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Document Indexer
Indexes and provides semantic search for local documents using a vector database and local language models.
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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.
Productivity & Workflow
Data Science & ML
Content Management
