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.github/workflows/test.yml
runs-on: ubuntu-latest steps: - name: Dump GitHub context env: GITHUB_CONTEXT: ${{ toJson(github) }} run: echo "$GITHUB_CONTEXT" - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.11" # Issue ref: https://github.com/actions/setup-python/issues/436 # cache: "pip"
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.github/workflows/publish.yml
id-token: write steps: - name: Dump GitHub context env: GITHUB_CONTEXT: ${{ toJson(github) }} run: echo "$GITHUB_CONTEXT" - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.10" # Issue ref: https://github.com/actions/setup-python/issues/436 # cache: "pip"
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docs/en/docs/advanced/dataclasses.md
```Python hl_lines="1 7-13 19" {!../../../docs_src/dataclasses/tutorial002.py!} ``` The dataclass will be automatically converted to a Pydantic dataclass. This way, its schema will show up in the API docs user interface: <img src="/img/tutorial/dataclasses/image01.png"> ## Dataclasses in Nested Data Structures You can also combine `dataclasses` with other type annotations to make nested data structures.
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.github/workflows/test-redistribute.yml
- fastapi-slim steps: - name: Dump GitHub context env: GITHUB_CONTEXT: ${{ toJson(github) }} run: echo "$GITHUB_CONTEXT" - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install build dependencies run: pip install build - name: Build source distribution env:
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docs/en/docs/how-to/async-sql-encode-databases.md
This section doesn't apply those ideas, to be equivalent to the counterpart in <a href="https://www.starlette.io/database/" class="external-link" target="_blank">Starlette</a>. ## Import and set up `SQLAlchemy` * Import `SQLAlchemy`. * Create a `metadata` object. * Create a table `notes` using the `metadata` object. ```Python hl_lines="4 14 16-22" {!../../../docs_src/async_sql_databases/tutorial001.py!}
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docs/en/docs/deployment/concepts.md
* Handled internally by a cloud provider as part of their services (read below 👇) Another option is that you could use a **cloud service** that does more of the work including setting up HTTPS. It could have some restrictions or charge you more, etc. But in that case, you wouldn't have to set up a TLS Termination Proxy yourself. I'll show you some concrete examples in the next chapters. ---
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docs/en/docs/advanced/generate-clients.md
code automatically. And if you **build** the client it will error out if you have any **mismatch** in the data used. So, you would **detect many errors** very early in the development cycle instead of having to wait for the errors to show up to your final users in production and then trying to debug where the problem is. ✨...
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docs/en/docs/advanced/behind-a-proxy.md
Up to here, everything would work as normally. But then, when you open the integrated docs UI (the frontend), it would expect to get the OpenAPI schema at `/openapi.json`, instead of `/api/v1/openapi.json`.
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docs/en/docs/tutorial/security/oauth2-jwt.md
You can learn how to use them and how they are integrated into **FastAPI** later in the **Advanced User Guide**. ## Recap With what you have seen up to now, you can set up a secure **FastAPI** application using standards like OAuth2 and JWT. In almost any framework handling the security becomes a rather complex subject quite quickly.
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docs/en/docs/tutorial/response-model.md
FastAPI does several things internally with Pydantic to make sure that those same rules of class inheritance are not used for the returned data filtering, otherwise you could end up returning much more data than what you expected. This way, you can get the best of both worlds: type annotations with **tooling support** and **data filtering**. ## See it in the docs
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