Multi-modal Knowledge Extraction

This tutorial demonstrates how Corvic AI enables you to extract knowledge from multiple data modalities simultaneously, including text, images, structured data, and visual content. Learn how to combine insights from different data types to build comprehensive knowledge representations.

What You’ll Learn

Extracting knowledge from multiple data modalities
Combining insights from text, images, and structured data
Building comprehensive knowledge representations
Using multi-modal knowledge in embeddings and agents

Key Concepts

Multi-modal knowledge extraction enables you to extract and combine knowledge from different data types simultaneously—text documents, images, structured tables, charts, and visual content. This approach captures a more complete picture of your data by leveraging information from all available modalities.

Unified Knowledge Representation

Corvic AI combines extracted knowledge from multiple modalities into unified representations that preserve relationships and context across different data types. This enables more comprehensive analysis and better understanding of your enterprise data. Gather data from multiple sources including text documents, images, structured tables, charts, and visual content. Ensure your data sources are properly organized and accessible for knowledge extraction.

Step 2: Configure Extraction Pipeline

Configure the knowledge extraction pipeline to process different data modalities. Set up extraction rules and parameters for each modality type to ensure comprehensive knowledge capture.

Step 3: Extract and Combine Knowledge

Extract knowledge from each modality and combine insights into unified representations. Corvic AI automatically handles the complexity of integrating knowledge from different data types while preserving relationships and context.

Step 4: Use in Applications

Use the extracted multi-modal knowledge in your embeddings, Corvic Tables, and agents. This enables applications that understand and leverage information from all available data types.

Benefits

Multi-modal knowledge extraction enables you to capture a more complete understanding of your data by leveraging information from all available modalities. This results in richer embeddings, more comprehensive Corvic Tables, and agents that can reason across different data types.

Best Practices

Organize your multi-modal data sources logically and ensure consistent metadata across different modalities. Configure extraction pipelines to capture the most relevant knowledge from each modality type, and validate that extracted knowledge accurately represents your data across all modalities.

Multi-Structural Embeddings

Create embeddings from multiple data structures.

Corvic Tables

Build Corvic Tables from multi-modal knowledge for distributed processing.

Agents

Use multi-modal knowledge in your agents.

Data Sources

Understand how to manage multi-modal data sources.

Video Tutorials