# Document retrieval

Node.js vector search implementation based on LanceDB and Ollama

This project builds a RAG-based HR chatbot. Through the MCP server as the function call center, it realizes the functions of PDF document uploading, parsing, retrieval, and natural language Q&A.

A development documentation server based on the MCP protocol, providing functions such as document crawling, local loading, precise search, and detail retrieval, to solve the document hallucination problem in AI development.

A development document server based on the MCP protocol, providing accurate framework document retrieval services and solving the API hallucination problem in AI development.

PatentSafe MCP is an MCP server used to connect to PatentSafe and retrieve documents. It supports accessing the database through Lucene queries and is suitable for MCP clients such as Claude.

mcp-rag-server is a service based on the Model Context Protocol (MCP) that supports Retrieval Augmented Generation (RAG) and can index documents and provide relevant context for large language models.

A document storage and retrieval system based on FastAPI and ChromaDB, providing API interfaces to manage collections and documents and supporting MCP service integration.

The SourceSync.ai MCP server is a service that implements the Model Context Protocol (MCP) for standardized interaction with the SourceSync.ai knowledge management platform, providing functions such as knowledge base management, content ingestion, document retrieval, and search.

This project provides two implementations of the MCP server, connecting Claude Desktop with Azure search capabilities, supporting document search and web search. You can choose to use the Azure AI Agent service or directly integrate Azure AI Search.

A simple MCP server for retrieving information from the official documentation of Langchain, Llama Index, and OpenAI, providing document search functionality.

An MCP protocol server that provides access to IETF documents for large language models

This project provides a Qdrant - based development container environment for file embedding and vector similarity search, supporting automatic indexing and retrieval of text, Markdown, and PDF files.

An MCP server based on Gemini and Vertex AI for private document search, improving search quality through data storage.

An MCP server integrating Azure AI Search Agent Service with Claude Desktop

Customized document retrieval server project

The Inkeep MCP server project is developed based on Python and manages and retrieves product document content through the RAG technology provided by the Inkeep platform.