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.
Sponsored
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
01Multi-format document support (PDF, TXT, MD, DOCX) and batch upload.
02Advanced vector search (semantic, keyword, hybrid) with metadata filtering.
03Secure user management via Supabase Authentication.
04Streamlit Web Interface for RAG system management.
05Model Context Protocol (MCP) Server integration for AI assistants.
0650 GitHub stars
Use Cases
01Managing and searching large collections of internal or external documents efficiently.
02Integrating AI assistants with private document repositories for enhanced knowledge retrieval and contextual understanding.
03Building custom RAG applications for document question-answering systems.