Structured Knowledge Retrieval for Accurate RAG Question Matching
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Solution Overview
Problem
Existing data processing methods in large language models under the RAG architecture face inefficiencies and inaccuracies in knowledge retrieval and recall due to the large volume of unstructured data that needs to be matched, affecting the efficiency and accuracy of model responses.
Innovation Solution
Represent knowledge in a knowledge database using structured data, where each piece includes answer data and corresponding question data, enabling direct matching and reducing data processing volume, thereby improving retrieval efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unstructured data is used in knowledge database, then data storage flexibility is improved, but data processing efficiency and retrieval accuracy deteriorate due to large volume of data needing to be matched
Solution Approach 1:
The patent segments unstructured data into structured formats by extracting key information (questions and answers) and organizing them into discrete structured records. This segmentation allows the system to maintain storage flexibility while significantly improving processing efficiency through targeted matching of structured question-answer pairs rather than processing entire unstructured datasets.
Solution Approach 2:
The patent extracts essential information from unstructured data sources and separates it into distinct structured components (questions and answers). This extraction process reduces the data volume that needs to be processed during retrieval operations while preserving the essential meaning, thereby improving both efficiency and accuracy.
2Adaptability or versatility
If unstructured data is used in knowledge database, then data storage flexibility is improved, but retrieval accuracy deteriorates due to inability to ensure accurate matching
Solution Approach 1:
By segmenting unstructured data into discrete question-answer pairs with clear semantic boundaries, the system achieves precise matching during retrieval. The structured format allows for exact semantic matching between user queries and database entries, significantly improving retrieval accuracy compared to unstructured data processing.
Solution Approach 2:
The patent changes the data representation parameters from unstructured text to structured formats that include explicit question-answer relationships. This parameter transformation enables the system to accurately measure and match semantic similarity, improving retrieval precision while maintaining storage flexibility.
3Quantity of substance
If large volume of data is processed, then knowledge comprehensiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential question-answer pairs from large volumes of unstructured data, creating a condensed structured representation. This extraction maintains knowledge comprehensiveness while dramatically reducing the processing volume, allowing the system to retrieve accurate information quickly without processing entire large datasets.
Solution Approach 2:
The patent performs preliminary processing to convert and structure data in advance, organizing it into queryable formats before actual retrieval operations. This preliminary action reduces the computational burden during retrieval operations, maintaining comprehensive knowledge access while minimizing processing time and resource consumption.
Data Source
AI summary
The present disclosure relates to a data processing method, an apparatus, a medium, a device and a computer program product. The method includes: receiving retrieval data; matching the retrieval data with structured data in a knowledge database to obtain candidate question data and candidate answer data, where knowledge in the knowledge database is represented by the structured data, and each piece of the structured data includes one piece of answer data and at least one piece of question data corresponding to the answer data; and generating a target retrieval result corresponding to the retrieval data according to the candidate question data and the candidate answer data.