├── .gitignore ├── .python-version ├── README.md ├── app ├── endpoint.py ├── main.py ├── request.py └── router.py ├── pyproject.toml └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *.cover 45 | 46 | # Translations 47 | *.mo 48 | *.pot 49 | 50 | # Django stuff: 51 | *.log 52 | 53 | # Sphinx documentation 54 | docs/_build/ 55 | 56 | # PyBuilder 57 | target/ 58 | 59 | # DotEnv configuration 60 | .env 61 | 62 | # Database 63 | *.db 64 | *.rdb 65 | 66 | # Pycharm 67 | .idea 68 | 69 | # VS Code 70 | .vscode/ 71 | *.code-workspace 72 | 73 | # Spyder 74 | .spyproject/ 75 | 76 | # Jupyter NB Checkpoints 77 | .ipynb_checkpoints/ 78 | 79 | # Mac OS-specific storage files 80 | .DS_Store 81 | 82 | # vim 83 | *.swp 84 | *.swo 85 | 86 | # Mypy cache 87 | .mypy_cache/ 88 | 89 | # Exclude virtual environment 90 | .venv/ 91 | 92 | # Exclude trained models 93 | /models/ 94 | 95 | # exclude data from source control by default 96 | # /data/ 97 | 98 | # exclude lancedb from source control by default 99 | lancedb/ -------------------------------------------------------------------------------- /.python-version: -------------------------------------------------------------------------------- 1 | 3.12 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FastAPI for AI Applications 2 | 3 | ## What is FastAPI? 4 | 5 | [FastAPI](https://fastapi.tiangolo.com/) is a modern, high-performance web framework for building APIs with Python. For AI apps, it serves as the interface between your AI models and the outside world, allowing external systems to send data to your models and receive predictions or processing results. What makes FastAPI particularly appealing is its simplicity and elegance - it provides everything you need without unnecessary complexity. 6 | 7 | ```mermaid 8 | --- 9 | config: 10 | theme: neutral 11 | --- 12 | sequenceDiagram 13 | participant App as Application 14 | participant API as FastAPI Layer 15 | participant LLM as AI/LLM Service 16 | 17 | App->>API: Send request with data 18 | API->>API: Validate data 19 | API->>LLM: Process with AI model 20 | LLM->>API: Return results 21 | API->>App: Deliver formatted response 22 | ``` 23 | 24 | ### Why FastAPI for AI Engineering? 25 | 26 | 1. **Performance**: Built on Starlette and Pydantic, FastAPI is fast and just works. 27 | 2. **Automatic Documentation**: FastAPI automatically generates interactive API documentation (via Swagger UI and ReDoc) from your code and type annotations, making it easier for teams to collaborate. 28 | 3. **Type Safety**: Leveraging Pydantic, FastAPI provides automatic request validation and clear error messages, reducing the likelihood of runtime errors. 29 | 4. **Asynchronous Support**: Native support for async/await patterns allows your API to handle multiple requests efficiently while waiting for AI model responses. 30 | 5. **WebSocket Support**: For streaming AI responses or building real-time applications, FastAPI provides first-class WebSocket support. 31 | 32 | ## Learn More 33 | 34 | Beyond this README, [this tutorial](https://fastapi.tiangolo.com/tutorial/) shows you how to use FastAPI with most of its features, step by step. 35 | 36 | ## Quick Start 37 | 38 | 1. Clone repository 39 | ```bash 40 | git clone https://github.com/daveebbelaar/fastapi-tutorial.git 41 | ``` 42 | 43 | 2. Install dependencies: 44 | ```bash 45 | uv sync 46 | ``` 47 | 48 | 3. Run the application: 49 | ```bash 50 | cd app 51 | uvicorn main:app --reload 52 | ``` 53 | 54 | 3. Access your API: 55 | - API endpoints: http://localhost:8000/events 56 | - Interactive docs: http://localhost:8000/docs 57 | 58 | ### About Uvicorn 59 | 60 | Uvicorn is an ASGI server that actually runs your FastAPI application. While FastAPI defines your API structure and logic, Uvicorn is the server that handles HTTP connections and serves your application. 61 | 62 | Think of FastAPI as the blueprint for your API, and Uvicorn as the engine that powers it. 63 | 64 | The command `uvicorn main:app --reload` means: 65 | - `main`: Use the file named `main.py` 66 | - `:app`: Look for a variable named `app` within that file 67 | - `--reload`: Automatically restart the server when you change your code (useful during development) 68 | 69 | ### Default Port 70 | 71 | By default, Uvicorn runs on port 8000. This means: 72 | - Your API will be accessible at `http://localhost:8000` 73 | - `localhost` refers to your own computer 74 | - `8000` is the "door" or port number through which requests can access your API 75 | 76 | You can change this with the `--port` flag if needed: 77 | ```bash 78 | uvicorn main:app --port 5000 79 | ``` 80 | 81 | ## Structure 82 | 83 | - `main.py`: Application entry point that creates the FastAPI app 84 | - `router.py`: Routes incoming requests to the appropriate endpoint handlers 85 | - `endpoint.py`: Defines data models and endpoint logic for processing events 86 | 87 | This modular approach keeps your code organized as your AI application grows in complexity. 88 | 89 | > For comprehensive documentation, visit the [FastAPI official docs](https://fastapi.tiangolo.com/). 90 | 91 | ## Code Walkthrough 92 | 93 | Let's examine how our three files work together to create a clean API for processing AI events. 94 | 95 | ### 1. `main.py` - Application Entry Point 96 | 97 | ```python 98 | from fastapi import FastAPI 99 | from router import router as process_router 100 | 101 | app = FastAPI() 102 | app.include_router(process_router) 103 | ``` 104 | 105 | This file: 106 | 107 | - Creates the main FastAPI application instance 108 | - Imports and includes our router 109 | - Serves as the entry point for Uvicorn to run our application 110 | 111 | ### 2. `router.py` - Request Routing 112 | 113 | ```python 114 | from fastapi import APIRouter 115 | import endpoint 116 | 117 | router = APIRouter() 118 | router.include_router(endpoint.router, prefix="/events", tags=["events"]) 119 | ``` 120 | 121 | This file: 122 | 123 | - Creates a main router 124 | - Imports our endpoint module with its router 125 | - Adds the endpoint router with the prefix `/events` 126 | - Uses tags for documentation organization 127 | - Routes all requests that start with `/events` to our endpoint 128 | 129 | ### 3. `endpoint.py` - Core Logic 130 | 131 | ```python 132 | import json 133 | from http import HTTPStatus 134 | 135 | from fastapi import APIRouter 136 | from pydantic import BaseModel 137 | from starlette.responses import Response 138 | 139 | router = APIRouter() 140 | 141 | 142 | class EventSchema(BaseModel): 143 | """Event Schema""" 144 | 145 | event_id: str 146 | event_type: str 147 | event_data: dict 148 | 149 | 150 | @router.post("/", dependencies=[]) 151 | def handle_event( 152 | data: EventSchema, 153 | ) -> Response: 154 | print(data) 155 | 156 | # Return acceptance response 157 | return Response( 158 | content=json.dumps({"message": "Data received!"}), 159 | status_code=HTTPStatus.ACCEPTED, 160 | ) 161 | ``` 162 | 163 | This file: 164 | 165 | - Defines a Pydantic model `EventSchema` that validates incoming data 166 | - Creates an endpoint router 167 | - Defines a POST handler at `/` (which becomes `/events/` when mounted in router.py) 168 | - Accepts and validates incoming data against our schema 169 | - Returns a JSON response with HTTP status code 202 (Accepted) 170 | 171 | #### Key Components: 172 | 173 | 1. **Pydantic Model**: `EventSchema` defines the structure of valid incoming data: 174 | - `event_id`: A unique identifier for the event 175 | - `event_type`: The category or type of event 176 | - `event_data`: A dictionary containing the actual event data 177 | 178 | 2. **Router Decorator**: `@router.post("/")` creates a POST endpoint at the base path 179 | 180 | 3. **Request Handler**: `handle_event()` processes incoming data: 181 | - FastAPI automatically validates incoming JSON against `EventSchema` 182 | - Invalid data will be rejected with appropriate error messages 183 | - Valid data is passed to our function where we can process it 184 | 185 | 4. **Response**: Returns a simple JSON confirmation with status code 202 (Accepted) 186 | 187 | ## Sync vs. Async Endpoints in FastAPI 188 | 189 | FastAPI supports both synchronous and asynchronous request handlers. 190 | 191 | ### Synchronous Endpoints 192 | 193 | Synchronous endpoints use standard Python functions and block the server while processing: 194 | 195 | ```python 196 | @router.post("/sync") 197 | def sync_endpoint(data: EventSchema): 198 | # This blocks the server until completion 199 | result = process_data(data) 200 | return {"result": result} 201 | ``` 202 | 203 | **When to use:** For quick operations that complete rapidly (under 1 second) 204 | 205 | ### Asynchronous Endpoints 206 | 207 | Asynchronous endpoints use Python's `async`/`await` syntax and don't block the server: 208 | 209 | ```python 210 | @router.post("/async") 211 | async def async_endpoint(data: EventSchema): 212 | # This doesn't block the server 213 | result = await async_process_data(data) 214 | return {"result": result} 215 | ``` 216 | 217 | **When to use:** For operations that: 218 | 219 | - Involve I/O operations (API calls, database queries) 220 | - Take longer to process (complex AI inference) 221 | - Need to handle many concurrent requests 222 | 223 | ### Key Points 224 | 225 | - FastAPI is designed for async and performs best with async handlers 226 | - You can mix sync and async endpoints in the same application 227 | - For AI applications with external API calls or long processing, async is strongly recommended 228 | - With sync functions, you need more workers to handle the same number of concurrent requests 229 | 230 | ## Testing Your Endpoint 231 | 232 | To quickly test your FastAPI endpoint, you can use the `requests.py` file 233 | 234 | ### What This Script Does 235 | 236 | 1. Sets up the endpoint URL where your FastAPI server is running 237 | 2. Creates a sample event with: 238 | - A random UUID as the event ID 239 | - A test event type 240 | - A dictionary of sample event data 241 | 3. Sends a POST request to your endpoint 242 | 4. Prints the response status code and body 243 | 244 | ## Understanding API Methods: GET vs POST 245 | 246 | If you're new to APIs, think of the difference between GET and POST as similar to the difference between reading and writing. 247 | 248 | ### GET: Asking for Information 249 | 250 | A GET request is like asking someone a question. When you use GET in an API, you're simply requesting information without changing anything. 251 | 252 | For example, checking the weather on a website is a GET request - you're just asking "What's the weather today?" without changing the weather itself. 253 | 254 | In FastAPI, you'd use GET when you want to retrieve information: 255 | 256 | ```python 257 | @router.get("/status") 258 | def get_status(): 259 | return {"status": "online"} 260 | ``` 261 | 262 | This creates an endpoint that tells users about your API's status when they visit `/status`. 263 | 264 | ### POST: Sending Information to Process 265 | 266 | A POST request is like filling out and submitting a form. When you use POST, you're sending data that needs to be processed or stored. 267 | 268 | Imagine ordering food through a delivery app. You're not just asking a question; you're submitting information (your order) that will change something on the server (create a new order in their system). 269 | 270 | In FastAPI, you'd use POST when receiving data for your AI to process: 271 | 272 | ```python 273 | @router.post("/analyze") 274 | def analyze_text(data: TextSchema): 275 | # Process the text with your AI model 276 | return {"sentiment": "positive"} 277 | ``` 278 | 279 | This creates an endpoint that accepts text data, processes it, and returns an analysis. 280 | 281 | ### When to Use Each in AI Applications 282 | 283 | For your AI applications, use GET when users are retrieving information without changing state - like checking if a model is available or retrieving previously generated results. 284 | 285 | Use POST when users are sending data that your AI needs to process - like text for summarization, images for classification, or parameters for generation. 286 | 287 | ## Securing Your FastAPI Endpoint with Bearer Tokens 288 | 289 | Bearer token authentication is the recommended approach for modern APIs, including AI applications. It's more standardized and flexible than simple API keys. 290 | 291 | ### Implementing Bearer Token Authentication 292 | 293 | ```python 294 | from fastapi import APIRouter, Depends, HTTPException 295 | from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials 296 | 297 | router = APIRouter() 298 | 299 | # Create security scheme 300 | security = HTTPBearer() 301 | 302 | # In production, store this in environment variables 303 | API_TOKEN = "your-secret-token" 304 | 305 | @router.post("/") 306 | def handle_event( 307 | data: EventSchema, 308 | credentials: HTTPAuthorizationCredentials = Depends(security) 309 | ): 310 | # Validate the token 311 | if credentials.credentials != API_TOKEN: 312 | raise HTTPException( 313 | status_code=401, 314 | detail="Invalid authentication token" 315 | ) 316 | 317 | # Process the valid request 318 | return {"message": "Data received!"} 319 | ``` 320 | 321 | ### Sending Authenticated Requests 322 | 323 | ```python 324 | import requests 325 | import json 326 | 327 | # API endpoint 328 | url = "http://localhost:8000/events/" 329 | 330 | # Sample event data 331 | event_data = { 332 | "event_id": "123e4567-e89b-12d3-a456-426614174000", 333 | "event_type": "test_event", 334 | "event_data": {"message": "Hello AI world!"} 335 | } 336 | 337 | # Send POST request with Bearer token 338 | headers = { 339 | "Content-Type": "application/json", 340 | "Authorization": "Bearer your-secret-token" 341 | } 342 | 343 | response = requests.post( 344 | url=url, 345 | data=json.dumps(event_data), 346 | headers=headers 347 | ) 348 | ``` 349 | 350 | ### Why Bearer Tokens? 351 | 352 | Bearer tokens have become the standard for API authentication because they: 353 | 354 | 1. Follow OAuth 2.0 specifications used by major APIs worldwide 355 | 2. Can be easily extended to JWT (JSON Web Tokens) for more advanced use cases 356 | 3. Are supported by all API clients and languages 357 | 4. Work well with token management systems 358 | 359 | For production applications, consider using JWT tokens which allow you to include expiration times and additional claims in the token itself. 360 | 361 | For more advanced authentication options, refer to the [FastAPI Security documentation](https://fastapi.tiangolo.com/tutorial/security/). -------------------------------------------------------------------------------- /app/endpoint.py: -------------------------------------------------------------------------------- 1 | import json 2 | from http import HTTPStatus 3 | 4 | from fastapi import APIRouter 5 | from pydantic import BaseModel 6 | from starlette.responses import Response 7 | 8 | 9 | router = APIRouter() 10 | 11 | 12 | class EventSchema(BaseModel): 13 | """Event Schema""" 14 | 15 | event_id: str 16 | event_type: str 17 | event_data: dict 18 | 19 | 20 | """ 21 | Becuase of the router, every endpoint in this file is prefixed with /events/ 22 | """ 23 | 24 | 25 | @router.post("/", dependencies=[]) 26 | def handle_event( 27 | data: EventSchema, 28 | ) -> Response: 29 | print(data) 30 | 31 | # This is where you implement the AI logic to handle the event 32 | 33 | # Return acceptance response 34 | return Response( 35 | content=json.dumps({"message": "Data received!"}), 36 | status_code=HTTPStatus.ACCEPTED, 37 | ) 38 | -------------------------------------------------------------------------------- /app/main.py: -------------------------------------------------------------------------------- 1 | from fastapi import FastAPI 2 | from router import router as process_router 3 | 4 | app = FastAPI() 5 | app.include_router(process_router) 6 | -------------------------------------------------------------------------------- /app/request.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import json 3 | import uuid 4 | 5 | 6 | def send_test_event(): 7 | # API endpoint URL (adjust if your server runs on a different port) 8 | url = "http://localhost:8000/events/" 9 | 10 | # Sample event data 11 | event_data = { 12 | "event_id": str(uuid.uuid4()), # Generate a random UUID 13 | "event_type": "test_event", 14 | "event_data": { 15 | "message": "Can you explain how to use FastAPI?", 16 | }, 17 | } 18 | 19 | # Headers for JSON content 20 | headers = { 21 | "Content-Type": "application/json", 22 | } 23 | 24 | # Send POST request to the endpoint 25 | response = requests.post(url=url, data=json.dumps(event_data), headers=headers) 26 | 27 | # Print response information 28 | print(f"Status Code: {response.status_code}") 29 | print(f"Response: {response.json()}") 30 | print(f"Full Response Headers: {response.headers}") 31 | 32 | 33 | if __name__ == "__main__": 34 | send_test_event() 35 | -------------------------------------------------------------------------------- /app/router.py: -------------------------------------------------------------------------------- 1 | from fastapi import 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