Using the below example document:
<img width="85%" src="https://bradley.codes/static/img/jstream-levels.gif" alt="jstream"/>

we can choose to extract and act only the objects within the top-level array:
```go
f, _ := os.Open("input.json")
decoder := jstream.NewDecoder(f, 1) // extract JSON values at a depth level of 1
for mv := range decoder.Stream() {
  fmt.Printf("%v\n ", mv.Value)
}
```

output:
```

    /**
     * Transform Graph into a Collection of metadata objects that
     * could serve as a classpath for a particular scope
     *
     * @param dirtyGraph - dependency graph
     * @param scope - which classpath to extract
     * @param resolve - whether to resolve artifacts.
     * @return Collection of metadata objects in the linked subgraph of the graph which
     *             contains the graph.getEntry() vertice
     */

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

**FastAPI** will use that *temporal* response to extract the status code (also cookies and headers), and will put them in the final response that contains the value you returned, filtered by any `response_model`.

            """
        ),
    ] = _Unset,
    alias: Annotated[
        Optional[str],
        Doc(
            """
            An alternative name for the parameter field.

            This will be used to extract the data and for the generated OpenAPI.
            It is particularly useful when you can't use the name you want because it
            is a Python reserved keyword or similar.
            """
        ),
    ] = None,

/// tip

Prefer to use the `Annotated` version if possible.

///

```Python hl_lines="9-13  17"
{!> ../../docs_src/query_param_models/tutorial001_py310.py!}
```

////

**FastAPI** will **extract** the data for **each field** from the **query parameters** in the request and give you the Pydantic model you defined.

## Check the Docs

You can see the query parameters in the docs UI at `/docs`:

        this(time, properties != null ? properties.get(BUILD_TIMESTAMP_FORMAT_PROPERTY) : null);
    }

    /**
     *
     * @deprecated Use {@link #MavenBuildTimestamp(Date, Map)} or extract the format and pass it
     *             to {@link #MavenBuildTimestamp(Date, String)} instead.
     */
    @Deprecated
    public MavenBuildTimestamp(Date time, Properties properties) {

    // Trace the operation now (create a node in the graph).
    TF_ExecuteOperation(add_op, 2, inputs, add_outputs, s);
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    TF_DeleteAbstractOp(add_op);
    // Extract the resulting tensor.
    add_output1 = TF_OutputListGet(add_outputs, 0);
    TF_DeleteOutputList(add_outputs);
  }

  // Same with a second "Add" computing `arg1 + arg1`.
  TF_AbstractTensor* add_output2;
  {

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

**FastAPI** will use that *temporal* response to extract the headers (also cookies and status code), and will put them in the final response that contains the value you returned, filtered by any `response_model`.

And you could do this even if the data type in the request is not JSON.

For example, in this application we don't use FastAPI's integrated functionality to extract the JSON Schema from Pydantic models nor the automatic validation for JSON. In fact, we are declaring the request content type as YAML, not JSON:

//// tab | Pydantic v2

```Python hl_lines="17-22  24"

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

**FastAPI** will use that *temporal* response to extract the cookies (also headers and status code), and will put them in the final response that contains the value you returned, filtered by any `response_model`.