Building a product search API with GPT-4 Vision, Pydantic, and FastAPI
In previous blog posts, we showed that Pydantic is well suited to steering language models and validating their outputs.
The application of Pydantic extends beyond merely managing outputs of these text-based models. In this post, we present a guide on how to develop a product search API that uses Pydantic as a link between GPT-4 Vision and FastAPI. Pydantic will be used to structure both the data extraction processes as well as FastAPI requests and responses.
The combination of Pydantic, FastAPI, and OpenAI's GPT models creates a powerful stack for the development of AI applications, characterized by:
- Pydantic's Schema Validation: This feature guarantees the uniformity and adherence to predefined schemas across the application, an essential factor for managing outputs from AI models.
- FastAPI's Performance and Ease of Use: FastAPI serves as the optimal framework for crafting responsive APIs that can fulfill the requirements of AI applications. This is further enhanced by its seamless integration with Pydantic, which aids in data validation and serialization.
- OpenAI's GPT-4 Vision Capabilities: The inclusion of GPT-4 Vision introduces a layer of advanced AI intelligence, empowering applications with the ability to accurately interpret and analyze visual data.