mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-05 11:12:47 +00:00
⚡ Building applications with LLMs through composability ⚡
fc802d8f9f
The vectorstore feature table in the documentation was showing incorrect information for the "IDs in add Documents" capability. Most vectorstores were marked as ❌ (not supported) when they actually support extracting IDs from documents. ## Problem The issue was an inconsistency between two sources of truth: - **JavaScript feature table** (`docs/src/theme/FeatureTables.js`): Hardcoded `idsInAddDocuments: false` for most vectorstores - **Python script** (`docs/scripts/vectorstore_feat_table.py`): Correctly showed `"IDs in add Documents": True` for most vectorstores ## Root Cause All vectorstores inherit the base `VectorStore.add_documents()` method which automatically extracts document IDs: ```python # From libs/core/langchain_core/vectorstores/base.py lines 277-284 if "ids" not in kwargs: ids = [doc.id for doc in documents] # If there's at least one valid ID, we'll assume that IDs should be used. if any(ids): kwargs["ids"] = ids ``` Since no vectorstores override `add_documents()`, they all inherit this behavior and support IDs in documents. ## Solution Updated `idsInAddDocuments` from `false` to `true` for 13 vectorstores: - AstraDBVectorStore, Chroma, Clickhouse, DatabricksVectorSearch - ElasticsearchStore, FAISS, InMemoryVectorStore, MongoDBAtlasVectorSearch - PGVector, PineconeVectorStore, Redis, Weaviate, SQLServer The other 4 vectorstores (CouchbaseSearchVectorStore, Milvus, openGauss, QdrantVectorStore) were already correctly marked as `true`. ## Impact Users visiting https://python.langchain.com/docs/integrations/vectorstores/ will now see accurate information. The "IDs in add Documents" column will correctly show ✅ for all vectorstores instead of incorrectly showing ❌ for most of them. This aligns with the API documentation which states: "if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence" - clearly indicating that document IDs are supported. Fixes #30622. <!-- START COPILOT CODING AGENT TIPS --> --- 💬 Share your feedback on Copilot coding agent for the chance to win a $200 gift card! Click [here](https://survey.alchemer.com/s3/8343779/Copilot-Coding-agent) to start the survey. --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com> |
||
|---|---|---|
| .devcontainer | ||
| .github | ||
| cookbook | ||
| docs | ||
| libs | ||
| scripts | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .readthedocs.yaml | ||
| CITATION.cff | ||
| LICENSE | ||
| Makefile | ||
| MIGRATE.md | ||
| poetry.toml | ||
| pyproject.toml | ||
| README.md | ||
| SECURITY.md | ||
| uv.lock | ||
| yarn.lock | ||
Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
pip install -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external / internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- LangSmith - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- LangGraph Platform - Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.