Twelve Labs Embed API enables developers to get multimodal embeddings that power advanced video understanding use cases, from semantic video search and data curation to content recommendation and video RAG systems.
With Twelve Labs, contextual vector representations can be generated that capture the relationship between visual expressions, body language, spoken words, and overall context within videos. Databricks Mosaic AI Vector Search provides a robust, scalable infrastructure for indexing and querying high-dimensional vectors. This blog post will guide you through harnessing these complementary technologies to unlock new possibilities in video AI applications.
Integrating Twelve Labs Embed API with Databricks Mosaic AI Vector Search addresses key challenges in video AI, such as efficient processing of large-scale video datasets and accurate multimodal content representation. This integration reduces development time and resource needs for advanced video applications, enabling complex queries across vast video libraries and enhancing overall workflow efficiency.
The unified approach to handling multimodal data is particularly noteworthy. Instead of juggling separate models for text, image, and audio analysis, users can now work with a single, coherent representation that captures the essence of video content in its entirety. This not only simplifies deployment architecture but also enables more nuanced and context-aware applications, from sophisticated content recommendation systems to advanced video search engines and automated content moderation tools.
Moreover, this integration extends the capabilities of the Databricks ecosystem, allowing seamless incorporation of video understanding into existing data pipelines and machine learning workflows. Whether companies are developing real-time video analytics, building large-scale content classification systems, or exploring novel applications in Generative AI, this combined solution provides a powerful foundation. It pushes the boundaries of what's possible in video AI, opening up new avenues for innovation and problem-solving in industries ranging from media and entertainment to security and healthcare.
Twelve Labs Embed API represents a significant advancement in multimodal embedding technology, specifically designed for video content. Unlike traditional approaches that rely on frame-by-frame analysis or separate models for different modalities, this API generates contextual vector representations that capture the intricate interplay of visual expressions, body language, spoken words, and overall context within videos.
The Embed API offers several key features that make it particularly powerful for AI engineers working with video data. First, it provides flexibility for any modality present in videos, eliminating the need for separate text-only or image-only models. Second, it employs a video-native approach that accounts for motion, action, and temporal information, ensuring a more accurate and temporally coherent interpretation of video content. Lastly, it creates a unified vector space that integrates embeddings from all modalities, facilitating a more holistic understanding of the video content.
For AI engineers, the Embed API opens up new possibilities in video understanding tasks. It enables more sophisticated content analysis, improved semantic search capabilities, and enhanced recommendation systems. The API's ability to capture subtle cues and interactions between different modalities over time makes it particularly valuable for applications requiring a nuanced understanding of video content, such as emotion recognition, context-aware content moderation, and advanced video retrieval systems.
Before integrating Twelve Labs Embed API with Databricks Mosaic AI Vector Search, be sure you have the following prerequisites:
To begin, set up the Databricks environment and install the necessary libraries:
1. Create a new Databricks workspace
2. Create a new cluster or connect to an existing cluster
Almost any ML cluster will work for this application. The below settings are provided for those seeking optimal price performance.
3. Create a new notebook in your Databricks workspace
4. Install the Twelve Labs and Mosaic AI Vector Search SDKs
In the first cell of your notebook, run the following Python command:
5. Set up Twelve Labs authentication
In the next cell, add the following Python code:
Note: For enhanced security, it's recommended to use Databricks secrets to store your API key rather than hard coding it or using environment variables.
Note: The Embed API is currently in private beta, but any user can request access by simply filling out this form. Within a few hours, you will receive a confirmation email that you can now start using the Embed API.
Use the provided generate_embedding function to generate multimodal embeddings using Twelve Labs Embed API. This function is designed as a Pandas user-defined function (UDF) to work efficiently with Spark DataFrames in Databricks. It encapsulates the process of creating an embedding task, monitoring its progress, and retrieving the results.
Next, create a process_url function, which takes the video URL as string input and invokes a wrapper call to the Twelve Labs Embed API - returning an array<float>.