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## Description This PR adds documentation for the new ZeusDB vector store integration with LangChain. ## Motivation ZeusDB is a high-performance vector database (Python/Rust backend) designed for AI applications that need fast similarity search and real-time vector ops. This integration brings ZeusDB's capabilities to the LangChain ecosystem, giving developers another production-oriented option for vector storage and retrieval. **Key Features:** - **User-Friendly Python API**: Intuitive interface that integrates seamlessly with Python ML workflows - **High Performance**: Powered by a robust Rust backend for lightning-fast vector operations - **Enterprise Logging**: Comprehensive logging capabilities for monitoring and debugging production systems - **Advanced Features**: Includes product quantization and persistence capabilities - **AI-Optimized**: Purpose-built for modern AI applications and RAG pipelines ## Changes - Added provider documentation: `docs/docs/integrations/providers/zeusdb.mdx` (installation, setup). - Added vector store documentation: `docs/docs/integrations/vectorstores/zeusdb.ipynb` (quickstart for creating/querying a ZeusDBVectorStore). - Registered langchain-zeusdb in `libs/packages.yml` for discovery. ## Target users - AI/ML engineers building RAG pipelines - Data scientists working with large document collections - Developers needing high-throughput vector search - Teams requiring near real-time vector operations ## Testing - Followed LangChain's "How to add standard tests to an integration" guidance. - Code passes format, lint, and test checks locally. - Tested with LangChain Core 0.3.74 - Works with Python 3.10 to 3.13 ## Package Information **PyPI:** https://pypi.org/project/langchain-zeusdb **Github:** https://github.com/ZeusDB/langchain-zeusdb
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.
- Chat LangChain: Ask questions & chat with our documentation.