Implementing the Vision Agent

Now let's implement the core functionality in our run.py file, following the same pattern as our reference chat agent module. We will set up our vision agent to accept a single image, and a question.

Wow. That was a lot of code! Let's break down what this implementation does:

• Core Components:

• SimpleVisionAgent class that handles vision analysis

  • Initializes with OpenAI API key and base URL
  • Configures system prompts and deployment settings
  • Implements vision method for image analysis
  • Handles API requests and response parsing

• run() function as the module entry point

  • Validates and processes module run inputs
  • Creates SimpleVisionAgent instance
  • Dynamically calls requested tool method
  • Returns analysis results

• Key Features:

• Direct OpenAI Vision API integration
• Configurable model parameters
• Support for image URL inputs
• Robust error handling and logging

• Input Processing:

• Accepts standardized input schema:
  • tool_name: Specifies "vision" operation
  • tool_input_data: Takes image URL
• Formats API request with:
  • Model configuration
  • Multi-modal message structure
  • Image URL embedding

• Error Handling:

• Validates API key presence
• Handles HTTP request errors
• Logs failures with details
• Raises informative exceptions

• Configuration:

• Environment-based setup
• Deployment configuration for:
  • Model selection and parameters
  • System prompts
  • API endpoints and auth

In the next section, we'll cover various ways to test our agent.