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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