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⚡Open Source Python Framework

Seamless
Auto-Vectorization
Framework

Add one decorator. Get Vector DB storage, semantic caching, and distributed tracing — automatically.

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example.py
from vectorwave import vectorize

@vectorize(
    collection="documents",
    cache=True,
    trace=True
)
def process_document(text: str) -> dict:
    """Your logic here - we handle the rest."""
    return {"content": text, "processed": True}

# That's it. Auto-vectorization enabled. ✨
CORE FEATURES

Everything you need for intelligent vectorization

Built for AI engineers who want to ship faster without sacrificing quality.

AI Documentation

Automatically generate embeddings and store in your preferred Vector DB. Supports Pinecone, Weaviate, Qdrant, and more.

Semantic Caching

Intelligent cache layer that understands context. Reduce redundant computations by up to 80% with similarity-based retrieval.

Distributed Tracing

Full observability out of the box. Track every vectorization call with OpenTelemetry-compatible spans and metrics.

ECOSYSTEM

Intelligent Workflow

Three powerful tools working together seamlessly.

VectorWave

Core vectorization engine with decorator-based API

VectorSurfer

Interactive exploration and visualization dashboard

VectorCheck

Testing and validation toolkit for vector quality

01

Vectorize

Decorate your functions with @vectorize

→
02

Explore

Visualize and navigate your vector space

→
03

Validate

Ensure quality with automated testing

Ready to ride the wave?

Start building intelligent, vectorized applications in minutes.

pip install vectorwave
Read the DocsQuick Start Guide