MindSearch, as a pioneering AI search system, is committed to tackling complex web information retrieval and integration challenges by simulating human thinking patterns. Its unique design concept and advanced technical architecture endow it with a series of notable features that together form its powerful functional system.
Firstly, MindSearch adopts the innovative design of a multi-agent framework. This framework is primarily composed of two types of agents, namely WebPlanner and WebSearcher. WebPlanner plays a crucial role in overall planning within the system, acting like an intelligent navigator that exercises macro control and planning over search tasks. WebSearcher, on the other hand, focuses on the specific search work, serving as the executor of information retrieval, responsible for finding information that meets the requirements in the vast ocean of the web.
Secondly, graphical reasoning is another key feature of MindSearch. It can cleverly decompose complex problems into the form of a directed acyclic graph. In this way, the system can gradually construct a solution to the problem, just like building a complex structure, starting from the basic framework and step by step perfecting the entire structure, making the problem-solving process clearer and more organized.
Moreover, code-driven planning is also a highlight of MindSearch. WebPlanner generates Python code to manipulate the graphical structure, a process that fully utilizes the powerful programming capabilities of large language models. With the precision and logic of code, the system can handle various complex tasks more efficiently, achieving fine-tuning and optimization of the graphical structure to better serve the goals of information retrieval and integration.
Additionally, a hierarchical retrieval strategy is well applied in MindSearch. WebSearcher adopts a coarse-to-fine search method, a strategy that effectively deals with the vast amount of web content. Like using a sieve to filter items, it first filters out a large amount of irrelevant information from a broader perspective, and then gradually refines the filtering conditions to accurately find the required information, improving the efficiency and accuracy of information retrieval.
Finally, MindSearch achieves effective context management through its multi-agent design. When dealing with long text information, this design can better manage the context environment, avoiding the loss and confusion of information, thereby improving the overall processing efficiency of the system.
A large number of experimental results have shown that MindSearch has achieved remarkable performance improvements in both closed and open-ended question answering tasks. Compared with existing systems such as ChatGPT and Perplexity.ai, it can provide users with more in-depth, extensive, and accurate answers, bringing a better search experience and information acquisition effect.