Search Result Summaries Using LLM Caching to Cut Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search engines lack the ability to programmatically generate summaries of search results in real-time based on the relevance of search queries, leading to increased latency and user frustration due to the need for manual review of multiple search results.
Innovation Solution
A language model is used to generate summaries of search results in real-time by extracting relevant snippets and generating a summary based on input search queries, with caching for future use and optimized rendering to improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a language model generates summaries of search results in real-time, then information synthesis capability is improved, but computational resource consumption increases
Solution Approach 1:
The patent implements a caching mechanism that pre-generates and stores summaries of search results. When a search query is received, the system first checks the cache for existing summaries before invoking the language model. This preliminary action reduces the frequency of computationally expensive LLM operations while maintaining real-time summary availability for users.
Solution Approach 2:
The system selectively applies language model processing only to search results that require summarization, rather than processing all search results uniformly. The caching mechanism also stores summaries locally for frequently accessed queries, creating local quality optimization where computational resources are concentrated on high-value operations.
2Loss of information
If summaries are generated in real-time for every search query, then response relevance is improved, but latency increases
Solution Approach 1:
The caching mechanism pre-computes and stores summaries for search queries. When a user submits a search query, the system checks the cache first and returns cached summaries immediately if available, eliminating the real-time generation latency for repeated queries while maintaining response relevance.
Solution Approach 2:
The system creates and stores copies of generated summaries in a cache database. These cached copies are then served to users for subsequent similar queries, eliminating the need to regenerate summaries and reducing latency while preserving the relevance and quality of the original language model-generated content.
3Measurement precision
If multiple search results are processed to extract snippets, then summary accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts and caches snippets from search results in advance, storing them for future use. When generating summaries, the language model receives pre-extracted snippets rather than having to process full search results, maintaining summary accuracy while reducing processing time.
Solution Approach 2:
The system extracts only the essential snippet information from each search result, separating the key relevant content from the full search result text. This extraction process creates condensed input data that maintains accuracy for summary generation while significantly reducing the processing burden and time required.
Data Source
AI summary
Technology is disclosed for programmatically generating a summary by a language model of search results based on the corresponding relevance of the search results to an input search query to a search engine. A user inputs a search query into a search engine and the search engine determines and ranks a set of search results based on the relevance of each of the search results. A snippet of information is determined for the most relevant search results to the input search query. The snippets of information are used to generate an input prompt to a language model with an instruction to generate a summary of the snippets of information based on the input search query. The generated summary is provided in response to the user to the search query and/or is cached in order to provide the generated summary in response to similar search queries.