The lightweight, game-changing solution for AI memory at scale
Memvid revolutionizes AI memory management by encoding text data into videos, enabling lightning-fast semantic search across millions of text chunks with sub-second retrieval times. Unlike traditional vector databases that consume massive amounts of RAM and storage, Memvid compresses your knowledge base into compact video files while maintaining instant access to any piece of information.
mem.mp4
- 🎥 Video-as-Database: Store millions of text chunks in a single MP4 file
- 🔍 Semantic Search: Find relevant content using natural language queries
- 💬 Built-in Chat: Conversational interface with context-aware responses
- 📚 PDF Support: Direct import and indexing of PDF documents
- 🚀 Fast Retrieval: Sub-second search across massive datasets
- 💾 Efficient Storage: 10x compression compared to traditional databases
- 🔌 Pluggable LLMs: Works with OpenAI, Anthropic, or local models
- 🌐 8000 Offline-First: No internet required after video generation
- 🔧 Simple API: Get started with just 3 lines of code
- 📖 Digital Libraries: Index thousands of books in a single video file
- 🎓 Educational Content: Create searchable video memories of course materials
- 📰 News Archives: Compress years of articles into manageable video databases
- 💼 Corporate Knowledge: Build company-wide searchable knowledge bases
- 🔬 Research Papers: Quick semantic search across scientific literature
- 📝 Personal Notes: Transform your notes into a searchable AI assistant
- Video as Database: Store millions of text chunks in a single MP4 file
- Instant Retrieval: Sub-second semantic search across massive datasets
- 10x Storage Efficiency: Video compression reduces memory footprint dramatically
- Zero Infrastructure: No database servers, just files you can copy anywhere
- Offline-First: Works completely offline once videos are generated
- Minimal Dependencies: Core functionality in ~1000 lines of Python
- CPU-Friendly: Runs efficiently without GPU requirements
- Portable: Single video file contains your entire knowledge base
- Streamable: Videos can be streamed from cloud storage
pip install memvidpip install memvid PyPDF2# Create a new project directory
mkdir my-memvid-project
cd my-memvid-project
# Create virtual environment
python -m venv venv
# Activate it
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate
# Install memvid
pip install memvid
# For PDF support:
pip install PyPDF2from memvid import MemvidEncoder, MemvidChat
# Create video memory from text chunks
chunks = ["Important fact 1", "Important fact 2", "Historical event details"]
encoder = MemvidEncoder()
encoder.add_chunks(chunks)
encoder.build_video("memory.mp4", "memory_index.json")
# Chat with your memory
chat = MemvidChat("memory.mp4", "memory_index.json")
chat.start_session()
response = chat.chat("What do you know about historical events?")
print(response)from memvid import MemvidEncoder
import os
# Load documents
encoder = MemvidEncoder(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
encoder.build_video(
"knowledge_base.mp4",
"knowledge_index.json",
fps=30, # Higher FPS = more chunks per second
frame_size=512 # Larger frames = more data per frame
)from memvid import MemvidRetriever
# Initialize retriever
retriever = MemvidRetriever("knowledge_base.mp4", "knowledge_index.json")
# Semantic search
results = retriever.search("machine learning algorithms", top_k=5)
for chunk, score in results:
print(f"Score: {score:.3f} | {chunk[:100]}...")
# Get context window
context = retriever.get_context("explain neural networks", max_tokens=2000)
print(context)from memvid import MemvidInteractive
# Launch interactive chat UI
interactive = MemvidInteractive("knowledge_base.mp4", "knowledge_index.json")
interactive.run() # Opens web interface at http://localhost:7860The examples/file_chat.py script provides a comprehensive way to test Memvid with your own documents:
# Process a directory of documents
python examples/file_chat.py --input-dir /path/to/documents --provider google
# Process specific files
python examples/file_chat.py --files doc1.txt doc2.pdf --provider openai
# Use H.265 compression (requires Docker)
python examples/file_chat.py --input-dir docs/ --codec h265 --provider google
# Custom chunking for large documents
python examples/file_chat.py --files large.pdf --chunk-size 2048 --overlap 32 --provider google
# Load existing memory
python examples/file_chat.py --load-existing output/my_memory --provider google# 1. Create a new directory and set up environment
mkdir book-chat-demo
cd book-chat-demo
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# 2. Install dependencies
pip install memvid PyPDF2
# 3. Create book_chat.py
cat > book_chat.py << 'EOF'
from memvid import MemvidEncoder, chat_with_memory
import os
# Your PDF file
book_pdf = "book.pdf" # Replace with your PDF path
# Build video memory
encoder = MemvidEncoder()
encoder.add_pdf(book_pdf)
encoder.build_video("book_memory.mp4", "book_index.json")
# Chat with the book
api_key = os.getenv("OPENAI_API_KEY") # Optional: for AI responses
chat_with_memory("book_memory.mp4", "book_index.json", api_key=api_key)
EOF
# 4. Run it
export OPENAI_API_KEY="your-api-key" # Optional
python book_chat.pyfrom sentence_transformers import SentenceTransformer
# Use custom embedding model
custom_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
encoder = MemvidEncoder(embedding_model=custom_model)# For maximum compression
encoder.build_video(
"compressed.mp4",
"index.json",
fps=60, # More frames per second
frame_size=256, # Smaller frames
video_codec='h265', # Better compression
crf=28 # Compression quality (lower = better quality)
)# Process large datasets in parallel
encoder = MemvidEncoder(n_workers=8)
encoder.add_chunks_parallel(massive_chunk_list)ModuleNotFoundError: No module named 'memvid'
# 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\activateImportError: PyPDF2 is required for PDF support
pip install PyPDF2LLM API Key Issues
# Set your API key (get one at https://platform.openai.com)
export GOOGLE_API_KEY="AIzaSyB1-..." # macOS/Linux
# Or on Windows:
set GOOGLE_API_KEY=AIzaSyB1-...Large PDF Processing
# For very large PDFs, use smaller chunk sizes
encoder = MemvidEncoder()
encoder.add_pdf("large_book.pdf", chunk_size=400, overlap=50)We welcome contributions! Please see our Contributing Guide for details.
# Run tests
pytest tests/
# Run with coverage
pytest --cov=memvid tests/
# Format code
black memvid/| Feature | Memvid | Vector DBs | Traditional DBs |
|---|---|---|---|
| Storage Efficiency | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ |
| Setup Complexity | Simple | Complex | Complex |
| Semantic Search | ✅ | ✅ | ❌ |
| Offline Usage | ✅ | ❌ | ✅ |
| Portability | File-based | Server-based | Server-based |
| Scalability | Millions | Millions | Billions |
| Cost | Free | $$$$ | $$$ |
Check out the examples/ directory for:
- Building memory from Wikipedia dumps
- Creating a personal knowledge base
- Multi-language support
- Real-time memory updates
- Integration with popular LLMs
- 📖 Documentation - Comprehensive guides
- 💬 Discussions - Ask questions
- 🐛 Issue Tracker - Report bugs
- 🌟 Show & Tell - Share your projects
MIT License - see LICENSE file for details.
Created by Olow304 and the Memvid community.
Built with ❤️ using:
- sentence-transformers - State-of-the-art embeddings for semantic search
- OpenCV - Computer vision and video processing
- qrcode - QR code generation
- FAISS - Efficient similarity search
- PyPDF2 - PDF text extraction
Special thanks to all contributors who help make Memvid better!
Ready to revolutionize your AI memory management? Install Memvid and start building! 🚀