vision

Overview ¶

Package vision Basic Vision Example This example demonstrates how to use the Atomic Agents framework to analyze images with text, specifically focusing on extracting structured information from nutrition labels using GPT-4 Vision capabilities.

## Features

- Image Analysis: Process nutrition label images using GPT-4 Vision - Structured Data Extraction: Convert visual information into structured Pydantic models - Multi-Image Processing: Analyze multiple nutrition labels simultaneously - Comprehensive Nutritional Data: Extract detailed nutritional information including:

• Basic nutritional facts (calories, fats, proteins, etc.)
• Serving size information
• Vitamin and mineral content
• Product details

## Components

1. Nutrition Label Schema (`NutritionLabel`) Defines the structure for storing nutrition information, including: - Macronutrients (fats, proteins, carbohydrates) - Micronutrients (vitamins and minerals) - Serving information - Product details
2. Input/Output Schemas - `NutritionAnalysisInput`: Handles input images and analysis instructions - `NutritionAnalysisOutput`: Structures the extracted nutrition information
3. Nutrition Analyzer Agent A specialized agent configured with: - GPT-4 Vision capabilities - Custom system prompts for nutrition label analysis - Structured data validation