Natural Language Formula Generation With Operator Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Configuring data analysis tools to execute complex algorithms requires programming expertise and extensive knowledge of available functionality, leading to decreased productivity and increased risk of logical errors due to poorly documented functionalities.
Innovation Solution
A system that generates data processing instructions from a natural language description using Large Language Models (LLMs) and a vector store, allowing users to input a natural language description of a calculation formula, which is then decomposed into components, searched for similar operators, and validated before execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users configure data analysis tools manually with programming expertise, then execution precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary system consisting of a language model and vector store that translates natural language queries into executable data processing instructions. This mediator layer converts user-friendly natural language into precise technical operations, allowing users to achieve accurate execution without programming expertise. The language model acts as the intermediary that bridges the gap between simple user input and complex data analysis requirements.
2Reliability
If users manually configure data processing instructions, then control over execution is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing and storing information about available data sources, operators, and functions in the vector store before user queries arrive. When a user submits a natural language query, the system can immediately search for relevant pre-organized information and generate appropriate instructions without requiring the user to manually configure each parameter. This pre-prepared knowledge base enables rapid response while maintaining execution control.
3Manufacturing precision
If detailed functionality documentation is provided, then execution precision is improved, but ease of operation deteriorates
Solution Approach 1:
The language model serves as an intermediary that internally references comprehensive functionality documentation while presenting simplified natural language interaction to users. The model has access to detailed operator descriptions, parameter specifications, and usage examples stored in the vector store, but it translates these complex details into simple natural language responses. This allows the system to maintain execution precision through access to detailed documentation while presenting ease of operation through natural language interaction.
Data Source
AI summary
Systems and methods include reception of a natural language description of a calculation formula and metadata of a data source, generation of a first prompt to prompt determination of calculation components of the calculation formula based on the description, reception of a plurality of calculation components from a text generation model in response to the first prompt, determination, for each of the plurality of calculation components, of metadata of each of one or more similar operators to a calculation component, generation of a second prompt to determine the calculation formula based on the natural language description, the metadata of the data source and the metadata of each of the one or more similar operators, and reception of the calculation formula from the text generation model in response to the second prompt.