Multi-Modal Semantic Search

Text, image, video, audio, semantic search, asynchronous worker, and more. It's time to unify them all.
No more database development.

Weeks of Setup
vs
Minutes to Ship

Traditional multi-modal infrastructure requires complex orchestration of multiple services. OneNode eliminates this entirely.

Traditional Setup

26-70 hours
Vector Database Setup4-8 hrs
Object Storage Config2-6 hrs
Embedding Pipelines8-16 hrs
Background Jobs4-12 hrs
Integration & Debug8-24 hrs

OneNode

1 minute
Install
pip install onenode
Initialize
client = OneNode()
db = client.db("my_app")
Everything included:
✓ Vector search
✓ Object storage
✓ Background jobs
✓ AI models
✓ Auto-scaling
✓ Monitoring

Stop configuring infrastructure. Start building intelligent applications.

Enterprise Infrastructure
Simplified

OneNode orchestrates battle-tested infrastructure behind a single API. Get enterprise capabilities without enterprise complexity.

terminal
# Install
pip install onenode

# Initialize
client = OneNode()
db = client.db("ai_app")
collection = db.collection("docs")

Powered by industry leaders

MongoDB
Document DB
Pinecone
Vector Search
Redis
Caching
Amazon S3
Storage
OpenAI
AI Models

Zero Configuration

No setup, no maintenance, no scaling decisions. Just write code and deploy.

Production Ready

Built on proven infrastructure used by millions of applications worldwide.

Auto-Scaling

Seamlessly scales from prototype to enterprise without changing a line of code.

One Input,
Multiple Destinations

Your data automatically flows through our intelligent pipeline, optimized for every storage system simultaneously.

Your Data

OneNode

AI Pipeline

Processing

Vector Search

Semantic embeddings

Document Store

Structured metadata

File Storage

Original media files

app.py

Single API call

result = collection.insert({
"image": Image("sunset.jpg").enable_index(),
"caption": Text("Beautiful sunset").enable_index()
})

Automatic distribution to:

Vector embeddings → Pinecone
Metadata → MongoDB
Image file → AWS S3

Stop wrestling with data pipelines.
Start building intelligent applications.

Watch Your Data
Come Alive

From raw input to intelligent search. Experience automated multimodal processing.

1. Raw Input

Upload images, videos, documents, or text. Processing begins automatically.

Sample input

2. AI Understanding

Advanced models generate detailed, context-aware descriptions.

This image shows an aerial view of the Golden Gate Bridge in San Francisco, California. In the foreground are the green hills of the Marin Headlands, with a winding road leading toward the bridge. The Golden Gate Bridge spans the Golden Gate Strait, connecting the Marin Peninsula to the San Francisco Peninsula. In the background is the San Francisco skyline, including tall buildings like the Salesforce Tower. To the right are residential areas of San Francisco, such as the Richmond and Sunset Districts, extending toward Ocean Beach. The water below the bridge is the San Francisco Bay, and a few sailboats can be seen on the water. The photo appears to be taken during the morning or late afternoon based on the lighting and shadows.

3. Vector Transformation

Content is embedded into vectors for semantic search.

0.12
-0.34
0.56
0.78
-0.90
0.23
-0.45
0.67
... and 1528 more dimensions ...

Simplified vector representation (1536 dimensions)