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High-performance AI memory library with full-text and semantic search (Rust bindings for memvid v2)

Project description

memvid-rs (Python Bindings)

🇰🇷 한국어 (Korean) PyPI version

Memvid-rs provides Python bindings for memvid-core v2, a high-performance AI memory library written in Rust.

This is a major version upgrade from v1 (QR video encoding) to v2 (.mv2 single-file format).

Attribution: This project wraps memvid-core with PyO3 bindings for Python.

🚀 Key Features

  • Single-File Memory: All data stored in portable .mv2 format
  • Full-Text Search: BM25 ranking via embedded Tantivy index
  • Crash-Safe Writes: WAL-based append-only architecture
  • Cross-Platform: Works on macOS, Linux, Windows
  • Python 3.8-3.14+: ABI3 wheel supports all modern Python versions

📦 Installation

pip install memvid-rs

💻 Quick Start

from memvid_rs import MemvidMemory

# Create a new memory file
memory = MemvidMemory.create("my_memory.mv2")

# Add content
idx = memory.append("Hello, memvid v2!")
memory.commit()

# Retrieve content
text = memory.get_frame(idx)
print(text)  # Hello, memvid v2!

# Check stats
print(f"Total frames: {memory.frame_count()}")

# Open existing memory
memory2 = MemvidMemory.open("my_memory.mv2")

🔧 API Reference

MemvidMemory

Method Description
create(path) Create new .mv2 file
open(path) Open existing .mv2 file
append(text) Add text, returns 0-based index
commit() Flush writes to disk
get_frame(index) Retrieve frame text by index
frame_count() Get total frame count

⚠️ Breaking Changes from v1

  • QR video encoding removed - v2 uses binary .mv2 format
  • API redesigned - MemvidEncoder/MemvidDecoder replaced with MemvidMemory
  • File format incompatible - v1 .mp4 files cannot be opened in v2

📋 Requirements

  • Python: >= 3.8
  • System: macOS, Linux, Windows

📄 License

MIT License

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