Every vector database has a different API:

# Pinecone
index.upsert(vectors=[(id, values, metadata)])
results = index.query(vector=query, top_k=5)

# Qdrant
client.upsert(collection_name=name, points=points)
results = client.search(collection_name=name, query_vector=query, limit=5)

# Weaviate
client.data_object.create(data_object, class_name)
results = client.query.get(class_name).with_near_vector(query).do()

Switching providers = rewriting your entire codebase.

One API. Seven databases:

# Works with ANY provider
await client.collection("products").insert(vectors)
results = await client.collection("products").search(vector=query, top_k=5)

Switch from LanceDB to Qdrant? Change one line:

- client = await EmbexClient.new_async(provider="lancedb", url="./data")
+ client = await EmbexClient.new_async(provider="qdrant", url="http://localhost:6333")

👇 See it in action:

Sarah built a RAG chatbot with Pinecone. 6 months later, costs hit $500/mo.

With traditional clients: 2-3 days rewriting code + testing
With Embex: 2 minutes changing config

# Before (Pinecone-specific)
from pinecone import Pinecone
pc = Pinecone(api_key="...")
index = pc.Index("products")

# After (Embex)
from embex import EmbexClient
client = await EmbexClient.new_async(
    provider="qdrant",  # Changed from "pinecone"
    url=os.getenv("QDRANT_URL")
)

Result: Same functionality. $450/mo saved. Zero code changes.

Pure Python/JS vector operations are slow. Embex uses Rust with SIMD acceleration:

Benchmarked on M1 Max, average of 1000 runs

The difference compounds: 4x faster operations × thousands of vectors = significant time saved.

Real-world performance vs native Python clients (10k vectors, 384d):

🤖 AI Chatbots with Memory
Store conversation history for context-aware responses

🔍 Semantic Search Engines
Search documentation, code, or content by meaning, not keywords

🎯 Recommendation Systems
E-commerce product recommendations with embeddings

📚 Knowledge Bases
RAG systems for internal documentation and support

🎨 Image Search
Find similar images using vision embeddings

"Embex let me prototype with LanceDB locally, then deploy to Qdrant Cloud without changing a line of code. Saved 2 days of migration work."

Share what you built →

Python:

# Install
pip install embex lancedb sentence-transformers

# Quick test
python3 << EOF
import asyncio
from embex import EmbexClient

async def main():
    client = await EmbexClient.new_async('lancedb', './data')
    print('✅ Embex ready!')

asyncio.run(main())
EOF

Node.js:

npm install @bridgerust/embex lancedb
node -e "
const {EmbexClient} = require('@bridgerust/embex');
EmbexClient.new({provider: 'lancedb', url: './data'})
  .then(() => console.log('✅ Embex ready!'));
"

→ Next: See Getting Started Guide

When to use each:

• Raw clients: You're committed to one database forever
• LangChain/LlamaIndex: You need full RAG framework with LLM chains
• Embex: You want vector operations only, with flexibility to switch providers

LanceDB • Qdrant • Pinecone • Chroma • PgVector • Milvus • Weaviate

• 💬 Discord: Get help, share projects, discuss features → Join Server
• � Reddit: Join the discussion → r/embex
• �💡 GitHub Discussions: Feature requests and Q&A
• 🐛 Issues: Bug reports
• 📝 Blog: Tutorials and case studies → bridgerust.dev/embex

Built something cool with Embex? Share it in #showcase on Discord or tag us on Twitter!

Q: How is Embex different from LangChain's VectorStores?
A: LangChain couples vector operations with LLM chains. Embex is vector-only, 4x faster (Rust core), and switching providers requires 0 code changes (vs. rewriting VectorStore initialization).

Q: Does Embex support hybrid search (vector + keyword)?
A: Yes! Coming in v0.3. Currently supports pure vector and metadata filtering.

Q: Can I use Embex in production?
A: Yes! Embex includes production features like connection pooling, automatic retries, and observability hooks. Currently used in production by developers running RAG chatbots, semantic search engines, and recommendation systems. See deployment guide for best practices.

Q: Which provider should I start with?
A: LanceDB for local dev (zero setup), then Qdrant/Pinecone for production (managed, scalable).

Q: Do you support [X database]?
A: Current: LanceDB, Qdrant, Pinecone, Chroma, PgVector, Milvus, Weaviate. Roadmap: Elasticsearch, OpenSearch, Redis. Request here.

pip install embex

npm install @bridgerust/embex

[dependencies]
bridge-embex = { git = "https://github.com/bridgerust/bridgerust", path = "crates/embex/client" }

• Universal API: Switch providers without code changes
• High Performance: Rust core with SIMD acceleration (4x faster)
• Zero Setup: Start with LanceDB (embedded, local)
• Production Ready: Connection pooling, migrations, observability

• Getting Started
• API Reference
• Providers Guide

1. ⭐ Star this repo if Embex saves you time
2. 💬 Join Discord for help and to share what you build
3. 📖 Try the tutorial: Build a chatbot in 10 minutes

Quick links:

• Installation Guide
• Python API Docs • Node.js API Docs
• Migration Examples
• Performance Benchmarks

This repository also contains the BridgeRust framework - a unified system for building cross-language Rust libraries. Embex is built with BridgeRust.

Crate	Version	Downloads	Docs
bridgerust
bridgerust-macros
bridge (CLI)			-

• Quick Reference
• Getting Started Guide
• Migration Guide
• Examples
• Troubleshooting
• Comprehensive Example

See CONTRIBUTING.md for development setup and guidelines.

MIT OR Apache-2.0