Unlocking the Power of Multimodal Visual AI Agents with NVIDIA NIM
Summary
The exponential growth of visual data has made manual review and analysis virtually impossible. To solve this challenge, vision-language models (VLMs) are emerging as powerful tools, combining visual perception of images and videos with text-based reasoning. With NVIDIA NIM microservices, building these advanced visual AI agents is easier and more efficient than ever. This article guides you through the process of designing and building intelligent visual AI agents using NVIDIA NIM microservices.
The Need for Multimodal AI Agents
The world is filled with diverse types of data, from images to videos to text. Traditional AI models are limited to processing a single type of data, which can lead to missed opportunities and increased risks. Multimodal AI agents, on the other hand, can process and integrate multiple data types simultaneously, providing a more comprehensive understanding of complex scenarios.
What are Vision-Language Models (VLMs)?
VLMs are a type of AI model that combines visual perception of images and videos with text-based reasoning. These models can process images, videos, and text, enabling them to interpret visual data and generate text-based outputs. VLMs are versatile and can be fine-tuned for specific use cases or prompted for tasks such as Q&A based on visual inputs.
NVIDIA NIM Microservices
NVIDIA NIM microservices provide a set of inference microservices that include industry-standard APIs, domain-specific code, optimized inference engines, and enterprise runtime. These microservices enable developers to create dynamic agents tailored to their unique business needs. With NIM microservices, building advanced visual AI agents is easier and more efficient than ever.
Types of Vision AI Models
To build a robust visual AI agent, you have the following core types of vision models at your disposal:
- VLMs: These models combine visual perception of images and videos with text-based reasoning.
- Embedding models: These models generate embeddings for text and images, enabling multimodal search and few-shot classification.
- Computer vision (CV) models: These models provide essential building blocks for developing intelligent visual AI agents, enhancing accuracy for tasks like object detection and parsing complex documents.
Building Visual AI Agents with NIM Microservices
NVIDIA NIM microservices provide a flexible and streamlined way to build visual AI agents. With NIM microservices, you can access a range of vision AI models, including VLMs, embedding models, and CV models. These models can be easily integrated into your workflows through simple REST APIs, allowing for efficient model inference on text, images, and videos.
Real-World Applications
NVIDIA NIM microservices can be applied to create powerful visual AI agents for a variety of use cases, including:
- Streaming video alerts: Analyze remote camera footage to detect early signs of wildfires or other critical events.
- Structured text extraction: Extract critical information buried within charts, tables, and images in business documents.
- Multimodal search: Retrieve images that match a given text query, enabling highly flexible and accurate search results.
- Few-shot classification: Classify images with minimal training data, enabling rapid deployment of AI-powered applications.
Getting Started
To get started with building your own visual AI agents, use the code provided in the NVIDIA Metropolis NIM Workflows GitHub repository as a foundation. This repository includes Jupyter notebook tutorials and demos that showcase how to use NIM APIs to build or integrate them into your applications.
Example Workflows
The NVIDIA Metropolis NIM Workflows GitHub repository includes several example workflows that demonstrate how to use NIM microservices to build powerful visual AI agents. These workflows include:
- VLM streaming video alerts agent: Analyze remote camera footage to detect early signs of wildfires or other critical events.
- Structured text extraction agent: Extract critical information buried within charts, tables, and images in business documents.
- Few-shot classification with NV-DINOv2 agent: Classify images with minimal training data, enabling rapid deployment of AI-powered applications.
- Multimodal search with NV-CLIP agent: Retrieve images that match a given text query, enabling highly flexible and accurate search results.
Table: Comparison of VLMs
| Model | Size | Latency | Capabilities |
|---|---|---|---|
| VILA (40B) | 40B | Low | General-purpose VLM |
| Neva (22B) | 22B | Medium | NVGPT + CLIP integration |
| Llama 3.2 (90B/11B) | 90B/11B | High | High-resolution processing |
| phi-3.5-vision (4.2B) | 4.2B | Low | Specialized OCR |
Table: Embedding Models
| Model | Size | Latency | Capabilities |
|---|---|---|---|
| EnV-CLIP | Low | Unified embedding space for text and images | |
| EnV-Dof 2 | Medium | High-resolution image embeddings |
Table: Computer Vision Models
| Model | Size | Latency | Capabilities |
|---|---|---|---|
| DINO | Low | Open vocabulary object detection | |
| OCR-Net | Medium | Optical character detection and recognition | |
| ChangeT | High | Pixel-level difference detection between images | |
| Retail Object Detection | Low | Pre-trained for common retail items |
Conclusion
Building advanced visual AI agents is easier and more efficient than ever with NVIDIA NIM microservices. By combining visual perception of images and videos with text-based reasoning, VLMs provide a powerful tool for transforming visual data into actionable insights. With NIM microservices, you can create dynamic agents tailored to your unique business needs, enabling real-time decision-making and automation. Get started today by exploring the NVIDIA Metropolis NIM Workflows GitHub repository and building your own visual AI agents.