Follow the steps below to create an API for your application using FastAPI and integrate a large language model (LLM). Before diving into the implementation, make sure to familiarize yourself with the basics of FastAPI.
First, refer to the getting_started folder for a quick introduction to FastAPI. Inside this folder, you’ll find resources that will help you understand:
- How to install FastAPI.
- Basic structure of a FastAPI application.
- How to create routes, handle requests, and define API endpoints.
By reviewing the getting_started folder, you'll learn essential FastAPI concepts like request handling, path parameters, and response models, which will be useful in the next steps.
Once you're familiar with the basics, proceed to the provided Jupyter Notebook where you can start building the API for your LLM. The notebook contains step-by-step code that shows how to:
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Set Up Uvicorn: Learn how to run a FastAPI server with Uvicorn.
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Integrate Your LLM: Follow the code to integrate a large language model (LLM) of your choice, such as GPT or BERT, into the FastAPI application. You'll find examples for loading models and creating endpoints to serve predictions from the model.
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Define API Endpoints: Learn how to create
POSTrequests to send input text to the LLM, process it, and return the generated output as a response. -
Test the API: The notebook contains examples of how to test the API using tools like
requestsor the built-in FastAPI documentation UI (Swagger). You can send sample data to the LLM API and see the responses in real-time.
By the end of this step, you’ll have a running FastAPI server that serves your LLM model as an API.