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7.30._small.mp4
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 andcn
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 usingDuckDuckGo
.
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.