Search Engine with RAG and MCP Server

@arkeodev

リポジトリ

基本情報

カテゴリ

検索

ライセンス

MIT

トランスポート

stdio

作者

arkeodev

Search Engine with RAG and MCP Server について

MCP Server supported search engine

Search Engine with RAG and MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "search-engine-with-rag-and-mcp": {
      "command": "python",
      "args": [
        "-m",
        "src.core.main",
        "your search query"
      ]
    }
  }
}

Search Engine with RAG and MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Search Engine with RAG and MCP?

Search Engine with RAG and MCP is a search engine that combines LangChain, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Ollama to create an agentic AI system capable of searching the web, retrieving information, and providing relevant answers.

How to use Search Engine with RAG and MCP?

Install dependencies with pip or Poetry, then run the application in one of three modes: direct search (python -m src.core.main "query"), agent mode (--agent), or MCP server mode (--server). Optionally configure host and port for server mode, and set up Ollama for local LLM usage.

Key features of Search Engine with RAG and MCP

Web search via Exa API and content retrieval via FireCrawl
RAG (Retrieval-Augmented Generation) for relevant information extraction
MCP server for standardized tool invocation
Support for local LLMs (Ollama) and cloud LLMs (OpenAI)
Three operation modes: direct search, agent, or server
Asynchronous processing for efficient web operations

Use cases of Search Engine with RAG and MCP

Perform direct web searches and retrieve summarized answers
Deploy an agentic AI that uses search and RAG tools independently
Run an MCP server that exposes search and retrieval tools to MCP clients
Combine local LLMs with external web data for privacy-sensitive queries

よくある質問

What runtime and dependencies are required?

Python 3.13+ is required. Dependencies include LangChain, MCP libraries, embeddings, FAISS, and API clients for Exa and FireCrawl. Optional: Ollama for local LLM, OpenAI for cloud LLM.

Where does data (embeddings, logs) live?

The project creates `data/` directories for data storage and `logs/` for log files (auto-created). FAISS vector stores and document chunks are managed locally.

What API keys are needed?

API keys for Exa and FireCrawl are mandatory. An OpenAI API key or Ollama local installation is optional depending on LLM choice.

How do I switch between local and cloud LLMs?

Set the appropriate environment variables in `.env`. For Ollama, set `OLLAMA_BASE_URL` and `OLLAMA_MODEL`; for OpenAI, set `OPENAI_API_KEY`.

What transports and auth does the MCP server use?

The MCP server listens on a configurable host and port (default: likely `localhost:8000`). No authentication mechanism is mentioned in the README.

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