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MCP Server with FAISS for RAG

@ProbonoBonobo

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基本信息

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记忆与知识

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stdio

作者

ProbonoBonobo

关于 MCP Server with FAISS for RAG

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#mathgpt#math-solver#math-assistant

MCP Server with FAISS for RAG 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "sui-mcp-server": {
      "command": "pipx",
      "args": [
        "ensurepath"
      ]
    }
  }
}

MCP Server with FAISS for RAG 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Server with FAISS for RAG?

A proof-of-concept Machine Conversation Protocol (MCP) server that allows an AI agent to query a FAISS vector database and retrieve relevant documents for Retrieval-Augmented Generation (RAG). It integrates FastAPI, FAISS, GitHub Move file extraction, and LLM support, targeting developers building RAG workflows.

How to use MCP Server with FAISS for RAG?

Install with pipx (pipx install -e .) or manually (pip install -r requirements.txt). Then use CLI commands like mcp-download, mcp-search-index, mcp-index, mcp-query, mcp-rag, and mcp-server. Optionally set GITHUB_TOKEN and OPENAI_API_KEY in .env. Start the server with mcp-server or python main.py, then query via MCP API at /mcp/action.

Key features of MCP Server with FAISS for RAG

  • FastAPI server with MCP endpoints
  • FAISS vector database integration
  • Document chunking and embedding
  • GitHub Move file extraction and processing
  • LLM integration for complete RAG workflow
  • Simple client example and sample documents

Use cases of MCP Server with FAISS for RAG

  • Querying Sui Move documentation using a vector database
  • Building a custom RAG pipeline for Move language development
  • Indexing and retrieving GitHub-sourced Move files
  • Running an MCP server for AI agents to perform retrieval-augmented generation

常见问题

1

What are the runtime requirements for MCP Server with FAISS for RAG?

The server requires Python 3 and dependencies listed in `requirements.txt`, including FAISS, FastAPI, and an LLM provider (OpenAI or others). A GitHub token is optional for higher API rate limits.

2

How does the RAG pipeline work in MCP Server with FAISS for RAG?

1. User submits a question. 2. System retrieves relevant documents from the FAISS vector database. 3. Retrieved documents are formatted into a context prompt. 4. The prompt is sent to an LLM for an enhanced answer. 5. The LLM’s response is returned to the user.

3

Where are the indexed documents stored?

The FAISS index is saved to `data/faiss_index.bin` by default, and the downloaded Move files go into `docs/move_files/`. You can customize the index path and document directory with command-line options.

4

How can I extend MCP Server with FAISS for RAG?

Add authentication and security, support more document types, integrate with other LLM providers, improve Move parsing, and add monitoring/logging as described in the Extended section of the README.

5

What license is MCP Server with FAISS for RAG distributed under?

MIT license.

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