MindSearch: A New AI Search Framework from Shanghai AI Lab Aims to RevolutionizeInformation Retrieval
Shanghai, China – The Shanghai Artificial Intelligence Laboratory (SAIL) has unveiled a groundbreaking new AI search framework called MindSearch. Designed to tackle complex information retrieval challenges, MindSearch leverages the power of large language models(LLMs) and multi-agent systems to deliver more comprehensive and accurate search results than traditional search engines.
MindSearch is built upon the InternLM2.5 7B dialogue model, allowing it to process information from hundreds of web pages within minutes, a task that would typically take humans hours. Its unique approach, inspired by human cognitive processes, involves a multi-agent framework thatsimulates human thinking, prioritizing planning before executing searches. This strategy significantly enhances the accuracy and completeness of the retrieved information.
Key Features of MindSearch:
- Complex Query Processing: MindSearch breaks down complex user queries into smaller,manageable sub-problems, enabling more precise searches for relevant information.
- Dynamic Graph Construction: The framework constructs a directed acyclic graph (DAG) to mimic the human problem-solving process, progressively refining queries and exploring potential solutions.
- Parallel Information Retrieval: Utilizing a multi-agent architecture, MindSearchenables parallel searches for multiple sub-problems, boosting the speed and efficiency of information retrieval.
- Hierarchical Retrieval Strategy: MindSearch employs a hierarchical retrieval strategy, starting with a broad information gathering phase followed by a deeper analysis of the most valuable pages to extract relevant information.
- Context Management: Themulti-agent system effectively manages contextual information, ensuring the coherence and completeness of information throughout the retrieval and integration process.
- Response Generation: MindSearch synthesizes the retrieved information to generate accurate, comprehensive, and insightful responses to the original complex query.
- Performance Enhancement: Through its innovative features,MindSearch significantly improves the quality, depth, and breadth of answers in both closed-set and open-set question-answering tasks.
- Human Preference Alignment: The generated responses are designed to align with human preferences, making MindSearch’s answers more appealing to human evaluators compared to other AI searchengines.
Technical Underpinnings of MindSearch:
- WebPlanner: This advanced planner decomposes user queries into multiple sub-problems and uses dynamic graph construction (DAG) to simulate a multi-step information-seeking mental model.
- WebSearcher: WebSearcher executes hierarchical information retrieval, retrieving and aggregating valuable information from the internet based on the sub-problems assigned by WebPlanner.
- Multi-Agent Collaboration: WebPlanner and WebSearcher act as independent agents, handling problem decomposition and information retrieval tasks, respectively, enabling parallel processing and effective information integration.
- Dynamic Graph Construction:Through code generation and execution, MindSearch dynamically constructs a logical graph representing the problem-solving process, allowing the LLM to progressively refine queries and retrieve relevant information.
- Context Management: Effective context state transfer between agents ensures that crucial information is not lost during information retrieval and integration.
Applications ofMindSearch:
- Academic Research: Researchers can utilize MindSearch to quickly gather and organize vast amounts of literature, supporting their research endeavors.
- Market Analysis: Businesses can leverage MindSearch to collect market data, analyze competitor information, and monitor industry trends.
- News Reporting: Journalists can employ MindSearch to gather background information on news events and rapidly write reports.
- Legal Research: Legal professionals can use MindSearch to collect relevant legal provisions, cases, and precedents, aiding in legal analysis and case preparation.
- Technical Support: Technical support teams can utilize MindSearch to swiftly find solutions and steps forresolving technical issues.
Open Source and Availability:
MindSearch is fully open-source, allowing users to freely experience and deploy it locally. The project’s website, online demo, GitHub repository, and technical paper are publicly available, providing access to its code, documentation, and research findings.
TheFuture of Information Retrieval:
MindSearch represents a significant leap forward in AI-powered search technology. Its ability to understand and respond to complex queries, combined with its human-inspired approach, has the potential to revolutionize how we access and process information. As the framework continues to evolve, it promises to become an invaluabletool for researchers, businesses, and individuals seeking comprehensive and accurate information in a rapidly evolving digital landscape.
【source】https://ai-bot.cn/mindsearch/
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