@positive666
基本情報
A lightweight, pure web search solution for large language models, supporting multi-engine aggregated search, deep reflection and result evaluation. A balanced approach between web search and deep research, providing a framework-free implementation and mcp server for easy develo
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": {
"deep_search_lightning": {
"command": "python",
"args": [
"test_demo.py"
]
}
}
}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。
Deep Search Lighting is a lightweight, pure web search solution for large language models that supports multi-engine aggregated search, deep reflection, and result evaluation. It is designed for developers needing a framework-free, easy-to-integrate search capability across models of any size.
Install dependencies via pip install -r requirements.txt, rename .env.examples to .env, and fill in your model API information (OpenAI‑style APIs). Run the test case with python test_demo.py, launch the Streamlit demo with streamlit run streamlit_app.py, or start the built‑in MCP server with python mcp_server.py and connect the client via python langgraph_mcp_client.py.
It is framework‑free, works with free APIs, supports models of any size, and includes a reflection mechanism for self‑evaluation, all while keeping implementation simple without web parsing or text chunking.
Python 3.11 and packages listed in `requirements.txt`; optionally install `requirements_langchain.txt` for LangChain support.
Baidu (free, no key), DuckDuckGo (free but needs VPN), Bocha (requires API key), and Tavily (requires registration key). Baidu is enabled by default.
Set up your `.env` file with model API details, then run `python mcp_server.py` and `python langgraph_mcp_client.py`.
No explicit cost or performance limit is stated, but the project emphasizes using free APIs for most engines and supporting adjustable depth to trade speed versus result quality.
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