

Omagent
Overview :
OmAgent is a sophisticated multimodal intelligent agent system dedicated to leveraging multimodal large language models and other multimodal algorithms to accomplish compelling tasks. It encompasses a lightweight intelligent agent framework, omagent_core, meticulously designed to address multimodal challenges. OmAgent comprises three core components: Video2RAG, DnCLoop, and Rewinder Tool, respectively responsible for long video understanding, complex problem decomposition, and information retrieval.
Target Users :
OmAgent is designed for developers and researchers, particularly those interested in multimodal algorithms, large language models, and agent technology. It is suitable for professionals who work on complex tasks like long video understanding and analysis, helping them efficiently realize innovative ideas.
Use Cases
Construct a system using OmAgent that can automatically analyze and summarize the content of long videos.
Utilize the DnCLoop component to decompose a complex research project into manageable subtasks.
Leverage the Rewinder Tool to quickly locate and retrieve key information during video analysis.
Features
Video2RAG: Transforms long video understanding into a multimodal RAG task, breaking the limitations of video length.
DnCLoop: Employs a divide-and-conquer algorithmic paradigm, recursively decomposing complex problems into task trees.
Rewinder Tool: A 'progress bar' tool designed to address the issue of video information loss, enabling the agent to autonomously rewind for details.
Supports custom configuration files for flexible task processing parameter settings.
Offers a quick start guide to simplify the task processing workflow.
Supports video understanding tasks, enhancing video feature retrieval through milvus vector databases and optional facial recognition algorithms.
Optional open vocabulary detection (OVD) service enhances recognition capabilities for diverse objects.
How to Use
Install a Python environment with a version of 3.10 or higher.
Navigate to the omagent-core directory and install omagent_core using pip.
Install any additional dependencies as needed, such as OpenAI GPT or other MLLMs.
Create a configuration file and set the necessary variables, such as API addresses and API keys.
Configure the run.py script to define the task processing logic.
Run python run.py to start OmAgent and begin using it by inputting queries or tasks.
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