https://github.com/arimanyus/hentaivid
Culturally-compliant video storage. Embeds searchable text chunks into pixelated media for lightning-fast semantic search. Zero-database, maximum compliance.
https://github.com/arimanyus/hentaivid
embedded-ai knowledge-base knowledge-graph llm machine-learning nlp offline-first-memory opencv python retrieval-augmented-generation semantic-search video-processing
Last synced: about 2 months ago
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Culturally-compliant video storage. Embeds searchable text chunks into pixelated media for lightning-fast semantic search. Zero-database, maximum compliance.
- Host: GitHub
- URL: https://github.com/arimanyus/hentaivid
- Owner: arimanyus
- Created: 2025-06-08T06:21:59.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-08T07:18:57.000Z (about 1 year ago)
- Last Synced: 2025-06-23T16:05:13.373Z (12 months ago)
- Topics: embedded-ai, knowledge-base, knowledge-graph, llm, machine-learning, nlp, offline-first-memory, opencv, python, retrieval-augmented-generation, semantic-search, video-processing
- Language: Python
- Homepage:
- Size: 92.8 KB
- Stars: 13
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🎌 Hentaivid
**Revolutionary RAG-compatible video storage format that embeds text chunks into QR codes hidden inside pixelated regions of adult content. Zero-database semantic search with maximum cultural authenticity.**
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/psf/black)
> *"My boss thinks I'm researching state-of-the-art data retrieval methods. He's not wrong."* - Anonymous FAANG Sr Engineer
## 🚀 What is Hentaivid?
Hentaivid is a breakthrough video-based knowledge storage system that leverages legally mandated pixelation in Japanese adult content as a steganographic medium for embedding searchable text data. By utilizing the culturally-required censorship regions as QR code carriers, Hentaivid creates a novel form of contextual data storage that is both semantically searchable and culturally compliant.
### ✨ Key Features
- 🎯 **Zero Database Required** - All data embedded directly in video files
- 🔍 **Lightning-Fast Semantic Search** - FAISS-powered vector similarity
- 🎌 **Culturally Authentic** - Respects Japanese pixelation standards
- 📱 **QR Code Integration** - Industry-standard data encoding
- 🎬 **Video-Native Storage** - No external dependencies for data retrieval
- 🔒 **Steganographic Security** - Data hidden in plain sight
- 📈 **Scalable Architecture** - Handle millions of text chunks
- 🌐 **Cross-Platform Compatible** - Works on any system with video playback
## 🔬 Technical Architecture
View Culturally-Compliant Architecture Diagram
## 📦 Installation
### System Requirements
- Python 3.8+
- OpenCV 4.0+
- At least 4GB RAM (8GB+ recommended for large corpora)
- GPU acceleration recommended for large-scale processing
### Quick Install
```bash
# Create a new project directory
mkdir my-hentaivid-project
cd my-hentaivid-project
# Create virtual environment
python -m venv venv
# Activate it
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate
# Install hentaivid
pip install -r requirements.txt
```
### For Advanced Pixelation Detection:
```bash
pip install tensorflow # For deep learning-based region detection
pip install scikit-image # For advanced image processing
```
## 🎯 Quick Start
### Basic Usage
```python
from hentaivid import HentaividEncoder, HentaividChat
# Create video memory from text chunks
chunks = ["Machine learning fundamentals", "Deep learning architectures", "Neural network optimization"]
encoder = HentaividEncoder()
encoder.add_chunks(chunks)
encoder.build_video("knowledge.mp4", "knowledge_index.faiss")
# Query your embedded knowledge
chat = HentaividChat("knowledge.mp4", "knowledge_index.faiss")
response = chat.search("What are neural networks?")
print(response)
```
### Building Knowledge Base from Documents
```python
from hentaivid import HentaividEncoder
import os
# Load documents
encoder = HentaividEncoder(chunk_size=512, overlap=50)
# Add text files
for file in os.listdir("documents"):
with open(f"documents/{file}", "r") as f:
encoder.add_text(f.read(), metadata={"source": file})
# Build optimized video with cultural compliance
encoder.build_video(
"knowledge_base.mp4",
"knowledge_index.faiss",
pixelation_strategy="authentic_japanese", # Ensures cultural accuracy
fps=30, # Higher FPS = more chunks per second
censorship_region_size="standard" # Optimized for QR code readability
)
```
### Advanced Search & Retrieval
```python
from hentaivid import HentaividRetriever
# Initialize retriever
retriever = HentaividRetriever("knowledge_base.mp4", "knowledge_index.faiss")
# Semantic search with cultural context
results = retriever.search("artificial intelligence", top_k=5)
for chunk, score, frame_timestamp in results:
print(f"Score: {score:.3f} | Frame: {frame_timestamp} | {chunk[:100]}...")
# Get contextual information
context = retriever.get_context("explain transformers", max_tokens=2000)
print(context)
```
### Interactive Knowledge Interface
```python
from hentaivid import HentaividInteractive
# Launch interactive interface
interactive = HentaividInteractive("knowledge_base.mp4", "knowledge_index.faiss")
interactive.run() # Opens interface at http://localhost:7860
```
### Testing with CLI
The main script provides comprehensive testing capabilities:
```bash
# Process a text file with video
python main.py input_video.mp4 knowledge.txt --output_path enhanced_video.mp4
# Use custom pixelation detection
python main.py input_video.mp4 data.txt --detector advanced --min_region_size 2000
# High-quality output with cultural authenticity
python main.py content.mp4 corpus.txt --quality high --cultural_compliance strict
```
### Complete Example: Research Paper Knowledge Base
```bash
# 1. Create a new directory and set up environment
mkdir research-knowledge-base
cd research-knowledge-base
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# 2. Install dependencies
pip install -r requirements.txt
# 3. Create research_base.py
cat > research_base.py << 'EOF'
from hentaivid import HentaividEncoder, search_knowledge_base
import os
# Your research papers directory
papers_dir = "papers/" # Place your PDFs here
# Build video memory with academic optimization
encoder = HentaividEncoder(
chunk_size=1024, # Larger chunks for academic content
overlap=128,
embedding_model="all-mpnet-base-v2" # Higher quality embeddings
)
# Process all papers
for paper in os.listdir(papers_dir):
if paper.endswith('.pdf'):
encoder.add_pdf(os.path.join(papers_dir, paper))
encoder.build_video(
"research_memory.mp4",
"research_index.faiss",
pixelation_strategy="high_density", # More data per frame
cultural_compliance="standard"
)
# Search your research
search_knowledge_base("research_memory.mp4", "research_index.faiss")
EOF
# 4. Run it
python research_base.py
```
## 🛠️ Advanced Configuration
### Custom Pixelation Detection
```python
from hentaivid.detector import AdvancedPixelationDetector
# Use deep learning for region detection
detector = AdvancedPixelationDetector(
model_type="cnn", # or "traditional", "hybrid"
confidence_threshold=0.85,
min_region_area=1000
)
encoder = HentaividEncoder(pixelation_detector=detector)
```
### Video Optimization
```python
# For maximum data density
encoder.build_video(
"ultra_dense.mp4",
"index.faiss",
fps=60, # More frames per second
pixelation_density="maximum", # Pack more QR codes
video_codec='h265', # Better compression
cultural_accuracy="strict" # Maintains authenticity
)
```
### Distributed Processing
```python
# Process large video collections in parallel
encoder = HentaividEncoder(n_workers=8)
encoder.add_videos_parallel(video_list)
```
## 🐛 Troubleshooting
### Common Issues
**ModuleNotFoundError: No module named 'hentaivid'**
```bash
# Make sure you're using the right Python
which python # Should show your virtual environment path
# If not, activate your virtual environment:
source venv/bin/activate # On Windows: venv\Scripts\activate
```
**ImportError: OpenCV is required for video processing**
```bash
pip install opencv-python
```
**Pixelation Detection Issues**
```python
# For videos with non-standard pixelation
encoder = HentaividEncoder()
encoder.set_detection_params(
sensitivity="high",
cultural_variant="modern_japanese", # or "classic", "international"
region_validation="strict"
)
```
**Large Video Processing**
```bash
# For very large video files, use chunked processing
python main.py large_video.mp4 corpus.txt --batch_size 100 --memory_efficient
```
## 🤝 Contributing
We welcome contributions! Please see our Contributing Guide for details.
```bash
# Run tests
pytest tests/
# Run with coverage
pytest --cov=hentaivid tests/
# Format code
black hentaivid/
```
## 🆚 Comparison with Traditional Solutions
| Feature | Hentaivid | Vector DBs | Traditional DBs |
|---------|-----------|------------|----------------|
| Storage Efficiency | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ |
| Cultural Compliance | ⭐⭐⭐⭐⭐ | ❌ | ❌ |
| Setup Complexity | Simple | Complex | Complex |
| Semantic Search | ✅ | ✅ | ❌ |
| Offline Usage | ✅ | ❌ | ✅ |
| Steganographic Security | ✅ | ❌ | ❌ |
| Video Integration | Native | ❌ | ❌ |
| Scalability | Millions | Millions | Billions |
| Cost | Free | $$$$ | $$$ |
## 📚 Examples
Check out the examples/ directory for:
* Building knowledge bases from academic papers
* Creating culturally-compliant content libraries
* Multi-language support with unicode QR encoding
* Real-time knowledge retrieval systems
* Integration with popular LLMs
### Pixelation Detection Pipeline
1. **Frame Analysis** - Detect rectangular uniformity patterns
2. **Cultural Validation** - Ensure compliance with Japanese standards
3. **Region Optimization** - Maximize QR code readability
4. **Temporal Consistency** - Maintain coherent embedding across frames
### QR Code Optimization
- **Error Correction**: Optimized for video compression artifacts
- **Data Density**: Variable sizing based on region availability
- **Encoding Strategy**: UTF-8 with compression for maximum efficiency
### Embedding Architecture
```
Text Corpus → Chunking → Embeddings → FAISS Index
↓
Video Frames → Pixelation Detection → QR Generation → Video Output
```
## 🆘 Getting Help
* 📖 **Documentation** - Comprehensive guides and API reference
* 💬 **Discussions** - Ask questions and share experiences
* 🐛 **Issue Tracker** - Report bugs and request features
* 🌟 **Show & Tell** - Share your knowledge bases
## 🔗 Links
* **GitHub Repository** - [github.com/user/hentaivid](https://github.com/user/hentaivid)
* **Documentation** - [hentaivid.readthedocs.io](https://hentaivid.readthedocs.io)
* **Cultural Guidelines** - [Japanese Pixelation Standards](https://example.com/guidelines)
## 📄 License
MIT License - see LICENSE file for details.
## 🙏 Acknowledgments
Created with respect for Japanese cultural standards and the open-source community.
Built with ❤️ using:
* **sentence-transformers** - State-of-the-art embeddings for semantic search
* **OpenCV** - Computer vision and video processing
* **qrcode** - QR code generation and optimization
* **FAISS** - Efficient similarity search and clustering
* **pyzbar** - QR code detection and decoding
Special thanks to:
- The Japanese Ministry of Cultural Affairs for pixelation standards
- The global computer vision community
- All contributors who help advance culturally-aware technology
---
**Ready to revolutionize your knowledge storage with cultural authenticity? Install Hentaivid and start building!** 🚀
## About
Revolutionary RAG-compatible video storage format that embeds text chunks into QR codes hidden inside pixelated regions. Culturally compliant, semantically searchable, zero-database architecture.
### Topics
`python` `nlp` `opencv` `machine-learning` `ai` `cultural-compliance` `video-processing` `knowledge-base` `semantic-search` `faiss` `rag` `vector-database` `llm` `qr-codes` `steganography` `japanese-standards` `pixelation` `adult-content`
### Resources
🌟 **Star this repo** if Hentaivid helps your projects!
### License
MIT license
---
*Hentaivid: Where technology meets culture, and knowledge transcends boundaries.* 🎌