Multimodal RAG Retrieval Using Modality-Aware Chunking
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Solution Overview
Problem
Existing data ingestion and retrieval systems for retrieval augmented generation (RAG) agents are inefficient in handling multimodal documents, leading to unwanted resource consumption and misalignment or hallucination due to inaccurate retrieval of documents, particularly when the data is diverse in nature.
Innovation Solution
A multimodal data ingestion and retrieval system that parses documents into text, image, and graphical chunks, generates summaries and embeddings, and uses semantic and embedding searches to efficiently retrieve relevant data for query responses, integrating Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) for accurate multimodal data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RAG systems ingest and retrieve documents without modality awareness, then the system is simpler to implement, but retrieval accuracy deteriorates and hallucination occurs due to inability to accurately determine which data to retrieve for diverse queries
Solution Approach 1:
The system segments documents into distinct modality chunks (text chunks, image chunks, video chunks, audio chunks) based on their content type. Each chunk is processed and retrieved independently with modality-specific handling, enabling accurate retrieval for diverse queries while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The retrieval system dynamically adapts its behavior based on query modality requirements. When a query requests specific modality data (e.g., images, videos), the system dynamically adjusts retrieval parameters and selects appropriate data types, enabling high retrieval accuracy for diverse queries without requiring the entire system to be overly complex
2Measurement precision
If the system retrieves too few documents to ensure accuracy, then retrieval precision improves, but information completeness deteriorates due to missed information
Solution Approach 1:
The system applies different retrieval strategies to different modality chunks based on their local characteristics and relevance to the query. Text chunks, image chunks, video chunks, and audio chunks are retrieved with modality-aware filtering and ranking, ensuring that the right amount of information is retrieved for each modality type without wasting resources on irrelevant data
Solution Approach 2:
The system changes retrieval parameters dynamically based on query requirements and data modality. Retrieval thresholds, chunk sizes, and search depths are adjusted according to the specific modality being queried, allowing the system to retrieve sufficient information for completeness while maintaining precision through modality-specific parameter optimization
3Loss of information
If the system retrieves too many documents or performs unneeded encoding and decoding, then information completeness improves, but computational resource consumption increases and misalignment or hallucination is introduced
Solution Approach 1:
The system extracts only the necessary modality chunks needed to answer the query by analyzing query requirements and filtering out irrelevant data types. This selective extraction prevents unnecessary encoding and decoding operations, reducing computational resource consumption while maintaining information completeness through targeted retrieval of relevant modalities
Solution Approach 2:
The system uses feedback mechanisms to evaluate retrieval results and adjust subsequent retrieval operations. By monitoring which modality chunks are actually needed and comparing them against retrieval results, the system optimizes computational resource allocation and prevents misalignment or hallucination through iterative refinement of retrieval strategies
Data Source
AI summary
Techniques for multimodal document retrieval are disclosed herein. Multimodal documents that include both textual and graphical components are retrieved from a knowledge base by a multimodal retrieval augmented generation (RAG) agent in response to a query. The documents and/or components or chunks thereof are retrievable by the RAG agent from the knowledge base using the semantic summaries and/or vector search of embeddings in the knowledge base that are generated from text extracted from processing non-textual components of the data. The RAG agent classifies the query type to determine whether to use a semantic match for text or image summaries, full text semantic search, vector cosine similarity search, and/or other multimodal vector search. The RAG agent performs types of searches selected based on the modality used to generate the response to the query.