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

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!

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):

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

To Qdrant Cloud (Prod):

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.

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