vectx-core
| Crates.io | vectx-core |
| lib.rs | vectx-core |
| version | 0.2.7 |
| created_at | 2025-12-22 12:50:23.810104+00 |
| updated_at | 2025-12-22 12:50:23.810104+00 |
| description | Core library for vectX vector database - HNSW indexing, SIMD operations, BM25 search |
| homepage | |
| repository | https://github.com/antonellof/vectX |
| max_upload_size | |
| id | 1999665 |
| size | 110,831 |
Antonello Fratepietro (antonellof)
documentation
https://docs.rs/vectx-core
README
vectX
A fast, in-memory vector database with 100% Qdrant API compatibility.
vectX is a drop-in replacement for Qdrant, providing high-performance vector similarity search with full API compatibility.
Quick Start
# Run vectX
docker run -p 6333:6333 antonellofratepietro/vectx:latest
# Create collection
curl -X PUT localhost:6333/collections/products -d '{
"vectors": {"size": 1536, "distance": "Cosine"}
}'
# Insert points
curl -X PUT localhost:6333/collections/products/points -d '{
"points": [{
"id": 1,
"vector": [0.1, 0.2, 0.3, ...],
"payload": {"name": "iPhone", "price": 999}
}]
}'
# Search
curl -X POST localhost:6333/collections/products/points/search -d '{
"vector": [0.1, 0.2, 0.3, ...],
"limit": 10
}'
Key Features
| Feature | Description |
|---|---|
| 100% Qdrant Compatible | Use existing Qdrant clients, drop-in replacement |
| High Performance | Fast in-memory vector search with HNSW indexing |
| Full Vector DB | Search, filter, facets, scroll, recommendations |
| Persistence | WAL and snapshot support for data durability |
| REST & gRPC APIs | Full Qdrant API compatibility |
| Multi-vector Support | Named vectors and sparse vectors |
REST API
Full Qdrant API compatibility:
| Endpoint | Description |
|---|---|
PUT /collections/{name} |
Create collection |
POST /collections/{name}/points/search |
Vector similarity search |
POST /collections/{name}/points/scroll |
Filter and browse with pagination |
POST /collections/{name}/points/recommend |
Recommendations from examples |
POST /collections/{name}/facet |
Aggregated counts by field |
PUT /collections/{name}/points |
Upsert points |
GET /collections/{name}/points |
Get points by IDs |
DELETE /collections/{name}/points |
Delete points |
Examples
Example applications demonstrating vectX usage:
- Data Chatbot — Next.js demo with CSV upload and vector search
- Fastest RAG Stack — RAG application with Streamlit UI
Installation
# Docker
docker run -p 6333:6333 antonellofratepietro/vectx:latest
# Cargo
cargo install vectx
# Source
git clone https://github.com/antonellof/vectX.git && cd vectX && cargo run --release
Use with Qdrant Clients
import { QdrantClient } from '@qdrant/js-client-rest';
const client = new QdrantClient({ url: 'http://localhost:6333' });
// All standard Qdrant operations work
await client.search('products', { vector: [...], limit: 10 });
Documentation
- Quick Start — Get running in 5 minutes
- API Reference — REST and gRPC endpoints
- Architecture — System design and internals
- Docker Guide — Container deployment
- Persistence — WAL, snapshots, storage
- Performance — Benchmarks and tuning
License
MIT OR Apache-2.0