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

Before setting up the API, you need to configure environment variables. Rename the .env.example file to .env and fill in the required values.

mv .env.example .env
# Open .env and add your keys and model configurations

Setup FastAPI Server.

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

• --lang: language of the model, en for English and cn for Chinese.

• --model_format: format of the model.

  • internlm_server for InternLM2.5-7b-chat with local server. (InternLM2.5-7b-chat has been better optimized for Chinese.)
  • gpt4 for GPT4. if you want to use other models, please modify models

• --search_engine: Search engine.

  • DuckDuckGoSearch for search engine for DuckDuckGo.
  • BingSearch for Bing search engine.
  • BraveSearch for Brave search web api engine.
  • GoogleSearch for Google Serper web search api engine.

  Please set your Web Search engine API key as the WEB_SEARCH_API_KEY environment variable unless you are using DuckDuckGo.

Providing following frontend interfaces,

• React

# Install Node.js and npm
# for Ubuntu
sudo apt install nodejs npm
# for windows
# download from https://nodejs.org/zh-cn/download/prebuilt-installer
# Install dependencies
cd frontend/React
npm install
npm start

Details can be found in React

• Gradio

python frontend/mindsearch_gradio.py

• Streamlit

streamlit run frontend/mindsearch_streamlit.py

To use a different type of web search API, modify the searcher_type attribute in the searcher_cfg located in mindsearch/agent/__init__.py. Currently supported web search APIs include:

• GoogleSearch
• DuckDuckGoSearch
• BraveSearch
• BingSearch

For example, to change to the Brave Search API, you would configure it as follows:

BingBrowser(
 searcher_type='BraveSearch',
 topk=2,
 api_key=os.environ.get('BRAVE_API_KEY', 'YOUR BRAVE API')
)

For users who prefer to interact with the backend directly, use the backend_example.py script. This script demonstrates how to send a query to the backend and process the response.

python backend_example.py

Make sure you have set up the environment variables and the backend is running before executing the script.

python -m mindsearch.terminal

This project is released under the Apache 2.0 license.

If you find this project useful in your research, please consider cite:

@article{chen2024mindsearch,
 title={MindSearch: Mimicking Human Minds Elicits Deep AI Searcher},
 author={Chen, Zehui and Liu, Kuikun and Wang, Qiuchen and Liu, Jiangning and Zhang, Wenwei and Chen, Kai and Zhao, Feng},
 journal={arXiv preprint arXiv:2407.20183},
 year={2024}
}

Explore our additional research on large language models, focusing on LLM agents.

• Lagent: A lightweight framework for building LLM-based agents
• AgentFLAN: An innovative approach for constructing and training with high-quality agent datasets (ACL 2024 Findings)
• T-Eval: A Fine-grained tool utilization evaluation benchmark (ACL 2024)