* @return The deserialized metadata, never {@code null}.
     * @throws IOException If the metadata could not be deserialized.
     * @throws MetadataParseException If the input format could not be parsed.
     */
    Metadata read(File input, Map<String, ?> options) throws IOException, MetadataParseException;

    /**

     * @return The deserialized settings, never {@code null}.
     * @throws IOException If the settings could not be deserialized.
     * @throws SettingsParseException If the input format could not be parsed.
     */
    Settings read(File input, Map<String, ?> options) throws IOException, SettingsParseException;

    /**

     * @return The deserialized toolchains, never {@code null}.
     * @throws IOException If the toolchains could not be deserialized.
     * @throws ToolchainsParseException If the input format could not be parsed.
     */
    PersistedToolchains read(File input, Map<String, ?> options) throws IOException, ToolchainsParseException;

    /**

///

An environment variable (also known as "**env var**") is a variable that lives **outside** of the Python code, in the **operating system**, and could be read by your Python code (or by other programs as well).

Environment variables could be useful for handling application **settings**, as part of the **installation** of Python, etc.

## Create and Use Env Vars { #create-and-use-env-vars }

This client could be a browser with a frontend, a code from someone else, an IoT device, etc.

You could need to tell the client that:

* The client doesn't have enough privileges for that operation.
* The client doesn't have access to that resource.
* The item the client was trying to access doesn't exist.
* etc.

///

## Use Cases { #use-cases }

You could use this if you want to stream pure strings, for example directly from the output of an **AI LLM** service.

You could also use it to stream **large binary files**, where you stream each chunk of data as you read it, without having to read it all in memory at once.

You could also stream **video** or **audio** this way, it could even be generated as you process and send it.

     *
     * @param originatingArtifact the artifact that was being resolved
     * @param resolvedArtifacts   artifacts that could be resolved
     * @param missingArtifacts    artifacts that could not be resolved
     * @param remoteRepositories  remote repositories where the missing artifacts were not found
     */
    public MultipleArtifactsNotFoundException(

     * @return The deserialized model, never {@code null}.
     * @throws IOException If the model could not be deserialized.
     * @throws ModelParseException If the input format could not be parsed.
     */
    Model read(File input, Map<String, ?> options) throws IOException, ModelParseException;

    /**

So, you could add additional data to the automatically generated schema.

For example, you could decide to read and validate the request with your own code, without using the automatic features of FastAPI with Pydantic, but you could still want to define the request in the OpenAPI schema.

You could do that with `openapi_extra`:

If you use a `requirements.txt` file you could specify the version with:

```txt
fastapi[standard]==0.112.0
```

that would mean that you would use exactly the version `0.112.0`.

Or you could also pin it with:

```txt
fastapi[standard]>=0.112.0,<0.113.0
```