padmadb 0.1.2

A blazing-fast, lightweight in-memory vector database built in pure Python.

PadmaDB 🪷

A blazing-fast, lightweight in-memory vector database built in pure Python and NumPy. Pinecone, Milvus, and Weaviate are incredible for enterprise-scale billions of vectors. But when you are building local AI apps, testing RAG pipelines, or running lightweight microservices, spinning up a massive cloud infrastructure is overkill. PadmaDB solves this by providing a hyper-fast, zero-dependency (beyond NumPy/FastAPI) vector search engine that runs locally and persists to disk.

🚀 Features

Pure Python/NumPy Engine: No C++ compilers, Docker containers, or cloud APIs required.

Exact Nearest Neighbor (k-NN): Guarantees 100% recall using optimized float32 Cosine Similarity matrix multiplications.

RESTful Architecture: Ships with a built-in FastAPI server, allowing it to act as an independent microservice database.

Local Persistence: Automatically serializes vector state to your local disk.

📦 Installation

pip install padmadb

⚡ Quickstart

1. Start the Database Server You can launch the server in one line of code:

from padmadb.server import run_server

if __name__ == "__main__":
    # Runs the database engine on [http://0.0.0.0:8000](http://0.0.0.0:8000)
    run_server()

2. Connect and Query (Client SDK) In a separate Python script or Jupyter Notebook:

from padmadb import PadmaClient

client = PadmaClient(host="http://localhost:8000")

1. Insert a 384-dimensional vector (e.g., from all-MiniLM-L6-v2)

vector_data = [0.12, -0.45, 0.88, ...] # Must be length 384
client.insert(
    vec_id="doc_1", 
    vector=vector_data, 
    metadata={"text": "Machine learning is fascinating."}
)

2. Search for the Top-K nearest neighbors

results = client.search(vector=vector_data, top_k=1)
print(results)

🧠 Architecture & Roadmap

PadmaDB v0.1.0 utilizes a brute-force exact search mechanism tailored for datasets up to 100,000 vectors where in-memory matrix multiplication is effectively instantaneous.

Upcoming in v0.2.x:

Implementation of threading.Lock() for concurrent write-safety. Transition from JSON persistence to memory-mapped .npy binary arrays for faster cold boots. HNSW (Hierarchical Navigable Small World) indexing for billion-scale querying.

🤝 Contributing

PadmaDB is fully open-source. We warmly welcome collaborators, researchers, and cloners!

Clone the repo, fork it, break it, fix it, and submit a PR! Let's build the ultimate local AI database together.