Typer

Overview: The Monorepo Challenge Managing multiple Python applications within a single repository often leads to a "dependency hell" of local virtual environments and duplicated logic. uv workspaces solve this by allowing developers to manage several apps—like a FastAPI web service and a Typer CLI tool—using a shared lockfile and a unified virtual environment. This structure maintains a "DRY" (Don't Repeat Yourself) codebase where internal logic lives in private packages used across the entire project. Prerequisites To follow this guide, you should have a solid grasp of Python development, including experience with virtual environments and `pyproject.toml` configuration. You should also have uv installed, as it serves as the primary engine for workspace orchestration. Key Libraries & Tools - **uv**: An extremely fast Python package and project manager. - **FastAPI**: A modern web framework for building APIs. - **Typer**: A library for creating CLI applications. - **OpenAI**: Used here for summarizing text via GPT models. - **httpx** & **Beautiful%20Soup**: Tools for web requests and HTML parsing. Code Walkthrough: Configuring the Workspace 1. Root Configuration In the project root, create a `pyproject.toml` that defines the workspace members. This tells uv which directories to treat as part of the collective environment. ```toml [project] name = "my-monorepo" version = "0.1.0" dependencies = ["python-dotenv"] [tool.uv.workspace] members = ["packages/*"] ``` 2. Defining Local Sources To import an internal package (e.g., `core`) without publishing it to PyPI, use the `tool.uv.sources` table. This points uv to the local file path. ```toml [tool.uv.sources] core = { workspace = true } ``` 3. Syncing the Environment Run the following command at the root to create a single `.venv` that encompasses all dependencies for every package in the workspace: ```bash uv sync ``` Syntax Notes: The Workspace Source Pattern The `workspace = true` flag is a specific uv convention. It ensures that when you run `uv sync` or `uv run`, the tool looks internally for the dependency rather than searching the public registry. This allows for seamless cross-package imports like `from core.news import fetch_headlines` across different applications. Practical Examples Consider an automation repo containing dozens of small scripts. Instead of each script having its own `httpx` version, they all draw from the workspace root. When you update a shared accounting layer in a `core` package, every web-hook listener and data-extraction tool in the repository immediately benefits from the update without manual re-installs. Tips & Gotchas - **VS Code Support**: Sometimes the IDE struggles with workspace-level imports. Selecting the root virtual environment as your Python interpreter usually resolves type-checking issues. - **When to Avoid**: If your apps are completely unrelated or you intend to release a package independently on PyPI, separate repositories remain the better choice. Workspaces thrive on high overlap and shared context.