FastAPI has redefined web development with its speed and simplicity. However, as applications grow, so do the intricacies of managing endpoints, validating input data, handling errors, and ensuring optimal performance. In this post, we'll explore how the FastAPI agent can help you move quickly with FastAPI projects. Whether you're a seasoned coder or new to the framework, the agent will optimize your workflow and enhance your development speed.
The FastAPI AI agent not only understands your project inside-out, but also knows everything about FastAPI's playbook. It combines what it knows about your work with FastAPI's instruction manual to give you the best advice. This way, the agent can help you add new parts to your API, write tests, or find and fix glitches. Put simply - it's like having a teammate who's familiar with both your project's quirks and the rules of the FastAPI game.
Real-World Use Case: Adding a Reports Feature to Your Inventory Management API
Let's dive into a practical scenario where you're adding a reports feature to your existing inventory management API, which includes endpoints for products, categories, and sales transactions. You can watch a video that covers the entire process here:
Describing the required feature
Start by specifying the reports API you want to implement, in this case it’s a simple GET endpoint that receives start and end date and returns a few numbers from the inventory data. Based on this input, the assistant can automatically generate the necessary API endpoints, complete with route paths, request parameters, and response structures. It leverages its knowledge of your API structure and FastAPI documentation to ensure the generated endpoints align with the existing design.
Activate the agent
Once the specifications are in place, the AI agent can start running. Inside the project directory we will run the command $ fine-dev proxy, that will allow the agent to operate locally. Then, on the Fine web platform, we will press ctrl+p to open the agent palette and select the FastAPI agent.
⚠️ Notice: if you haven’t deployed the FastAPI agent to your workspace, you will not see it in the list of agents. To deploy the agent, follow the instructions here.
The agent will start by providing the list of steps it intends to perform and asking for confirmation. We will confirm and move forward.
Validation and Pull Request
After the agent completed its operation, you will find that the reports endpoint was added to the main API, and that a test has been added as well. The tests follow the guidelines from the FastAPI documentation. That’s pretty great!
By leveraging AI agents, we've streamlined the process of adding a feature to our existing API. It has harnessed its knowledge of our project, combined with FastAPI best practices, to provide tailored support at every step – from endpoint generation to testing and documentation. With this help, we've expanded the capabilities of our API while maintaining code quality and adhering to FastAPI's principles.
Moving forward
If you are using FastAPI, consider exploring the possibilities that AI agents offer. While FastAPI is just one framework, AI agents can extend their support to a wide array of tools and technologies. Our GitHub repository showcases practical examples of how the AI agents can integrate with different projects, check it out!
I invite you to join us in the journey of discovering how AI agents can enhance development processes. Share your experiences and observations as you experiment with our AI agents in your projects, helping us refine and expand its capabilities. Join our discord community, develop your own custom agents, and contribute to shaping the way developers interact with code!
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