What to expect in v2

Early-access notice
Memvid v1 is still experimental. The file format and API may change until we lock in a stable release.

Memvid v2 – what’s next

• Living-Memory Engine – keep adding new data and let LLMs remember it across sessions.
• Capsule Context – shareable .mv2 capsules, each with its own rules and expiry.
• Time-Travel Debugging – rewind or branch any chat to review or test.
• Smart Recall – local cache guesses what you’ll need and loads it in under 5 ms.
• Codec Intelligence – auto-tunes AV1 now and future codecs later, so files keep shrinking.
• CLI & Dashboard – simple tools for branching, analytics, and one-command cloud publish.

Sneak peek of Memvid v2 - a living memory engine that can be used to chat with your knowledge base.

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Memvid v1

Memvid - Turn millions of text chunks into a single, searchable video file

Memvid compresses an entire knowledge base into MP4 files while keeping millisecond-level semantic search. Think of it as SQLite for AI memory portable, efficient, and self-contained. By encoding text as QR codes in video frames, we deliver 50-100× smaller storage than vector databases with zero infrastructure.

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Why Video Compression Changes Everything 🚀

What it enables	How video codecs make it possible
50-100× smaller storage	Modern video codecs compress repetitive visual patterns (QR codes) far better than raw embeddings
Sub-100ms retrieval	Direct frame seek via index → QR decode → your text. No server round-trips
Zero infrastructure	Just Python and MP4 files-no DB clusters, no Docker, no ops
True portability	Copy or stream memory.mp4-it works anywhere video plays
Offline-first design	After encoding, everything runs without internet

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Under the Hood - Memvid v1 🔍

1. Text → QR → Frame
   Each text chunk becomes a QR code, packed into video frames. Modern codecs excel at compressing these repetitive patterns.

2. Smart indexing
   Embeddings map queries → frame numbers. One seek, one decode, millisecond results.

3. Codec leverage
   30 years of video R&D means your text gets compressed better than any custom algorithm could achieve.

4. Future-proof
   Next-gen codecs (AV1, H.266) automatically make your memories smaller and faster-no code changes needed.

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Installation

pip install memvid
# For PDF support
pip install memvid PyPDF2

Quick Start

from memvid import MemvidEncoder, MemvidChat

# Create video memory from text
chunks = ["NASA founded 1958", "Apollo 11 landed 1969", "ISS launched 1998"]
encoder = MemvidEncoder()
encoder.add_chunks(chunks)
encoder.build_video("space.mp4", "space_index.json")

# Chat with your memory
chat = MemvidChat("space.mp4", "space_index.json")
response = chat.chat("When did humans land on the moon?")
print(response)  # References Apollo 11 in 1969

Real-World Examples

Documentation Assistant

from memvid import MemvidEncoder
import os

encoder = MemvidEncoder(chunk_size=512)

# Index all markdown files
for file in os.listdir("docs"):
    if file.endswith(".md"):
        with open(f"docs/{file}") as f:
            encoder.add_text(f.read(), metadata={"file": file})

encoder.build_video("docs.mp4", "docs_index.json")

PDF Library Search

# Index multiple PDFs
encoder = MemvidEncoder()
encoder.add_pdf("deep_learning.pdf")
encoder.add_pdf("machine_learning.pdf") 
encoder.build_video("ml_library.mp4", "ml_index.json")

# Semantic search across all books
from memvid import MemvidRetriever
retriever = MemvidRetriever("ml_library.mp4", "ml_index.json")
results = retriever.search("backpropagation", top_k=5)

Interactive Web UI

from memvid import MemvidInteractive

# Launch at http://localhost:7860
interactive = MemvidInteractive("knowledge.mp4", "index.json")
interactive.run()

Advanced Features

Scale Optimization

# Maximum compression for huge datasets
encoder.build_video(
    "compressed.mp4",
    "index.json", 
    fps=60,              # More frames/second
    frame_size=256,      # Smaller QR codes
    video_codec='h265',  # Better compression
    crf=28              # Quality tradeoff
)

Custom Embeddings

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-mpnet-base-v2')
encoder = MemvidEncoder(embedding_model=model)

Parallel Processing

encoder = MemvidEncoder(n_workers=8)
encoder.add_chunks_parallel(million_chunks)

CLI Usage

# Process documents
python examples/file_chat.py --input-dir /docs --provider openai

# Advanced codecs
python examples/file_chat.py --files doc.pdf --codec h265

# Load existing
python examples/file_chat.py --load-existing output/memory

Performance

• Indexing: ~10K chunks/second on modern CPUs
• Search: <100ms for 1M chunks (includes decode)
• Storage: 100MB text → 1-2MB video
• Memory: Constant 500MB RAM regardless of size

What’s Coming in v2

• Delta encoding: Time-travel through knowledge versions
• Streaming ingest: Add to videos in real-time
• Cloud dashboard: Web UI with API management
• Smart codecs: Auto-select AV1/HEVC per content
• GPU boost: 100× faster bulk encoding

Get Involved

Memvid is redefining AI memory. Join us:

• ⭐ Star on GitHub
• 🐛 Report issues or request features
• 🔧 Submit PRs (we review quickly!)
• 💬 Discuss video-based AI memory