This technology introduces a client-side document embedding cache designed to enhance the responsiveness of conversational search systems. By caching document embeddings relevant to a conversation's initial topic, it enables faster and more relevant responses to successive queries, leveraging dense retrieval models for semantic understanding. This innovation not only achieves a high hit rate but also maintains the integrity of search results.
For example, when a user inquires about "weather patterns," and later asks about "rainfall in the forest," the system could quickly provides specific, relevant documents from the cache based on the semantic linkage established by the initial query, thus streamlining the search process within the conversational context.
BACKGROUND
In the development of conversational agents, the shift from keyword-based to semantically enriched searches poses new challenges, particularly with audio input from devices like smart speakers. Traditional search methods fall short in understanding and retaining the context of multi-turn conversations, leading to inefficiencies. Addressing this, this approach significantly reduces search times by utilizing a compact, efficient spatial index for nearest-neighbor similarity queries, revolutionizing conversational search with its emphasis on context and previous interactions.
Benefit
Market Application
Publications
Intellectual property status
Granted Patent
Patent number : Patent No.: US 2023/0267126 A1
Where : USA
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