What is OneNode?
AI-Native
Built for LLM applications with semantic understanding at its core
Multimodal
Search across text, images, and structured data with natural language
Unified
One platform for documents, vectors, and objects—no complex integrations
How It Works
OneNode uses a two-step process to enable semantic search across all data types
Content Analysis
Images are analyzed by vision models to generate descriptive text. All content is normalized into a text representation.
Semantic Embedding
All text content is converted into semantic vectors that capture meaning, enabling intelligent cross-modal search capabilities.
Architecture
Three storage layers unified under a single API
Document Store
MongoDB-compatible JSON document storage with flexible schemas
Vector Search
High-performance semantic search with embedding models
Object Storage
Scalable file storage with automatic content analysis
Why OneNode
Developer Experience
Application Capabilities
Explore our comprehensive documentation to learn how OneNode can power your next AI application. Start with the core concepts and dive deeper into specific features.
Core Operations
Insert, find, update, and delete documents
Multimodal Types
Text and Image classes with semantic indexing
Collections
Create and manage document collections
Query Syntax
Filters, projections, and update operators
LLM Models
Available embedding and vision models
Quick Start
Jump right in with your first insert