MindSearch
M
Mindsearch
Overview :
MindSearch is a multi-agent network search engine framework based on large language models (LLM), with performance akin to Perplexity.ai Pro. Users can easily deploy their own search engines, supporting both proprietary large language models (such as GPT and Claude) and open-source large language models (like InternLM2.5-7b-chat). It features the ability to tackle any real-world problem by utilizing internet knowledge to provide in-depth and comprehensive knowledge base answers; displays detailed solution pathways to improve the credibility and usability of final responses; offers an optimized UI experience with various interface options including React, Gradio, Streamlit, and Terminal; and dynamically constructs graphs that break down user queries into atomic sub-problems within the graph, progressively expanding it based on WebSearcher results.
Target Users :
MindSearch is suitable for developers and enterprises looking to build personalized search engines, especially professionals seeking to leverage AI technology to enhance search efficiency and depth.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 159.8K
Use Cases
Developers use MindSearch to build personalized AI search engines.
Companies leverage MindSearch to improve the efficiency of their internal knowledge base searches.
Research institutions utilize MindSearch for in-depth academic resource searching.
Features
Resolve any real-world issues using online knowledge.
Deep knowledge discovery, browsing hundreds of pages to answer questions.
Detailed solution pathways enhance the credibility and usability of final responses.
Optimized UI experience with multiple user interface options.
Dynamic graph construction process that gradually expands the graph based on search results.
How to Use
1. Install dependencies: Install the required Python libraries according to the MindSearch requirements.txt file.
2. Set up the MindSearch API: Configure the API using the FastAPI server, setting the language and model formats.
3. Set up the MindSearch front end: Choose one of React, Gradio, Streamlit, or Terminal for front-end development as needed.
4. Start the server: Run the backend service of MindSearch to ensure the API responds correctly.
5. Test functionality: Enter queries through the front-end interface to test MindSearch's search and response capabilities.
6. Debug and optimize: Adjust parameters based on test results to enhance search results and user experience.
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase