9,000+ Downloads in 2 Weeks: I Just Built and Published

Two weeks ago, I published Embex to PyPI and npm.

Today: 9,000+ downloads (7K Python, 2K Node.js).

I made one LinkedIn post after publishing. That's it. And it didn't even got likes or comments.

Here's what I built.

What is Embex?

A universal ORM for vector databases. One API that works across 7 different databases.

The problem:

Every vector database has a completely 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 from Pinecone to Qdrant means rewriting your entire data layer.

With Embex:

# Works with ANY provider

client = await EmbexClient.new_async(provider="lancedb", url="./data")

await client.insert("products", vectors)

results = await client.search("products", vector=query, top_k=5)



# Switch to Qdrant? Change ONE line:

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

Same code. Zero vendor lock-in.

Why I Built It

I needed to test different vector databases for a project. Writing separate implementations for each one seemed wasteful.

So I built one API that works with all of them.

The Tech Stack

Core:

• Rust (performance-critical operations)


• PyO3 (Python bindings)


• Napi-rs (Node.js bindings)


• SIMD instructions (vector math acceleration)

Why Rust?

Vector operations are CPU-intensive. Rust + SIMD is ~4x faster than pure Python/JavaScript for normalization, similarity calculations, and filtering.

Supported Databases:

• LanceDB (embedded, file-based)


• Qdrant (managed/self-hosted)


• Pinecone (managed)


• Chroma (embedded/server)


• PgVector (PostgreSQL extension)


• Milvus (self-hosted/cloud)


• Weaviate (managed/self-hosted)

The Launch

Published to PyPI and npm. Made one LinkedIn post. Went back to building.

Downloads started coming in. 9,000+ in two weeks.

I don't know where the traffic came from. PyPI/npm search, probably. Maybe GitHub. I haven't looked at analytics closely.

What I Noticed

23% of downloads are Node.js. I expected mostly Python. Apparently people are building with JavaScript too.

Multi-language support matters. Supporting both Python and Node.js from day one expanded reach.

Example Usage

Python:

from embex import EmbexClient, Vector

from sentence_transformers import SentenceTransformer



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

model = SentenceTransformer('all-MiniLM-L6-v2')



await client.create_collection("docs", dimension=384)



vectors = [Vector(

    id="1",

    vector=model.encode("your text").tolist(),

    metadata={"text": "your text"}

)]

await client.insert("docs", vectors)



results = await client.search(

    "docs",

    vector=model.encode("query").tolist(),

    top_k=5

)

Node.js:

const { EmbexClient } = require('@bridgerust/embex');

const { pipeline } = require('@xenova/transformers');



const embedder = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');

const client = await EmbexClient.new({ provider: 'qdrant', url: 'http://localhost:6333' });



await client.insert('docs', documents.map(doc => ({

  id: doc.id,

  vector: embedder(doc.content),

  metadata: { title: doc.title }

})));



const results = await client.search(

  'docs',

  embedder('search query'),

  { top_k: 10 }

);

What's Next

Short term:

• Hybrid search (vector + keyword)


• Performance optimizations


• More examples

Medium term:

• Elasticsearch/OpenSearch support


• Redis vector support


• Migration utilities

Try It

Python:

pip install embex lancedb sentence-transformers

Node.js:

npm install @bridgerust/embex lancedb @xenova/transformers

Quick test:

import asyncio

from embex import EmbexClient



async def main():

    client = await EmbexClient.new_async('lancedb', './data')

    await client.create_collection('test', dimension=384)

    print('✅ Works')



asyncio.run(main())

Links:

• GitHub: https://github.com/bridgerust/bridgerust
  


• Docs: https://bridgerust.dev/embex/introduction
  

That's it. I built something I needed. Published it. People downloaded it.

Read the full story on my blog: kologojosias.com/embex-9k-downloads

Still building.