Multi-Modal RAG Research & Implementation

A comprehensive exploration of Multi-Modal Retrieval Augmented Generation (MRAG) systems, focusing on understanding and implementing different approaches to handle various modalities including text, images, video, and audio.

Core Concepts

Multi-Modal Embeddings

• Creates modality-independent vector representations
• Enables unified vector space for different content types (text, images, video, audio)
• Allows cross-modal similarity search and retrieval
• Can be achieved through:
  1. Training a single multi-modal embedding model
  2. Unifying different specialized embedding models

Image Understanding in LLMs

• Image Embedding vs Vision Models
  • Image embedding models: Convert image to single vector
  • Vision models: Process images in patches for detailed understanding
• Document Understanding
  • Feature extraction through patches
  • OCR extraction
  • Spatial cues awareness (e.g., DocLLM)
  • Hidden information detection capabilities

Multi-Modal RAG Approaches

Option 1: Direct Multi-Modal Approach

• Uses multi-modal embeddings for both images and text
• Retrieves content using similarity search
• Passes raw images and text to multi-modal LLM
• Pros: Direct approach
• Cons: Requires sophisticated multi-modal embedding model

Option 2: Image-to-Text Conversion

• Converts images to text summaries using multi-modal LLM
• Embeds and retrieves text only
• Uses text-only LLM for synthesis
• Pros: Simpler implementation
• Cons: May lose visual nuances

Option 3: Hybrid Approach (Recommended)

• Extracts features from images using multi-modal LLM
• Embeds summaries while maintaining raw image references
• Passes both raw images and text to multi-modal LLM
• Pros:
  • Best balance of efficiency and accuracy
  • Doesn't require multi-modal embedding model
  • Maintains original context
• Cons: More complex implementation

Implementation Details

Document Processing with Unstructured Library

Supports multiple document types:

• Text files (.txt)
• Office documents (.docx, .pptx, .xlsx)
• PDFs with various partitioning strategies:
  • "auto": Automatic strategy selection
  • "hi_res": High-resolution layout analysis
  • "ocr_only": Image text extraction
  • "fast": Quick text extraction
• Images (.png, .jpg, .heic)
• Emails, web pages, e-books, etc.

Storage Architecture

• Document Store: Raw content storage
• Vector Store: Embedding storage
• Linking Mechanism: Metadata-based connection between stores

Future Research Directions

• Custom MRAG pipeline without framework abstractions
• Performance optimization for local deployment
• Integration with newer multi-modal models
• Enhanced evaluation frameworks
• Improved handling of complex document structures

References

1. RACM3: Improving Vision Model Performance through MRAG
2. DocLLM Paper: Enhanced PDF Parsing

Key Insights

1. Modality Independence: Embeddings should work across different content types
2. Feature Extraction: Different approaches for different content types
3. Storage Strategy: Dual storage (raw + vector) with proper linking is crucial
4. Model Selection: Balance between efficiency and accuracy

Getting Started

Prerequisites

• Python 3.9+
• Tesseract for OCR
• Poppler for PDF processing

Attribution & Background

This repository evolved from concepts learned through DeepLearning.AI's educational content on multi-modal RAG systems. The foundational implementation draws inspiration from their course materials, while incorporating significant original research, modifications, and extended implementations. This represents a learning journey from basic concepts to advanced practical applications.

Repository Contents

• Tutorial-Inspired Base: Core concepts and basic implementations based on DeepLearning.AI course materials
• Original Extensions:
  • Custom RAG pipeline implementations
  • Research findings on embedding models
  • Extended documentation and practical insights
  • Modified architectures for improved performance
  • Additional experimental implementations

The work presented here represents both learning from established educational resources and original research/implementation work. All modifications, extensions, and documentation beyond the basic concepts are original contributions.