Vector search without
the infrastructure
Embedded vector database for Python and Node.js.
No server. No Docker. No configuration. Just pip install.
import omendb
# Create database — no server, no setup
db = omendb.open("./vectors", dimensions=768)
# Add vectors with metadata
db.set([{
"id": "doc_1",
"vector": embedding, # from any embedding model
"text": "Your document text here",
"metadata": {"category": "docs"}
}])
# Search with optional filtering
results = db.search(query_embedding, k=10)
results = db.search(query_embedding, k=10, filter={"category": "docs"}) Why embedded?
Skip the infrastructure. Your data stays local, latency stays low, and cloud bills stay at zero.
Data stays local
Your vectors never leave your machine. Perfect for sensitive data, air-gapped environments, or when you just don't want another vendor.
Sub-millisecond latency
No network round-trips. Search runs in-process with your application. HNSW + SIMD gives you 10,000+ queries per second.
$0 cloud bills
No managed service fees. No per-query pricing. No usage tiers. Just a library that runs wherever Python or Node.js runs.
Performance
Search queries per second with 10K vectors
Tested on Apple M3 Max and Intel i9-13900KF · Batch queries achieve 6-8x higher throughput
Features
Vector Search
Find similar vectors fast using HNSW. Supports L2, cosine, and dot product distance. Tune speed vs accuracy to your needs.
Hybrid Search
Combine keyword matching with semantic similarity. Get the best of traditional search and vector search in one query.
Metadata Filtering
Filter results by any field in your metadata. Combine conditions with AND/OR. Stays fast even with selective filters.
Quantization
Reduce memory 4-8x while keeping 98-99% accuracy. One flag to enable, automatic rescoring keeps results precise.
Persistence
Data saved to a single file, crash-safe by default. Fast startup even with millions of vectors. Or use in-memory for testing.
Multi-language
Native support for Python, Node.js, and Rust. Same simple API across all languages.
Works with your stack
Get started in seconds
pip install omendb npm install omendb cargo add omendb