Multimodal RAG Retrieval Using Chunked Summaries and Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data ingestion and retrieval systems for retrieval augmented generation (RAG) agents are inefficient and resource-intensive when handling multimodal documents, leading to unwanted computational and storage costs, missed information, and misalignment or hallucination in responses due to inaccurate retrieval of textual and non-textual components.
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, integrating multimodal Large Language Models (LLMs) for accurate response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional unimodal RAG systems are used for multimodal documents, then system simplicity is maintained, but retrieval accuracy and completeness deteriorate
Solution Approach 1:
The document is segmented into multiple chunks based on modality (text, image, graphical data) and then further divided into smaller segments. Each chunk is processed independently through modality-specific encoders, allowing the system to handle diverse content types effectively while maintaining overall system manageability.
Solution Approach 2:
The system creates a unified embedding space that accommodates multiple modalities (text, image, graphical data) through a common encoder architecture. This universal approach allows the system to retrieve and process any modality of data using the same retrieval mechanisms, improving accuracy without requiring separate specialized systems for each modality.
2Reliability
If all document components are encoded and stored for retrieval, then retrieval completeness is improved, but computational and storage costs increase
Solution Approach 1:
Documents are divided into meaningful chunks that preserve semantic coherence. By segmenting at the chunk level rather than processing entire documents, the system retrieves only relevant portions for each query, reducing unnecessary computational encoding and storage operations while maintaining complete information availability when needed.
Solution Approach 2:
Different encoders are applied to different modalities based on their specific characteristics. Text uses text encoders, images use vision encoders, and graphical data uses specialized encoders. This localized processing approach ensures optimal encoding for each modality type, improving retrieval quality while avoiding the computational overhead of applying all encoders to all data uniformly.
3Measurement precision
If multiple encoders are used for different modalities, then encoding accuracy is improved, but system complexity and misalignment risks increase
Solution Approach 1:
Multiple modality-specific encoders are merged into a unified embedding space through a common projection layer or attention mechanism. This allows text, image, and graphical data encoders to operate independently with their specialized architectures while their outputs are integrated into a consistent representation space, maintaining encoding accuracy for each modality while enabling coherent cross-modality retrieval and reducing misalignment issues.
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.