='dedicated'` or `/completions` "
- "or `/chat/completions` for `endpoint_api_type='serverless'`"
+ "or `/models/chat/completions` "
+ "for `endpoint_api_type='serverless'`"
)
return field_value
@@ -455,16 +456,17 @@ def validate_endpoint_api_type(
"Endpoints of type `dedicated` should follow the format "
"`https://..inference.ml.azure.com/score`."
" If your endpoint URL ends with `/completions` or"
- "`/chat/completions`, use `endpoint_api_type='serverless'` instead."
+ "`/models/chat/completions`,"
+ "use `endpoint_api_type='serverless'` instead."
)
if field_value == AzureMLEndpointApiType.serverless and not (
endpoint_url.endswith("/completions") # type: ignore[union-attr]
- or endpoint_url.endswith("/chat/completions") # type: ignore[union-attr]
+ or endpoint_url.endswith("/models/chat/completions") # type: ignore[union-attr]
):
raise ValueError(
"Endpoints of type `serverless` should follow the format "
- "`https://..inference.ml.azure.com/chat/completions`"
- " or `https://..inference.ml.azure.com/chat/completions`"
+ "`https://..inference.ml.azure.com/completions`"
+ " or `https://..inference.ml.azure.com/models/chat/completions`"
)
return field_value
diff --git a/libs/community/langchain_community/llms/friendli.py b/libs/community/langchain_community/llms/friendli.py
index 74b1ef7d199f8..d33c80eb39a42 100644
--- a/libs/community/langchain_community/llms/friendli.py
+++ b/libs/community/langchain_community/llms/friendli.py
@@ -16,14 +16,19 @@
from pydantic import Field, SecretStr
-def _stream_response_to_generation_chunk(stream_response: Any) -> GenerationChunk:
+def _stream_response_to_generation_chunk(
+ stream_response: Any,
+) -> GenerationChunk:
"""Convert a stream response to a generation chunk."""
- if stream_response.event == "token_sampled":
- return GenerationChunk(
- text=stream_response.text,
- generation_info={"token": str(stream_response.token)},
- )
- return GenerationChunk(text="")
+ if not stream_response.get("choices", None):
+ return GenerationChunk(text="")
+ return GenerationChunk(
+ text=stream_response.choices[0].text,
+ # generation_info=dict(
+ # finish_reason=stream_response.choices[0].get("finish_reason", None),
+ # logprobs=stream_response.choices[0].get("logprobs", None),
+ # ),
+ )
class BaseFriendli(Serializable):
@@ -34,7 +39,7 @@ class BaseFriendli(Serializable):
# Friendli Async client.
async_client: Any = Field(default=None, exclude=True)
# Model name to use.
- model: str = "mixtral-8x7b-instruct-v0-1"
+ model: str = "meta-llama-3.1-8b-instruct"
# Friendli personal access token to run as.
friendli_token: Optional[SecretStr] = None
# Friendli team ID to run as.
@@ -107,7 +112,7 @@ class Friendli(LLM, BaseFriendli):
from langchain_community.llms import Friendli
friendli = Friendli(
- model="mixtral-8x7b-instruct-v0-1", friendli_token="YOUR FRIENDLI TOKEN"
+ model="meta-llama-3.1-8b-instruct", friendli_token="YOUR FRIENDLI TOKEN"
)
"""
diff --git a/libs/community/langchain_community/llms/solar.py b/libs/community/langchain_community/llms/solar.py
index b5ea74bd6323b..8bfaecd50350f 100644
--- a/libs/community/langchain_community/llms/solar.py
+++ b/libs/community/langchain_community/llms/solar.py
@@ -44,7 +44,7 @@ class SolarCommon(BaseModel):
base_url: str = SOLAR_SERVICE_URL_BASE
solar_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
"""Solar API key. Get it here: https://console.upstage.ai/services/solar"""
- model_name: str = Field(default="solar-1-mini-chat", alias="model")
+ model_name: str = Field(default="solar-mini", alias="model")
"""Model name. Available models listed here: https://console.upstage.ai/services/solar"""
max_tokens: int = Field(default=1024)
temperature: float = 0.3
diff --git a/libs/community/langchain_community/tools/slack/base.py b/libs/community/langchain_community/tools/slack/base.py
index 23a3fa6c99f94..38eb8f7d3f3c4 100644
--- a/libs/community/langchain_community/tools/slack/base.py
+++ b/libs/community/langchain_community/tools/slack/base.py
@@ -10,7 +10,14 @@
from langchain_community.tools.slack.utils import login
if TYPE_CHECKING:
+ # This is for linting and IDE typehints
from slack_sdk import WebClient
+else:
+ try:
+ # We do this so pydantic can resolve the types when instantiating
+ from slack_sdk import WebClient
+ except ImportError:
+ pass
class SlackBaseTool(BaseTool): # type: ignore[override]
diff --git a/libs/community/langchain_community/utilities/duckduckgo_search.py b/libs/community/langchain_community/utilities/duckduckgo_search.py
index ede5c12298710..328161e7cc277 100644
--- a/libs/community/langchain_community/utilities/duckduckgo_search.py
+++ b/libs/community/langchain_community/utilities/duckduckgo_search.py
@@ -28,9 +28,9 @@ class DuckDuckGoSearchAPIWrapper(BaseModel):
Options: d, w, m, y
"""
max_results: int = 5
- backend: str = "api"
+ backend: str = "auto"
"""
- Options: api, html, lite
+ Options: auto, html, lite
"""
source: str = "text"
"""
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index bf755468c29af..f9573f4b69e97 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/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.
[[package]]
name = "aiohappyeyeballs"
@@ -971,6 +971,20 @@ files = [
[package.extras]
test = ["pytest (>=6)"]
+[[package]]
+name = "execnet"
+version = "2.1.1"
+description = "execnet: rapid multi-Python deployment"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
+ {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
+]
+
+[package.extras]
+testing = ["hatch", "pre-commit", "pytest", "tox"]
+
[[package]]
name = "executing"
version = "2.1.0"
@@ -1815,7 +1829,7 @@ files = [
[[package]]
name = "langchain"
-version = "0.3.13"
+version = "0.3.14"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@@ -1825,7 +1839,7 @@ develop = true
[package.dependencies]
aiohttp = "^3.8.3"
async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.3.26"
+langchain-core = "^0.3.29"
langchain-text-splitters = "^0.3.3"
langsmith = ">=0.1.17,<0.3"
numpy = [
@@ -1844,7 +1858,7 @@ url = "../langchain"
[[package]]
name = "langchain-core"
-version = "0.3.27"
+version = "0.3.29"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
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@@ -3254,6 +3257,26 @@ files = [
[package.dependencies]
watchdog = ">=2.0.0"
+[[package]]
+name = "pytest-xdist"
+version = "3.6.1"
+description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+execnet = ">=2.1"
+pytest = ">=7.0.0"
+
+[package.extras]
+psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
+testing = ["filelock"]
+
[[package]]
name = "python-dateutil"
version = "2.9.0.post0"
@@ -3858,14 +3881,61 @@ description = "Database Abstraction Library"
optional = false
python-versions = ">=3.7"
files = [
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+ {file = "SQLAlchemy-2.0.36-cp39-cp39-win32.whl", hash = "sha256:9fe53b404f24789b5ea9003fc25b9a3988feddebd7e7b369c8fac27ad6f52f28"},
+ {file = "SQLAlchemy-2.0.36-cp39-cp39-win_amd64.whl", hash = "sha256:af148a33ff0349f53512a049c6406923e4e02bf2f26c5fb285f143faf4f0e46a"},
{file = "SQLAlchemy-2.0.36-py3-none-any.whl", hash = "sha256:fddbe92b4760c6f5d48162aef14824add991aeda8ddadb3c31d56eb15ca69f8e"},
{file = "sqlalchemy-2.0.36.tar.gz", hash = "sha256:7f2767680b6d2398aea7082e45a774b2b0767b5c8d8ffb9c8b683088ea9b29c5"},
]
@@ -4634,4 +4704,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "fe58adfe92b997ebd50a0b1f36bb4a4a2296a5e2203ced651363302a246655b1"
+content-hash = "285e19251cdc78fc8dbde4ccf887f0ca35a0800e757223bb4abefa471a04a33a"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 6053686eed041..dde6e9a1bbb89 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-community"
-version = "0.3.13"
+version = "0.3.14"
description = "Community contributed LangChain integrations."
authors = []
license = "MIT"
@@ -33,8 +33,8 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
-langchain-core = "^0.3.27"
-langchain = "^0.3.13"
+langchain-core = "^0.3.29"
+langchain = "^0.3.14"
SQLAlchemy = ">=1.4,<3"
requests = "^2"
PyYAML = ">=5.3"
@@ -102,6 +102,7 @@ pytest-mock = "^3.10.0"
pytest-socket = "^0.6.0"
syrupy = "^4.0.2"
requests-mock = "^1.11.0"
+pytest-xdist = "^3.6.1"
[[tool.poetry.group.test.dependencies.cffi]]
version = "<1.17.1"
python = "<3.10"
diff --git a/libs/community/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py b/libs/community/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py
index 928c26898c9e7..47163c859520b 100644
--- a/libs/community/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py
+++ b/libs/community/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py
@@ -61,7 +61,7 @@ def _assert_with_parser(parser: BaseBlobParser, splits_by_page: bool = True) ->
assert metadata["source"] == str(LAYOUT_PARSER_PAPER_PDF)
if splits_by_page:
- assert metadata["page"] == 0
+ assert int(metadata["page"]) == 0
def _assert_with_duplicate_parser(parser: BaseBlobParser, dedupe: bool = False) -> None:
diff --git a/libs/community/tests/integration_tests/document_loaders/test_pdf.py b/libs/community/tests/integration_tests/document_loaders/test_pdf.py
index 50c9fde29d918..66ce4ce3fa587 100644
--- a/libs/community/tests/integration_tests/document_loaders/test_pdf.py
+++ b/libs/community/tests/integration_tests/document_loaders/test_pdf.py
@@ -1,3 +1,4 @@
+import os
from pathlib import Path
from typing import Sequence, Union
@@ -17,7 +18,7 @@
def test_unstructured_pdf_loader_elements_mode() -> None:
"""Test unstructured loader with various modes."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = UnstructuredPDFLoader(str(file_path), mode="elements")
+ loader = UnstructuredPDFLoader(file_path, mode="elements")
docs = loader.load()
assert len(docs) == 2
@@ -26,7 +27,7 @@ def test_unstructured_pdf_loader_elements_mode() -> None:
def test_unstructured_pdf_loader_paged_mode() -> None:
"""Test unstructured loader with various modes."""
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
- loader = UnstructuredPDFLoader(str(file_path), mode="paged")
+ loader = UnstructuredPDFLoader(file_path, mode="paged")
docs = loader.load()
assert len(docs) == 16
@@ -35,7 +36,7 @@ def test_unstructured_pdf_loader_paged_mode() -> None:
def test_unstructured_pdf_loader_default_mode() -> None:
"""Test unstructured loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = UnstructuredPDFLoader(str(file_path))
+ loader = UnstructuredPDFLoader(file_path)
docs = loader.load()
assert len(docs) == 1
@@ -44,26 +45,26 @@ def test_unstructured_pdf_loader_default_mode() -> None:
def test_pdfminer_loader() -> None:
"""Test PDFMiner loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = PDFMinerLoader(str(file_path))
+ loader = PDFMinerLoader(file_path)
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
- loader = PDFMinerLoader(str(file_path))
+ loader = PDFMinerLoader(file_path)
docs = loader.load()
assert len(docs) == 1
# Verify that concatenating pages parameter works
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = PDFMinerLoader(str(file_path), concatenate_pages=True)
+ loader = PDFMinerLoader(file_path, concatenate_pages=True)
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
- loader = PDFMinerLoader(str(file_path), concatenate_pages=False)
+ loader = PDFMinerLoader(file_path, concatenate_pages=False)
docs = loader.load()
assert len(docs) == 16
@@ -72,13 +73,13 @@ def test_pdfminer_loader() -> None:
def test_pdfminer_pdf_as_html_loader() -> None:
"""Test PDFMinerPDFasHTMLLoader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = PDFMinerPDFasHTMLLoader(str(file_path))
+ loader = PDFMinerPDFasHTMLLoader(file_path)
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
- loader = PDFMinerPDFasHTMLLoader(str(file_path))
+ loader = PDFMinerPDFasHTMLLoader(file_path)
docs = loader.load()
assert len(docs) == 1
@@ -87,13 +88,13 @@ def test_pdfminer_pdf_as_html_loader() -> None:
def test_pypdfium2_loader() -> None:
"""Test PyPDFium2Loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = PyPDFium2Loader(str(file_path))
+ loader = PyPDFium2Loader(file_path)
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
- loader = PyPDFium2Loader(str(file_path))
+ loader = PyPDFium2Loader(file_path)
docs = loader.load()
assert len(docs) == 16
@@ -102,13 +103,13 @@ def test_pypdfium2_loader() -> None:
def test_pymupdf_loader() -> None:
"""Test PyMuPDF loader."""
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = PyMuPDFLoader(str(file_path))
+ loader = PyMuPDFLoader(file_path)
docs = loader.load()
assert len(docs) == 1
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
- loader = PyMuPDFLoader(str(file_path))
+ loader = PyMuPDFLoader(file_path)
docs = loader.load()
assert len(docs) == 16
@@ -123,20 +124,21 @@ def test_pymupdf_loader() -> None:
assert len(docs) == 1
+@pytest.mark.skipif(
+ not os.environ.get("MATHPIX_API_KEY"), reason="Mathpix API key not found"
+)
def test_mathpix_loader() -> None:
file_path = Path(__file__).parent.parent / "examples/hello.pdf"
- loader = MathpixPDFLoader(str(file_path))
+ loader = MathpixPDFLoader(file_path)
docs = loader.load()
assert len(docs) == 1
- print(docs[0].page_content) # noqa: T201
file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf"
- loader = MathpixPDFLoader(str(file_path))
+ loader = MathpixPDFLoader(file_path)
docs = loader.load()
assert len(docs) == 1
- print(docs[0].page_content) # noqa: T201
@pytest.mark.parametrize(
@@ -187,8 +189,8 @@ def test_mathpix_loader() -> None:
1,
False,
),
- (str(Path(__file__).parent.parent / "examples/hello.pdf"), ["FORMS"], 1, False),
- (str(Path(__file__).parent.parent / "examples/hello.pdf"), [], 1, False),
+ (Path(__file__).parent.parent / "examples/hello.pdf", ["FORMS"], 1, False),
+ (Path(__file__).parent.parent / "examples/hello.pdf", [], 1, False),
(
"s3://amazon-textract-public-content/langchain/layout-parser-paper.pdf",
["FORMS", "TABLES", "LAYOUT"],
@@ -222,7 +224,7 @@ def test_amazontextract_loader(
@pytest.mark.skip(reason="Requires AWS credentials to run")
def test_amazontextract_loader_failures() -> None:
# 2-page PDF local file system
- two_page_pdf = str(
+ two_page_pdf = (
Path(__file__).parent.parent / "examples/multi-page-forms-sample-2-page.pdf"
)
loader = AmazonTextractPDFLoader(two_page_pdf)
diff --git a/libs/community/tests/unit_tests/document_loaders/test_imports.py b/libs/community/tests/unit_tests/document_loaders/test_imports.py
index ddeaf734b0fe8..33c988d1b9294 100644
--- a/libs/community/tests/unit_tests/document_loaders/test_imports.py
+++ b/libs/community/tests/unit_tests/document_loaders/test_imports.py
@@ -43,6 +43,7 @@
"CassandraLoader",
"CSVLoader",
"ChatGPTLoader",
+ "CloudBlobLoader",
"CoNLLULoader",
"CollegeConfidentialLoader",
"ConcurrentLoader",
diff --git a/libs/community/tests/unit_tests/llms/test_friendli.py b/libs/community/tests/unit_tests/llms/test_friendli.py
index 6fd4593d93d3b..18cf5c3364425 100644
--- a/libs/community/tests/unit_tests/llms/test_friendli.py
+++ b/libs/community/tests/unit_tests/llms/test_friendli.py
@@ -114,14 +114,14 @@ async def test_friendli_ainvoke(
@pytest.mark.requires("friendli")
def test_friendli_stream(mock_friendli_client: Mock, friendli_llm: Friendli) -> None:
"""Test stream with friendli."""
+ mock_choice_0 = Mock()
+ mock_choice_0.text = "Hello "
+ mock_choice_1 = Mock()
+ mock_choice_1.text = "langchain"
mock_chunk_0 = Mock()
- mock_chunk_0.event = "token_sampled"
- mock_chunk_0.text = "Hello "
- mock_chunk_0.token = 0
+ mock_chunk_0.choices = [mock_choice_0]
mock_chunk_1 = Mock()
- mock_chunk_1.event = "token_sampled"
- mock_chunk_1.text = "Friendli"
- mock_chunk_1.token = 1
+ mock_chunk_1.choices = [mock_choice_1]
mock_stream = MagicMock()
mock_chunks = [mock_chunk_0, mock_chunk_1]
mock_stream.__iter__.return_value = mock_chunks
@@ -129,7 +129,7 @@ def test_friendli_stream(mock_friendli_client: Mock, friendli_llm: Friendli) ->
mock_friendli_client.completions.create.return_value = mock_stream
stream = friendli_llm.stream("Hello langchain")
for i, chunk in enumerate(stream):
- assert chunk == mock_chunks[i].text
+ assert chunk == mock_chunks[i].choices[0].text
mock_friendli_client.completions.create.assert_called_once_with(
model=friendli_llm.model,
@@ -149,22 +149,22 @@ async def test_friendli_astream(
mock_friendli_async_client: AsyncMock, friendli_llm: Friendli
) -> None:
"""Test async stream with friendli."""
+ mock_choice_0 = Mock()
+ mock_choice_0.text = "Hello "
+ mock_choice_1 = Mock()
+ mock_choice_1.text = "langchain"
mock_chunk_0 = Mock()
- mock_chunk_0.event = "token_sampled"
- mock_chunk_0.text = "Hello "
- mock_chunk_0.token = 0
+ mock_chunk_0.choices = [mock_choice_0]
mock_chunk_1 = Mock()
- mock_chunk_1.event = "token_sampled"
- mock_chunk_1.text = "Friendli"
- mock_chunk_1.token = 1
+ mock_chunk_1.choices = [mock_choice_1]
mock_stream = AsyncMock()
mock_chunks = [mock_chunk_0, mock_chunk_1]
- mock_stream.__aiter__.return_value = mock_chunks
+ mock_stream.__aiter__.return_value = iter(mock_chunks)
mock_friendli_async_client.completions.create.return_value = mock_stream
stream = friendli_llm.astream("Hello langchain")
async for i, chunk in aenumerate(stream):
- assert chunk == mock_chunks[i].text
+ assert chunk == mock_chunks[i].choices[0].text
mock_friendli_async_client.completions.create.assert_awaited_once_with(
model=friendli_llm.model,
diff --git a/libs/community/tests/unit_tests/test_dependencies.py b/libs/community/tests/unit_tests/test_dependencies.py
index a5c8c7c12c100..0368f3a143624 100644
--- a/libs/community/tests/unit_tests/test_dependencies.py
+++ b/libs/community/tests/unit_tests/test_dependencies.py
@@ -91,6 +91,7 @@ def test_test_group_dependencies(poetry_conf: Mapping[str, Any]) -> None:
"pytest-mock",
"pytest-socket",
"pytest-watcher",
+ "pytest-xdist",
"responses",
"syrupy",
"requests-mock",
diff --git a/libs/core/Makefile b/libs/core/Makefile
index 61ac2dcc6971e..169e49a497967 100644
--- a/libs/core/Makefile
+++ b/libs/core/Makefile
@@ -12,7 +12,7 @@ test tests:
-u LANGCHAIN_API_KEY \
-u LANGSMITH_TRACING \
-u LANGCHAIN_PROJECT \
- poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+ poetry run pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE)
test_watch:
env \
diff --git a/libs/core/langchain_core/_api/deprecation.py b/libs/core/langchain_core/_api/deprecation.py
index c7d1e87b3bac8..0e254053d8819 100644
--- a/libs/core/langchain_core/_api/deprecation.py
+++ b/libs/core/langchain_core/_api/deprecation.py
@@ -360,10 +360,9 @@ def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
_addendum,
]
details = " ".join([component.strip() for component in components if component])
- package = (
- _package or _name.split(".")[0].replace("_", "-") if "." in _name else None
+ package = _package or (
+ _name.split(".")[0].replace("_", "-") if "." in _name else None
)
- since_str = f"{package}=={since}" if package else since
if removal:
if removal.startswith("1.") and package and package.startswith("langchain"):
removal_str = f"It will not be removed until {package}=={removal}."
@@ -372,7 +371,7 @@ def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
else:
removal_str = ""
new_doc = f"""\
-.. deprecated:: {since_str} {details} {removal_str}
+.. deprecated:: {since} {details} {removal_str}
{old_doc}\
"""
diff --git a/libs/core/langchain_core/_api/internal.py b/libs/core/langchain_core/_api/internal.py
index 0dca40ad668a8..e5ef4300ff6d3 100644
--- a/libs/core/langchain_core/_api/internal.py
+++ b/libs/core/langchain_core/_api/internal.py
@@ -14,10 +14,9 @@ def is_caller_internal(depth: int = 2) -> bool:
frame = frame.f_back
if frame is None:
return False
- caller_module = inspect.getmodule(frame)
- if caller_module is None:
- return False
- caller_module_name = caller_module.__name__
+ # Directly access the module name from the frame's global variables
+ module_globals = frame.f_globals
+ caller_module_name = module_globals.get("__name__", "")
return caller_module_name.startswith("langchain")
finally:
del frame
diff --git a/libs/core/langchain_core/prompts/base.py b/libs/core/langchain_core/prompts/base.py
index 042b2d77ea80f..803302f1898d0 100644
--- a/libs/core/langchain_core/prompts/base.py
+++ b/libs/core/langchain_core/prompts/base.py
@@ -264,7 +264,7 @@ def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate
"""Return a partial of the prompt template.
Args:
- kwargs: Union[str, Callable[[], str], partial variables to set.
+ kwargs: Union[str, Callable[[], str]], partial variables to set.
Returns:
BasePromptTemplate: A partial of the prompt template.
diff --git a/libs/core/langchain_core/prompts/few_shot.py b/libs/core/langchain_core/prompts/few_shot.py
index 96e70c17d95cb..40f1d86a64ccc 100644
--- a/libs/core/langchain_core/prompts/few_shot.py
+++ b/libs/core/langchain_core/prompts/few_shot.py
@@ -282,7 +282,7 @@ class FewShotChatMessagePromptTemplate(
]
example_prompt = ChatPromptTemplate.from_messages(
- [('human', '{input}'), ('ai', '{output}')]
+ [('human', 'What is {input}?'), ('ai', '{output}')]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
diff --git a/libs/core/langchain_core/tools/retriever.py b/libs/core/langchain_core/tools/retriever.py
index b59b49a3d317a..b1036058ca515 100644
--- a/libs/core/langchain_core/tools/retriever.py
+++ b/libs/core/langchain_core/tools/retriever.py
@@ -1,11 +1,12 @@
from __future__ import annotations
from functools import partial
-from typing import Optional
+from typing import Literal, Optional, Union
from pydantic import BaseModel, Field
from langchain_core.callbacks import Callbacks
+from langchain_core.documents import Document
from langchain_core.prompts import (
BasePromptTemplate,
PromptTemplate,
@@ -28,11 +29,16 @@ def _get_relevant_documents(
document_prompt: BasePromptTemplate,
document_separator: str,
callbacks: Callbacks = None,
-) -> str:
+ response_format: Literal["content", "content_and_artifact"] = "content",
+) -> Union[str, tuple[str, list[Document]]]:
docs = retriever.invoke(query, config={"callbacks": callbacks})
- return document_separator.join(
+ content = document_separator.join(
format_document(doc, document_prompt) for doc in docs
)
+ if response_format == "content_and_artifact":
+ return (content, docs)
+
+ return content
async def _aget_relevant_documents(
@@ -41,12 +47,18 @@ async def _aget_relevant_documents(
document_prompt: BasePromptTemplate,
document_separator: str,
callbacks: Callbacks = None,
-) -> str:
+ response_format: Literal["content", "content_and_artifact"] = "content",
+) -> Union[str, tuple[str, list[Document]]]:
docs = await retriever.ainvoke(query, config={"callbacks": callbacks})
- return document_separator.join(
+ content = document_separator.join(
[await aformat_document(doc, document_prompt) for doc in docs]
)
+ if response_format == "content_and_artifact":
+ return (content, docs)
+
+ return content
+
def create_retriever_tool(
retriever: BaseRetriever,
@@ -55,6 +67,7 @@ def create_retriever_tool(
*,
document_prompt: Optional[BasePromptTemplate] = None,
document_separator: str = "\n\n",
+ response_format: Literal["content", "content_and_artifact"] = "content",
) -> Tool:
"""Create a tool to do retrieval of documents.
@@ -66,6 +79,11 @@ def create_retriever_tool(
model, so should be descriptive.
document_prompt: The prompt to use for the document. Defaults to None.
document_separator: The separator to use between documents. Defaults to "\n\n".
+ response_format: The tool response format. If "content" then the output of
+ the tool is interpreted as the contents of a ToolMessage. If
+ "content_and_artifact" then the output is expected to be a two-tuple
+ corresponding to the (content, artifact) of a ToolMessage (artifact
+ being a list of documents in this case). Defaults to "content".
Returns:
Tool class to pass to an agent.
@@ -76,12 +94,14 @@ def create_retriever_tool(
retriever=retriever,
document_prompt=document_prompt,
document_separator=document_separator,
+ response_format=response_format,
)
afunc = partial(
_aget_relevant_documents,
retriever=retriever,
document_prompt=document_prompt,
document_separator=document_separator,
+ response_format=response_format,
)
return Tool(
name=name,
@@ -89,4 +109,5 @@ def create_retriever_tool(
func=func,
coroutine=afunc,
args_schema=RetrieverInput,
+ response_format=response_format,
)
diff --git a/libs/core/langchain_core/utils/function_calling.py b/libs/core/langchain_core/utils/function_calling.py
index e56e7dd49ba8e..dd3468d59e532 100644
--- a/libs/core/langchain_core/utils/function_calling.py
+++ b/libs/core/langchain_core/utils/function_calling.py
@@ -75,12 +75,7 @@ def _rm_titles(kv: dict, prev_key: str = "") -> dict:
return new_kv
-@deprecated(
- "0.1.16",
- alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
- removal="1.0",
-)
-def convert_pydantic_to_openai_function(
+def _convert_pydantic_to_openai_function(
model: type,
*,
name: Optional[str] = None,
@@ -121,6 +116,13 @@ def convert_pydantic_to_openai_function(
}
+convert_pydantic_to_openai_function = deprecated(
+ "0.1.16",
+ alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
+ removal="1.0",
+)(_convert_pydantic_to_openai_function)
+
+
@deprecated(
"0.1.16",
alternative="langchain_core.utils.function_calling.convert_to_openai_tool()",
@@ -144,7 +146,7 @@ def convert_pydantic_to_openai_tool(
Returns:
The tool description.
"""
- function = convert_pydantic_to_openai_function(
+ function = _convert_pydantic_to_openai_function(
model, name=name, description=description
)
return {"type": "function", "function": function}
@@ -155,12 +157,7 @@ def _get_python_function_name(function: Callable) -> str:
return function.__name__
-@deprecated(
- "0.1.16",
- alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
- removal="1.0",
-)
-def convert_python_function_to_openai_function(
+def _convert_python_function_to_openai_function(
function: Callable,
) -> FunctionDescription:
"""Convert a Python function to an OpenAI function-calling API compatible dict.
@@ -186,13 +183,20 @@ def convert_python_function_to_openai_function(
error_on_invalid_docstring=False,
include_injected=False,
)
- return convert_pydantic_to_openai_function(
+ return _convert_pydantic_to_openai_function(
model,
name=func_name,
description=model.__doc__,
)
+convert_python_function_to_openai_function = deprecated(
+ "0.1.16",
+ alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
+ removal="1.0",
+)(_convert_python_function_to_openai_function)
+
+
def _convert_typed_dict_to_openai_function(typed_dict: type) -> FunctionDescription:
visited: dict = {}
from pydantic.v1 import BaseModel
@@ -201,7 +205,7 @@ def _convert_typed_dict_to_openai_function(typed_dict: type) -> FunctionDescript
type[BaseModel],
_convert_any_typed_dicts_to_pydantic(typed_dict, visited=visited),
)
- return convert_pydantic_to_openai_function(model) # type: ignore
+ return _convert_pydantic_to_openai_function(model) # type: ignore
_MAX_TYPED_DICT_RECURSION = 25
@@ -272,12 +276,7 @@ def _convert_any_typed_dicts_to_pydantic(
return type_
-@deprecated(
- "0.1.16",
- alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
- removal="1.0",
-)
-def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
+def _format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
"""Format tool into the OpenAI function API.
Args:
@@ -290,7 +289,7 @@ def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
is_simple_oai_tool = isinstance(tool, simple.Tool) and not tool.args_schema
if tool.tool_call_schema and not is_simple_oai_tool:
- return convert_pydantic_to_openai_function(
+ return _convert_pydantic_to_openai_function(
tool.tool_call_schema, name=tool.name, description=tool.description
)
else:
@@ -312,6 +311,13 @@ def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
}
+format_tool_to_openai_function = deprecated(
+ "0.1.16",
+ alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
+ removal="1.0",
+)(_format_tool_to_openai_function)
+
+
@deprecated(
"0.1.16",
alternative="langchain_core.utils.function_calling.convert_to_openai_tool()",
@@ -326,7 +332,7 @@ def format_tool_to_openai_tool(tool: BaseTool) -> ToolDescription:
Returns:
The tool description.
"""
- function = format_tool_to_openai_function(tool)
+ function = _format_tool_to_openai_function(tool)
return {"type": "function", "function": function}
@@ -408,15 +414,15 @@ def convert_to_openai_function(
if function_copy and "properties" in function_copy:
oai_function["parameters"] = function_copy
elif isinstance(function, type) and is_basemodel_subclass(function):
- oai_function = cast(dict, convert_pydantic_to_openai_function(function))
+ oai_function = cast(dict, _convert_pydantic_to_openai_function(function))
elif is_typeddict(function):
oai_function = cast(
dict, _convert_typed_dict_to_openai_function(cast(type, function))
)
elif isinstance(function, BaseTool):
- oai_function = cast(dict, format_tool_to_openai_function(function))
+ oai_function = cast(dict, _format_tool_to_openai_function(function))
elif callable(function):
- oai_function = cast(dict, convert_python_function_to_openai_function(function))
+ oai_function = cast(dict, _convert_python_function_to_openai_function(function))
else:
msg = (
f"Unsupported function\n\n{function}\n\nFunctions must be passed in"
diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock
index 53962198d8297..eadfb8d460c70 100644
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -523,6 +523,20 @@ files = [
[package.extras]
test = ["pytest (>=6)"]
+[[package]]
+name = "execnet"
+version = "2.1.1"
+description = "execnet: rapid multi-Python deployment"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
+ {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
+]
+
+[package.extras]
+testing = ["hatch", "pre-commit", "pytest", "tox"]
+
[[package]]
name = "executing"
version = "2.1.0"
@@ -2160,6 +2174,26 @@ files = [
tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""}
watchdog = ">=2.0.0"
+[[package]]
+name = "pytest-xdist"
+version = "3.6.1"
+description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pytest_xdist-3.6.1-py3-none-any.whl", hash = "sha256:9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7"},
+ {file = "pytest_xdist-3.6.1.tar.gz", hash = "sha256:ead156a4db231eec769737f57668ef58a2084a34b2e55c4a8fa20d861107300d"},
+]
+
+[package.dependencies]
+execnet = ">=2.1"
+pytest = ">=7.0.0"
+
+[package.extras]
+psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
+testing = ["filelock"]
+
[[package]]
name = "python-dateutil"
version = "2.9.0.post0"
@@ -3104,4 +3138,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "5accfdfd412486fbf7bb3ef18f00e75db40599034428651ef014b0bc3927ddfa"
+content-hash = "65d2f612fead6395befc285353347bf82d09044ce832c278f8b35e4f179caebb"
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 2bba5c7ff609d..65e40fafb09f8 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-core"
-version = "0.3.28"
+version = "0.3.29"
description = "Building applications with LLMs through composability"
authors = []
license = "MIT"
@@ -84,17 +84,20 @@ classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_ini
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
+
[tool.poetry.group.typing.dependencies]
mypy = ">=1.10,<1.11"
types-pyyaml = "^6.0.12.2"
types-requests = "^2.28.11.5"
types-jinja2 = "^2.11.9"
+
[tool.poetry.group.dev.dependencies]
jupyter = "^1.0.0"
setuptools = "^67.6.1"
grandalf = "^0.8"
+
[tool.poetry.group.test.dependencies]
pytest = "^8"
freezegun = "^1.2.2"
@@ -105,6 +108,7 @@ pytest-asyncio = "^0.21.1"
grandalf = "^0.8"
responses = "^0.25.0"
pytest-socket = "^0.7.0"
+pytest-xdist = "^3.6.1"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1.24.0"
python = "<3.12"
@@ -113,12 +117,15 @@ python = "<3.12"
version = ">=1.26.0,<3"
python = ">=3.12"
+
[tool.poetry.group.test_integration.dependencies]
+
[tool.poetry.group.typing.dependencies.langchain-text-splitters]
path = "../text-splitters"
develop = true
+
[tool.poetry.group.test.dependencies.langchain-tests]
path = "../standard-tests"
develop = true
diff --git a/libs/core/tests/unit_tests/test_tools.py b/libs/core/tests/unit_tests/test_tools.py
index c0d8c962dd31e..03e7fcc01e5e4 100644
--- a/libs/core/tests/unit_tests/test_tools.py
+++ b/libs/core/tests/unit_tests/test_tools.py
@@ -30,8 +30,13 @@
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
-from langchain_core.messages import ToolMessage
+from langchain_core.callbacks.manager import (
+ CallbackManagerForRetrieverRun,
+)
+from langchain_core.documents import Document
+from langchain_core.messages import ToolCall, ToolMessage
from langchain_core.messages.tool import ToolOutputMixin
+from langchain_core.retrievers import BaseRetriever
from langchain_core.runnables import (
Runnable,
RunnableConfig,
@@ -2118,6 +2123,57 @@ def my_tool(val: int, other_val: Annotated[dict, "my annotation"]) -> str:
assert schema.__annotations__ == expected_type_hints
+def test_create_retriever_tool() -> None:
+ class MyRetriever(BaseRetriever):
+ def _get_relevant_documents(
+ self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+ ) -> list[Document]:
+ return [Document(page_content=f"foo {query}"), Document(page_content="bar")]
+
+ retriever = MyRetriever()
+ retriever_tool = tools.create_retriever_tool(
+ retriever, "retriever_tool_content", "Retriever Tool Content"
+ )
+ assert isinstance(retriever_tool, BaseTool)
+ assert retriever_tool.name == "retriever_tool_content"
+ assert retriever_tool.description == "Retriever Tool Content"
+ assert retriever_tool.invoke("bar") == "foo bar\n\nbar"
+ assert retriever_tool.invoke(
+ ToolCall(
+ name="retriever_tool_content",
+ args={"query": "bar"},
+ id="123",
+ type="tool_call",
+ )
+ ) == ToolMessage(
+ "foo bar\n\nbar", tool_call_id="123", name="retriever_tool_content"
+ )
+
+ retriever_tool_artifact = tools.create_retriever_tool(
+ retriever,
+ "retriever_tool_artifact",
+ "Retriever Tool Artifact",
+ response_format="content_and_artifact",
+ )
+ assert isinstance(retriever_tool_artifact, BaseTool)
+ assert retriever_tool_artifact.name == "retriever_tool_artifact"
+ assert retriever_tool_artifact.description == "Retriever Tool Artifact"
+ assert retriever_tool_artifact.invoke("bar") == "foo bar\n\nbar"
+ assert retriever_tool_artifact.invoke(
+ ToolCall(
+ name="retriever_tool_artifact",
+ args={"query": "bar"},
+ id="123",
+ type="tool_call",
+ )
+ ) == ToolMessage(
+ "foo bar\n\nbar",
+ artifact=[Document(page_content="foo bar"), Document(page_content="bar")],
+ tool_call_id="123",
+ name="retriever_tool_artifact",
+ )
+
+
@pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Testing pydantic v2.")
def test_tool_args_schema_pydantic_v2_with_metadata() -> None:
from pydantic import BaseModel as BaseModelV2
diff --git a/libs/langchain/Makefile b/libs/langchain/Makefile
index e06cd2e65d1a9..4da2edd811560 100644
--- a/libs/langchain/Makefile
+++ b/libs/langchain/Makefile
@@ -19,7 +19,7 @@ coverage:
$(TEST_FILE)
test tests:
- poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+ poetry run pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE)
extended_tests:
poetry run pytest --disable-socket --allow-unix-socket --only-extended tests/unit_tests
diff --git a/libs/langchain/langchain/_api/deprecation.py b/libs/langchain/langchain/_api/deprecation.py
index ecd5a71b8964f..6d5f2cf6895f1 100644
--- a/libs/langchain/langchain/_api/deprecation.py
+++ b/libs/langchain/langchain/_api/deprecation.py
@@ -11,11 +11,13 @@
"LangChain agents will continue to be supported, but it is recommended for new "
"use cases to be built with LangGraph. LangGraph offers a more flexible and "
"full-featured framework for building agents, including support for "
- "tool-calling, persistence of state, and human-in-the-loop workflows. See "
- "LangGraph documentation for more details: "
- "https://langchain-ai.github.io/langgraph/. Refer here for its pre-built "
- "ReAct agent: "
- "https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/"
+ "tool-calling, persistence of state, and human-in-the-loop workflows. For "
+ "details, refer to the "
+ "`LangGraph documentation `_"
+ " as well as guides for "
+ "`Migrating from AgentExecutor `_" # noqa: E501
+ " and LangGraph's "
+ "`Pre-built ReAct agent `_." # noqa: E501
)
diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock
index f892ddfcda4e0..154ae27fa58cc 100644
--- a/libs/langchain/poetry.lock
+++ b/libs/langchain/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.
[[package]]
name = "aiohappyeyeballs"
@@ -1043,6 +1043,20 @@ files = [
[package.extras]
test = ["pytest (>=6)"]
+[[package]]
+name = "execnet"
+version = "2.1.1"
+description = "execnet: rapid multi-Python deployment"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
+ {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
+]
+
+[package.extras]
+testing = ["hatch", "pre-commit", "pytest", "tox"]
+
[[package]]
name = "executing"
version = "2.1.0"
@@ -1976,7 +1990,7 @@ files = [
[[package]]
name = "langchain-core"
-version = "0.3.26"
+version = "0.3.29"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@@ -2001,7 +2015,7 @@ url = "../core"
[[package]]
name = "langchain-openai"
-version = "0.2.12"
+version = "0.2.14"
description = "An integration package connecting OpenAI and LangChain"
optional = true
python-versions = ">=3.9,<4.0"
@@ -2009,8 +2023,8 @@ files = []
develop = true
[package.dependencies]
-langchain-core = "^0.3.21"
-openai = "^1.55.3"
+langchain-core = "^0.3.27"
+openai = "^1.58.1"
tiktoken = ">=0.7,<1"
[package.source]
@@ -2776,41 +2790,30 @@ files = [
{file = "pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348"},
{file = "pandas-2.2.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d9c45366def9a3dd85a6454c0e7908f2b3b8e9c138f5dc38fed7ce720d8453ed"},
{file = "pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86976a1c5b25ae3f8ccae3a5306e443569ee3c3faf444dfd0f41cda24667ad57"},
- {file = "pandas-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b8661b0238a69d7aafe156b7fa86c44b881387509653fdf857bebc5e4008ad42"},
{file = "pandas-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:37e0aced3e8f539eccf2e099f65cdb9c8aa85109b0be6e93e2baff94264bdc6f"},
{file = "pandas-2.2.3-cp310-cp310-win_amd64.whl", hash = "sha256:56534ce0746a58afaf7942ba4863e0ef81c9c50d3f0ae93e9497d6a41a057645"},
{file = "pandas-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:66108071e1b935240e74525006034333f98bcdb87ea116de573a6a0dccb6c039"},
{file = "pandas-2.2.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7c2875855b0ff77b2a64a0365e24455d9990730d6431b9e0ee18ad8acee13dbd"},
- {file = "pandas-2.2.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd8d0c3be0515c12fed0bdbae072551c8b54b7192c7b1fda0ba56059a0179698"},
{file = "pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c124333816c3a9b03fbeef3a9f230ba9a737e9e5bb4060aa2107a86cc0a497fc"},
- {file = "pandas-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:63cc132e40a2e084cf01adf0775b15ac515ba905d7dcca47e9a251819c575ef3"},
{file = "pandas-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:29401dbfa9ad77319367d36940cd8a0b3a11aba16063e39632d98b0e931ddf32"},
{file = "pandas-2.2.3-cp311-cp311-win_amd64.whl", hash = "sha256:3fc6873a41186404dad67245896a6e440baacc92f5b716ccd1bc9ed2995ab2c5"},
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-
[[package]]
name = "typing-extensions"
version = "4.12.2"
@@ -4629,22 +4674,6 @@ files = [
[package.extras]
dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
-[[package]]
-name = "urllib3"
-version = "1.26.20"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
-files = [
- {file = "urllib3-1.26.20-py2.py3-none-any.whl", hash = "sha256:0ed14ccfbf1c30a9072c7ca157e4319b70d65f623e91e7b32fadb2853431016e"},
- {file = "urllib3-1.26.20.tar.gz", hash = "sha256:40c2dc0c681e47eb8f90e7e27bf6ff7df2e677421fd46756da1161c39ca70d32"},
-]
-
-[package.extras]
-brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
-
[[package]]
name = "urllib3"
version = "2.2.3"
@@ -4662,6 +4691,23 @@ h2 = ["h2 (>=4,<5)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
+[[package]]
+name = "vcrpy"
+version = "4.3.0"
+description = "Automatically mock your HTTP interactions to simplify and speed up testing"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "vcrpy-4.3.0-py2.py3-none-any.whl", hash = "sha256:8fbd4be412e8a7f35f623dd61034e6380a1c8dbd0edf6e87277a3289f6e98093"},
+ {file = "vcrpy-4.3.0.tar.gz", hash = "sha256:49c270ce67e826dba027d83e20d25b67a5885487697e97bca6dbdf53d750a0ac"},
+]
+
+[package.dependencies]
+PyYAML = "*"
+six = ">=1.5"
+wrapt = "*"
+yarl = "*"
+
[[package]]
name = "vcrpy"
version = "6.0.2"
@@ -4675,10 +4721,7 @@ files = [
[package.dependencies]
PyYAML = "*"
-urllib3 = [
- {version = "<2", markers = "platform_python_implementation == \"PyPy\" or python_version < \"3.10\""},
- {version = "*", markers = "platform_python_implementation != \"PyPy\" and python_version >= \"3.10\""},
-]
+urllib3 = {version = "*", markers = "platform_python_implementation != \"PyPy\" and python_version >= \"3.10\""}
wrapt = "*"
yarl = "*"
@@ -4979,4 +5022,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "da2bb8e1aa0cdc1fd625160145acf9441b78e03b8e3298304f49dfcb3ad17161"
+content-hash = "8263b9b9697f3251c51de95c653f97704713cef0c14dbc4f537dbdc8a901fd12"
diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml
index e05fd98080b36..e0e2d5326ce97 100644
--- a/libs/langchain/pyproject.toml
+++ b/libs/langchain/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain"
-version = "0.3.13"
+version = "0.3.14"
description = "Building applications with LLMs through composability"
authors = []
license = "MIT"
@@ -33,7 +33,7 @@ langchain-server = "langchain.server:main"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
-langchain-core = "^0.3.26"
+langchain-core = "^0.3.29"
langchain-text-splitters = "^0.3.3"
langsmith = ">=0.1.17,<0.3"
pydantic = "^2.7.4"
@@ -99,6 +99,7 @@ pytest-mock = "^3.10.0"
pytest-socket = "^0.6.0"
syrupy = "^4.0.2"
requests-mock = "^1.11.0"
+pytest-xdist = "^3.6.1"
[[tool.poetry.group.test.dependencies.cffi]]
version = "<1.17.1"
python = "<3.10"
diff --git a/libs/langchain/tests/unit_tests/test_dependencies.py b/libs/langchain/tests/unit_tests/test_dependencies.py
index 664e313e0f5aa..285daa620173f 100644
--- a/libs/langchain/tests/unit_tests/test_dependencies.py
+++ b/libs/langchain/tests/unit_tests/test_dependencies.py
@@ -91,6 +91,7 @@ def test_test_group_dependencies(poetry_conf: Mapping[str, Any]) -> None:
"pytest-mock",
"pytest-socket",
"pytest-watcher",
+ "pytest-xdist",
"responses",
"syrupy",
"requests-mock",
diff --git a/libs/packages.yml b/libs/packages.yml
index 810e54c5a857b..ce75cf8672cb0 100644
--- a/libs/packages.yml
+++ b/libs/packages.yml
@@ -1,4 +1,4 @@
-# this file is used to definet he packages that are used in the project
+# this file is used to define the packages that are used in the project
# it is EXPERIMENTAL and may be removed in the future
packages:
@@ -270,7 +270,7 @@ packages:
downloads_updated_at: '2024-12-23T20:10:11.816059+00:00'
- name: langchain-box
path: libs/box
- repo: langchain-ai/langchain-box
+ repo: box-community/langchain-box
downloads: 2749
downloads_updated_at: '2024-12-23T20:10:11.816059+00:00'
- name: langchain-tests
@@ -308,3 +308,24 @@ packages:
repo: crate/langchain-cratedb
downloads: 362
downloads_updated_at: '2024-12-23T20:53:27.001852+00:00'
+- name: langchain-modelscope
+ path: .
+ repo: modelscope/langchain-modelscope
+ downloads: 0
+- name: langchain-falkordb
+ path: .
+ repo: kingtroga/langchain-falkordb
+ downloads: 610
+ downloads_updated_at: '2025-01-02T20:23:02.544257+00:00'
+- name: langchain-dappier
+ path: .
+ repo: DappierAI/langchain-dappier
+ downloads: 0
+- name: langchain-pull-md
+ path: .
+ repo: chigwell/langchain-pull-md
+ downloads: 0
+- name: langchain-kuzu
+ path: .
+ repo: kuzudb/langchain-kuzu
+ downloads: 0
diff --git a/libs/partners/anthropic/Makefile b/libs/partners/anthropic/Makefile
index 0bab6dfe077fd..cdb61e6081453 100644
--- a/libs/partners/anthropic/Makefile
+++ b/libs/partners/anthropic/Makefile
@@ -7,7 +7,10 @@ all: help
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest -vvv --timeout 30 --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest -vvv --timeout 30 $(TEST_FILE)
test_watch:
diff --git a/libs/partners/anthropic/poetry.lock b/libs/partners/anthropic/poetry.lock
index 3186e11cc9e42..2729e80cf9c2f 100644
--- a/libs/partners/anthropic/poetry.lock
+++ b/libs/partners/anthropic/poetry.lock
@@ -1343,4 +1343,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "7d087b04b53259355c5e0ebef8b777f0534849bcead105a0de3eb284461019c3"
+content-hash = "c22d86d391e97dc8befa33fbd7ca674410eb318e2bef08e28284449b2a57aedf"
diff --git a/libs/partners/anthropic/pyproject.toml b/libs/partners/anthropic/pyproject.toml
index adf12952fb639..8224f59585a67 100644
--- a/libs/partners/anthropic/pyproject.toml
+++ b/libs/partners/anthropic/pyproject.toml
@@ -66,6 +66,7 @@ pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
defusedxml = "^0.7.1"
pytest-timeout = "^2.3.1"
+pytest-socket = "^0.7.0"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
diff --git a/libs/partners/box/README.md b/libs/partners/box/README.md
deleted file mode 100644
index d2a21ac8ce910..0000000000000
--- a/libs/partners/box/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-This package has moved!
-
-https://github.com/langchain-ai/langchain-box/tree/main/libs/box
diff --git a/libs/partners/chroma/Makefile b/libs/partners/chroma/Makefile
index 3f635f57498c3..7818baa86158d 100644
--- a/libs/partners/chroma/Makefile
+++ b/libs/partners/chroma/Makefile
@@ -7,7 +7,10 @@ all: help
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
diff --git a/libs/partners/chroma/poetry.lock b/libs/partners/chroma/poetry.lock
index f4b4192f448a0..11ea60f0e50b0 100644
--- a/libs/partners/chroma/poetry.lock
+++ b/libs/partners/chroma/poetry.lock
@@ -1941,6 +1941,20 @@ pytest = ">=6.2.5"
[package.extras]
dev = ["pre-commit", "pytest-asyncio", "tox"]
+[[package]]
+name = "pytest-socket"
+version = "0.7.0"
+description = "Pytest Plugin to disable socket calls during tests"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+ {file = "pytest_socket-0.7.0-py3-none-any.whl", hash = "sha256:7e0f4642177d55d317bbd58fc68c6bd9048d6eadb2d46a89307fa9221336ce45"},
+ {file = "pytest_socket-0.7.0.tar.gz", hash = "sha256:71ab048cbbcb085c15a4423b73b619a8b35d6a307f46f78ea46be51b1b7e11b3"},
+]
+
+[package.dependencies]
+pytest = ">=6.2.5"
+
[[package]]
name = "pytest-watcher"
version = "0.3.5"
@@ -2824,4 +2838,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4"
-content-hash = "2d6bc4b9a18a322c326c3f7d5786c4b196a997458e6d2ca4043cb6b7a4a123b3"
+content-hash = "24ab2f4c5777fdd73a46207600a2ec66b401acae4fb1e3be28a3a4097160ca03"
diff --git a/libs/partners/chroma/pyproject.toml b/libs/partners/chroma/pyproject.toml
index 8a7e18bdf1bf3..156de25d4e275 100644
--- a/libs/partners/chroma/pyproject.toml
+++ b/libs/partners/chroma/pyproject.toml
@@ -80,7 +80,11 @@ syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
# hack to make sure py3.9 compatible versionof onnxruntime is installed for testing
-onnxruntime = [{version = "<1.20", python = "<3.10"}, {version = "*", python = ">=3.10"}]
+onnxruntime = [
+ { version = "<1.20", python = "<3.10" },
+ { version = "*", python = ">=3.10" },
+]
+pytest-socket = "^0.7.0"
[[tool.poetry.group.test.dependencies.langchain-core]]
path = "../../core"
develop = true
@@ -94,15 +98,22 @@ python = "<3.9"
path = "../../standard-tests"
develop = true
+
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
+
[tool.poetry.group.test_integration.dependencies]
+
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
# hack to make sure py3.9 compatible versionof onnxruntime is installed for testing
-onnxruntime = [{version = "<1.20", python = "<3.10"}, {version = "*", python = ">=3.10"}]
+onnxruntime = [
+ { version = "<1.20", python = "<3.10" },
+ { version = "*", python = ">=3.10" },
+]
+
[tool.poetry.group.dev.dependencies]
[[tool.poetry.group.dev.dependencies.langchain-core]]
@@ -114,6 +125,7 @@ python = ">=3.9"
version = ">=0.1.40,<0.3"
python = "<3.9"
+
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
types-requests = "^2.31.0.20240406"
diff --git a/libs/partners/fireworks/Makefile b/libs/partners/fireworks/Makefile
index 4b2ef400f1c11..e1c8e80ac0b32 100644
--- a/libs/partners/fireworks/Makefile
+++ b/libs/partners/fireworks/Makefile
@@ -7,7 +7,10 @@ all: help
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
diff --git a/libs/partners/fireworks/poetry.lock b/libs/partners/fireworks/poetry.lock
index 10c837ed56011..367a83b2b046e 100644
--- a/libs/partners/fireworks/poetry.lock
+++ b/libs/partners/fireworks/poetry.lock
@@ -2054,4 +2054,4 @@ propcache = ">=0.2.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "6ad4d438ed6657e39e016ae7b54a69f229d36aa33d32f3397d0098fa7fae7449"
+content-hash = "45e6234a03b287c77a8d16017ccc56237315693f46a2f4f6b0a07a6f7c7ecc87"
diff --git a/libs/partners/fireworks/pyproject.toml b/libs/partners/fireworks/pyproject.toml
index c187500417c81..6c6bf26430a70 100644
--- a/libs/partners/fireworks/pyproject.toml
+++ b/libs/partners/fireworks/pyproject.toml
@@ -62,6 +62,7 @@ pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
+pytest-socket = "^0.7.0"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
diff --git a/libs/partners/groq/Makefile b/libs/partners/groq/Makefile
index 3ab3161a837a6..710d9829ae5ba 100644
--- a/libs/partners/groq/Makefile
+++ b/libs/partners/groq/Makefile
@@ -7,7 +7,11 @@ all: help
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
-test tests integration_test integration_tests:
+
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
diff --git a/libs/partners/groq/langchain_groq/__init__.py b/libs/partners/groq/langchain_groq/__init__.py
index e1f8568f815c5..746b62ad1058a 100644
--- a/libs/partners/groq/langchain_groq/__init__.py
+++ b/libs/partners/groq/langchain_groq/__init__.py
@@ -1,3 +1,4 @@
from langchain_groq.chat_models import ChatGroq
+from langchain_groq.version import __version__
-__all__ = ["ChatGroq"]
+__all__ = ["ChatGroq", "__version__"]
diff --git a/libs/partners/groq/langchain_groq/chat_models.py b/libs/partners/groq/langchain_groq/chat_models.py
index 5868e9cc6a3a4..45e3cf792b323 100644
--- a/libs/partners/groq/langchain_groq/chat_models.py
+++ b/libs/partners/groq/langchain_groq/chat_models.py
@@ -86,6 +86,8 @@
)
from typing_extensions import Self
+from langchain_groq.version import __version__
+
class ChatGroq(BaseChatModel):
"""`Groq` Chat large language models API.
@@ -386,6 +388,10 @@ def validate_environment(self) -> Self:
if self.temperature == 0:
self.temperature = 1e-8
+ default_headers = {"User-Agent": f"langchain/{__version__}"} | dict(
+ self.default_headers or {}
+ )
+
client_params: Dict[str, Any] = {
"api_key": (
self.groq_api_key.get_secret_value() if self.groq_api_key else None
@@ -393,7 +399,7 @@ def validate_environment(self) -> Self:
"base_url": self.groq_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
- "default_headers": self.default_headers,
+ "default_headers": default_headers,
"default_query": self.default_query,
}
diff --git a/libs/partners/groq/langchain_groq/version.py b/libs/partners/groq/langchain_groq/version.py
new file mode 100644
index 0000000000000..8d54cafc7e33b
--- /dev/null
+++ b/libs/partners/groq/langchain_groq/version.py
@@ -0,0 +1,9 @@
+"""Main entrypoint into package."""
+
+from importlib import metadata
+
+try:
+ __version__ = metadata.version(__package__)
+except metadata.PackageNotFoundError:
+ # Case where package metadata is not available.
+ __version__ = ""
diff --git a/libs/partners/groq/poetry.lock b/libs/partners/groq/poetry.lock
index 3f29ec17b1d2c..68741a463e33a 100644
--- a/libs/partners/groq/poetry.lock
+++ b/libs/partners/groq/poetry.lock
@@ -13,13 +13,13 @@ files = [
[[package]]
name = "anyio"
-version = "4.7.0"
+version = "4.8.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
files = [
- {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
- {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
+ {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+ {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
]
[package.dependencies]
@@ -30,7 +30,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
trio = ["trio (>=0.26.1)"]
[[package]]
@@ -46,116 +46,103 @@ files = [
[[package]]
name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.7"
files = [
- {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4f9fc98dad6c2eaa32fc3af1417d95b5e3d08aff968df0cd320066def971f9a6"},
- {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0de7b687289d3c1b3e8660d0741874abe7888100efe14bd0f9fd7141bcbda92b"},
- {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5ed2e36c3e9b4f21dd9422f6893dec0abf2cca553af509b10cd630f878d3eb99"},
- {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:40d3ff7fc90b98c637bda91c89d51264a3dcf210cade3a2c6f838c7268d7a4ca"},
- {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1110e22af8ca26b90bd6364fe4c763329b0ebf1ee213ba32b68c73de5752323d"},
- {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:86f4e8cca779080f66ff4f191a685ced73d2f72d50216f7112185dc02b90b9b7"},
- {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7f683ddc7eedd742e2889d2bfb96d69573fde1d92fcb811979cdb7165bb9c7d3"},
- {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:27623ba66c183eca01bf9ff833875b459cad267aeeb044477fedac35e19ba907"},
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+ {file = "orjson-3.10.13.tar.gz", hash = "sha256:eb9bfb14ab8f68d9d9492d4817ae497788a15fd7da72e14dfabc289c3bb088ec"},
]
[[package]]
@@ -1129,13 +1123,13 @@ files = [
[[package]]
name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
- {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+ {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+ {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
]
[package.extras]
@@ -1189,4 +1183,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "a22fc552b6be33c63b9d1b9661463046e8d8af121aaa1415e5f119cb07d87f7f"
+content-hash = "67cfd541db15dfbe8284cc5d29cc26e3ba17f90f10a2d41caef28fb804667c64"
diff --git a/libs/partners/groq/pyproject.toml b/libs/partners/groq/pyproject.toml
index b211b572f1d5c..12fbf4b2e8ff4 100644
--- a/libs/partners/groq/pyproject.toml
+++ b/libs/partners/groq/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-groq"
-version = "0.2.2"
+version = "0.2.3"
description = "An integration package connecting Groq and LangChain"
authors = []
readme = "README.md"
@@ -20,7 +20,7 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
-langchain-core = "^0.3.27"
+langchain-core = "^0.3.29"
groq = ">=0.4.1,<1"
[tool.ruff.lint]
diff --git a/libs/partners/groq/tests/unit_tests/test_imports.py b/libs/partners/groq/tests/unit_tests/test_imports.py
index 264ec51dcfff7..eba04950b16de 100644
--- a/libs/partners/groq/tests/unit_tests/test_imports.py
+++ b/libs/partners/groq/tests/unit_tests/test_imports.py
@@ -1,6 +1,6 @@
from langchain_groq import __all__
-EXPECTED_ALL = ["ChatGroq"]
+EXPECTED_ALL = ["ChatGroq", "__version__"]
def test_all_imports() -> None:
diff --git a/libs/partners/huggingface/Makefile b/libs/partners/huggingface/Makefile
index 62ab3e92260b4..bb5a8018b95d5 100644
--- a/libs/partners/huggingface/Makefile
+++ b/libs/partners/huggingface/Makefile
@@ -8,7 +8,10 @@ TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
diff --git a/libs/partners/huggingface/poetry.lock b/libs/partners/huggingface/poetry.lock
index 245d3723c28ca..755eb2003a02c 100644
--- a/libs/partners/huggingface/poetry.lock
+++ b/libs/partners/huggingface/poetry.lock
@@ -2411,6 +2411,20 @@ pytest = ">=7.0.0"
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
+[[package]]
+name = "pytest-socket"
+version = "0.7.0"
+description = "Pytest Plugin to disable socket calls during tests"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+ {file = "pytest_socket-0.7.0-py3-none-any.whl", hash = "sha256:7e0f4642177d55d317bbd58fc68c6bd9048d6eadb2d46a89307fa9221336ce45"},
+ {file = "pytest_socket-0.7.0.tar.gz", hash = "sha256:71ab048cbbcb085c15a4423b73b619a8b35d6a307f46f78ea46be51b1b7e11b3"},
+]
+
+[package.dependencies]
+pytest = ">=6.2.5"
+
[[package]]
name = "pytest-watcher"
version = "0.3.5"
@@ -3875,4 +3889,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "2d468da765374084b6ad008d1b3e6d4d12f3c1d696cea84f2245f49012691272"
+content-hash = "8ca5060b227b1c9ab5f16ea660bb5868a79e9233f93a850b6ffddc5ac7b5e81c"
diff --git a/libs/partners/huggingface/pyproject.toml b/libs/partners/huggingface/pyproject.toml
index c725ed90afe79..493c8907413e7 100644
--- a/libs/partners/huggingface/pyproject.toml
+++ b/libs/partners/huggingface/pyproject.toml
@@ -59,6 +59,7 @@ optional = true
pytest = "^7.3.0"
pytest-asyncio = "^0.21.1"
pytest-watcher = "^0.3.4"
+pytest-socket = "^0.7.0"
[[tool.poetry.group.test.dependencies.scipy]]
version = "^1"
diff --git a/libs/partners/pinecone/Makefile b/libs/partners/pinecone/Makefile
index d2f1c9265f7bd..048ea097895fc 100644
--- a/libs/partners/pinecone/Makefile
+++ b/libs/partners/pinecone/Makefile
@@ -7,7 +7,10 @@ all: help
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
diff --git a/libs/partners/pinecone/langchain_pinecone/embeddings.py b/libs/partners/pinecone/langchain_pinecone/embeddings.py
index d3cdd73fe7d9b..f7f9e361deb57 100644
--- a/libs/partners/pinecone/langchain_pinecone/embeddings.py
+++ b/libs/partners/pinecone/langchain_pinecone/embeddings.py
@@ -106,7 +106,6 @@ def validate_environment(self) -> Self:
self._client = client
# Ensure async_client is lazily initialized
- _ = self.async_client
return self
def _get_batch_iterator(self, texts: List[str]) -> Iterable:
diff --git a/libs/partners/pinecone/poetry.lock b/libs/partners/pinecone/poetry.lock
index 3122fd088d064..7bc0bc1d060bf 100644
--- a/libs/partners/pinecone/poetry.lock
+++ b/libs/partners/pinecone/poetry.lock
@@ -98,13 +98,13 @@ speedups = ["Brotli", "aiodns", "brotlicffi"]
[[package]]
name = "aiosignal"
-version = "1.3.1"
+version = "1.3.2"
description = "aiosignal: a list of registered asynchronous callbacks"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
files = [
- {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
- {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
+ {file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5"},
+ {file = "aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54"},
]
[package.dependencies]
@@ -123,24 +123,24 @@ files = [
[[package]]
name = "anyio"
-version = "4.6.2.post1"
+version = "4.8.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
files = [
- {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
- {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
+ {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+ {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
trio = ["trio (>=0.26.1)"]
[[package]]
@@ -156,146 +156,133 @@ files = [
[[package]]
name = "attrs"
-version = "24.2.0"
+version = "24.3.0"
description = "Classes Without Boilerplate"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
files = [
- {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
- {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
+ {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
+ {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
]
[package.extras]
benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
[[package]]
name = "certifi"
-version = "2024.8.30"
+version = "2024.12.14"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
- {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
- {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
+ {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
+ {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
]
[[package]]
name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.7"
files = [
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description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
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packaging = ">=23.2,<25"
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description = "An integration package connecting OpenAI and LangChain"
optional = false
python-versions = ">=3.9,<4.0"
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type = "directory"
url = "../openai"
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+ {file = "pydantic_core-2.27.2.tar.gz", hash = "sha256:eb026e5a4c1fee05726072337ff51d1efb6f59090b7da90d30ea58625b1ffb39"},
]
[package.dependencies]
@@ -1346,13 +1365,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
[[package]]
name = "pytest"
-version = "7.4.4"
+version = "8.3.4"
description = "pytest: simple powerful testing with Python"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
files = [
- {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
- {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
+ {file = "pytest-8.3.4-py3-none-any.whl", hash = "sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6"},
+ {file = "pytest-8.3.4.tar.gz", hash = "sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761"},
]
[package.dependencies]
@@ -1360,29 +1379,29 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
iniconfig = "*"
packaging = "*"
-pluggy = ">=0.12,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
[package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
[[package]]
name = "pytest-asyncio"
-version = "0.21.2"
+version = "0.25.1"
description = "Pytest support for asyncio"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
files = [
- {file = "pytest_asyncio-0.21.2-py3-none-any.whl", hash = "sha256:ab664c88bb7998f711d8039cacd4884da6430886ae8bbd4eded552ed2004f16b"},
- {file = "pytest_asyncio-0.21.2.tar.gz", hash = "sha256:d67738fc232b94b326b9d060750beb16e0074210b98dd8b58a5239fa2a154f45"},
+ {file = "pytest_asyncio-0.25.1-py3-none-any.whl", hash = "sha256:c84878849ec63ff2ca509423616e071ef9cd8cc93c053aa33b5b8fb70a990671"},
+ {file = "pytest_asyncio-0.25.1.tar.gz", hash = "sha256:79be8a72384b0c917677e00daa711e07db15259f4d23203c59012bcd989d4aee"},
]
[package.dependencies]
-pytest = ">=7.0.0"
+pytest = ">=8.2,<9"
[package.extras]
-docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
-testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
+docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1)"]
+testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
[[package]]
name = "pytest-mock"
@@ -1401,6 +1420,20 @@ pytest = ">=6.2.5"
[package.extras]
dev = ["pre-commit", "pytest-asyncio", "tox"]
+[[package]]
+name = "pytest-socket"
+version = "0.7.0"
+description = "Pytest Plugin to disable socket calls during tests"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+ {file = "pytest_socket-0.7.0-py3-none-any.whl", hash = "sha256:7e0f4642177d55d317bbd58fc68c6bd9048d6eadb2d46a89307fa9221336ce45"},
+ {file = "pytest_socket-0.7.0.tar.gz", hash = "sha256:71ab048cbbcb085c15a4423b73b619a8b35d6a307f46f78ea46be51b1b7e11b3"},
+]
+
+[package.dependencies]
+pytest = ">=6.2.5"
+
[[package]]
name = "pytest-watcher"
version = "0.3.5"
@@ -1767,13 +1800,13 @@ files = [
[[package]]
name = "six"
-version = "1.16.0"
+version = "1.17.0"
description = "Python 2 and 3 compatibility utilities"
optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
files = [
- {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
- {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+ {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
+ {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
]
[[package]]
@@ -1938,13 +1971,13 @@ files = [
[[package]]
name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
- {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+ {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+ {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
]
[package.extras]
@@ -2094,4 +2127,4 @@ propcache = ">=0.2.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "b19080a915cafa4786cc8238c11e89b05e496912674d8915d22fe1fe85604b22"
+content-hash = "c6bfaf9d0fdb71fc6aa80ee07d08a558c6ae414481e47f1feb56edcbd0d16bb1"
diff --git a/libs/partners/pinecone/pyproject.toml b/libs/partners/pinecone/pyproject.toml
index 7c33a4502f861..53c80ac9f00bc 100644
--- a/libs/partners/pinecone/pyproject.toml
+++ b/libs/partners/pinecone/pyproject.toml
@@ -1,5 +1,5 @@
[build-system]
-requires = [ "poetry-core>=1.0.0",]
+requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
@@ -20,21 +20,26 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
-langchain-core = "^0.3.21"
+langchain-core = "^0.3.29"
pinecone = "^5.4.0"
aiohttp = ">=3.9.5,<3.10"
numpy = ">=1.26.0,<2.0.0"
+langchain-tests = "^0.3.7"
[tool.ruff.lint]
-select = [ "E", "F", "I", "T201",]
+select = ["E", "F", "I", "T201"]
[tool.coverage.run]
-omit = [ "tests/*",]
+omit = ["tests/*"]
[tool.pytest.ini_options]
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
-markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them",]
+markers = [
+ "requires: mark tests as requiring a specific library",
+ "compile: mark placeholder test used to compile integration tests without running them",
+]
asyncio_mode = "auto"
+asyncio_default_fixture_loop_scope = "function"
[tool.poetry.group.test]
optional = true
@@ -52,12 +57,13 @@ optional = true
optional = true
[tool.poetry.group.test.dependencies]
-pytest = "^7.3.0"
+pytest = "^8"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
-pytest-asyncio = "^0.21.1"
+pytest-asyncio = ">=0.25.0,<1"
+pytest-socket = "^0.7.0"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
diff --git a/libs/partners/pinecone/tests/integration_tests/test_embeddings.py b/libs/partners/pinecone/tests/integration_tests/test_embeddings.py
index 56c9cd3e45e6f..1d2bb7a0fd88e 100644
--- a/libs/partners/pinecone/tests/integration_tests/test_embeddings.py
+++ b/libs/partners/pinecone/tests/integration_tests/test_embeddings.py
@@ -1,19 +1,24 @@
import time
+from typing import AsyncGenerator
import pytest
from langchain_core.documents import Document
from pinecone import Pinecone, ServerlessSpec # type: ignore
from langchain_pinecone import PineconeEmbeddings, PineconeVectorStore
+from tests.integration_tests.test_vectorstores import DEFAULT_SLEEP
DIMENSION = 1024
INDEX_NAME = "langchain-pinecone-embeddings"
MODEL = "multilingual-e5-large"
+NAMESPACE_NAME = "test_namespace"
-@pytest.fixture()
-def embd_client() -> PineconeEmbeddings:
- return PineconeEmbeddings(model=MODEL)
+@pytest.fixture(scope="function")
+async def embd_client() -> AsyncGenerator[PineconeEmbeddings, None]:
+ client = PineconeEmbeddings(model=MODEL)
+ yield client
+ await client.async_client.close()
@pytest.fixture
@@ -44,6 +49,7 @@ def test_embed_query(embd_client: PineconeEmbeddings) -> None:
assert len(out) == DIMENSION
+@pytest.mark.asyncio
async def test_aembed_query(embd_client: PineconeEmbeddings) -> None:
out = await embd_client.aembed_query("Hello, world!")
assert isinstance(out, list)
@@ -57,6 +63,7 @@ def test_embed_documents(embd_client: PineconeEmbeddings) -> None:
assert len(out[0]) == DIMENSION
+@pytest.mark.asyncio
async def test_aembed_documents(embd_client: PineconeEmbeddings) -> None:
out = await embd_client.aembed_documents(["Hello, world!", "This is a test."])
assert isinstance(out, list)
@@ -68,7 +75,10 @@ def test_vector_store(
embd_client: PineconeEmbeddings, pc_index: Pinecone.Index
) -> None:
vectorstore = PineconeVectorStore(index_name=INDEX_NAME, embedding=embd_client)
- vectorstore.add_documents([Document("Hello, world!"), Document("This is a test.")])
- time.sleep(5)
- resp = vectorstore.similarity_search(query="hello")
+ vectorstore.add_documents(
+ [Document("Hello, world!"), Document("This is a test.")],
+ namespace=NAMESPACE_NAME,
+ )
+ time.sleep(DEFAULT_SLEEP) # Increase wait time to ensure indexing is complete
+ resp = vectorstore.similarity_search(query="hello", namespace=NAMESPACE_NAME)
assert len(resp) == 2
diff --git a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py
index 184825a8174a8..1d19c2ef8cc23 100644
--- a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py
+++ b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py
@@ -8,7 +8,8 @@
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings # type: ignore[import-not-found]
-from pinecone import PodSpec
+from langchain_tests.integration_tests.vectorstores import VectorStoreIntegrationTests
+from pinecone import ServerlessSpec
from pytest_mock import MockerFixture # type: ignore[import-not-found]
from langchain_pinecone import PineconeVectorStore
@@ -20,52 +21,46 @@
DEFAULT_SLEEP = 20
-class TestPinecone:
+class TestPinecone(VectorStoreIntegrationTests):
index: "pinecone.Index"
+ pc: "pinecone.Pinecone"
@classmethod
- def setup_class(cls) -> None:
+ def setup_class(self) -> None:
import pinecone
client = pinecone.Pinecone(api_key=os.environ["PINECONE_API_KEY"])
index_list = client.list_indexes()
- for i in index_list:
- if i["name"] == INDEX_NAME:
- client.delete_index(INDEX_NAME)
- break
- if len(index_list) > 0:
- time.sleep(DEFAULT_SLEEP) # prevent race with creation
+ if INDEX_NAME in [
+ i["name"] for i in index_list
+ ]: # change to list comprehension
+ client.delete_index(INDEX_NAME)
+ time.sleep(DEFAULT_SLEEP) # prevent race with subsequent creation
client.create_index(
name=INDEX_NAME,
dimension=DIMENSION,
metric="cosine",
- spec=PodSpec(environment="gcp-starter"),
+ spec=ServerlessSpec(cloud="aws", region="us-west-2"),
)
- cls.index = client.Index(INDEX_NAME)
-
- # insure the index is empty
- index_stats = cls.index.describe_index_stats()
- assert index_stats["dimension"] == DIMENSION
- if index_stats["namespaces"].get(NAMESPACE_NAME) is not None:
- assert index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == 0
+ self.index = client.Index(INDEX_NAME)
+ self.pc = client
@classmethod
- def teardown_class(cls) -> None:
- index_stats = cls.index.describe_index_stats()
- for _namespace_name in index_stats["namespaces"].keys():
- cls.index.delete(delete_all=True, namespace=_namespace_name)
+ def teardown_class(self) -> None:
+ self.pc.delete_index()
@pytest.fixture(autouse=True)
def setup(self) -> None:
# delete all the vectors in the index
print("called") # noqa: T201
- try:
- self.index.delete(delete_all=True, namespace=NAMESPACE_NAME)
- time.sleep(DEFAULT_SLEEP) # prevent race condition with previous step
- except Exception:
- # if namespace not found
- pass
+ index_stats = self.index.describe_index_stats()
+ if index_stats["total_vector_count"] > 0:
+ try:
+ self.index.delete(delete_all=True, namespace=NAMESPACE_NAME)
+ except Exception:
+ # if namespace not found
+ pass
@pytest.fixture
def embedding_openai(self) -> OpenAIEmbeddings:
diff --git a/libs/partners/pinecone/tests/unit_tests/test_embeddings.py b/libs/partners/pinecone/tests/unit_tests/test_embeddings.py
index 924b4e79662ad..57a59f787133b 100644
--- a/libs/partners/pinecone/tests/unit_tests/test_embeddings.py
+++ b/libs/partners/pinecone/tests/unit_tests/test_embeddings.py
@@ -1,4 +1,10 @@
+from typing import Any, Type
+from unittest.mock import patch
+
+import aiohttp
+import pytest
from langchain_core.utils import convert_to_secret_str
+from langchain_tests.unit_tests.embeddings import EmbeddingsTests
from langchain_pinecone import PineconeEmbeddings
@@ -6,23 +12,72 @@
MODEL_NAME = "multilingual-e5-large"
-def test_default_config() -> None:
- e = PineconeEmbeddings(
- pinecone_api_key=API_KEY, # type: ignore[call-arg]
- model=MODEL_NAME,
- )
- assert e.batch_size == 96
+@pytest.fixture(autouse=True)
+def mock_pinecone() -> Any:
+ """Mock Pinecone client for all tests."""
+ with patch("langchain_pinecone.embeddings.PineconeClient") as mock:
+ yield mock
+
+
+class TestPineconeEmbeddingsStandard(EmbeddingsTests):
+ """Standard LangChain embeddings tests."""
+
+ @property
+ def embeddings_class(self) -> Type[PineconeEmbeddings]:
+ """Get the class under test."""
+ return PineconeEmbeddings
+
+ @property
+ def embedding_model_params(self) -> dict:
+ """Get the parameters for initializing the embeddings model."""
+ return {
+ "model": MODEL_NAME,
+ "pinecone_api_key": API_KEY,
+ }
+
+
+class TestPineconeEmbeddingsConfig:
+ """Additional configuration tests for PineconeEmbeddings."""
+
+ def test_default_config(self) -> None:
+ """Test default configuration is set correctly."""
+ embeddings = PineconeEmbeddings(model=MODEL_NAME, pinecone_api_key=API_KEY) # type: ignore
+ assert embeddings.batch_size == 96
+ assert embeddings.query_params == {"input_type": "query", "truncation": "END"}
+ assert embeddings.document_params == {
+ "input_type": "passage",
+ "truncation": "END",
+ }
+ assert embeddings.dimension == 1024
+ def test_custom_config(self) -> None:
+ """Test custom configuration overrides defaults."""
+ embeddings = PineconeEmbeddings(
+ model=MODEL_NAME,
+ api_key=API_KEY,
+ batch_size=128,
+ query_params={"custom": "param"},
+ document_params={"other": "param"},
+ )
+ assert embeddings.batch_size == 128
+ assert embeddings.query_params == {"custom": "param"}
+ assert embeddings.document_params == {"other": "param"}
-def test_default_config_with_api_key() -> None:
- e = PineconeEmbeddings(api_key=API_KEY, model=MODEL_NAME)
- assert e.batch_size == 96
+ @pytest.mark.asyncio
+ async def test_async_client_initialization(self) -> None:
+ """Test async client is initialized correctly and only when needed."""
+ embeddings = PineconeEmbeddings(model=MODEL_NAME, api_key=API_KEY)
+ assert embeddings._async_client is None
+ # Access async_client property
+ client = embeddings.async_client
+ assert client is not None
+ assert isinstance(client, aiohttp.ClientSession)
-def test_custom_config() -> None:
- e = PineconeEmbeddings(
- pinecone_api_key=API_KEY, # type: ignore[call-arg]
- model=MODEL_NAME,
- batch_size=128,
- )
- assert e.batch_size == 128
+ # Ensure headers are set correctly
+ expected_headers = {
+ "Api-Key": API_KEY.get_secret_value(),
+ "Content-Type": "application/json",
+ "X-Pinecone-API-Version": "2024-10",
+ }
+ assert client._default_headers == expected_headers
diff --git a/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py b/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py
index 36f4c7d3a210f..4be0445539270 100644
--- a/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py
+++ b/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py
@@ -1,6 +1,6 @@
from unittest.mock import Mock
-from langchain_pinecone.vectorstores import Pinecone, PineconeVectorStore
+from langchain_pinecone.vectorstores import PineconeVectorStore
def test_initialization() -> None:
@@ -9,7 +9,7 @@ def test_initialization() -> None:
index = Mock()
embedding = Mock()
text_key = "xyz"
- Pinecone(index, embedding, text_key)
+ PineconeVectorStore(index, embedding, text_key)
def test_id_prefix() -> None:
diff --git a/libs/partners/qdrant/Makefile b/libs/partners/qdrant/Makefile
index ffab929526bd5..5dfd343da44ad 100644
--- a/libs/partners/qdrant/Makefile
+++ b/libs/partners/qdrant/Makefile
@@ -8,7 +8,10 @@ TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
diff --git a/libs/partners/qdrant/poetry.lock b/libs/partners/qdrant/poetry.lock
index 41fd237e2b117..2a2935582cdd5 100644
--- a/libs/partners/qdrant/poetry.lock
+++ b/libs/partners/qdrant/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 = "annotated-types"
@@ -1556,6 +1556,20 @@ pytest = ">=6.2.5"
[package.extras]
dev = ["pre-commit", "pytest-asyncio", "tox"]
+[[package]]
+name = "pytest-socket"
+version = "0.7.0"
+description = "Pytest Plugin to disable socket calls during tests"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+ {file = "pytest_socket-0.7.0-py3-none-any.whl", hash = "sha256:7e0f4642177d55d317bbd58fc68c6bd9048d6eadb2d46a89307fa9221336ce45"},
+ {file = "pytest_socket-0.7.0.tar.gz", hash = "sha256:71ab048cbbcb085c15a4423b73b619a8b35d6a307f46f78ea46be51b1b7e11b3"},
+]
+
+[package.dependencies]
+pytest = ">=6.2.5"
+
[[package]]
name = "pytest-watcher"
version = "0.3.5"
@@ -2141,4 +2155,4 @@ fastembed = ["fastembed"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4"
-content-hash = "2a325d2b9028b0f32aa3cc7a15c22fe32568452e8c91cfc5b68642faed09a013"
+content-hash = "d3da6307476f136c84041e9256868c1e571bffb612216d0e962734c6005407a1"
diff --git a/libs/partners/qdrant/pyproject.toml b/libs/partners/qdrant/pyproject.toml
index 13e9dc8236bd0..20f950c1e09d3 100644
--- a/libs/partners/qdrant/pyproject.toml
+++ b/libs/partners/qdrant/pyproject.toml
@@ -66,6 +66,7 @@ syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
requests = "^2.31.0"
+pytest-socket = "^0.7.0"
[[tool.poetry.group.test.dependencies.langchain-core]]
path = "../../core"
develop = true
diff --git a/libs/partners/voyageai/Makefile b/libs/partners/voyageai/Makefile
index 0d2d194ce0449..4c8bb2bc0fe91 100644
--- a/libs/partners/voyageai/Makefile
+++ b/libs/partners/voyageai/Makefile
@@ -7,7 +7,10 @@ all: help
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
diff --git a/libs/partners/voyageai/poetry.lock b/libs/partners/voyageai/poetry.lock
index 8acd96b3ed1b4..d3fb3cee78097 100644
--- a/libs/partners/voyageai/poetry.lock
+++ b/libs/partners/voyageai/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"
@@ -676,7 +676,7 @@ files = [
[[package]]
name = "langchain-core"
-version = "0.3.25"
+version = "0.3.29"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@@ -1418,6 +1418,20 @@ pytest = ">=6.2.5"
[package.extras]
dev = ["pre-commit", "pytest-asyncio", "tox"]
+[[package]]
+name = "pytest-socket"
+version = "0.7.0"
+description = "Pytest Plugin to disable socket calls during tests"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+ {file = "pytest_socket-0.7.0-py3-none-any.whl", hash = "sha256:7e0f4642177d55d317bbd58fc68c6bd9048d6eadb2d46a89307fa9221336ce45"},
+ {file = "pytest_socket-0.7.0.tar.gz", hash = "sha256:71ab048cbbcb085c15a4423b73b619a8b35d6a307f46f78ea46be51b1b7e11b3"},
+]
+
+[package.dependencies]
+pytest = ">=6.2.5"
+
[[package]]
name = "pytest-watcher"
version = "0.3.5"
@@ -1905,4 +1919,4 @@ propcache = ">=0.2.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "e554f41a0c54d62654ba52624e92749d684ac6ef6483b5488c2c8810009e6fa9"
+content-hash = "c124f82eb6590af74422548d8261e425486f206e818e5848a5c43b523e601c30"
diff --git a/libs/partners/voyageai/pyproject.toml b/libs/partners/voyageai/pyproject.toml
index f68f16826dd54..a891839f90ce0 100644
--- a/libs/partners/voyageai/pyproject.toml
+++ b/libs/partners/voyageai/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-voyageai"
-version = "0.1.3"
+version = "0.1.4"
description = "An integration package connecting VoyageAI and LangChain"
authors = []
readme = "README.md"
@@ -20,7 +20,7 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
-langchain-core = "^0.3.15"
+langchain-core = "^0.3.29"
voyageai = ">=0.3.2,<1"
pydantic = ">=2,<3"
@@ -57,6 +57,7 @@ pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
+pytest-socket = "^0.7.0"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1.24.0"
python = "<3.12"
diff --git a/libs/partners/xai/Makefile b/libs/partners/xai/Makefile
index 1626a01bc493c..963563e2d7099 100644
--- a/libs/partners/xai/Makefile
+++ b/libs/partners/xai/Makefile
@@ -8,7 +8,10 @@ TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
-test tests integration_test integration_tests:
+test tests:
+ poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+
+integration_test integration_tests:
poetry run pytest $(TEST_FILE)
######################
diff --git a/libs/partners/xai/poetry.lock b/libs/partners/xai/poetry.lock
index 294cdfa338637..7b605b8b14bf4 100644
--- a/libs/partners/xai/poetry.lock
+++ b/libs/partners/xai/poetry.lock
@@ -2122,4 +2122,4 @@ propcache = ">=0.2.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "5f922370eb32d79a26e769e13b0cee1f09d6caa7fb4f61f43e77ee0eb0d61dad"
+content-hash = "ab1d2f38bf7404b05ad63b5ebfabd149d65760bbc0ee96c2d2f539912ac8f8ad"
diff --git a/libs/partners/xai/pyproject.toml b/libs/partners/xai/pyproject.toml
index a6872c3913bb3..7b3f776cb0a30 100644
--- a/libs/partners/xai/pyproject.toml
+++ b/libs/partners/xai/pyproject.toml
@@ -75,12 +75,15 @@ docarray = "^0.32.1"
langchain-openai = { path = "../openai", develop = true }
langchain-core = { path = "../../core", develop = true }
langchain-tests = { path = "../../standard-tests", develop = true }
+pytest-socket = "^0.7.0"
+
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
+
[tool.poetry.group.test_integration.dependencies]
[[tool.poetry.group.test_integration.dependencies.numpy]]
version = "^1"
@@ -91,15 +94,18 @@ version = "^1.26.0"
python = ">=3.12"
+
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
+
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
types-requests = "^2"
langchain-core = { path = "../../core", develop = true }
+
[tool.poetry.group.dev.dependencies]
langchain-core = { path = "../../core", develop = true }
diff --git a/libs/text-splitters/Makefile b/libs/text-splitters/Makefile
index f806ab5f97b1f..c5fcfecc05709 100644
--- a/libs/text-splitters/Makefile
+++ b/libs/text-splitters/Makefile
@@ -7,7 +7,7 @@ all: help
TEST_FILE ?= tests/unit_tests/
test tests:
- poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+ poetry run pytest -n auto --disable-socket --allow-unix-socket $(TEST_FILE)
integration_test integration_tests:
poetry run pytest tests/integration_tests/
diff --git a/libs/text-splitters/poetry.lock b/libs/text-splitters/poetry.lock
index b85cb94d34b02..6819ef0f632ce 100644
--- a/libs/text-splitters/poetry.lock
+++ b/libs/text-splitters/poetry.lock
@@ -666,6 +666,20 @@ files = [
[package.extras]
test = ["pytest (>=6)"]
+[[package]]
+name = "execnet"
+version = "2.1.1"
+description = "execnet: rapid multi-Python deployment"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
+ {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
+]
+
+[package.extras]
+testing = ["hatch", "pre-commit", "pytest", "tox"]
+
[[package]]
name = "executing"
version = "2.1.0"
@@ -1412,7 +1426,7 @@ files = [
[[package]]
name = "langchain-core"
-version = "0.3.26"
+version = "0.3.29"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@@ -2870,6 +2884,26 @@ files = [
tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""}
watchdog = ">=2.0.0"
+[[package]]
+name = "pytest-xdist"
+version = "3.6.1"
+description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pytest_xdist-3.6.1-py3-none-any.whl", hash = "sha256:9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7"},
+ {file = "pytest_xdist-3.6.1.tar.gz", hash = "sha256:ead156a4db231eec769737f57668ef58a2084a34b2e55c4a8fa20d861107300d"},
+]
+
+[package.dependencies]
+execnet = ">=2.1"
+pytest = ">=7.0.0"
+
+[package.extras]
+psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
+testing = ["filelock"]
+
[[package]]
name = "python-dateutil"
version = "2.9.0.post0"
@@ -4768,4 +4802,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "a756bff9e65722e2109c6187b057060fdc601074bca0f22035a9f3c4d63b04e2"
+content-hash = "cdc4edb327b2a72ce35cda46d12dcd77403aa32ddf210e6ae96b32d98deba9b3"
diff --git a/libs/text-splitters/pyproject.toml b/libs/text-splitters/pyproject.toml
index 683d00b9ceb1e..0c2fbf00afec2 100644
--- a/libs/text-splitters/pyproject.toml
+++ b/libs/text-splitters/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-text-splitters"
-version = "0.3.4"
+version = "0.3.5"
description = "LangChain text splitting utilities"
authors = []
license = "MIT"
@@ -36,7 +36,7 @@ ignore_missing_imports = "True"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
-langchain-core = "^0.3.26"
+langchain-core = "^0.3.29"
[tool.ruff.lint]
select = ["E", "F", "I", "T201", "D"]
@@ -75,6 +75,7 @@ convention = "google"
ruff = "^0.5"
+
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
lxml-stubs = "^0.5.1"
@@ -82,11 +83,13 @@ types-requests = "^2.31.0.20240218"
tiktoken = "^0.8.0"
+
[tool.poetry.group.dev.dependencies]
jupyter = "^1.0.0"
+
[tool.poetry.group.test.dependencies]
pytest = "^8"
freezegun = "^1.2.2"
@@ -94,6 +97,8 @@ pytest-mock = "^3.10.0"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
pytest-socket = "^0.7.0"
+pytest-xdist = "^3.6.1"
+
[tool.poetry.group.test_integration]
@@ -106,16 +111,19 @@ transformers = "^4.47.0"
sentence-transformers = { version = ">=2.6.0", python = "<3.13" }
+
[tool.poetry.group.lint.dependencies.langchain-core]
path = "../core"
develop = true
+
[tool.poetry.group.dev.dependencies.langchain-core]
path = "../core"
develop = true
+
[tool.poetry.group.test.dependencies.langchain-core]
path = "../core"
develop = true