Ephemeral Query Views for Evolving Database Schemas
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Solution Overview
Problem
Conventional AI tools struggle to autonomously comprehend and navigate structured environments and accurately retrieve information from databases with evolving schemas, leading to high compute load and model drift due to changes in database structures.
Innovation Solution
A query engine that automatically discovers schemas in evolving, block-based structures, constructs queries based on natural language input, and generates ephemeral views, using reference-based schema definitions to minimize compute resources and adapt to schema changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional AI tools are used to query databases with evolving schemas, then the system can handle natural language prompts, but the compute load increases and model drift occurs due to schema changes
Solution Approach 1:
The schema is segmented into multiple ephemeral views that are generated on-demand based on user questions. Instead of loading the entire schema into memory, the system divides it into smaller, context-relevant portions that are created temporarily for each query operation, reducing overall memory consumption and compute load.
Solution Approach 2:
The system implements dynamic schema representation through ephemeral views that are created and destroyed as needed. The schema structure adapts dynamically to user questions by generating relevant views only when required, allowing the system to respond to schema changes without requiring full model retraining or extensive recomputation.
2Measurement precision
If the entire schema is loaded into context for AI query generation, then query accuracy improves, but memory consumption increases
Solution Approach 1:
Instead of providing the entire schema uniformly to the AI model, the system generates localized ephemeral views that contain only the specific schema portions relevant to each user question. This localizes the information quality to match the specific query needs, maintaining accuracy for relevant portions while minimizing overall memory consumption.
Solution Approach 2:
The system performs preliminary analysis of the user question to identify which schema portions are relevant before generating the ephemeral view. This preliminary action allows the system to pre-filter and prepare only the necessary schema information, ensuring query accuracy is maintained while avoiding loading unnecessary data into memory.
3Measurement precision
If prompt engineering techniques are used to improve query accuracy, then retrieval accuracy improves, but the system cannot handle unknown or changed database structures
Solution Approach 1:
The system dynamically generates ephemeral views that adapt to the specific database structure relevant to each user question. When the underlying database schema changes, the system automatically generates updated ephemeral views reflecting the new structure, maintaining retrieval accuracy without requiring manual prompt engineering updates or knowledge of the schema changes.
Solution Approach 2:
The system performs self-updating by automatically generating new ephemeral views when database schemas change. Instead of relying on external prompt engineering techniques that would need manual adjustment, the system autonomously adapts to schema changes by creating updated views, maintaining both accuracy and adaptability.
Data Source
AI summary
A system can generate and render, at a computing device, a page comprising a graphical user interface (GUI) that includes a displayable item from a first block of a block-based data structure. The system can use at least a portion of a natural language prompt received by the GUI to cause a neural network (e.g., an LLM) to generate a query executable against the block-based data structure to return a result set comprising a second block. The second block is not initially displayed at the page and can be discoverable by determining query context using at least a portion of the natural language prompt in combination with a property of the first block. The system can cause the generated query to be executed against the block-based data structure to generate and display, at the GUI, (i) an ephemeral view comprising the result set and (ii) a query tuning control configured to enable dynamic tuning of the generated query via the GUI.