RAG Retrieval with Intermediate Matching and Mixed Granularity
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Solution Overview
Problem
Traditional Retrieval-Augmented Generation (RAG) systems rely solely on direct connections to external data sources, leading to inaccurate and contextually irrelevant responses, lacking the ability to leverage semantic similarity and latent space relations for precise information retrieval.
Innovation Solution
Implementing an intermediate matching process that identifies semantically similar values in historical case data and utilizing mixed granularity scoring to balance document and chunk relevance, ensuring responses are contextually relevant and closely related to past solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RAG systems use direct connections to external data sources, then the system structure is simple, but the response accuracy and contextual relevance deteriorate
Solution Approach 1:
The patent introduces an intermediate processing layer between the external data source and the RAG system. This intermediary component performs semantic similarity matching and mixed granularity scoring on retrieved data before presenting it to the model, thereby improving response accuracy without requiring fundamental changes to the core RAG architecture.
Solution Approach 2:
The system performs preliminary processing of retrieved data through semantic similarity matching and granularity-based scoring before the data is used by the generative model. This pre-processing step ensures that only highly relevant and contextually appropriate information is presented to the model, improving response reliability in advance.
2Reliability
If semantic similarity matching is implemented in the retrieval process, then the contextual relevance of responses improves, but the processing time increases
Solution Approach 1:
The patent applies different processing granularities to different portions of retrieved data. Instead of uniformly processing all data at the same level, the system uses mixed granularity scoring that applies more intensive semantic analysis only where needed, thereby improving contextual relevance while minimizing additional processing time.
3Measurement precision
If mixed granularity scoring is used to balance document and chunk relevance, then the precision of information retrieval improves, but the computational complexity increases
Solution Approach 1:
The patent segments the retrieval evaluation process into multiple granularity levels: document-level relevance assessment and chunk-level relevance assessment. By dividing the evaluation into these distinct segments with different scoring mechanisms, the system achieves higher retrieval precision while managing computational complexity through structured modular processing.
Data Source
AI summary
Systems and methods are provided to implement improvements to the RAG process. For example, the system may receive a search query with a first search term and implement an intermediate matching process to identify semantically similar values between the first search term and terms in an existing knowledge base. The system may also determine at least one of the semantically similar values within a latent space proximity of a second search term. Based on the second search term, the system may retrieve an external data source utilizing a mixed granularity process.