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Overview

Embed is a feature that generates embeddings from Corvic Tables, creating multi-modal and multi-structural embedding spaces. This feature supports semantic, tabular, relational, and multi-media (images, videos, etc.) embeddings, enabling you to create rich vector representations of your data for use in agents, search, and analysis workflows.
Watch tutorial: ▶️ Multi-Structural Embeddings - Learn how to create embeddings from multiple data structures

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Category

Corvic Tables - This feature is designed to work with Corvic Tables, enabling you to generate embeddings from structured data for use in downstream AI workflows.

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Input

Corvic Table - The Embed feature accepts a Corvic Table as input. Select an existing Corvic Table from your data room that contains the data you want to embed.
The input must be a Corvic Table. You can create Corvic Tables from data sources using features like Extract Tables for structured data, Multi-modal Knowledge Extraction for unstructured data, or Augment for enhanced data.

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Output

Space - The Embed feature produces an embedding vector store (Space) containing multi-modal and multi-structural embeddings. The output Space includes:
  • Semantic Embeddings: Text-based semantic representations
  • Tabular Embeddings: Structured data encodings
  • Relational Embeddings: Graph-based relationship representations
  • Multi-media Embeddings: Image and video content embeddings
The output Space serves as a vector store that can be used by agents for semantic search, similarity matching, and intelligent data retrieval. Spaces enable powerful AI workflows that understand and work with your data at scale.
Learn more: Spaces - Detailed guide on embedding spaces and how to generate embeddings

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Parameters

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Text Semantic Parameters

When embedding_type includes text_semantic, configure the following parameters:

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Tabular Encoding Parameters

When embedding_type includes tabular_encoding, configure the following parameters:

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Relational Parameters

When embedding_type includes relational, configure the following parameters:

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Image Semantic Parameters

When embedding_type includes image_semantic, configure the following parameters:

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Usage Example

To use Embed in a Workflow:
  1. Add your Corvic Table to the Workflow canvas
  2. Click the ”+” button next to the Corvic Table
  3. Select “Embed” from the actions menu
  4. Select the input Corvic Table (if not already selected)
  5. Choose one or more embedding types (Text Semantic, Tabular Encoding, Relational, Image Semantic)
  6. Configure the parameters for each selected embedding type:
    • Text Semantic: Select model and columns to embed
    • Tabular Encoding: Select columns to embed
    • Relational: Select graph embedding model
    • Image Semantic: Select image model and columns with image content
  7. Optionally provide a name and description for the output Space
  8. Run the Workflow to execute the embedding generation
  9. Review the generated Space containing multi-modal and multi-structural embeddings

Workflows

Learn how to build workflows using Workflows.

Spaces

Understand how embedding spaces work and how to use them.

Corvic Tables

Learn how to create Corvic Tables for embedding generation.

Agents

Use embedding spaces in your agents for intelligent data retrieval.