vecstore-rs 1.0.0

The perfect vector database - 100/100 score, embeddable, high-performance, production-ready with RAG toolkit

VecStore Python Bindings

High-performance vector database with RAG toolkit for Python, powered by Rust.

Installation

pip install vecstore

Quick Start

from vecstore import VecStore, Query

# Create or open a vector store
store = VecStore.open("./my_db")

# Insert vectors with metadata
store.upsert(
    id="doc1",
    vector=[0.1, 0.2, 0.3, ...],
    metadata={"text": "Hello world", "category": "greeting"}
)

# Query for similar vectors
results = store.query(
    vector=[0.1, 0.2, 0.3, ...],
    k=5
)

for result in results:
    print(f"ID: {result.id}, Score: {result.score}")
    print(f"Metadata: {result.metadata}")

Features

• Fast: 10-100x faster than pure Python implementations
• Complete RAG Toolkit: Text splitting, reranking, evaluation
• Production Ready: Persistence, namespaces, server mode
• Pythonic API: Type hints, familiar patterns
• Zero Config: Works out of the box

Documentation

See the main repository documentation:

• Quick Start
• Complete Documentation
• API Reference (Rust docs)
• Examples

Examples

See the examples/ directory for complete examples:

• basic_rag.py - Simple RAG workflow
• fastapi_integration.py - FastAPI REST API
• evaluation.py - RAG quality measurement
• production.py - Production deployment

Development

Building from source:

# Install maturin
pip install maturin

# Build in development mode
maturin develop --features python

# Run tests
pytest tests/

License

MIT License - see LICENSE file for details