Natural Language Query Construction for Dynamic Block Schemas
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Solution Overview
Problem
AI models face high compute load and model drift when querying large, evolving database schemas, limiting their ability to handle ad hoc, free-form questions and user-modifiable queries.
Innovation Solution
A query engine that automatically discovers schemas in dynamic, block-based structures, using natural language input to construct queries and present results via user-modifiable ephemeral views, minimizing compute resources by referencing schema definitions rather than copying data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are used to query large, evolving database schemas, then the ability to handle ad hoc questions is improved, but compute load and model drift increase
Solution Approach 1:
The system segments the database schema into multiple tables with defined relationships, allowing the AI model to query only relevant portions of the schema rather than processing the entire schema structure. This reduces compute load while maintaining the ability to handle ad hoc questions about specific data domains.
Solution Approach 2:
The system performs preliminary schema validation and structure verification before executing AI-generated queries. By pre-processing and validating the schema structure, the system reduces the computational burden on the AI model during query execution and minimizes model drift caused by schema changes.
2Adaptability or versatility
If AI models query evolving database schemas, then query flexibility is improved, but model drift increases
Solution Approach 1:
The system implements feedback mechanisms that monitor schema changes and automatically update the AI model's understanding of the database structure. When schema changes are detected, the system feeds this information back to the model, allowing it to adapt to evolving schemas while maintaining query flexibility and reducing model drift.
Solution Approach 2:
The system dynamically adjusts query parameters and schema representation based on detected changes in the database structure. By changing parameters such as table relationships, data types, and query constraints in response to schema evolution, the system maintains model reliability while preserving query flexibility.
3Measurement precision
If schema definitions are copied for querying, then query accuracy is improved, but compute resources increase
Solution Approach 1:
The system creates lightweight copies of only the necessary schema definition elements required for each specific query, rather than copying entire schema structures. This selective copying approach maintains query accuracy by providing the AI model with relevant schema information while minimizing the computational resources consumed during query processing.
Data Source
AI summary
A multimodal content management system having a block-based data structure can include a query engine configured to perform automatic data discovery for natural language queries. The system can generate and render, at a computing device, a page comprising a graphical user interface (GUI) with a displayable item from a first block of a block-based data structure. The system can generate and bind, to the page, a schema definition comprising a first reference to the first block and a second reference to a set of blocks, wherein the first block is relationally linked to the set of blocks via the second reference. The system can use at least a portion of a natural language prompt, received at the GUI, to generate an input feature for a large language model, the input feature having a schema-question pair that includes at least a portion of the schema definition. The large language model can generate a query configured to operate on the block-based data structure.