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  1. fastapi/encoders.py

            Doc(
                """
                Pydantic's `include` parameter, passed to Pydantic models to set the
                fields to include.
                """
            ),
        ] = None,
        exclude: Annotated[
            Optional[IncEx],
            Doc(
                """
                Pydantic's `exclude` parameter, passed to Pydantic models to set the
                fields to exclude.
                """
            ),
        ] = None,
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Thu Apr 18 21:56:59 UTC 2024
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  2. docs_src/sql_databases_peewee/sql_app/main.py

    import time
    from typing import List
    
    from fastapi import Depends, FastAPI, HTTPException
    
    from . import crud, database, models, schemas
    from .database import db_state_default
    
    database.db.connect()
    database.db.create_tables([models.User, models.Item])
    database.db.close()
    
    app = FastAPI()
    
    sleep_time = 10
    
    
    async def reset_db_state():
        database.db._state._state.set(db_state_default.copy())
        database.db._state.reset()
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Thu Mar 26 19:09:53 UTC 2020
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  3. docs/en/docs/tutorial/response-model.md

    You can declare the type used for the response by annotating the *path operation function* **return type**.
    
    You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.
    
    //// tab | Python 3.10+
    
    ```Python hl_lines="16  21"
    {!> ../../docs_src/response_model/tutorial001_01_py310.py!}
    ```
    
    ////
    
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
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  4. scripts/playwright/query_param_models/image01.py

        page.get_by_role("button", name="Try it out").click()
        page.get_by_role("heading", name="Servers").click()
        # Manually add the screenshot
        page.screenshot(path="docs/en/docs/img/tutorial/query-param-models/image01.png")
    
        # ---------------------
        context.close()
        browser.close()
    
    
    process = subprocess.Popen(
        ["fastapi", "run", "docs_src/query_param_models/tutorial001.py"]
    )
    try:
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Tue Sep 17 18:54:10 UTC 2024
    - 1.3K bytes
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  5. docs/en/data/external_links.yml

    hould-try-7c0ac7eebb3e title: 5 Advanced Features of FastAPI You Should Try - author: Kaustubh Gupta author_link: https://medium.com/@kaustubhgupta1828/ link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/ title: Deploying ML Models as API Using FastAPI and Heroku - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/ title: Using GitHub Actions to Deploy a FastAPI Project to Heroku author_link: http...
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Thu Oct 24 18:39:34 UTC 2024
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  6. docs/en/docs/advanced/events.md

    ## Use Case
    
    Let's start with an example **use case** and then see how to solve it with this.
    
    Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖
    
    The same models are shared among requests, so, it's not one model per request, or one per user or something similar.
    
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
    - 7.8K bytes
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  7. docs_src/path_params/tutorial005.py

    from enum import Enum
    
    from fastapi import FastAPI
    
    
    class ModelName(str, Enum):
        alexnet = "alexnet"
        resnet = "resnet"
        lenet = "lenet"
    
    
    app = FastAPI()
    
    
    @app.get("/models/{model_name}")
    async def get_model(model_name: ModelName):
        if model_name is ModelName.alexnet:
            return {"model_name": model_name, "message": "Deep Learning FTW!"}
    
        if model_name.value == "lenet":
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Fri Aug 26 13:26:03 UTC 2022
    - 546 bytes
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  8. fastapi/security/base.py

    from fastapi.openapi.models import SecurityBase as SecurityBaseModel
    
    
    class SecurityBase:
        model: SecurityBaseModel
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Fri Dec 07 15:12:16 UTC 2018
    - 141 bytes
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  9. docs/en/docs/advanced/settings.md

    Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.
    
    The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.
    
    You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.
    
    //// tab | Pydantic v2
    
    ```Python hl_lines="2  5-8  11"
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
    - 12.9K bytes
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  10. docs/en/docs/tutorial/security/simple-oauth2.md

        disabled = user_dict["disabled"],
        hashed_password = user_dict["hashed_password"],
    )
    ```
    
    /// info
    
    For a more complete explanation of `**user_dict` check back in [the documentation for **Extra Models**](../extra-models.md#about-user_indict){.internal-link target=_blank}.
    
    ///
    
    ## Return the token
    
    The response of the `token` endpoint must be a JSON object.
    
    Registered: Sun Oct 27 07:19:11 UTC 2024
    - Last Modified: Sun Oct 06 20:36:54 UTC 2024
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