Building A FastAPI Feature In 5 Minutes Using An AI Agent

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. In this article, we will explore how to build a FastAPI feature in just 5 minutes using an AI agent.

Step 1: Setup FastAPI

First, we need to install FastAPI using pip:

pip install fastapi

Once FastAPI is installed, we can create a new Python file and import the necessary modules:


from fastapi import FastAPI
app = FastAPI()


Step 2: Create an AI Agent

Next, we need to create an AI agent that will handle the logic for our FastAPI feature. We can use an existing AI agent or train a new one using machine learning libraries such as TensorFlow or PyTorch. For the purpose of this article, let’s assume we have an AI agent named “ai_agent”.

Step 3: Define FastAPI Endpoint

Now, we can define a FastAPI endpoint that will use our AI agent to perform some task. For example, let’s create an endpoint that takes an input text and returns a prediction using our AI agent:


@app.post("/predict")
async def predict(text: str):
prediction = ai_agent.predict(text)
return {"prediction": prediction}


Step 4: Run FastAPI Server

Finally, we can run the FastAPI server to test our feature. We can do this by adding the following code at the bottom of our Python file:


if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)


Now, if we run our Python file and navigate to http://localhost:8000/docs, we should see the FastAPI documentation page where we can test our “/predict” endpoint.

Conclusion

In just 5 minutes, we have successfully built a FastAPI feature using an AI agent. With FastAPI’s easy-to-use syntax and powerful features, building and deploying APIs has never been easier. By integrating AI agents into FastAPI, we can create intelligent and efficient web applications that deliver real value to users.