Video Insight (a.k.a VideoRAG)

A multimodal Retrieval Augmented Generation (RAG) system for intelligent video search and question answering.

Overview

VIDEO RAG enables contextual understanding of video content through advanced multimodal embeddings, precise vector search, and large vision-language models. It creates a seamless bridge between visual content and natural language understanding, allowing users to query videos using natural language and receive accurate, context-aware responses.

Architecture

The system employs a multi-stage pipeline architecture:

1. Video Processing

   • Frame extraction at optimized sampling rates
   • Transcript extraction and temporal alignment
   • Multi-modal metadata extraction and indexing

2. Embedding Generation

   • Text embedding using OpenAI's embedding models
   • Frame-text pair vectorization into unified semantic space
   • Dimension optimization for performance and accuracy

3. Vector Database

   • LanceDB for high-performance vector storage and retrieval
   • Multimodal document indexing with rich metadata
   • Efficient similarity search with configurable parameters

4. LLM Integration

   • GPT-4V (now merged with general GPT models) for advanced vision-language comprehension
   • Context-aware prompt engineering for improved accuracy
   • Hybrid retrieval combining visual and textual elements

Key Technologies

LLaVA (Large Language-and-Vision Assistant)

• End-to-end trained multimodal model that connects a vision encoder and LLM for comprehensive visual and language understanding
• Capable of complex tasks like explaining charts, recognizing text in images, and identifying fine details
• Enables the system to not just see images but understand their content, context, and meaning within videos
• Provides reasoning capabilities about visual content that traditional systems lack

BridgeTower

• Multimodal embedding model that creates unified representations of text and images
• Creates high-quality semantic vectors that capture the relationship between visual content and language
• Allows for precise similarity search across modalities by placing related concepts in the same semantic space
• Helps the system understand conceptual connections between what is being shown and discussed in videos

LanceDB

• High-performance vector database optimized for machine learning applications
• Provides efficient storage and retrieval of multimodal embeddings
• Enables complex similarity search operations with low latency
• Scales horizontally to accommodate growing video libraries and increasing query loads
• Supports rich metadata storage alongside vector embeddings for comprehensive retrieval

Key Capabilities

• Multimodal Understanding: Processes and comprehends both visual content and spoken/written text in videos
• Temporal Awareness: Maintains context of when events occur in video timeline
• Contextual Retrieval: Intelligently selects the most relevant video segments for each query
• Conversational Interface: Supports multi-turn dialogue about video content

Technical Differentiators

• Visual-only Content Analysis: Can extract meaning from purely visual content with no audio
• High-precision Frame Selection: Identifies and retrieves precisely the most relevant frames
• Temporal Context Preservation: Maintains video timeline awareness in responses
• Semantic Search: Goes beyond keyword matching to understand conceptual meaning

Use Cases

• Educational Content Navigation: Find specific concepts within lecture videos
• Technical Documentation: Extract procedures from instructional videos
• Content Summarization: Generate concise summaries of lengthy video content
• Research Analysis: Extract insights from recorded experiments or simulations
• Media Intelligence: Search and analyze trends across video libraries

Installation & Usage

Currently building. Soon to come ...

Acknowledgements

Inspired by Intel Labs' resources on Large Multimodal Models (LMM) and BridgeTower led by Vasudev Lal which is also covered by Deep Learning AI.