I Created OctaneDB – The Lightning-Fast Python Vector Database!

Rijin Raju — Sat, 23 Aug 2025 06:45:55 +0000

What is OctaneDB?
OctaneDB is an open-source, high-performance vector database written in Python.
It lets you store, index, and rapidly search millions of text, image, or custom embeddings using state-of-the-art similarity search algorithms.

Key Features
Faster Than Pinecone/ChromaDB: Sub-millisecond queries, >3,000 vectors/sec insert rate.

Advanced Indexing: HNSW for ultra-fast approximate search, FlatIndex for exact matches.

Flexible Storage: In-memory or persistent HDF5 mode.

Text Embedding Built-In: Auto text-to-vector with sentence-transformers.

GPU Acceleration: CUDA support out of the box.

Powerful Search: Batch search, advanced metadata filtering (AND/OR/NOT logic).

Easy Integration: ChromaDB-compatible API for seamless migration.

Open Source: MIT licensed, totally free for all uses!

Try it Online or Locally!
Get Started:

bash
pip install octanedb
Quick Example:

python
from octanedb import OctaneDB
db = OctaneDB(dimension=384, embedding_model="all-MiniLM-L6-v2")
db.create_collection("documents")
db.add(
ids=["doc1", "doc2"],
documents=["About pineapple", "About oranges"]
)
results = db.search_text(query_text="fruit", k=2)
print(results)
Use Cases
Semantic search

NLP & document retrieval

Recommendation engines

Image embedding similarity

RAG pipelines in AI/LLM

Exploratory research

Features Coming Soon
Live Multi-Tenancy

Direct LLM Integration

Hybrid Scalar/Vector Queries

Instant Index Updates (feedback wanted!)

Get Involved!
Try it, star it, and contribute on GitHub

Share your benchmarks and real-world results!

What problems do you face with vector DBs?
Drop your ideas, feature requests, or open an issue!

Screenshot

Open to Feedback, Collaboration, and Questions!
Let's build the next era of search and AI together 🤝
GitHub – RijinRaju/octanedb

Forem: Rijin Raju

I Created OctaneDB – The Lightning-Fast Python Vector Database!