Embex (Node.js)

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: Full TypeScript support.

📦 Installation

``bash
npm install @bridgerust/embex lancedb @xenova/transformers
`

⚡ Quick Start

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

`typescript
import { EmbexClient, Vector } from "@bridgerust/embex";
import { pipeline } from "@xenova/transformers";

async function main() {
// 1. Setup Embedding Model
const generateEmbedding = await pipeline(
"feature-extraction",
"Xenova/all-MiniLM-L6-v2"
);
const embed = async (text: string) => {
const output = await generateEmbedding(text, {
pooling: "mean",
normalize: true,
});
return Array.from(output.data);
};

// 2. Initialize Client (uses LanceDB embedded)
const client = await EmbexClient.newAsync("lancedb", "./data");

// 3. Create Collection (384 dimensions for MiniLM)
await client.createCollection("products", 384);

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

const vectors: Vector[] = [];
for (const doc of documents) {
vectors.push({
id: doc.id,
vector: await embed(doc.text),
metadata: { text: doc.text },
});
}

await client.insert("products", vectors);

// 5. Search
const query = "smartphone";
const results = await client.search({
collection_name: "products",
vector: await embed(query),
limit: 1,
});

console.log(Query: '${query}');
console.log(Match: ${results[0].metadata.text});
}

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):

`typescript
const client = await EmbexClient.newAsync("lancedb", "./data");
`

To Qdrant Cloud (Prod):

`typescript
const client = new EmbexClient(
"qdrant",
"https://your-cluster.qdrant.io",
process.env.QDRANT_API_KEY
);
`

🔄 Data Migration

Move data between providers effortlessly using the built-in DataMigrator.

`typescript
import { EmbexClient, DataMigrator } from "@bridgerust/embex";

// 1. Setup clients
const source = await EmbexClient.newAsync("lancedb", "./local_data");
const dest = new EmbexClient("qdrant", "http://prod-db:6333");

// 2. Migrate
const migrator = new DataMigrator(source, dest);
const result = await migrator.migrateSimple(
"products", // source
"products_v2" // destination
);

console.log(Migrated ${result.pointsMigrated} points!);
``

🔗 Resources

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