FastAPI Exercise Application for Copilot Trainings

The purpose of this repository is to be used as an exercise application in GitHub Copilot trainings for Python developers.

Technology Stack and Features

• ⚡ FastAPI for the Python backend API.
  • 🧰 SQLModel for the Python SQL database interactions (ORM).
  • 🔍 Pydantic, used by FastAPI, for the data validation and settings management.
  • 💾 PostgreSQL as the SQL database.
• 🐋 Docker Compose for development and production.
• 🔒 Secure password hashing by default.
• 🔑 JWT (JSON Web Token) authentication.
• 📫 Email based password recovery.
• ✅ Tests with Pytest.

How to get started

TODO: Simplified instructions of how to

• Install requirements (Docker, uv)
• Run the local development environment (docker compose)
• Run unit tests

Exercises for the Copilot traning

1. Create a custom instructions file

Generate an instruction file in freestyle markdown that will include information you want to add to all Copilot prompts. The instruction file is simply a markdown file that resides in .github/copilot-instructions.md. The instructions can include for example the language that you want Copilot to answer in, the structure of those answers, some coding best practices or company policies, etc.

For example,

All Python files must adhere to PEP 8 guidelines.

Answer the prompt and only the prompt. Nothing else.

are just some lines you could add into your instruction file

• Create .github/copilot-instructions.md
• Ask Copilot for Python best practices and add those to instructions file
• Review suggested changes and fix accordingly
• Think about what kind of contents would be useful in the file, e.g. - Project structure - Tehcnology stack, e.g. application and testing frameworks, data stores etc. - Design patterns - Description of the application purpose, business logic, use cases - Coding conventions - Instructions for building, running tests etc.
• In the following exercises, take notice how the file is automatically added to all prompts. See if the contests could be refined based on the effectiveness of the prompts.

2. Initialize a MCP server for PostgreSQL

• Racap of MCP servers
  • MCP (Model Context Protocol) servers provide a standardized way to interact with various tools, services, and systems.
  • MCP server is run locally on the developer's workstation e.g. as a docker container
  • Developers can initialize and manage MCP servers directly from their IDE or command-line tools.
  • They allow seamless integration with Copilot for managing and interacting with external resources.
  • They support multiple types of integrations, including databases, APIs, and cloud services.
  • MCP servers enable real-time exploration, execution, and interaction with connected resources.
  • A wide variety of MCP servers are aldready available
  • In the following exercise PostgreSQL MCP server will be configured and used
• Exercise: Initialize a MCP server for PostgreSQL (VS Code)
  • VS Code => Shift+CMD+P
  • > MCP: Add server...
  • Docker image => mcp/postgres
  • Select "Allow"
  • Postgres URL: postgresql://postgres:[email protected]:5432/app
  • Select "Workspace"
  • Start the server by clicking on the play button in mcp.json
  • The query tool provided by the MCP server should be now available in the tools menu in the agent mode prompt box
  • Try out the tool e.g. with the following prompt: "What is the schema of my database #query"
  • Think about how the information provided by the MCP server could be utilised in prompts targeting the codebase

3. Prompt files

Create a prompt file to tell Copilot to follow specific instructions when generating new API endpoints. These instructions are:

• All methods must be under 30 lines in length.
• All method parameters must have proper type hints in place
• When raising a HTTPException, there must be details added to the raise.
• All new endpoints must have a sufficient docstring.

Like a copilot-instructions.md file, prompt files are custom instructions you can add to your prompts, but only for the context of the current prompt. These files are named <name>.prompt.md and they need to be separately added as a context to your prompts.

4. Comparison: Agent mode vs Edits

Prompt Copilot Edits and Copilot Agent mode to create a new API route to add an Authors model and compare the results.

We have our books model in place, but books don't have appropraite authors. Each book should have one author and any author may have written more than one book. We need to refactor our codebase to address this change. Also, the new Author model needs to tested with proper unit tests.

Consider the following questions:

• How do the responses differ from the each other?
• Which mode should you use in each scenario?

About This Repository

Forked from fastapi/full-stack-fastapi-template. Frontend removed to make the project suitable for Python developers.