Multimodal Data Types

OneNode supports multimodal data types that make working with AI and embeddings simple. With built-in multimodal data types, you can store text, images, and other media in your database and have them automatically embedded for semantic search.

What Multimodal Data Types Do For You

• Automatic Embedding: Just wrap your content in a multimodal data type, and OneNode handles the embedding process
• Semantic Search Across Different Modalities: Query text, images, and other media types using natural language in a single search
• No External Media Storage Needed: Store your media directly in OneNode—no need for separate file storage systems or CDNs
• Async by Default: All embedding and processing happens asynchronously, keeping your application fast and responsive

Quick Example

See how easy it is to use multimodal data types in your applications:

Python

JavaScript

from onenode import Text, Image

# Step 1: Create Text instance
description_text = Text("This is a detailed description that will be embedded for semantic search")

# Step 2: Enable indexing for Text
description_text.enable_index()

# Step 3: Create Image instance (file type auto-detected from extension)
thumbnail_image = Image("thumbnail.jpg")

# Step 4: Enable indexing for Image
thumbnail_image.enable_index()

# Step 5: Create document with multimodal fields
document = {
    "title": "My First Document",
    "description": description_text,
    "thumbnail": thumbnail_image
}

# Step 6: Store in OneNode
collection.insert([document])

# Step 7: Search semantically across all embedded fields
results = collection.find({"$semanticSearch": "design principles"})

Available Multimodal Data Types

• Text: For text data ranging from short phrases to long documents
• Image: For images (accepts files, binary data, and base64 encoded data)
• Coming Soon: EmbVideo, EmbFile, EmbAudio, and Emb3D for additional media types

How can we improve this documentation?

Got question? Email us and we'll get back to you within 24 hours.