模型上下文协议服务器

Search Engine with RAG and MCP

A powerful 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.

Features

Web search capabilities using the Exa API
Web content retrieval using FireCrawl
RAG (Retrieval-Augmented Generation) for more relevant information extraction
MCP (Model Context Protocol) server for standardized tool invocation
Support for both local LLMs via Ollama and cloud-based LLMs via OpenAI
Flexible architecture supporting direct search, agent-based search, or server mode
Comprehensive error handling and graceful fallbacks
Python 3.13+ with type hints
Asynchronous processing for efficient web operations

Architecture

This project integrates several key components:

Search Module: Uses Exa API to search the web and FireCrawl to retrieve content
RAG Module: Embeds documents, chunks them, and stores them in a FAISS vector store
MCP Server: Provides a standardized protocol for tool invocation
Agent: LangChain-based agent that uses the search and RAG capabilities

Project Structure

search-engine-with-rag-and-mcp/
├── LICENSE              # MIT License
├── README.md            # Project documentation
├── data/                # Data directories
├── docs/                # Documentation
│   └── env_template.md  # Environment variables documentation
├── logs/                # Log files directory (auto-created)
├── src/                 # Main package (source code)
│   ├── __init__.py      
│   ├── core/            # Core functionality
│   │   ├── __init__.py
│   │   ├── main.py      # Main entry point
│   │   ├── search.py    # Web search module
│   │   ├── rag.py       # RAG implementation
│   │   ├── agent.py     # LangChain agent
│   │   └── mcp_server.py # MCP server implementation
│   └── utils/           # Utility modules
│       ├── __init__.py
│       ├── env.py       # Environment variable loading
│       └── logger.py    # Logging configuration
├── pyproject.toml       # Poetry configuration
├── requirements.txt     # Project dependencies
└── tests/               # Test directory

Getting Started

Prerequisites

Python 3.13+
Poetry (optional, for development)
API keys for Exa and FireCrawl
(Optional) Ollama installed locally
(Optional) OpenAI API key

Installation

Clone the repository

git clone https://github.com/yourusername/search-engine-with-rag-and-mcp.git
cd search-engine-with-rag-and-mcp

Install dependencies

# Using pip
pip install -r requirements.txt

# Or using poetry
poetry install

Create a .env file (use docs/env_template.md as a reference)

Usage

The application has three main modes of operation:

1. Direct Search Mode (Default)

# Using pip
python -m src.core.main "your search query"

# Or using poetry
poetry run python -m src.core.main "your search query"

2. Agent Mode

python -m src.core.main --agent "your search query"

3. MCP Server Mode

python -m src.core.main --server

You can also specify custom host and port:

python -m src.core.main --server --host 0.0.0.0 --port 8080

Using Ollama (Optional)

To use Ollama for local embeddings and LLM capabilities:

Install Ollama: https://ollama.ai/
Pull a model:

ollama pull mistral:latest

Set the appropriate environment variables in your .env file:

OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=mistral:latest

Development

This project follows these best practices:

Code formatting: Black and isort for consistent code style
Type checking: mypy for static type checking
Linting: flake8 for code quality
Testing: pytest for unit and integration tests
Environment Management: python-dotenv for managing environment variables
Logging: Structured logging to both console and file

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

LangChain for the agent framework
Model Context Protocol for the standardized tool invocation
Ollama for local LLM capabilities
Exa for web search capabilities
FireCrawl for web content retrieval

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

提供工具调用的标准化协议，使AI系统能够通过与LangChain、RAG和Ollama的集成来搜索网络、检索信息并提供相关答案。

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