Indexes and provides semantic search for local documents using a vector database and local language models.
Sponsored
The Document Indexer transforms your local files into a searchable knowledge base, allowing for intelligent querying and summarization directly on your machine. It acts as a Python-based Model Context Protocol (MCP) server, automatically monitoring designated folders for new or modified documents. Leveraging LanceDB for vector storage and Ollama for local LLM integration, it provides powerful semantic search capabilities across various file formats, ensuring your data remains private while enabling advanced document retrieval and cataloging.
Key Features
01Real-time Document Monitoring
02MCP Integration exposing search and catalog tools
03Local LLM Integration (Ollama) for summarization and keyword extraction
04Multi-format Support (PDF, Word, TXT, MD, RTF)
054 GitHub stars
06Vector Search for semantic queries using LanceDB
Use Cases
01Cataloging and summarizing local files to get an overview of indexed content.
02Searching personal or research document collections using natural language queries.
03Integrating local document search capabilities into AI assistants like Claude Desktop.
