User Feedback Querying With LLM Summaries and Vector Embeddings
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Solution Overview
Problem
Conventional approaches struggle with generating real-time insights from large-scale user feedback data, often leading to inaccuracy due to reliance on summaries or samplings, and are limited by SQL queries and filters, which do not allow for detailed exploration of feedback data.
Innovation Solution
A method involving embedding user feedback and summaries into a vector space, using a Large Language Model (LLM) to generate natural-language summaries, and querying these embeddings for real-time, detailed insights through natural-language queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional SQL queries and filters are used on large databases, then the system can handle large volumes of feedback data, but real-time insights cannot be generated and the analysis is limited to available filters
Solution Approach 1:
The patent pre-generates and stores natural language summaries of feedback data along with their vector embeddings before queries are executed. This preliminary action allows the system to quickly retrieve and analyze pre-processed summaries rather than querying raw data in real-time, thus achieving both real-time performance and detailed insights
Solution Approach 2:
The patent introduces vector embeddings as an intermediary representation between the raw feedback data and the natural language queries. These embeddings enable semantic similarity search and allow the system to interpret and query feedback data using natural language without being constrained by traditional SQL filters, achieving both speed and analytical depth
2Productivity
If summaries or samplings of feedback data are used, then query speed improves, but accuracy of insights deteriorates due to loss of detailed information
Solution Approach 1:
The patent creates vector embedding copies of the feedback data that preserve the semantic meaning and key information. These embedding representations serve as efficient proxies that maintain the essential characteristics of the original data, enabling fast querying without sacrificing accuracy in the insights generated
Solution Approach 2:
The system pre-computes and stores natural language summaries with their vector embeddings in advance. When queries are executed, the system retrieves these pre-processed summaries rather than processing raw data, achieving both speed and accuracy by having the essential information ready beforehand
3Loss of information
If detailed exploration of feedback data is enabled through natural language queries, then insight quality improves, but system complexity increases
Solution Approach 1:
The patent replaces the mechanical SQL query system with a natural language processing approach using vector embeddings and semantic similarity search. This substitution allows users to query feedback data using intuitive natural language rather than learning complex SQL syntax and filter systems, improving accessibility while maintaining detailed analysis capabilities
Solution Approach 2:
The patent changes the parameter space from traditional database filters to vector embedding dimensions. By transforming the query mechanism from filter-based to similarity-based search in embedding space, the system enables detailed exploration through natural language while managing complexity through the use of pre-computed embeddings
Data Source
AI summary
In one embodiment, a method includes accessing a set of user feedback, each user feedback in the set including natural-language feedback. The method further includes embedding each user feedback in the set into a vector embedding space; generating, by an LLM and based on the set of user feedback, a number of natural language summaries, each natural language summary corresponding to at least some of the user feedback in the set; and embedding each natural language summary in the vector embedding space. The method further includes receiving a query including a request for user-feedback information; embedding the query in the vector embedding space; and returning a query response that includes one or more natural-language summaries generated by an LLM, based on a similarity between the embedded query and one or more of (1) the embedded natural language summaries and (2) the embedded user feedback.