MindSearch Deep Search

What does this agent do, and when should you use it?

MindSearch is an LLM-based multi-agent web-search framework positioned for experiences like Perplexity.ai Pro and SearchGPT. It runs as a FastAPI service and can launch asynchronous agents with --asy. The backend can use an InternLM local server or GPT4-format model and supports DuckDuckGo, Bing, Brave, Google Serper, and Tencent Search. The project offers React, Gradio, and Streamlit frontends; the updated UI is intended to show simultaneous multi-query search. backend_example.py can send queries to a running backend and process its responses directly.

After copying .env.example to .env and filling in model and search settings, run python -m mindsearch.app --lang en --model_format internlm_server --search_engine DuckDuckGoSearch --asy to start the FastAPI service. The service selects English or Chinese model settings through --lang, chooses internlm_server or gpt4 format through --model_format, and chooses its web provider through --search_engine. Search providers other than DuckDuckGo generally require WEB_SEARCH_API_KEY; Tencent Search requires TENCENT_SEARCH_SECRET_ID and TENCENT_SEARCH_SECRET_KEY. Queries can be submitted through the React, Gradio, or Streamlit frontends, or by running backend_example.py against the backend and processing the returned response. To change the web-search API, edit searcher_cfg in mindsearch/agent/__init__.py and set its searcher_type.

1. A team that needs to self-host a multi-agent web-search service for English or Chinese queries.
2. A developer running an InternLM2.5-7b-chat local server who wants to connect it to a web-search workflow.
3. An application developer with Brave, Bing, Google Serper, or Tencent Search credentials who needs to switch search providers.
4. A frontend team that wants a React interface showing several searches happening at the same time.
5. An engineer integrating a running FastAPI backend into a custom client using backend_example.py.

How do you install or deploy this agent?

git clone https://github.com/InternLM/MindSearch
cd MindSearch
pip install -r requirements.txt
mv .env.example .env

Edit .env with the required model and search configuration. For a search service other than DuckDuckGo, set WEB_SEARCH_API_KEY; for Tencent Search, set TENCENT_SEARCH_SECRET_ID and TENCENT_SEARCH_SECRET_KEY. The React frontend additionally requires Node.js and npm; run npm install in frontend/React.

How do you use this agent?

Start the backend:

python -m mindsearch.app --lang en --model_format internlm_server --search_engine DuckDuckGoSearch --asy

You can change --lang to cn, use gpt4 as the model format, or select BingSearch, BraveSearch, GoogleSearch, or TencentSearch. Once the backend is running, execute python backend_example.py to send a query. Optional frontends are python frontend/mindsearch_gradio.py, streamlit run frontend/mindsearch_streamlit.py, or npm start in frontend/React after configuring the Vite proxy. For local debugging, run python -m mindsearch.terminal.

What are this agent's strengths and limitations?

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Recent releases

Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.

1. v0.1.0 2024-11-05
   Release notes excerpt

   An open-source AI Search Engine Framework with Perplexity.ai Pro performance Technical Report: https://arxiv.org/abs/2407.20183 Search engines provide extensive and the latest web information but often fail to tailor search results to align with complex human intentions. Inspired by the remarkable progress of Large Language Models (LLMs), recent works enhance search engines with LLMs. However, these methods still obtain unsatisfying performance due to three critical problems: …

   View on GitHub ↗

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