Sequential RAG Pipelined Search Query Generation for Accurate SPL
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Solution Overview
Problem
Individuals with varying levels of programming expertise often face challenges in utilizing unfamiliar programming languages, particularly in generating complex pipelined search query statements, due to the vast number of languages and rapid introduction of new ones, necessitating improved tools for automated code generation.
Innovation Solution
A pipelined search query generation engine utilizing large language models (LLMs) with retrieval augmented generation (RAG) processes to generate, explain, or answer queries in Search Processing Language (SPL), incorporating user-specific historical data and context through vector databases to enhance prompt augmentation and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional manual coding approaches are used, then users with programming expertise can write complex queries, but users with varying programming expertise struggle to generate accurate pipelined search query statements
Solution Approach 1:
The patent introduces a language model as an intermediary between the user and the query generation system. The language model receives natural language input from users with varying programming expertise, processes it through learned patterns, and outputs accurate pipelined search query statements. This intermediary bridges the gap between simple natural language input and complex structured query output, enabling users without programming expertise to generate accurate queries.
Solution Approach 2:
The patent replaces manual mechanical coding processes with an automated language model system. Instead of requiring users to manually construct pipelined search query statements using programming knowledge, the system uses natural language processing and machine learning to automatically generate the queries. This substitution eliminates the need for users to understand the underlying programming syntax while maintaining query accuracy.
2Adaptability or versatility
If more programming languages are introduced to expand functionality, then system versatility improves, but the complexity of learning and utilizing these languages increases
Solution Approach 1:
The patent implements a universal language model that can handle multiple programming languages and query formats through a single unified system. The language model is trained on diverse datasets including natural language, programming code, and pipelined search query statements, enabling it to translate between different languages and formats. This multi-functional approach allows the system to support numerous programming languages without requiring separate specialized systems for each language.
Solution Approach 2:
The patent uses parameter changes in the language model to adapt to different programming languages and query styles. The model adjusts its internal parameters and processing mechanisms based on the input language and desired output format, allowing it to maintain high performance across diverse programming languages while presenting a simplified interface to users.
3Measurement precision
If retrieval augmented generation is implemented to improve query accuracy, then context relevance improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing relevant contextual information in a retrieval-augmented generation system. The system proactively retrieves and prepares relevant context data before the actual query generation process, so that when a user submits a query, the necessary context is already available for immediate use. This preliminary preparation reduces the processing time required during query generation while maintaining high context relevance.
Solution Approach 2:
The patent applies partial action by selectively retrieving only the necessary portion of contextual information needed for each specific query, rather than processing all available data. The retrieval-augmented generation system identifies and processes only the relevant context snippets required for accurate query generation, avoiding unnecessary computational resources and time spent on irrelevant data processing.
Data Source
AI summary
Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through novel processes for performing retrieval augmented generation (RAG). In some examples, such a method includes operations of receiving a natural language user-provided prompt, performing a first RAG process including retrieving first RAG data including natural language, programming syntax pairings from a vector database and performing a second RAG process including retrieving second RAG data including additional programming syntax examples from a vector database that have a second level of similarity with programming syntax of the natural language, programming syntax pairings retrieved during the first RAG process. The method may also include generating an auto-generated prompt requesting generation of programming code by a large language model (LLM) and generating a graphical user interface that displays the response to the auto-generated prompt from the LLM.