Back office ops · Production

Integrating Twelve Labs Embed API with Databricks Mosaic AI Vector Search for multimodal video understanding

The problem

Building video AI applications requires handling large-scale video datasets with accurate multimodal content representation, historically requiring separate models for text, image, and audio analysis and creating complex deployment architectures.

Workflow diagram · grounded in source
1
Video URLs provided as input
trigger
“First, create a source DataFrame with video URLs and metadata”
2
Generate multimodal embeddings
ai_action
“contextual vector representations can be generated that capture the relationship between visual expressions, body language, spoken words, and overall context within videos”
3
Store embeddings in Delta Table
integration
“create a source Delta Table to store video metadata and the embeddings generated by Twelve Labs Embed API. This table will serve as the foundation for a Vector Search index”
4
Create Delta Sync Index
integration
“create a Delta Sync Index that will automatically stay in sync with your videos_source_embeddings Delta table”
5
Convert text query to embedding
ai_action
“get the embedding for a text query using Twelve Labs Embed API”
6
Similarity search returns videos
output
“find videos similar to a given text query by leveraging the power of multimodal embeddings”
Reported outcome

The integration reduces development time and resource needs for advanced video applications, enables complex queries across vast video libraries, and enhances overall workflow efficiency through a unified multimodal embedding space.

Reported metrics
Development time and resource needsreduces development time and resource needs
Overall workflow efficiencyenhancing overall workflow efficiency
Reported stack
Twelve Labs Embed APIDatabricks Mosaic AI Vector SearchMarengo-retrieval-2.6
Source
https://www.databricks.com/blog/mastering-multimodal-ai-twelve-labs
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The integration reduces development time and resource needs for advanced video applications, enables complex queries across vast video libraries, and enhances overall workflow efficiency through a unified multimodal e…

What tools did this team use?

Twelve Labs Embed API, Databricks Mosaic AI Vector Search, Marengo-retrieval-2.6.

What results were reported?

Development time and resource needs: reduces development time and resource needs; Overall workflow efficiency: enhancing overall workflow efficiency (source-reported, not independently verified).

How is this back office ops AI workflow structured?

Video URLs provided as input → Generate multimodal embeddings → Store embeddings in Delta Table → Create Delta Sync Index → Convert text query to embedding → Similarity search returns videos.

Related back office ops cases