Class swarmauri_middleware_gzipcompression.GzipCompressionMiddleware.GzipCompressionMiddleware

swarmauri_middleware_gzipcompression.GzipCompressionMiddleware.GzipCompressionMiddleware

GzipCompressionMiddleware(app=None, **kwargs)

Bases: MiddlewareBase

Middleware that provides gzip compression for outgoing responses.

Source code in swarmauri_base/middlewares/MiddlewareBase.py

def __init__(self, app: Optional[ASGIApp] = None, **kwargs: Any) -> None:
    super().__init__(**kwargs)

    async def _noop_app(
        scope: Scope, receive: ReceiveCallable, send: SendCallable
    ) -> None:
        return None

    self._app: ASGIApp = app or _noop_app

type class-attribute instance-attribute

type = 'ComponentBase'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = MIDDLEWARE.value

version class-attribute instance-attribute

version = '0.1.0'

app property

app

dispatch async

dispatch(request, call_next)

Dispatches the request to the next middleware in the chain.

Source code in swarmauri_middleware_gzipcompression/GzipCompressionMiddleware.py

async def dispatch(
    self, request: Request, call_next: Callable[[Request], Any]
) -> Any:
    """Dispatches the request to the next middleware in the chain."""

    # Process the request by calling the next middleware in the chain
    response = await call_next(request)

    # Check if the response is a Response object
    if not isinstance(response, Response):
        return response

    # Check if the response content is not already compressed
    if "gzip" in str(response.headers.get("Content-Encoding", "")):
        _logger.debug("Response is already gzipped, skipping compression")
        return response

    # Check if the client supports gzip encoding
    if "gzip" not in request.headers.get("Accept-Encoding", "").lower():
        _logger.debug("Client does not support gzip encoding, skipping compression")
        return response

    # Check if the response content type is compressible
    content_type = response.headers.get("Content-Type", "")
    if "application/json" not in content_type and "text/" not in content_type:
        _logger.debug(
            f"Content type {content_type} is not compressible, skipping compression"
        )
        return response

    # Get the response body
    response_body = response.body

    # Compress the response body using gzip
    buffer = io.BytesIO()
    with gzip.GzipFile(fileobj=buffer, mode="w") as gzip_file:
        if response_body:
            if isinstance(response_body, str):
                gzip_file.write(response_body.encode("utf-8"))
            else:
                gzip_file.write(response_body)

    compressed_body = buffer.getvalue()

    # Create a new response with the compressed body
    new_response = Response(
        content=compressed_body,
        status_code=response.status_code,
        headers=dict(response.headers),
        media_type=response.media_type,
    )

    # Add or update the Content-Encoding header
    new_response.headers["Content-Encoding"] = "gzip"

    # Update the Content-Length header
    new_response.headers["Content-Length"] = str(len(compressed_body))

    _logger.debug("Successfully compressed response using gzip")
    return new_response

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger

on_scope async

on_scope(scope)

Hook executed when the middleware receives a scope.

Source code in swarmauri_base/middlewares/MiddlewareBase.py

async def on_scope(self, scope: Scope) -> Scope:
    """Hook executed when the middleware receives a scope."""

    return scope

on_receive async

on_receive(scope, message)

Hook executed for every message received from the client.

Source code in swarmauri_base/middlewares/MiddlewareBase.py

async def on_receive(self, scope: Scope, message: Message) -> Message:
    """Hook executed for every message received from the client."""

    return message

on_send async

on_send(scope, message)

Hook executed before messages are sent to the client.

Source code in swarmauri_base/middlewares/MiddlewareBase.py

async def on_send(self, scope: Scope, message: Message) -> Message:
    """Hook executed before messages are sent to the client."""

    return message

bind

bind(app)

Bind the downstream ASGI application to the middleware.

Source code in swarmauri_base/middlewares/MiddlewareBase.py

def bind(self, app: ASGIApp) -> None:
    """Bind the downstream ASGI application to the middleware."""

    self._app = app

call_next async

call_next(scope, receive, send)

Invoke the next ASGI application in the chain.

Source code in swarmauri_base/middlewares/MiddlewareBase.py

async def call_next(
    self, scope: Scope, receive: ReceiveCallable, send: SendCallable
) -> None:
    """Invoke the next ASGI application in the chain."""

    await self.app(scope, receive, send)