Dynamic Schema Integration for LLM Query Token Limits
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Solution Overview
Problem
Existing systems struggle to effectively integrate dataset schemas with natural language prompts to generate queries, leading to inefficiencies in query generation and result accuracy.
Innovation Solution
A system that integrates dataset schemas with natural language prompts by generating feature vectors for both and using a Large Language Model (LLM) to generate queries based on similarity criteria and semantic relationships, ensuring accurate and relevant query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dataset schemas are integrated with natural language prompts to generate queries, then query accuracy and relevance are improved, but system complexity increases
Solution Approach 1:
The system segments the complex query generation process into distinct modules: a schema integration module that compares feature vectors of dataset schemas with natural language prompts, a filtering module that selects relevant schemas based on similarity criteria, and a query generation module that uses the selected schemas to generate accurate queries. This segmentation manages complexity by breaking down the integration task into manageable, specialized components.
Solution Approach 2:
The system introduces feature vectors as an intermediary representation between dataset schemas and natural language prompts. By converting both schemas and prompts into comparable feature vector formats, the system enables meaningful comparison and integration without directly combining complex structured schemas with unstructured text, thereby managing complexity while improving query accuracy.
2Measurement precision
If multiple dataset schemas are integrated with natural language prompts, then query relevance is improved, but processing time increases
Solution Approach 1:
The system applies partial action by selecting only a subset of dataset schemas that meet the similarity criteria rather than integrating all available schemas. The filtering mechanism identifies and processes only the most relevant schemas based on feature vector comparison, reducing processing time while maintaining query relevance through selective integration of pertinent schema information.
Solution Approach 2:
The system changes the parameter of schema selection from including all schemas to including only those that satisfy similarity criteria. By adjusting the selection parameters based on feature vector similarity thresholds, the system optimizes the balance between query relevance (benefiting from multiple relevant schemas) and processing time (reduced by excluding irrelevant schemas).
Data Source
AI summary
Techniques for integrating one or more dataset schemas with a natural language prompt to generate a query for obtaining results to the natural language prompt are disclosed. In some embodiments, a method comprises the following: receiving user input comprising a natural language prompt; generating an instruction for a Large Language Model (LLM) to generate a query, wherein the instruction specifies the natural language prompt and a first subset of dataset schemas; submitting the instruction to the LLM, wherein the LLM generates the query based on the instruction; receiving the query from the LLM, wherein the query is based on and directed to the first subset of dataset schemas; executing the query on the data repository to generate a set of one or more results based on the first subset of dataset schemas; and storing the set of one or more results in response to the natural language prompt.