RAG Retrieval Mixing With Metadata Extraction for LLM Accuracy
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Solution Overview
Problem
Large language models (LLMs) suffer from hallucinations and untrained knowledge, necessitating a cost-effective approach to improve accuracy and relevance in responses.
Innovation Solution
A retrieval-augmented generation (RAG) system that utilizes metadata extraction and search engine mixing, employing multiple retrievers to enhance data retrieval and response generation through vector databases and artificial intelligence models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single retriever is used in the RAG system, then the device complexity is reduced, but the response accuracy and relevance deteriorate due to hallucinations and untrained knowledge
Solution Approach 1:
The system segments the retrieval function into multiple specialized retrievers (e.g., dense retriever, sparse retriever, hybrid retriever) that each handle different aspects of the search query. This segmentation allows each retriever to be optimized for specific types of queries while collectively providing comprehensive and accurate results, thereby improving response accuracy without requiring a single complex monolithic system
Solution Approach 2:
The system merges the results from multiple different retrievers through a unified framework that combines their outputs. This merging process integrates the strengths of each retriever type (dense, sparse, hybrid) to produce a more accurate and relevant set of retrieved documents, improving overall system reliability while distributing complexity across multiple simpler components
2Reliability
If multiple retrievers are employed to improve response quality, then the response accuracy improves, but the loss of time increases due to multiple retrieval operations
Solution Approach 1:
The system implements periodic or staged retrieval operations where different retrievers are activated based on the query characteristics. Rather than running all retrievers simultaneously for every query, the system periodically selects and activates appropriate retrievers based on query analysis, reducing unnecessary retrieval operations and associated time losses while maintaining high response accuracy when needed
Solution Approach 2:
The system performs partial retrieval actions by activating only the necessary subset of retrievers for each specific query rather than always running all retrievers. This partial action approach reduces the time loss associated with unnecessary retrieval operations while still achieving high response accuracy by selecting the appropriate retrievers for each query's specific requirements
3Measurement precision
If metadata extraction is performed to enhance retrieval quality, then the measurement precision of retrieved results improves, but the device complexity increases due to additional processing steps
Solution Approach 1:
The system extracts metadata from documents as a separate, independent processing step that can be performed independently of the main retrieval logic. This extraction process isolates the complex metadata processing from the core retrieval mechanism, allowing metadata to be used for enhanced precision while keeping the retrieval system itself relatively simple and maintainable
Solution Approach 2:
The system introduces metadata as an intermediary layer between the raw documents and the retrieval process. This intermediary metadata structure provides precise filtering and ranking capabilities without requiring complex modifications to the core retrieval algorithm, thereby improving measurement precision while maintaining processing simplicity through a clear separation of concerns
Data Source
AI summary
Disclosed is a method and system for advanced retrieval-augmented generation through metadata and search engine mixing. A retrieval-augmented generation method may include acquiring data of a user; constructing a vector database by embedding the data of the user; receiving and embedding a query of the user; retrieving data corresponding to the embedded query of the user from the vector database using a plurality of retrievers, each retriever of the plurality of retrievers differing from each other; and generating a response to the query of the user by inputting a prompt to an artificial intelligence model, the prompt including the query and the data retrieved using each retriever of the plurality of retrievers.