🎥 Video RAG: Semantic Video Search & AI Summarization

Transform your videos into searchable, explainable knowledge with AI

Features • Demo • Installation • Usage • How It Works

🌟 Overview

Video RAG is a powerful Retrieval-Augmented Generation system that enables semantic search within videos using natural language. Simply upload a video, ask a question, and get relevant frames with AI-generated summaries—no manual scrubbing required!

🎯 What Makes This Special?

🔍 Natural Language Search: Query videos like "person wearing red jacket" or "car accident"
⚡ Lightning Fast: FAISS-powered similarity search through thousands of frames
🧠 AI Summaries: Context-aware explanations powered by Google's Gemini
🎬 Video Clips: Extract and download specific scenes automatically
💾 Smart Caching: Process once, search instantly on subsequent runs

✨ Features

Core Capabilities

Feature	Description
🎞️ Multi-Format Support	Upload `.mp4`, `.mov`, `.avi` videos
⚙️ Configurable Sampling	Adjust FPS (0.5-5) for speed vs. accuracy trade-off
🔍 Semantic Search	CLIP-powered understanding of visual content
📊 Visual Results	Storyboard view + individual frames with timestamps
🎬 Clip Extraction	Download matched video segments
💬 AI Summaries	Gemini-generated contextual explanations
🗂️ Smart Caching	Skip re-processing for previously analyzed videos
📈 Research Metrics	Comprehensive evaluation metrics for academic papers

Advanced Features

Batch Processing: Efficient GPU utilization for large videos
Progress Tracking: Real-time feedback during processing
Multiple Models: Choose between CLIP variants for different use cases
Responsive UI: Clean, intuitive interface with sidebar controls
Research Evaluation: Calculate Top-K Retrieval %, MAP, MRR, NDCG, Precision@K, Recall@K

📊 Research Metrics

Video RAG now includes comprehensive retrieval metrics suitable for research papers and academic evaluation:

Available Metrics

Metric	Description	Use Case
Top-K Retrieval %	Percentage of relevant chunks found in top-K results	Primary metric for retrieval coverage
Precision@K	Fraction of top-K results that are relevant	Measures result quality
Recall@K	Fraction of all relevant items found in top-K	Measures completeness
Mean Average Precision (MAP)	Average precision across all relevant positions	Overall ranking quality
Mean Reciprocal Rank (MRR)	Inverse rank of first relevant item	Early precision metric
NDCG@K	Normalized Discounted Cumulative Gain	Position-aware ranking quality
Avg Similarity Score	Mean similarity of top-K results	Confidence measure

Using Metrics

Enable Metrics in the sidebar under "📊 Research Metrics"
Provide Ground Truth (optional): Enter comma-separated frame IDs that are relevant to your query
- Example: 120,450,780,1200
Run Search as usual
View Metrics in the expandable "📊 Retrieval Metrics" section
Export Results as JSON or CSV for research papers

Example Usage

# In the UI, enable metrics and provide ground truth
Ground Truth Frame IDs: 120,450,780,1200

# After search, view metrics:
# 🎯 Top-K Retrieval: 75.0%
#   - 3/4 relevant chunks found in top-5
# 📈 Precision@5: 0.6000
# 📊 Recall@5: 0.7500
# ⭐ Average Precision: 0.7083
# 🔝 Reciprocal Rank: 1.0000
# 🏆 NDCG@5: 0.8631
# 💯 Avg Similarity Score: 0.8543

Export Formats

JSON: Detailed metrics with metadata, timestamps
CSV: Simple table format for Excel/analysis tools

🎬 Demo

Example Workflow

1. Upload: city_traffic.mp4
2. Query: "red car at intersection"
3. Results: 3 matched frames at [0:45, 1:23, 2:08]
4. Summary: "A red sedan waits at a traffic light during rush hour..."

Screenshots

📸 Click to view screenshots

Search Interface

Frame Results

AI Summary

💡 Tip: Add actual screenshots to a screenshots/ folder in your repo

🚀 Installation

Prerequisites

Python 3.10 or higher
4GB+ RAM recommended
(Optional) GPU for faster processing

Quick Start

# 1. Clone the repository
git clone https://github.com/akash4426/Video_RAG.git
cd Video_RAG

# 2. Create virtual environment
python -m venv venv

# Activate (macOS/Linux)
source venv/bin/activate

# Activate (Windows)
.\venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the app
streamlit run app.py

System Dependencies

macOS:

brew install ffmpeg

Ubuntu/Debian:

sudo apt-get update
sudo apt-get install ffmpeg libsm6 libxext6 libgl1

Windows: Download FFmpeg from ffmpeg.org and add to PATH

🔑 Configuration

Gemini API Setup

Get your API key from Google AI Studio
Add to Streamlit secrets:

Local Development: Create .streamlit/secrets.toml:

GEMINI_API_KEY = "your-api-key-here"

Streamlit Cloud:

Go to App Settings → Secrets
Add: GEMINI_API_KEY = "your-key"

Environment Variables

# Optional: Set cache directory
export VIDEORAG_CACHE_DIR=/path/to/cache

# Optional: Set device
export TORCH_DEVICE=cuda  # or cpu, mps

📖 Usage

Basic Search

# 1. Upload video through UI
# 2. Enter query: "person walking dog"
# 3. View matched frames + timestamps
# 4. Generate AI summary (optional)

Advanced Options

Setting	Description	Default
Model	CLIP variant	`clip-vit-base-patch32`
Sampling FPS	Frames per second	1.0
Batch Size	GPU batch processing	16
Top K	Number of results	3
Clip Window	Context duration (sec)	2.0

Example Queries

✅ "person wearing blue shirt"
✅ "car accident on highway"
✅ "sunset over ocean"
✅ "group of people dancing"
❌ "the color blue" (too abstract)
❌ "video from 2020" (no temporal reasoning)

🧠 How It Works

Architecture

┌─────────────┐
│ Video Input │
└──────┬──────┘
       │
       ▼
┌─────────────────┐
│ Frame Extraction│  ← OpenCV samples at specified FPS
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ CLIP Embeddings │  ← Convert frames to 512-D vectors
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  FAISS Indexing │  ← Build similarity search index
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Text Query     │  ← User input converted to embedding
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Similarity      │  ← Find nearest neighbors
│ Search          │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Gemini Summary  │  ← Generate natural language explanation
└─────────────────┘

Key Technologies

1. CLIP (OpenAI)

Purpose: Multimodal embeddings (vision + language)
How: Contrastive learning on 400M image-text pairs
Output: 512-dimensional vectors for both images and text

2. FAISS (Facebook AI)

Purpose: Efficient similarity search
How: Approximate nearest neighbor algorithms
Speed: Sub-millisecond search on millions of vectors

3. Gemini (Google)

Purpose: Multimodal AI summarization
How: Analyzes multiple frames + text context
Output: Natural language descriptions

Performance

Metric	Value
1 min video @ 1 FPS	~60 frames, 2-3 sec processing
Search latency	< 100ms
Memory usage	~500MB + video size
Cache size	~7MB per hour of video

🧩 Project Structure

Video_RAG/
├── app.py                 # Main Streamlit application
├── requirements.txt       # Python dependencies
├── packages.txt          # System dependencies
├── runtime.txt           # Python version
├── .streamlit/
│   ├── config.toml       # UI configuration
│   └── secrets.toml      # API keys (gitignored)
├── screenshots/          # Demo images
├── README.md
└── LICENSE

📊 Comparison with Alternatives

Feature	Video RAG	YouTube Search	Manual Scrubbing
Semantic Understanding	✅	⚠️ Metadata only	❌
Speed	✅ Instant	✅	❌ Very slow
Offline Support	✅	❌	✅
AI Summaries	✅	❌	❌
Custom Videos	✅	❌ Public only	✅

🛠️ Development

Running Tests

# Install dev dependencies
pip install pytest pytest-cov

# Run tests
pytest tests/

# With coverage
pytest --cov=app tests/

Code Quality

# Format code
black app.py

# Lint
flake8 app.py

# Type checking
mypy app.py

🤝 Contributing

Contributions are welcome! Here's how you can help:

Ways to Contribute

🐛 Report bugs via Issues
💡 Suggest features through Discussions
📝 Improve documentation
🔧 Submit pull requests

Development Workflow

# 1. Fork the repository
# 2. Create feature branch
git checkout -b feature/amazing-feature

# 3. Make changes and commit
git commit -m "Add amazing feature"

# 4. Push to your fork
git push origin feature/amazing-feature

# 5. Open a Pull Request

📝 Use Cases

🎓 Education

Navigate lecture videos by topic
Find specific demonstrations or examples

🎬 Content Creation

Locate B-roll footage quickly
Find specific scenes for editing

🔒 Security

Search surveillance footage by description
Incident investigation and analysis

📊 Research & Academia

Analyze video datasets semantically
Extract frames for annotation
Evaluate retrieval algorithms with comprehensive metrics
Benchmark video search systems with Top-K Retrieval %
Generate research paper results with exportable metrics (JSON/CSV)
Compare different CLIP models using standardized evaluation

🏢 Business

Meeting recap and highlight extraction
Training video search and organization

🚧 Roadmap

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

OpenAI CLIP for multimodal embeddings
Facebook FAISS for similarity search
Google Gemini for AI summaries
Streamlit for the amazing framework

👤 Author

Akash Karri

📧 Email: akashkarri2006@gmail.com
💼 LinkedIn: LinkedIn Profile
🐙 GitHub: @akash4426

⭐ Star History

📈 Stats

Made with ❤️ by Akash Karri

If you found this project helpful, consider giving it a ⭐!

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Name		Name	Last commit message	Last commit date
.streamlit		.streamlit
.vscode		.vscode
prob.dSYM/Contents		prob.dSYM/Contents
.gitignore		.gitignore
IMPLEMENTATION_SUMMARY.md		IMPLEMENTATION_SUMMARY.md
METRICS_EXAMPLE.md		METRICS_EXAMPLE.md
METRICS_GUIDE.md		METRICS_GUIDE.md
README.md		README.md
app.py		app.py
metrics.py		metrics.py
requirements.txt		requirements.txt
test_metrics_demo.py		test_metrics_demo.py

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