

Jockey
Overview :
Jockey is a conversational video agent built on top of Twelve Labs API and LangGraph. It integrates the capabilities of existing Large Language Models (LLMs) with Twelve Labs' API, utilizing LangGraph for task allocation to distribute the workload of complex video workflows to appropriate base models. LLMs are used for logically planning execution steps and interacting with users, while video-related tasks are passed to the Twelve Labs API powered by Video Foundation Models (VFMs) for native video processing, eliminating the need for intermediary representations like pre-generated captions.
Target Users :
Jockey is geared towards developers and teams handling complex video workflows, particularly those looking to leverage large language models to enhance their video content creation and editing processes. It caters to professional users requiring high customization and automation in video processing tasks.
Use Cases
Video editing teams automate video clipping and caption generation using Jockey.
Content creators utilize Jockey to generate video drafts and storyboards.
Educational institutions employ Jockey to create interactive video tutorials.
Features
Distributes the workload of complex video workflows by combining large language models with video processing APIs.
Utilizes LangGraph for task allocation, improving video processing efficiency.
Leverages LLMs to logically plan execution steps, enhancing user interaction experience.
Processes video tasks directly using video foundation models, eliminating the need for intermediary representations.
Supports customization and extension to adapt to diverse video-related use cases.
Offers terminal and LangGraph API server deployment options, flexibly catering to development and testing needs.
How to Use
1. Install necessary external dependencies such as FFmpeg, Docker, and Docker Compose.
2. Clone the Jockey GitHub repository to your local environment.
3. Create and activate a Python virtual environment, installing required Python packages.
4. Configure the .env file, adding necessary API keys and environment variables.
5. Deploy the Jockey API server using Docker Compose.
6. Test Jockey by running the instance through the terminal or utilize the LangGraph API server for end-to-end deployment.
7. Use the LangGraph Debugger UI for debugging and end-to-end testing.
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