Principles of Linked Problem Recommender System
Tavily comes with smart suggested questions in the search response through query semantic understanding and knowledge graph technology. When include_answer=True is set, the system not only returns the direct answer, but also generates 3-5 related question expansion packs. This function is based on GPT-4 and other large language models for intent recognition and rewriting of questions, for example, when searching for "quantum computing principles", it will suggest "the difference between quantum bits and classical bits" and other derived questions. This mechanism enables the AI dialog system to actively guide the topic in-depth, which is particularly effective in customer service and education applications, evolving single-round Q&A into structured knowledge transfer.
- Suggest that questions automatically adapt to the semantic field of the user's original query
- Continuously optimize the recommendation model using user click-through feedback
- Support for controlling the level of derived questions via the suggestions_depth parameter
- Deep integration with open source projects such as GPT Researcher
This answer comes from the articleTavily: Real-Time Information Search API Service for AIThe
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