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  1. docs/en/docs/tutorial/first-steps.md

    In the output, there's a line with something like:
    
    ```hl_lines="4"
    INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
    ```
    
    That line shows the URL where your app is being served, in your local machine.
    
    ### Check it
    
    Open your browser at <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>.
    
    You will see the JSON response as:
    
    ```JSON
    {"message": "Hello World"}
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  2. docs/en/docs/how-to/nosql-databases-couchbase.md

    In the code, a `Bucket` represents the main entrypoint of communication with the database.
    
    This utility function will:
    
    * Connect to a **Couchbase** cluster (that might be a single machine).
        * Set defaults for timeouts.
    * Authenticate in the cluster.
    * Get a `Bucket` instance.
        * Set defaults for timeouts.
    * Return it.
    
    ```Python hl_lines="12-21"
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  3. docs/en/data/external_links.yml

    Articles:
      English:
      - author: Kurtis Pykes - NVIDIA
        link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/
        title: Building a Machine Learning Microservice with FastAPI
      - author: Ravgeet Dhillon - Twilio
        link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi
        title: Booking Appointments with Twilio, Notion, and FastAPI
      - author: Abhinav Tripathi - Microsoft Blogs
    Others
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  4. 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. 🤖
    
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  5. docs/tr/docs/project-generation.md

    ## Full Stack FastAPI MongoDB
    
    ... müsaitliğime ve diğer faktörlere bağlı olarak daha sonra gelebilir. 😅 🎉
    
    ## Machine Learning modelleri, spaCy ve FastAPI
    
    GitHub: <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a>
    
    ### Machine Learning modelleri, spaCy ve FastAPI - Features
    
    * **spaCy** NER model entegrasyonu.
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  6. docs/en/docs/async.md

    * **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
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  7. docs/en/docs/deployment/index.md

    ## What Does Deployment Mean
    
    To **deploy** an application means to perform the necessary steps to make it **available to the users**.
    
    For a **web API**, it normally involves putting it in a **remote machine**, with a **server program** that provides good performance, stability, etc, so that your **users** can **access** the application efficiently and without interruptions or problems.
    
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  8. docs/en/docs/fastapi-cli.md

    By default it will listen on the IP address `127.0.0.1`, which is the IP for your machine to communicate with itself alone (`localhost`).
    
    ## `fastapi run`
    
    When you run `fastapi run`, it will run on production mode by default.
    
    It will have **auto-reload disabled** by default.
    
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  9. docs/es/docs/async.md

    * **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.
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  10. docs/en/docs/deployment/docker.md

    Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).
    
    This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).
    
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