embex 0.1.15

Embex: A Universal Vector Database ORM

Embex (Python)

The fastest way to add vector search to your app.

Embex is a universal vector database client that lets you start with zero setup and scale to production without rewriting code.

🚀 Features

• Start Simple: Use LanceDB (embedded) for zero-setup local development.
• Unified API: Switch to Qdrant, Pinecone, or Milvus just by changing the config.
• Performance: Powered by a shared Rust core with SIMD acceleration.
• Type Safety: Fully typed Python bindings.

📦 Installation

pip install embex lancedb sentence-transformers

⚡ Quick Start

Build semantic search in 5 minutes using LanceDB (embedded) and local embeddings. No API keys or Docker needed!

import asyncio
from embex import EmbexClient, Vector
from sentence_transformers import SentenceTransformer

async def main():
    # 1. Setup Embedding Model
    model = SentenceTransformer('all-MiniLM-L6-v2')

    # 2. Initialize Client (uses LanceDB embedded)
    client = await EmbexClient.new_async("lancedb://./data")

    # 3. Create Collection (384 dimensions for MiniLM)
    await client.create_collection("products", dimension=384)

    # 4. Insert Data
    documents = [
        {"id": "1", "text": "Apple iPhone 15", "category": "electronics"},
        {"id": "2", "text": "Samsung Galaxy S24", "category": "electronics"},
    ]

    vectors = []
    for doc in documents:
        vectors.append(Vector(
            id=doc["id"],
            vector=model.encode(doc["text"]).tolist(),
            metadata={"text": doc["text"]}
        ))

    await client.insert("products", vectors)

    # 5. Search
    query = "smartphone"
    results = await client.search(
        collection_name="products",
        vector=model.encode(query).tolist(),
        limit=1
    )

    print(f"Query: '{query}'")
    print(f"Match: {results[0].metadata['text']}")

if __name__ == "__main__":
    asyncio.run(main())

🗺️ Development → Production Roadmap

Stage	Recommendation	Why?
Day 1: Learning	LanceDB	Embedded. Zero setup. Free.
Week 2: Staging	Qdrant / Pinecone	Managed cloud. Connection pooling.
Month 1: Scale	Milvus	Distributed. Billion-scale vectors.
Anytime	PgVector	You already use PostgreSQL.

☁️ Switch Provider (Zero Code Changes)

Ready for production? Just change the initialization line.

From LanceDB (Dev):

client = await EmbexClient.new_async("lancedb://./data")

To Qdrant Cloud (Prod):

client = EmbexClient(
    provider="qdrant",
    url="https://your-cluster.qdrant.io",
    api_key="..."
)

🔗 Resources

• Full Documentation: bridgerust.dev/embex
• GitHub: bridgerust/bridgerust