base on 🔍 An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT) <div id="top"></div> <div align="center"> <img src="assets/logo.svg" style="width: 50%; height: auto;"> [📃 Paper](https://arxiv.org/abs/2407.20183) | [💻 Demo](https://internlm-chat.intern-ai.org.cn/) English | [简体中文](README_zh-CN.md) <https://github.com/user-attachments/assets/44ffe4b9-be26-4b93-a77b-02fed16e33fe> </div> </p> ## ✨ MindSearch: Mimicking Human Minds Elicits Deep AI Searcher ## 📅 Changelog - 2024/11/05: 🥳 MindSearch is now deployed on Puyu! 👉 [Try it](https://internlm-chat.intern-ai.org.cn/) 👈 - Refactored the agent module based on [Lagent v0.5](https://github.com/InternLM/lagent) for better performance in concurrency. - Improved the UI to embody the simultaneous multi-query search. ## ⚽️ Build Your Own MindSearch ### Step1: Dependencies Installation ```bash git clone https://github.com/InternLM/MindSearch cd MindSearch pip install -r requirements.txt ``` ### Step2: Setup Environment Variables Before setting up the API, you need to configure environment variables. Rename the `.env.example` file to `.env` and fill in the required values. ```bash mv .env.example .env # Open .env and add your keys and model configurations ``` ### Step3: Setup MindSearch API Setup FastAPI Server. ```bash python -m mindsearch.app --lang en --model_format internlm_server --search_engine DuckDuckGoSearch --asy ``` - `--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](./mindsearch/agent/models.py) - `--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. - `TencentSearch` for Tencent search api engine. Please set your Web Search engine API key as the `WEB_SEARCH_API_KEY` environment variable unless you are using `DuckDuckGo`, or `TencentSearch` that requires secret id as `TENCENT_SEARCH_SECRET_ID` and secret key as `TENCENT_SEARCH_SECRET_KEY`. - `--asy`: deploy asynchronous agents. ### Step4: Setup MindSearch Frontend Providing following frontend interfaces, - React First configurate the backend URL for Vite proxy. ```bash HOST="127.0.0.1" # modify as you need PORT=8002 sed -i -r "s/target:\s*\"\"/target: \"${HOST}:${PORT}\"/" frontend/React/vite.config.ts ``` ```bash # 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](./frontend/React/README.md) - Gradio ```bash python frontend/mindsearch_gradio.py ``` - Streamlit ```bash streamlit run frontend/mindsearch_streamlit.py ``` ## 🌐 Change Web Search API 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` - `TencentSearch` For example, to change to the Brave Search API, you would configure it as follows: ```python BingBrowser( searcher_type='BraveSearch', topk=2, api_key=os.environ.get('BRAVE_API_KEY', 'YOUR BRAVE API') ) ``` ## 🐞 Using the Backend Without Frontend 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. ```bash python backend_example.py ``` Make sure you have set up the environment variables and the backend is running before executing the script. ## 🐞 Debug Locally ```bash python -m mindsearch.terminal ``` ## 📝 License This project is released under the [Apache 2.0 license](LICENSE). ## Citation 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} } ``` ## Our Projects Explore our additional research on large language models, focusing on LLM agents. - [Lagent](https://github.com/InternLM/lagent): A lightweight framework for building LLM-based agents - [AgentFLAN](https://github.com/InternLM/Agent-FLAN): An innovative approach for constructing and training with high-quality agent datasets (ACL 2024 Findings) - [T-Eval](https://github.com/open-compass/T-Eval): A Fine-grained tool utilization evaluation benchmark (ACL 2024) ", Assign "at most 3 tags" to the expected json: {"id":"12210","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"