Search Engine with RAG and MCP

Name: Search Engine with RAG and MCP
Author: arkeodev

@arkeodev

代码仓库

关于 Search Engine with RAG and MCP

MCP Server supported search engine

基本信息

分类

搜索

许可证

MIT

运行时

python

传输方式

stdio

发布者

arkeodev

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

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 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

FAQ from Search Engine with RAG and MCP

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.