AI Todo Assistant API

A FastAPI backend project that combines a Todo CRUD API with an LLM-powered task extraction assistant.

Users can create todos manually, or describe tasks in natural language and let the AI extract tasks, due times, and save them into the SQLite database.

Features

• FastAPI backend
• Todo CRUD API
• SQLite persistence with SQLModel
• Request and response validation with Pydantic
• HTTP status codes and error handling
• Swagger UI API documentation
• Docker support
• Docker volume support for persistent SQLite data
• Bailian / DashScope LLM integration
• AI chat endpoint
• Natural language task extraction
• AI-generated todos with due time extraction

Tech Stack

• Python
• FastAPI
• Pydantic
• SQLModel
• SQLite
• Uvicorn
• Docker
• OpenAI-compatible SDK
• Alibaba Cloud Bailian / DashScope

Project Structure

fastapi-todo-api/
├── data/
│   └── todos.db
├── .dockerignore
├── .env
├── .env.example
├── .gitignore
├── Dockerfile
├── main.py
├── README.md
└── requirements.txt

Environment Variables

Create a .env file in the project root:

DASHSCOPE_API_KEY=your_dashscope_api_key_here
DASHSCOPE_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
DASHSCOPE_MODEL=qwen3.5-plus

Do not commit .env to GitHub.

Use .env.example as the public template.

Run Locally

Install dependencies:

pip install -r requirements.txt

Start the server:

uvicorn main:app --reload

Open API docs:

http://127.0.0.1:8000/docs

Run with Docker

Build the Docker image:

docker build -t fastapi-todo-api .

Run the container:

docker run -p 8000:8000 fastapi-todo-api

Open API docs:

http://127.0.0.1:8000/docs

If port 8000 is already in use:

docker run -p 8001:8000 fastapi-todo-api

Then open:

http://127.0.0.1:8001/docs

Run with Docker and Persistent SQLite Data

Create a local data directory:

mkdir data

Build the image:

docker build -t fastapi-todo-api .

Run with a volume:

docker run -p 8001:8000 -v "${PWD}/data:/app/data" fastapi-todo-api

For Windows PowerShell:

$projectPath = (Get-Location).Path
docker run -p 8001:8000 -v "${projectPath}\data:/app/data" fastapi-todo-api

The SQLite database will be stored in:

data/todos.db

API Endpoints

Basic Endpoints

Method	Path	Description
GET	/	Root endpoint
GET	/health	Health check
GET	/about	Project information
POST	/echo	Echo test endpoint
POST	/chat	Mock chat endpoint

Todo Endpoints

Method	Path	Description
GET	/todos	List all todos
POST	/todos	Create a todo
GET	/todos/{todo_id}	Get a todo by id
PATCH	/todos/{todo_id}	Update a todo
DELETE	/todos/{todo_id}	Delete a todo

AI Endpoints

Method	Path	Description
POST	/ai/chat	Chat with the LLM
POST	/ai/extract-tasks	Extract todo tasks from natural language
POST	/ai/create-todos	Extract tasks and save them into the database

Todo API Examples

Create a Todo

Request:

{
  "title": "Learn FastAPI",
  "due_time": "Tomorrow morning"
}

Response:

{
  "id": 1,
  "title": "Learn FastAPI",
  "completed": false,
  "due_time": "Tomorrow morning"
}

List Todos

Request:

GET /todos

Response:

[
  {
    "id": 1,
    "title": "Learn FastAPI",
    "completed": false,
    "due_time": "Tomorrow morning"
  }
]

Update a Todo

Request:

{
  "title": "Review FastAPI",
  "completed": true,
  "due_time": "Tonight"
}

Response:

{
  "id": 1,
  "title": "Review FastAPI",
  "completed": true,
  "due_time": "Tonight"
}

Delete a Todo

Request:

DELETE /todos/1

Response:

{
  "message": "Todo deleted"
}

AI API Examples

AI Chat

Endpoint:

POST /ai/chat

Request:

{
  "message": "用一句话鼓励我继续学习 FastAPI"
}

Response:

{
  "user_message": "用一句话鼓励我继续学习 FastAPI",
  "assistant_message": "继续加油，掌握 FastAPI 会让你具备构建现代 Web 服务的核心能力。",
  "model": "qwen3.5-plus"
}

Extract Tasks from Natural Language

Endpoint:

POST /ai/extract-tasks

Request:

{
  "text": "明天上午九点学习 FastAPI，下午三点写 Docker 总结，晚上复习 SQLModel"
}

Response:

{
  "tasks": [
    {
      "title": "学习 FastAPI",
      "time": "明天上午九点"
    },
    {
      "title": "写 Docker 总结",
      "time": "明天下午三点"
    },
    {
      "title": "复习 SQLModel",
      "time": "明天晚上"
    }
  ]
}

Create Todos with AI

Endpoint:

POST /ai/create-todos

Request:

{
  "text": "明天上午九点学习 FastAPI，下午三点写 Docker 总结，晚上复习 SQLModel"
}

Response:

{
  "extracted_tasks": [
    {
      "title": "学习 FastAPI",
      "time": "明天上午九点"
    },
    {
      "title": "写 Docker 总结",
      "time": "明天下午三点"
    },
    {
      "title": "复习 SQLModel",
      "time": "明天晚上"
    }
  ],
  "created_todos": [
    {
      "id": 1,
      "title": "学习 FastAPI",
      "completed": false,
      "due_time": "明天上午九点"
    },
    {
      "id": 2,
      "title": "写 Docker 总结",
      "completed": false,
      "due_time": "明天下午三点"
    },
    {
      "id": 3,
      "title": "复习 SQLModel",
      "completed": false,
      "due_time": "明天晚上"
    }
  ]
}

Current Version

v0.9 - AI Todo Assistant backend with Docker, SQLite persistence, and LLM-powered task creation.

What I Learned

• How to build a FastAPI backend
• How to design RESTful CRUD endpoints
• How to use Pydantic request and response models
• How to use path parameters and request bodies
• How to handle HTTP errors
• How to use SQLModel with SQLite
• How to persist data with a database
• How to Dockerize a FastAPI project
• How to use Docker volumes for persistent data
• How to call an OpenAI-compatible LLM API
• How to extract structured JSON from natural language
• How to connect AI output with backend business logic
• How to manage configuration with environment variables
• How to add basic application logging
• How to write API tests with pytest and TestClient
• How to use a separate test database

Next Steps

• Add filtering by completed status
• Add filtering by due time
• Add better time parsing
• Add tests with pytest
• Add logging and configuration cleanup
• Add a simple frontend
• Deploy the API online

Run Tests

Install test dependencies:

pip install -r requirements.txt

Run tests:

pytest

Current test coverage includes:

• Health check endpoint
• Todo creation
• Full Todo CRUD flow
• Request validation for empty title

The tests use a separate SQLite database:

data/test_todos.db

This avoids polluting the local development database:

data/todos.db