MemVid: Compress AI Memory 100× with Video Encoding

1. The Problem with Traditional AI Memory

Current Vector Database Limitations

Traditional RAG systems using vector databases face critical challenges:

The MemVid Revolution

What if you could replace your entire vector database with a single MP4 file?

• ✅ 100MB text → 1-2MB video (50-100× compression)
• ✅ <100ms retrieval for 1 million chunks
• ✅ 500MB RAM regardless of dataset size (constant memory)
• ✅ No database infrastructure - just FFmpeg and Python
• ✅ Portable - copy one file, deploy anywhere

How? By leveraging 30 years of video compression research. H.264/H.265 codecs are optimized for redundancy elimination—exactly what text embeddings have.

2. How MemVid Works

Core Concept: Text → QR Code → Video Frame

Step 1: Text Chunking
────────────────────────────────────────────────
"The Eiffel Tower was built in 1889..."
"Paris is the capital of France..."
"Machine learning models require data..."
           ↓

Step 2: QR Code Encoding
────────────────────────────────────────────────
Each chunk → QR code image (PNG)
[Chunk 0001] → ████ ▓▓▓▓ ████
[Chunk 0002] → ▓▓▓▓ ████ ▓▓▓▓
           ↓

Step 3: Video Concatenation
────────────────────────────────────────────────
QR codes → Video frames (30fps MP4)
Frame 0001: Chunk 0001
Frame 0002: Chunk 0002
Frame 0003: Chunk 0003
           ↓

Step 4: Embedding Index
────────────────────────────────────────────────
Text → Embedding (384D vector)
Embedding → Frame number mapping
Store in lightweight JSON (2-5MB for 1M chunks)
           ↓

Step 5: Semantic Search
────────────────────────────────────────────────
Query: "When was Eiffel Tower built?"
  1. Embed query (384D vector)
  2. Find nearest embedding in index
  3. Get frame number → 0001
  4. Seek video to frame 0001
  5. Decode QR → Return text

Why This Works

1. Video Codecs Exploit Redundancy

• QR codes have repetitive patterns (black/white blocks)
• H.264 uses intra-frame compression (same as PNG, but better)
• Result: 50-100× smaller than storing raw PNGs

2. Constant Memory with Streaming

• FFmpeg decodes frames on-the-fly - no need to load entire video
• Seek directly to frame N in O(log n) time
• Memory usage: 500MB fixed (decoder buffer + index)

3. Fast Retrieval with Smart Indexing

• Embedding index is tiny (2-5MB for 1M vectors)
• Nearest neighbor search: <10ms with FAISS/HNSWlib
• Video seek + decode: 50-80ms
• Total: <100ms end-to-end

3. Technical Architecture

System Components

┌─────────────────────────────────────────────────────────┐
│                    MemVid Architecture                  │
│                                                         │
│  ┌──────────────┐      ┌──────────────┐      ┌────────┐│
│  │  Text Chunks │─────→│ QR Encoder   │─────→│ Video  ││
│  │  (raw data)  │      │ (qrcode lib) │      │ (MP4)  ││
│  └──────────────┘      └──────────────┘      └────────┘│
│         │                                         ▲     │
│         │                                         │     │
│         ▼                                         │     │
│  ┌──────────────┐      ┌──────────────┐          │     │
│  │  Embeddings  │◄────┤ Embedding     │          │     │
│  │  (vectors)   │      │ Model (SBERT)│          │     │
│  └──────────────┘      └──────────────┘          │     │
│         │                                         │     │
│         ▼                                         │     │
│  ┌──────────────┐      ┌──────────────┐          │     │
│  │  Index File  │      │  FFmpeg      │──────────┘     │
│  │  (JSON/FAISS)│      │  Decoder     │                │
│  └──────────────┘      └──────────────┘                │
│                                                         │
│  Query: "Find docs about X" → Embed → Search Index →   │
│  Frame N → Seek Video → Decode QR → Return Text        │
└─────────────────────────────────────────────────────────┘

Technology Stack

Encoding Pipeline

• QR Generation: qrcode Python library (error correction level H)
• Video Encoding: FFmpeg with H.264/H.265 codec
• Embedding Model: SentenceTransformer (all-MiniLM-L6-v2, 384D)
• Index Storage: JSON for simple, FAISS for >100K vectors

Retrieval Pipeline

• Vector Search: Cosine similarity with HNSWlib/FAISS
• Video Decoding: FFmpeg seek + frame extraction
• QR Decoding: pyzbar library

File Format Breakdown

{
  "version": "1.0",
  "total_chunks": 10000,
  "video_path": "knowledge.mp4",
  "embedding_model": "all-MiniLM-L6-v2",
  "index": [
    {
      "frame": 0,
      "embedding": [0.123, -0.456, 0.789, ...],  // 384D vector
      "metadata": {
        "source": "docs/api.md",
        "timestamp": "2025-01-10"
      }
    },
    {
      "frame": 1,
      "embedding": [-0.234, 0.567, -0.890, ...],
      "metadata": {...}
    }
    // ... 10,000 entries
  ]
}
// File size: ~2.5MB for 10K chunks (250 bytes per entry)

4. Performance Benchmarks

Storage Compression

Retrieval Speed (1M chunks)

Note: MemVid trades slight latency increase (40-60ms) for 100× storage reduction and zero infrastructure. For most RAG use cases, <100ms is acceptable.

Memory Footprint

Indexing Speed

• 📊 ~10K chunks/second on modern CPU (AMD EPYC 7763)
• ⚡ ~35K chunks/second with GPU acceleration (CUDA QR encoding)
• 🎯 1M chunks indexed in ~2 minutes (CPU) or 30 seconds (GPU)

5. Implementation Guide

Installation

# Install dependencies
pip install memvid sentence-transformers qrcode[pil] pyzbar opencv-python

# Or build from source
git clone https://github.com/Olow304/memvid
cd memvid
pip install -e .

# Install FFmpeg (required for video encoding)
# Ubuntu/Debian
sudo apt-get install ffmpeg

# macOS
brew install ffmpeg

Basic Usage: Create Memory from Texts

from memvid import MemVid
from sentence_transformers import SentenceTransformer

# Initialize MemVid with embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')
memory = MemVid(embedding_model=model)

# Load documents
texts = [
    "The Eiffel Tower was built in 1889 for the World's Fair.",
    "Paris is the capital of France, with a population of 2.1M.",
    "Machine learning models require large datasets for training.",
    # ... 1 million more chunks
]

# Create video memory (this will take 2-3 minutes for 1M chunks)
memory.create(
    texts=texts,
    output_video="knowledge.mp4",
    output_index="knowledge_index.json",
    fps=30,  # Frames per second
    codec="libx264",  # H.264 for compatibility
    qr_error_correction="H"  # High error correction
)

print(f"✅ Created video: {memory.video_size / 1024**2:.2f} MB")
print(f"✅ Created index: {memory.index_size / 1024**2:.2f} MB")

Semantic Search & Retrieval

from memvid import MemVid
from sentence_transformers import SentenceTransformer

# Load existing memory
model = SentenceTransformer('all-MiniLM-L6-v2')
memory = MemVid.load(
    video_path="knowledge.mp4",
    index_path="knowledge_index.json",
    embedding_model=model
)

# Semantic search
query = "When was the Eiffel Tower built?"
results = memory.search(query, top_k=5)

for i, result in enumerate(results):
    print(f"\n#{i+1} (score: {result.score:.3f})")
    print(f"Text: {result.text}")
    print(f"Source: {result.metadata.get('source', 'N/A')}")

# Output:
# #1 (score: 0.892)
# Text: The Eiffel Tower was built in 1889 for the World's Fair.
# Source: docs/paris_landmarks.md

Advanced: Custom Embedding Models

# Use domain-specific embedding model
from sentence_transformers import SentenceTransformer

# For code search
model = SentenceTransformer('microsoft/codebert-base')

# For multilingual
model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')

# For medical/scientific
model = SentenceTransformer('pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb')

memory = MemVid(embedding_model=model)
# ... rest of the code

Production: FastAPI RAG Service

from fastapi import FastAPI
from memvid import MemVid
from sentence_transformers import SentenceTransformer
from pydantic import BaseModel

app = FastAPI()

# Load memory at startup
model = SentenceTransformer('all-MiniLM-L6-v2')
memory = MemVid.load(
    video_path="/data/knowledge.mp4",
    index_path="/data/knowledge_index.json",
    embedding_model=model
)

class Query(BaseModel):
    text: str
    top_k: int = 5

@app.post("/search")
async def search(query: Query):
    results = memory.search(query.text, top_k=query.top_k)
    return {
        "query": query.text,
        "results": [
            {
                "text": r.text,
                "score": r.score,
                "metadata": r.metadata
            }
            for r in results
        ]
    }

@app.get("/stats")
async def stats():
    return {
        "total_chunks": memory.total_chunks,
        "video_size_mb": memory.video_size / 1024**2,
        "index_size_mb": memory.index_size / 1024**2,
        "memory_usage_mb": memory.ram_usage / 1024**2
    }

6. Production Use Cases

1. Offline Documentation Search

2. Edge AI Knowledge Base

3. Multi-Tenant RAG SaaS

4. Time-Travel Debugging for AI Agents

7. MemVid vs Vector Databases

When to Use MemVid

• ✅ Edge/Offline deployments - No internet, limited resources
• ✅ Cost-sensitive projects - Avoid DB infrastructure costs
• ✅ Portable knowledge bases - Share as single file (USB, email)
• ✅ Read-heavy workloads - Rare updates, frequent retrieval
• ✅ Multi-tenant SaaS - 1 video per customer, S3 storage

When to Use Vector Databases

• ⚠️ Real-time updates - Frequent insertions/deletions (>100/sec)
• ⚠️ Ultra-low latency - Need <10ms retrieval (MemVid is 70-90ms)
• ⚠️ Complex filtering - Advanced metadata queries, hybrid search
• ⚠️ Distributed systems - Sharding, replication, HA requirements

8. Future Roadmap (v2)

Upcoming Features

1. Living-Memory Engine

• 🔄 Incremental updates - Append new frames without re-encoding
• 🗑️ Soft deletes - Mark frames as deleted, compact later
• 📈 Version control - Git-like branching for memory states

2. Smart Codec Selection

• 🎯 AV1 codec - 30-50% better compression than H.265
• ⚡ Hardware acceleration - NVENC (NVIDIA), QuickSync (Intel)
• 🔬 Per-chunk optimization - Different QR sizes based on text length

3. GPU Acceleration

• 🚀 CUDA QR encoding - 10× faster indexing (350K chunks/sec)
• 📊 Tensor Core search - GPU-accelerated similarity search
• 🎬 GPU video decoding - NVDEC for 50% faster retrieval

4. Time-Travel Debugging

• ⏰ Temporal indexing - Query "What did the agent know at 10:32 AM?"
• 📸 Snapshots - Create checkpoint videos for rollback
• 🔍 Diff visualization - Compare memory states across time

Experimental: Neural Codecs

Research is exploring using learned video codecs (e.g., Google's NN-based compression) to achieve 500-1000× compression by learning text embedding patterns.

Potential: 1M chunks → 500KB file (vs current 18MB)

9. Conclusion

MemVid represents a paradigm shift in AI memory storage - by repurposing video compression technology, it achieves 100× storage reduction while maintaining practical retrieval speeds.

Key Takeaways

• 🎯 100× compression - 1GB text → 10-20MB video
• ⚡ <100ms retrieval - Acceptable for most RAG use cases
• 💾 Constant 500MB RAM - No linear growth with data size
• 🚀 Zero infrastructure - Just FFmpeg and Python
• 📦 Portable - Single file deployment, works offline

Trade-offs to Consider

• ⚠️ Slower retrieval - 70ms vs 15ms for Qdrant (4-5× slower)
• ⚠️ Read-optimized - Updates require re-encoding (v2 will fix this)
• ⚠️ Limited filtering - Semantic search only, no complex metadata queries

When to Adopt MemVid

Choose MemVid if you:

• ✅ Deploy to edge devices or offline environments
• ✅ Want minimal infrastructure (no Kubernetes, no DB maintenance)
• ✅ Have cost constraints (avoid $500-1000/mo vector DB bills)
• ✅ Need portability (share knowledge base as single file)
• ✅ Can tolerate 70-100ms retrieval (vs 15ms for dedicated DBs)