"Unknown q: " + query.getClass() + " => " + query);
    }

    /**
     * Converts a TermQuery to a QueryBuilder with the given boost value.
     *
     * @param context the query context
     * @param termQuery the term query to convert
     * @param boost the boost value to apply
     * @return the converted QueryBuilder
     */

It would also mean that if you get data from the `Request` object directly (for example, read the body) it won't be validated, converted or documented (with OpenAPI, for the automatic API user interface) by FastAPI.

Although any other parameter declared normally (for example, the body with a Pydantic model) would still be validated, converted, annotated, etc.

But there are specific cases where it's useful to get the `Request` object.

                    "price": 35.4,
                    "tax": 3.2,
                },
            },
            "converted": {
                "summary": "An example with converted data",
                "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                "value": {
                    "name": "Bar",
                    "price": "35.4",

                                        },
                                    },
                                    "converted": {
                                        "summary": "An example with converted data",
                                        "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                                        "value": {"name": "Bar", "price": "35.4"},

    }

    /**
     * Converts a wildcard query to an appropriate OpenSearch QueryBuilder.
     *
     * @param context The query context containing field and search information
     * @param wildcardQuery The Lucene wildcard query to convert
     * @param boost The boost factor to apply to the query
     * @return The converted OpenSearch QueryBuilder
     */

For example, you cannot put a Pydantic model in a `JSONResponse` without first converting it to a `dict` with all the data types (like `datetime`, `UUID`, etc) converted to JSON-compatible types.

For those cases, you can use the `jsonable_encoder` to convert your data before passing it to a response:

{* ../../docs_src/response_directly/tutorial001_py310.py hl[5:6,20:21] *}

/// note | Technical Details

        return 10;
    }

    /**
     * Initializes the converter.
     *
     * @throws IOException if an I/O error occurs during initialization.
     */
    void init() throws IOException;

    /**
     * Converts the given text into a list of readings based on the specified field and languages.
     *
     * @param text the text to be converted.
     * @param field the field to be used for conversion.

You couldn't get this kind of editor support if you were working directly with `dict` instead of Pydantic models.

But you don't have to worry about them either, incoming dicts are converted automatically and your output is converted automatically to JSON too.

## Bodies of arbitrary `dict`s { #bodies-of-arbitrary-dicts }

You can also declare a body as a `dict` with keys of some type and values of some other type.

* `skip`: with a value of `0`
* `limit`: with a value of `10`

As they are part of the URL, they are "naturally" strings.

But when you declare them with Python types (in the example above, as `int`), they are converted to that type and validated against it.

All the same process that applied for path parameters also applies for query parameters:

* Editor support (obviously)

def test_swagger_ui():
    response = client.get("/docs")
    assert response.status_code == 200, response.text
    assert '"syntaxHighlight": false' in response.text, (
        "syntaxHighlight should be included and converted to JSON"
    )
    assert '"dom_id": "#swagger-ui"' in response.text, (
        "default configs should be preserved"
    )
    assert "presets: [" in response.text, "default configs should be preserved"