LLM Database Query System for Human-Readable Output
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Solution Overview
Problem
Large language models (LLMs) are limited in their ability to autonomously detect, debug, and optimize code, and they often generate results that lack human-readability and effective multimedia representation, hindering communication with users.
Innovation Solution
A system that includes a query evaluator to determine if a user question requires database queries, a query generator to identify relevant databases and generate queries, and a large language model to run queries and convert results into human-readable formats, including graphical/multimedia representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used to generate code and query results, then the capability to understand and generate human-like text is improved, but the ability to autonomously detect, debug, and optimize code deteriorates
Solution Approach 1:
The patent introduces an intermediary code analysis module between the LLM and the code execution environment. This module specializes in autonomous code detection, debugging, and optimization, allowing the LLM to maintain its text generation capabilities while the intermediary handles code quality improvement autonomously.
Solution Approach 2:
The system segments the code processing functionality into separate modules: one for generation (LLM) and another for analysis and optimization (code analysis module). This segmentation allows each component to specialize in its strength while working together as a unified system.
2Productivity
If LLMs generate code and query results, then the response capability is improved, but the human-readability and effective multimedia representation of results deteriorates
Solution Approach 1:
A results transformation module serves as an intermediary between the LLM's query results and the user interface. This module converts raw data into human-readable formats including tables, charts, and multimedia representations, bridging the gap between machine-generated output and user comprehension.
Solution Approach 2:
The system dynamically changes the presentation parameters of query results based on the type of data and user preferences. Numeric data may be transformed into statistical visualizations, text data into formatted tables, and complex structures into hierarchical displays, optimizing readability for different contexts.
Data Source
AI summary
A system for responding to questions that require database queries including: a query evaluator that trained or programmed to be capable of receiving a user input question and evaluating whether the question requires one or more database queries to answer; a query generator that trained or programmed to be capable of processing the question in order to identify at least one database from a library of databases may be queried to answer the question, and to generate a query output comprising a database schema for the at least one identified database and a query to be run on the identified databases; and a large language model trained to be capable of receiving the query output from the query generator, to run the query on the identified databases, and to generate a response; and methods relating to same.