RAG Retrieval Pipeline for Structured and Unstructured Data
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Solution Overview
Problem
Existing Retrieval-Augmented Generation (RAG) frameworks struggle to efficiently and accurately process both unstructured and structured data, particularly failing to retrieve relevant structured data due to dependency on natural language similarity searches.
Innovation Solution
An RAG framework that includes an RAG pre-processing platform to generate vector embeddings for unstructured data and summaries with metadata for structured data, combined with an RAG retriever platform to retrieve context-relevant information and an RAG reader platform to create a context-aware response using LLM prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RAG embedding process and vector search are used, then unstructured data retrieval is successful, but structured data retrieval fails
Solution Approach 1:
The patent segments the data processing pipeline into distinct components: unstructured data undergoes traditional embedding, while structured data is transformed into natural language queries separately. This segmentation allows each data type to be processed with appropriate methods, resolving the contradiction between structured data retrieval accuracy and data type compatibility.
Solution Approach 2:
The patent introduces an intermediary component that transforms structured data into natural language queries before embedding. This intermediary translation layer enables structured data to be processed through the existing vector search infrastructure, maintaining compatibility while improving retrieval accuracy for structured data types.
2Measurement precision
If vector search is performed on structured data directly, then processing speed is maintained, but retrieval accuracy deteriorates
Solution Approach 1:
The patent applies preliminary transformation to structured data by converting it into natural language queries before the embedding process. This preliminary action ensures that structured data is properly formatted for vector search, improving retrieval accuracy without significantly impacting processing efficiency since the transformation occurs during the pre-processing stage.
3Adaptability or versatility
If RAG framework is extended to support both unstructured and structured data, then comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data processing approach where both unstructured and structured data are converted into natural language representations and processed through the same embedding and vector search infrastructure. This multi-functionality allows the system to handle diverse data types without requiring entirely separate processing pipelines, thereby limiting the increase in system complexity while improving versatility.
Data Source
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AI summary
A Retrieval-Augmented Generation ("RAG") framework may include an RAG vector data store with information about vector embeddings. An RAG pre-processing platform may access unstructured data and perform vector embedding to generate a vector embedding for unstructured data to be stored in the RAG vector data store. The RAG pre-processing platform may also access structured data from the knowledge base and create a summary and metadata about the structured data to be stored in the RAG vector data store. An RAG retriever platform may receive a user prompt from a user, perform vector embedding, and retrieve context-relevant information for unstructured and structured data by searching for similar embeddings in the RAG vector data store. An RAG reader platform may combine the context-relevant information with the user prompt and an RAG prompt to create a LLM prompt. A context-aware response is then output to the user.