Natural Language Query Generation With Semantic Schema Validation
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Solution Overview
Problem
Conventional data systems require technical expertise for interacting with databases, limiting non-technical users' ability to extract insights, and face challenges in achieving high accuracy and precision in automated data query responses.
Innovation Solution
Utilizing semantic data and large language models to interpret natural language questions and generate structured queries, bridging the gap between user queries and database queries, and providing context-specific information to enhance accuracy and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing technologies and large language models are integrated into data systems, then ease of operation for non-technical users is improved, but device complexity increases
Solution Approach 1:
The patent introduces natural language processing technologies and large language models as intermediary components between users and the database system. These intermediaries translate user-friendly natural language queries into structured database queries, shielding non-technical users from complex SQL syntax while maintaining system functionality. The intermediary layer handles the complexity internally, allowing users to interact with the system through simple, intuitive language.
2Measurement precision
If semantic data and large language models are used to generate structured queries, then measurement precision of query responses is improved, but device complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and structuring semantic data before it needs to be queried. The system maintains pre-defined schemas, data models, and semantic relationships in advance, which are then rapidly matched against user queries. This preliminary organization of data and metadata enables precise query generation without requiring complex real-time processing, as the heavy lifting of data structuring has already been completed.
3Productivity
If automated query generation is implemented without human oversight, then productivity is improved, but reliability of query responses deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously monitors and learns from query generation outcomes. User corrections, query performance metrics, and result validation feedback are fed back into the large language model to refine its query generation capabilities. This closed-loop feedback system enables the automated system to improve its reliability over time while maintaining high productivity, as it learns from past performance without requiring constant human intervention.
Data Source
AI summary
Various embodiments described herein provide for systems, methods, devices, instructions, and like for generating a structured language data query based on a natural language question and semantic data associated with a schema of a data store (e.g., database or the like). In particular, some embodiments use a set of large language models to generate a structured language data query for a data store based on semantic data and the natural language question, determines whether the structured language data query is valid, causes the structured language data query to be performed on a data store in response to determining that the structured language data query is valid, and generating a response that comprises a query result from the data store.