Multimodal AI Agents: Building with Vision, Audio, and Text

The Rise of Multimodal AI

Multimodal AI agents can process and generate multiple types of content - text, images, audio, and video. This opens up entirely new use cases that were impossible with text-only models.

What Makes an Agent Multimodal?

A multimodal agent can:

• Understand images and describe their contents
• Generate images from text descriptions
• Process audio and transcribe speech
• Analyze videos frame by frame
• Combine multiple modalities in reasoning

Key Multimodal Models

Vision Models

• GPT-4 Vision - OpenAI's multimodal flagship
• Claude 3 - Anthropic's vision-capable model
• Gemini - Google's native multimodal model
• LLaVA - Open-source vision-language model

Audio Models

• Whisper - Speech-to-text transcription
• ElevenLabs - Text-to-speech generation
• MusicGen - Audio generation

Building a Vision Agent

import openai
import base64

def analyze_image(image_path, question):
    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode()
    
    response = openai.chat.completions.create(
        model="gpt-4-vision-preview",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": question},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{image_data}"
                        }
                    }
                ]
            }
        ]
    )
    return response.choices[0].message.content

Real-World Multimodal Applications

1. Medical Imaging - Analyze X-rays and MRIs
2. Quality Control - Inspect products for defects
3. Document Processing - Extract info from scanned documents
4. Accessibility - Describe images for visually impaired users
5. Content Moderation - Identify inappropriate images

Build your own multimodal agent in our Multimodal Agent Module.