关于
This project provides a Node.js implementation for performing vector search operations using LanceDB, leveraging Ollama's embedding model for generating vector embeddings. It showcases how to connect to a LanceDB database, create custom embedding functions, perform vector similarity searches against stored documents, and process/display search results. The setup allows for integration with Claude Desktop as an MCP service, streamlining the process of incorporating vector search capabilities into various applications.
主要功能
- Performs vector similarity search against stored documents
- Connects to LanceDB for vector storage and retrieval
- Includes example script for testing vector search functionality
- Utilizes Ollama for custom text embedding generation
- Integrates with Claude Desktop as an MCP service
使用案例
- Implementing semantic search in Node.js applications
- Building retrieval-augmented generation (RAG) systems
- Enhancing search capabilities with vector embeddings
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