Provides a vector search system for document retrieval using MongoDB Atlas Vector Search and Voyage AI embeddings.
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This powerful system facilitates efficient document retrieval by leveraging vector search. It intelligently ingests and chunks markdown documents, preserving their hierarchical structure, then generates high-quality contextual embeddings using Voyage AI's API. These documents and their corresponding embeddings are stored in MongoDB with parent-child relationships, enabling advanced semantic search. The tool offers a FastMCP server, allowing seamless integration with MCP clients for comprehensive document search capabilities, and supports flexible configuration of vector dimensions and chunking strategies.
Key Features
01Ingests and chunks markdown documents with hierarchical headers
02Generates embeddings using Voyage AI's contextual embeddings API
03Stores documents and embeddings in MongoDB with parent-child relationships
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05Supports configurable vector dimensions and chunking strategies
06Provides a FastMCP server for semantic document search
Use Cases
01Performing semantic document search for information retrieval
02Integrating vector search capabilities into MCP clients like Claude Desktop
03Building knowledge bases for AI applications requiring contextual document access
