LangConnect
Provides a Streamlit-based Retrieval-Augmented Generation (RAG) client with vector search, comprehensive document management, Supabase authentication, and Model Context Protocol (MCP) integration for AI assistants.
About
LangConnect is a comprehensive Retrieval-Augmented Generation (RAG) client application built with Streamlit, offering an intuitive interface for managing documents and performing advanced vector searches. It integrates with a FastAPI-based backend that leverages PostgreSQL with pgvector for efficient storage and similarity search, and provides secure user management via Supabase Authentication. Beyond its core RAG capabilities, LangConnect features multi-format document support, semantic, keyword, and hybrid search options, and integrates with the Model Context Protocol (MCP) server, enabling AI assistants like Claude to programmatically interact with your document collections for enhanced contextual understanding.
Key Features
- Multi-format document support (PDF, TXT, MD, DOCX) and batch upload.
- Advanced vector search (semantic, keyword, hybrid) with metadata filtering.
- Secure user management via Supabase Authentication.
- Streamlit Web Interface for RAG system management.
- Model Context Protocol (MCP) Server integration for AI assistants.
- 50 GitHub stars
Use Cases
- Managing and searching large collections of internal or external documents efficiently.
- Integrating AI assistants with private document repositories for enhanced knowledge retrieval and contextual understanding.
- Building custom RAG applications for document question-answering systems.
