Dynamic Document Chunk Selection for Accurate RAG Answers
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Solution Overview
Problem
Conventional retrieval-augmented generation (RAG) approaches struggle with selecting an optimal number of document chunks for processing, leading to incomplete or inaccurate responses due to fixed or threshold-based methods that fail to adapt to the varying complexity and information needs of diverse queries.
Innovation Solution
An adaptive strategy using supervised learning and query-aware chunking techniques dynamically determines the optimal number of document chunks based on the specificity of the input query, leveraging semantic similarity scores and distribution statistics through a regression model to enhance RAG processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed number of document chunks is used for RAG processing, then the processing method is simple and fast, but the response completeness and accuracy deteriorate due to inability to adapt to varying query complexity
Solution Approach 1:
The system dynamically determines the number of document chunks to retrieve based on the specificity of the input query using supervised learning and distribution statistics, rather than using a fixed number. This allows the retrieval process to adapt to varying query complexities, improving response accuracy while maintaining processing efficiency.
Solution Approach 2:
The system changes the parameter of document chunk count based on query characteristics. By analyzing distribution statistics of semantic similarity scores and using a regression model trained on query-specificity features, the system adjusts the number of chunks retrieved to match the information needs of each query, resolving the contradiction between fixed processing and adaptive accuracy.
2Ease of operation
If threshold-based methods are used to select document chunks, then the selection process is straightforward, but the adaptability to diverse query information needs deteriorates
Solution Approach 1:
The system changes the selection parameter from a fixed threshold to a dynamically determined chunk count based on query specificity. The regression model processes distribution statistics to determine an optimal chunk count for each query type, maintaining operational simplicity while significantly improving adaptability to diverse information needs.
Solution Approach 2:
The system uses feedback from distribution statistics analysis to adjust the number of chunks retrieved. By monitoring semantic similarity score distributions and using this feedback in the supervised learning model, the system adapts its retrieval strategy to match query requirements while maintaining a straightforward selection process.
3Loss of information
If more document chunks are retrieved, then the information completeness improves, but the noise infusion and information loss increase due to processing irrelevant chunks
Solution Approach 1:
The system optimizes the chunk count parameter to balance completeness and noise reduction. By using distribution statistics and supervised learning to determine the precise number of chunks needed for each query, the system retrieves sufficient information while avoiding the noise infusion that occurs with excessive chunk retrieval.
Solution Approach 2:
The system applies partial action by retrieving exactly the number of chunks needed rather than all available chunks. The supervised learning model determines the optimal subset size based on query specificity and distribution statistics, ensuring sufficient information retrieval while minimizing noise from irrelevant chunks.
Data Source
AI summary
An apparatus comprises at least one processing device configured to obtain a query comprising search text and a context identifying documents to be searched using the search text, to generate document chunks by parsing the documents, to determine a degree of specificity of the search text, and to determine a number of the document chunks to select for retrieval-augmented generation processing based on the determined degree of specificity. The at least one processing device is also configured to select a subset of the document chunks based on similarity between the document chunks and the search text, the subset including the determined number of document chunks. The at least one processing device is further configured to generate and apply a prompt including the selected subset of the document chunks to a machine learning system to generate an output, and to provide an answer to the query based on the output.