Natural Language Query Translation for Data Analytics
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Solution Overview
Problem
Current data analytics systems require technical expertise for query authoring, leading to long turnaround times and high costs, and are limited in providing standard quality measure catalogs and regulatory reporting capabilities, especially in complex domains like healthcare.
Innovation Solution
Implementing a large language model (LLM) with retrieval-augmented generation (RAG) to translate natural language queries into executable database queries, simplifying the process by allowing users to interact through a chat interface and automating measure creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analytics systems are used with manual query authoring, then query accuracy and data reliability are maintained, but technical expertise is required leading to long turnaround times and high costs
Solution Approach 1:
The patent introduces a large language model as an intermediary between the user and the data analytics system. The LLM translates natural language queries into executable code (SQL, Python, R), eliminating the need for users to have technical expertise while maintaining system functionality. This mediator handles the complexity of query translation, allowing non-technical users to interact with the system using everyday language.
Solution Approach 2:
The patent replaces manual mechanical query authoring processes with an automated AI-based system. Instead of requiring users to manually write and debug technical queries, the system uses natural language processing and code generation to automatically translate user intent into executable analytics code, significantly reducing turnaround time and technical barriers.
2Loss of time
If manual query authoring processes are used, then query precision is maintained, but turnaround time increases and costs rise
Solution Approach 1:
The system performs preliminary actions by pre-loading and indexing documentation, data dictionaries, and code libraries into the LLM's knowledge base before query execution. This preparation allows the LLM to quickly generate accurate code without requiring real-time manual intervention, significantly reducing turnaround time while maintaining query precision through access to pre-processed reference materials.
Solution Approach 2:
The patent replaces manual query authoring and debugging processes with automated AI code generation. The LLM systematically translates natural language queries into executable code, eliminating the time-consuming manual steps of query writing, syntax checking, and error debugging, thereby dramatically improving processing efficiency.
3Ease of operation
If automated LLM-based query translation is implemented, then ease of use and productivity are improved, but system complexity increases
Solution Approach 1:
The LLM serves as an intelligent intermediary layer that abstracts away system complexity from users. While the backend system architecture becomes more complex to support AI capabilities, the user interface remains simple and accessible through natural language input. The LLM handles complex translations internally, shielding users from underlying system complexity.
Solution Approach 2:
The patent implements a universal LLM-based interface that can handle multiple types of queries and translation tasks (SQL generation, Python scripting, R programming) through a single unified system. This multi-functional approach consolidates what would otherwise require multiple specialized tools, managing system complexity while providing broad accessibility.
4Adaptability or versatility
If traditional analytics systems are used, then implementation cost is controlled, but adaptability to different query types is limited
Solution Approach 1:
The patent implements a universal LLM-based interface that can handle multiple types of queries and translation tasks (SQL generation, Python scripting, R programming) through a single unified system. This multi-functional approach consolidates what would otherwise require multiple specialized tools, managing system complexity while providing broad accessibility.
Solution Approach 2:
The system adapts to different query types by dynamically changing parameters such as the target programming language, query complexity level, and data source configuration. The LLM adjusts its code generation strategy based on the specific query requirements, enabling versatile handling of diverse analytics tasks without requiring separate systems for each query type.
Data Source
AI summary
Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to provide hi-query AI for use with the data analytics environment. Systems and methods disclosed can provide for query processing and semantic analysis. The system can take a user's natural language question and run a semantic search to discern the query's intent and find tables relevant to the question, and generate a query to run against a data store or data warehouse.