LLM Query Translation for Proprietary Platform Data Retrieval
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Solution Overview
Problem
Existing systems face difficulties in efficiently extracting desired information from large datasets stored in proprietary formats and interacting with big data due to the need for specific commands, making it challenging for users to obtain relevant insights without intermediate tools.
Innovation Solution
A system utilizing a large language model (LLM) and generative AI to facilitate interactions between users and data platforms, allowing natural language questioning and transforming queries into tool-specific formats to retrieve data from databases, with components like extract, rewrite, route, text-to-tool, and tool-to-text functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users interact with proprietary data formats using specific commands, then data retrieval accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into proprietary data format commands. This mediator system includes a language model that converts casual questions into structured queries, allowing users to interact with complex data formats without learning specific commands, thus resolving the contradiction between retrieval accuracy and ease of operation
Solution Approach 2:
The system segments the data interaction process into distinct components: natural language input processing, query translation, data retrieval, and result formatting. By breaking down the complex interaction into manageable segments with specialized handling for each stage, the system maintains high retrieval accuracy while presenting a simplified interface to users
2Productivity
If intermediate tools are used to extract information from big data, then data processing capability is improved, but device complexity deteriorates
Solution Approach 1:
The patent merges multiple intermediate tools and functions into a unified natural language processing system. Instead of requiring separate tools for different data extraction tasks, the system combines query translation, data access, and result formatting into a single integrated interface, maintaining high data processing capability while reducing the apparent complexity for users
Solution Approach 2:
The natural language processing system serves multiple functions simultaneously: it acts as a query translator, a data access interface, a result formatter, and an error handler. This multi-functional approach consolidates what would otherwise require multiple separate tools, improving productivity without increasing the number of components users must manage
Data Source
AI summary
A method may include obtaining a platform data set. The method may also include providing a user interface operable to interact with the platform data set. The method may further include obtaining a natural language question from the user interface. The method may also include extracting at least one component from the natural language question. The method may further include identifying a first primitive and a second primitive. The method may also include transforming the natural language question into the tool query based on the at least one component using the first primitive. The method may further include executing the tool query in the tool using the second primitive to obtain query results from the platform data set associated with the natural language question. The method may also include providing the query results to the user interface in a natural language format.