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anthropic.calls

A module for calling Anthropic's Claude API.

AnthropicCall

Bases: BaseCall[AnthropicCallResponse, AnthropicCallResponseChunk, AnthropicTool, MessageParam]

A base class for calling Anthropic's Claude models.

Example:

from mirascope.anthropic import AnthropicCall


class BookRecommender(AnthropicCall):
    prompt_template = "Please recommend a {genre} book."

    genre: str


response = BookRecommender(genre="fantasy").call()
print(response.content)
#> There are many great books to read, it ultimately depends...
Source code in mirascope/anthropic/calls.py 
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class AnthropicCall(
    BaseCall[
        AnthropicCallResponse,
        AnthropicCallResponseChunk,
        AnthropicTool,
        MessageParam,
    ]
):
    """A base class for calling Anthropic's Claude models.

    Example:

    ```python
    from mirascope.anthropic import AnthropicCall


    class BookRecommender(AnthropicCall):
        prompt_template = "Please recommend a {genre} book."

        genre: str


    response = BookRecommender(genre="fantasy").call()
    print(response.content)
    #> There are many great books to read, it ultimately depends...
    ```
    """

    call_params: ClassVar[AnthropicCallParams] = AnthropicCallParams()
    _provider: ClassVar[str] = "anthropic"

    def messages(self) -> list[MessageParam]:
        """Returns the template as a formatted list of messages."""
        return self._parse_messages(
            [MessageRole.SYSTEM, MessageRole.USER, MessageRole.ASSISTANT]
        )  # type: ignore

    @retry
    def call(
        self, retries: Union[int, Retrying] = 0, **kwargs: Any
    ) -> AnthropicCallResponse:
        """Makes a call to the model using this `AnthropicCall` instance.

        Args:
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Returns:
            A `AnthropicCallResponse` instance.
        """
        messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
        user_message_param = self._get_possible_user_message(messages)
        client = get_wrapped_client(
            Anthropic(api_key=self.api_key, base_url=self.base_url), self
        )
        create = get_wrapped_call(
            client.messages.create,
            self,
            response_type=AnthropicCallResponse,
            tool_types=tool_types,
        )
        start_time = datetime.datetime.now().timestamp() * 1000
        message = create(
            messages=messages,
            stream=False,
            **kwargs,
        )
        return AnthropicCallResponse(
            response=message,
            user_message_param=user_message_param,
            tool_types=tool_types,
            start_time=start_time,
            end_time=datetime.datetime.now().timestamp() * 1000,
            cost=anthropic_api_calculate_cost(message.usage, message.model),
            response_format=self.call_params.response_format,
        )

    @retry
    async def call_async(
        self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
    ) -> AnthropicCallResponse:
        """Makes an asynchronous call to the model using this `AnthropicCall` instance.

        Args:
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Returns:
            A `AnthropicCallResponse` instance.
        """
        messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
        user_message_param = self._get_possible_user_message(messages)
        client = get_wrapped_async_client(
            AsyncAnthropic(api_key=self.api_key, base_url=self.base_url), self
        )
        create = get_wrapped_call(
            client.messages.create,
            self,
            is_async=True,
            response_type=AnthropicCallResponse,
            tool_types=tool_types,
        )
        start_time = datetime.datetime.now().timestamp() * 1000
        message = await create(
            messages=messages,
            stream=False,
            **kwargs,
        )
        return AnthropicCallResponse(
            response=message,
            user_message_param=user_message_param,
            tool_types=tool_types,
            start_time=start_time,
            end_time=datetime.datetime.now().timestamp() * 1000,
            cost=anthropic_api_calculate_cost(message.usage, message.model),
            response_format=self.call_params.response_format,
        )

    @retry
    def stream(
        self, retries: Union[int, Retrying] = 0, **kwargs: Any
    ) -> Generator[AnthropicCallResponseChunk, None, None]:
        """Streams the response for a call using this `AnthropicCall`.

        Args:
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Yields:
            An `AnthropicCallResponseChunk` for each chunk of the response.
        """
        messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
        user_message_param = self._get_possible_user_message(messages)
        client = get_wrapped_client(
            Anthropic(api_key=self.api_key, base_url=self.base_url), self
        )
        stream_fn = get_wrapped_call(
            client.messages.stream,
            self,
            response_chunk_type=AnthropicCallResponseChunk,
            tool_types=tool_types,
        )
        stream = stream_fn(messages=messages, **kwargs)
        if isinstance(stream, AbstractContextManager):
            with stream as message_stream:
                for chunk in message_stream:
                    yield AnthropicCallResponseChunk(
                        chunk=chunk,
                        user_message_param=user_message_param,
                        tool_types=tool_types,
                        response_format=self.call_params.response_format,
                    )
        else:
            for chunk in stream:  # type: ignore
                yield AnthropicCallResponseChunk(
                    chunk=chunk,  # type: ignore
                    user_message_param=user_message_param,
                    tool_types=tool_types,
                    response_format=self.call_params.response_format,
                )

    @retry
    async def stream_async(
        self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
    ) -> AsyncGenerator[AnthropicCallResponseChunk, None]:
        """Streams the response for an asynchronous call using this `AnthropicCall`.

        Args:
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Yields:
            An `AnthropicCallResponseChunk` for each chunk of the response.
        """
        messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
        user_message_param = self._get_possible_user_message(messages)
        client = get_wrapped_async_client(
            AsyncAnthropic(api_key=self.api_key, base_url=self.base_url), self
        )
        stream_fn = get_wrapped_call(
            client.messages.stream,
            self,
            is_async=True,
            response_chunk_type=AnthropicCallResponseChunk,
            tool_types=tool_types,
        )
        stream = stream_fn(messages=messages, **kwargs)
        if isinstance(stream, AbstractAsyncContextManager):
            async with stream as message_stream:
                async for chunk in message_stream:  # type: ignore
                    yield AnthropicCallResponseChunk(
                        chunk=chunk,
                        user_message_param=user_message_param,
                        tool_types=tool_types,
                        response_format=self.call_params.response_format,
                    )
        else:
            async for chunk in stream:  # type: ignore
                yield AnthropicCallResponseChunk(
                    chunk=chunk,
                    user_message_param=user_message_param,
                    tool_types=tool_types,
                    response_format=self.call_params.response_format,
                )

    ############################## PRIVATE METHODS ###################################

    def _setup_anthropic_kwargs(
        self,
        kwargs: dict[str, Any],
    ) -> tuple[
        list[MessageParam],
        dict[str, Any],
        Optional[list[Type[AnthropicTool]]],
    ]:
        """Overrides the `BaseCall._setup` for Anthropic specific setup."""
        kwargs, tool_types = self._setup(kwargs, AnthropicTool)
        messages = self.messages()
        system_message = ""
        if "system" in kwargs and kwargs["system"] is not None:
            system_message += f'{kwargs.pop("system")}'
        if messages[0]["role"] == "system":
            if system_message:
                system_message += "\n"
            system_message += messages.pop(0)["content"]
        if self.call_params.response_format == "json":
            if system_message:
                system_message += "\n\n"
            system_message += "Response format: JSON."
            messages.append(
                {
                    "role": "assistant",
                    "content": "Here is the JSON requested with only the fields "
                    "defined in the schema you provided:\n{",
                }
            )
            if "tools" in kwargs:
                tools = kwargs.pop("tools")
                messages[-1]["content"] = (
                    "For each JSON you output, output ONLY the fields defined by these "
                    "schemas. Include a `tool_name` field that EXACTLY MATCHES the "
                    "tool name found in the schema matching this tool:"
                    "\n{schemas}\n{json_msg}".format(
                        schemas="\n\n".join([str(tool) for tool in tools]),
                        json_msg=messages[-1]["content"],
                    )
                )
        if system_message:
            kwargs["system"] = system_message

        return messages, kwargs, tool_types

call(retries=0, **kwargs)

Makes a call to the model using this AnthropicCall instance.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.

 {}

Returns:

Type Description
AnthropicCallResponse

A AnthropicCallResponse instance.
Source code in mirascope/anthropic/calls.py 
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@retry
def call(
    self, retries: Union[int, Retrying] = 0, **kwargs: Any
) -> AnthropicCallResponse:
    """Makes a call to the model using this `AnthropicCall` instance.

    Args:
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Returns:
        A `AnthropicCallResponse` instance.
    """
    messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
    user_message_param = self._get_possible_user_message(messages)
    client = get_wrapped_client(
        Anthropic(api_key=self.api_key, base_url=self.base_url), self
    )
    create = get_wrapped_call(
        client.messages.create,
        self,
        response_type=AnthropicCallResponse,
        tool_types=tool_types,
    )
    start_time = datetime.datetime.now().timestamp() * 1000
    message = create(
        messages=messages,
        stream=False,
        **kwargs,
    )
    return AnthropicCallResponse(
        response=message,
        user_message_param=user_message_param,
        tool_types=tool_types,
        start_time=start_time,
        end_time=datetime.datetime.now().timestamp() * 1000,
        cost=anthropic_api_calculate_cost(message.usage, message.model),
        response_format=self.call_params.response_format,
    )

call_async(retries=0, **kwargs) async

Makes an asynchronous call to the model using this AnthropicCall instance.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.

 {}

Returns:

Type Description
AnthropicCallResponse

A AnthropicCallResponse instance.
Source code in mirascope/anthropic/calls.py 
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@retry
async def call_async(
    self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
) -> AnthropicCallResponse:
    """Makes an asynchronous call to the model using this `AnthropicCall` instance.

    Args:
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Returns:
        A `AnthropicCallResponse` instance.
    """
    messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
    user_message_param = self._get_possible_user_message(messages)
    client = get_wrapped_async_client(
        AsyncAnthropic(api_key=self.api_key, base_url=self.base_url), self
    )
    create = get_wrapped_call(
        client.messages.create,
        self,
        is_async=True,
        response_type=AnthropicCallResponse,
        tool_types=tool_types,
    )
    start_time = datetime.datetime.now().timestamp() * 1000
    message = await create(
        messages=messages,
        stream=False,
        **kwargs,
    )
    return AnthropicCallResponse(
        response=message,
        user_message_param=user_message_param,
        tool_types=tool_types,
        start_time=start_time,
        end_time=datetime.datetime.now().timestamp() * 1000,
        cost=anthropic_api_calculate_cost(message.usage, message.model),
        response_format=self.call_params.response_format,
    )

messages()

Returns the template as a formatted list of messages.

Source code in mirascope/anthropic/calls.py 
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def messages(self) -> list[MessageParam]:
    """Returns the template as a formatted list of messages."""
    return self._parse_messages(
        [MessageRole.SYSTEM, MessageRole.USER, MessageRole.ASSISTANT]
    )  # type: ignore

stream(retries=0, **kwargs)

Streams the response for a call using this AnthropicCall.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.

 {}

Yields:

Type Description
AnthropicCallResponseChunk

An AnthropicCallResponseChunk for each chunk of the response.
Source code in mirascope/anthropic/calls.py 
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@retry
def stream(
    self, retries: Union[int, Retrying] = 0, **kwargs: Any
) -> Generator[AnthropicCallResponseChunk, None, None]:
    """Streams the response for a call using this `AnthropicCall`.

    Args:
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Yields:
        An `AnthropicCallResponseChunk` for each chunk of the response.
    """
    messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
    user_message_param = self._get_possible_user_message(messages)
    client = get_wrapped_client(
        Anthropic(api_key=self.api_key, base_url=self.base_url), self
    )
    stream_fn = get_wrapped_call(
        client.messages.stream,
        self,
        response_chunk_type=AnthropicCallResponseChunk,
        tool_types=tool_types,
    )
    stream = stream_fn(messages=messages, **kwargs)
    if isinstance(stream, AbstractContextManager):
        with stream as message_stream:
            for chunk in message_stream:
                yield AnthropicCallResponseChunk(
                    chunk=chunk,
                    user_message_param=user_message_param,
                    tool_types=tool_types,
                    response_format=self.call_params.response_format,
                )
    else:
        for chunk in stream:  # type: ignore
            yield AnthropicCallResponseChunk(
                chunk=chunk,  # type: ignore
                user_message_param=user_message_param,
                tool_types=tool_types,
                response_format=self.call_params.response_format,
            )

stream_async(retries=0, **kwargs) async

Streams the response for an asynchronous call using this AnthropicCall.

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.

 {}

Yields:

Type Description
AsyncGenerator[AnthropicCallResponseChunk, None]

An AnthropicCallResponseChunk for each chunk of the response.
Source code in mirascope/anthropic/calls.py 
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@retry
async def stream_async(
    self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
) -> AsyncGenerator[AnthropicCallResponseChunk, None]:
    """Streams the response for an asynchronous call using this `AnthropicCall`.

    Args:
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Yields:
        An `AnthropicCallResponseChunk` for each chunk of the response.
    """
    messages, kwargs, tool_types = self._setup_anthropic_kwargs(kwargs)
    user_message_param = self._get_possible_user_message(messages)
    client = get_wrapped_async_client(
        AsyncAnthropic(api_key=self.api_key, base_url=self.base_url), self
    )
    stream_fn = get_wrapped_call(
        client.messages.stream,
        self,
        is_async=True,
        response_chunk_type=AnthropicCallResponseChunk,
        tool_types=tool_types,
    )
    stream = stream_fn(messages=messages, **kwargs)
    if isinstance(stream, AbstractAsyncContextManager):
        async with stream as message_stream:
            async for chunk in message_stream:  # type: ignore
                yield AnthropicCallResponseChunk(
                    chunk=chunk,
                    user_message_param=user_message_param,
                    tool_types=tool_types,
                    response_format=self.call_params.response_format,
                )
    else:
        async for chunk in stream:  # type: ignore
            yield AnthropicCallResponseChunk(
                chunk=chunk,
                user_message_param=user_message_param,
                tool_types=tool_types,
                response_format=self.call_params.response_format,
            )

AnthropicCallParams

Bases: BaseCallParams[AnthropicTool]

The parameters to use when calling d Claud API with a prompt.

Example:

from mirascope.anthropic import AnthropicCall, AnthropicCallParams


class BookRecommender(AnthropicCall):
    prompt_template = "Please recommend some books."

    call_params = AnthropicCallParams(
        model="anthropic-3-opus-20240229",
    )
Source code in mirascope/anthropic/types.py 
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class AnthropicCallParams(BaseCallParams[AnthropicTool]):
    """The parameters to use when calling d Claud API with a prompt.

    Example:

    ```python
    from mirascope.anthropic import AnthropicCall, AnthropicCallParams


    class BookRecommender(AnthropicCall):
        prompt_template = "Please recommend some books."

        call_params = AnthropicCallParams(
            model="anthropic-3-opus-20240229",
        )
    ```
    """

    max_tokens: int = 1000
    model: str = "claude-3-haiku-20240307"
    metadata: Optional[Metadata] = None
    stop_sequences: Optional[list[str]] = None
    system: Optional[str] = None
    temperature: Optional[float] = None
    top_k: Optional[int] = None
    top_p: Optional[float] = None
    extra_headers: Optional[Headers] = None
    extra_query: Optional[Query] = None
    extra_body: Optional[Body] = None
    timeout: Optional[Union[float, Timeout]] = 600

    response_format: Optional[Literal["json"]] = None

    model_config = ConfigDict(arbitrary_types_allowed=True)

    def kwargs(
        self,
        tool_type: Optional[Type[AnthropicTool]] = None,
        exclude: Optional[set[str]] = None,
    ) -> dict[str, Any]:
        """Returns the keyword argument call parameters."""
        extra_exclude = {"response_format"}
        exclude = extra_exclude if exclude is None else exclude.union(extra_exclude)
        return super().kwargs(tool_type, exclude)

kwargs(tool_type=None, exclude=None)

Returns the keyword argument call parameters.

Source code in mirascope/anthropic/types.py 
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def kwargs(
    self,
    tool_type: Optional[Type[AnthropicTool]] = None,
    exclude: Optional[set[str]] = None,
) -> dict[str, Any]:
    """Returns the keyword argument call parameters."""
    extra_exclude = {"response_format"}
    exclude = extra_exclude if exclude is None else exclude.union(extra_exclude)
    return super().kwargs(tool_type, exclude)

AnthropicCallResponse

Bases: BaseCallResponse[Message, AnthropicTool]

Convenience wrapper around the Anthropic Claude API.

When using Mirascope's convenience wrappers to interact with Anthropic models via AnthropicCall, responses using Anthropic.call() will return an AnthropicCallResponse, whereby the implemented properties allow for simpler syntax and a convenient developer experience.

Example:

from mirascope.anthropic import AnthropicCall


class BookRecommender(AnthropicCall):
    prompt_template = "Please recommend some books."


print(BookRecommender().call())
Source code in mirascope/anthropic/types.py 
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class AnthropicCallResponse(BaseCallResponse[Message, AnthropicTool]):
    """Convenience wrapper around the Anthropic Claude API.

    When using Mirascope's convenience wrappers to interact with Anthropic models via
    `AnthropicCall`, responses using `Anthropic.call()` will return an
    `AnthropicCallResponse`, whereby the implemented properties allow for simpler syntax
    and a convenient developer experience.

    Example:

    ```python
    from mirascope.anthropic import AnthropicCall


    class BookRecommender(AnthropicCall):
        prompt_template = "Please recommend some books."


    print(BookRecommender().call())
    ```
    """

    response_format: Optional[Literal["json"]] = None
    user_message_param: Optional[MessageParam] = None

    @property
    def message_param(self) -> MessageParam:
        """Returns the assistant's response as a message parameter."""
        return self.response.model_dump(include={"content", "role"})  # type: ignore

    @property
    def tools(self) -> Optional[list[AnthropicTool]]:
        """Returns the tools for the 0th choice message."""
        if not self.tool_types:
            return None

        if self.response_format == "json":
            # Note: we only handle single tool calls in JSON mode.
            tool_type = self.tool_types[0]
            return [
                tool_type.from_tool_call(
                    ToolUseBlock(
                        id="id",
                        input=json.loads(self.content),
                        name=tool_type.name(),
                        type="tool_use",
                    )
                )
            ]

        if self.response.stop_reason != "tool_use":
            raise RuntimeError(
                "Generation stopped with stop reason that is not `tool_use`. "
                "This is likely due to a limit on output tokens that is too low. "
                "Note that this could also indicate no tool is beind called, so we "
                "recommend that you check the output of the call to confirm. "
                f"Stop Reason: {self.response.stop_reason} "
            )

        extracted_tools = []
        for tool_call in self.response.content:
            if tool_call.type != "tool_use":
                continue
            for tool_type in self.tool_types:
                if tool_call.name == tool_type.name():
                    tool = tool_type.from_tool_call(tool_call)
                    extracted_tools.append(tool)
                    break

        return extracted_tools

    @property
    def tool(self) -> Optional[AnthropicTool]:
        """Returns the 0th tool for the 0th choice text block."""
        tools = self.tools
        if tools:
            return tools[0]
        return None

    @classmethod
    def tool_message_params(
        self, tools_and_outputs: list[tuple[AnthropicTool, str]]
    ) -> list[MessageParam]:
        """Returns the tool message parameters for tool call results."""
        return [
            {
                "role": "user",
                "content": [
                    ToolResultBlockParam(
                        tool_use_id=tool.tool_call.id,
                        type="tool_result",
                        content=[{"text": output, "type": "text"}],
                    )
                    for tool, output in tools_and_outputs
                ],
            }
        ]

    @property
    def content(self) -> str:
        """Returns the string text of the 0th text block."""
        block = self.response.content[0]
        return block.text if block.type == "text" else ""

    @property
    def model(self) -> str:
        """Returns the name of the response model."""
        return self.response.model

    @property
    def id(self) -> str:
        """Returns the id of the response."""
        return self.response.id

    @property
    def finish_reasons(self) -> Optional[list[str]]:
        """Returns the finish reason of the response."""
        return [str(self.response.stop_reason)]

    @property
    def usage(self) -> Usage:
        """Returns the usage of the message."""
        return self.response.usage

    @property
    def input_tokens(self) -> int:
        """Returns the number of input tokens."""
        return self.usage.input_tokens

    @property
    def output_tokens(self) -> int:
        """Returns the number of output tokens."""
        return self.usage.output_tokens

    def dump(self) -> dict[str, Any]:
        """Dumps the response to a dictionary."""
        return {
            "start_time": self.start_time,
            "end_time": self.end_time,
            "output": self.response.model_dump(),
        }

content: str property

Returns the string text of the 0th text block.

finish_reasons: Optional[list[str]] property

Returns the finish reason of the response.

id: str property

Returns the id of the response.

input_tokens: int property

Returns the number of input tokens.

message_param: MessageParam property

Returns the assistant's response as a message parameter.

model: str property

Returns the name of the response model.

output_tokens: int property

Returns the number of output tokens.

tool: Optional[AnthropicTool] property

Returns the 0th tool for the 0th choice text block.

tools: Optional[list[AnthropicTool]] property

Returns the tools for the 0th choice message.

usage: Usage property

Returns the usage of the message.

dump()

Dumps the response to a dictionary.

Source code in mirascope/anthropic/types.py 
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def dump(self) -> dict[str, Any]:
    """Dumps the response to a dictionary."""
    return {
        "start_time": self.start_time,
        "end_time": self.end_time,
        "output": self.response.model_dump(),
    }

tool_message_params(tools_and_outputs) classmethod

Returns the tool message parameters for tool call results.

Source code in mirascope/anthropic/types.py 
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@classmethod
def tool_message_params(
    self, tools_and_outputs: list[tuple[AnthropicTool, str]]
) -> list[MessageParam]:
    """Returns the tool message parameters for tool call results."""
    return [
        {
            "role": "user",
            "content": [
                ToolResultBlockParam(
                    tool_use_id=tool.tool_call.id,
                    type="tool_result",
                    content=[{"text": output, "type": "text"}],
                )
                for tool, output in tools_and_outputs
            ],
        }
    ]

AnthropicCallResponseChunk

Bases: BaseCallResponseChunk[MessageStreamEvent, AnthropicTool]

Convenience wrapper around the Anthropic API streaming chunks.

When using Mirascope's convenience wrappers to interact with Anthropic models via AnthropicCall, responses using AnthropicCall.stream() will yield AnthropicCallResponseChunk, whereby the implemented properties allow for simpler syntax and a convenient developer experience.

Example:

from mirascope.anthropic import AnthropicCall


class Math(AnthropicCall):
    prompt_template = "What is 1 + 2?"


content = ""
for chunk in Math().stream():
    content += chunk.content
    print(content)
#> 1
#  1 +
#  1 + 2
#  1 + 2 equals
#  1 + 2 equals
#  1 + 2 equals 3
#  1 + 2 equals 3.
Source code in mirascope/anthropic/types.py 
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class AnthropicCallResponseChunk(
    BaseCallResponseChunk[MessageStreamEvent, AnthropicTool]
):
    """Convenience wrapper around the Anthropic API streaming chunks.

    When using Mirascope's convenience wrappers to interact with Anthropic models via
    `AnthropicCall`, responses using `AnthropicCall.stream()` will yield
    `AnthropicCallResponseChunk`, whereby the implemented properties allow for simpler
    syntax and a convenient developer experience.

    Example:

    ```python
    from mirascope.anthropic import AnthropicCall


    class Math(AnthropicCall):
        prompt_template = "What is 1 + 2?"


    content = ""
    for chunk in Math().stream():
        content += chunk.content
        print(content)
    #> 1
    #  1 +
    #  1 + 2
    #  1 + 2 equals
    #  1 + 2 equals
    #  1 + 2 equals 3
    #  1 + 2 equals 3.
    ```
    """

    response_format: Optional[Literal["json"]] = None
    user_message_param: Optional[MessageParam] = None

    @property
    def type(
        self,
    ) -> Literal[
        "text",
        "input_json",
        "message_start",
        "message_delta",
        "message_stop",
        "content_block_start",
        "content_block_delta",
        "content_block_stop",
    ]:
        """Returns the type of the chunk."""
        return self.chunk.type

    @property
    def content(self) -> str:
        """Returns the string content of the 0th message."""
        if isinstance(self.chunk, ContentBlockStartEvent):
            return (
                self.chunk.content_block.text
                if isinstance(self.chunk.content_block, TextBlock)
                else ""
            )
        if isinstance(self.chunk, ContentBlockDeltaEvent):
            return (
                self.chunk.delta.text if isinstance(self.chunk.delta, TextDelta) else ""
            )
        return ""

    @property
    def model(self) -> Optional[str]:
        """Returns the name of the response model."""
        if isinstance(self.chunk, MessageStartEvent):
            return self.chunk.message.model
        return None

    @property
    def id(self) -> Optional[str]:
        """Returns the id of the response."""
        if isinstance(self.chunk, MessageStartEvent):
            return self.chunk.message.id
        return None

    @property
    def finish_reasons(self) -> Optional[list[str]]:
        """Returns the finish reason of the response."""
        if isinstance(self.chunk, MessageStartEvent):
            return [str(self.chunk.message.stop_reason)]
        return None

    @property
    def usage(self) -> Optional[Usage]:
        """Returns the usage of the message."""
        if isinstance(self.chunk, MessageStartEvent):
            return self.chunk.message.usage
        return None

    @property
    def input_tokens(self) -> Optional[int]:
        """Returns the number of input tokens."""
        if self.usage:
            return self.usage.input_tokens
        return None

    @property
    def output_tokens(self) -> Optional[int]:
        """Returns the number of output tokens."""
        if self.usage:
            return self.usage.output_tokens
        return None

content: str property

Returns the string content of the 0th message.

finish_reasons: Optional[list[str]] property

Returns the finish reason of the response.

id: Optional[str] property

Returns the id of the response.

input_tokens: Optional[int] property

Returns the number of input tokens.

model: Optional[str] property

Returns the name of the response model.

output_tokens: Optional[int] property

Returns the number of output tokens.

type: Literal['text', 'input_json', 'message_start', 'message_delta', 'message_stop', 'content_block_start', 'content_block_delta', 'content_block_stop'] property

Returns the type of the chunk.

usage: Optional[Usage] property

Returns the usage of the message.

AnthropicTool

Bases: BaseTool[ToolUseBlock]

A base class for easy use of tools with the Anthropic Claude client.

AnthropicTool internally handles the logic that allows you to use tools with simple calls such as AnthropicCallResponse.tool or AnthropicTool.fn, as seen in the example below.

Example:

from mirascope import AnthropicCall, AnthropicCallParams


def animal_matcher(fav_food: str, fav_color: str) -> str:
    """Tells you your most likely favorite animal from personality traits.

    Args:
        fav_food: your favorite food.
        fav_color: your favorite color.

    Returns:
        The animal most likely to be your favorite based on traits.
    """
    return "Your favorite animal is the best one, a frog."


class AnimalMatcher(AnthropicCall):
    prompt_template = """
    Tell me my favorite animal if my favorite food is {food} and my
    favorite color is {color}.
    """

    food: str
    color: str

    call_params = AnthropicCallParams(tools=[animal_matcher])


response = AnimalMatcher(food="pizza", color="red").call
tool = response.tool
print(tool.fn(**tool.args))
#> Your favorite animal is the best one, a frog.
Source code in mirascope/anthropic/tools.py 
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class AnthropicTool(BaseTool[ToolUseBlock]):
    '''A base class for easy use of tools with the Anthropic Claude client.

    `AnthropicTool` internally handles the logic that allows you to use tools with
    simple calls such as `AnthropicCallResponse.tool` or `AnthropicTool.fn`, as seen in
    the example below.

    Example:

    ```python
    from mirascope import AnthropicCall, AnthropicCallParams


    def animal_matcher(fav_food: str, fav_color: str) -> str:
        """Tells you your most likely favorite animal from personality traits.

        Args:
            fav_food: your favorite food.
            fav_color: your favorite color.

        Returns:
            The animal most likely to be your favorite based on traits.
        """
        return "Your favorite animal is the best one, a frog."


    class AnimalMatcher(AnthropicCall):
        prompt_template = """
        Tell me my favorite animal if my favorite food is {food} and my
        favorite color is {color}.
        """

        food: str
        color: str

        call_params = AnthropicCallParams(tools=[animal_matcher])


    response = AnimalMatcher(food="pizza", color="red").call
    tool = response.tool
    print(tool.fn(**tool.args))
    #> Your favorite animal is the best one, a frog.
    ```
    '''

    @classmethod
    def tool_schema(cls) -> ToolParam:
        """Constructs JSON tool schema for use with Anthropic's Claude API."""
        schema = super().tool_schema()
        return ToolParam(
            input_schema=schema["parameters"],
            name=schema["name"],
            description=schema["description"],
        )

    @classmethod
    def from_tool_call(cls, tool_call: ToolUseBlock) -> AnthropicTool:
        """Extracts an instance of the tool constructed from a tool call response.

        Given the tool call contents in a `Message` from an Anthropic call response,
        this method parses out the arguments of the tool call and creates an
        `AnthropicTool` instance from them.

        Args:
            tool_call: The list of `TextBlock` contents.

        Returns:
            An instance of the tool constructed from the tool call.

        Raises:
            ValidationError: if the tool call doesn't match the tool schema.
        """
        model_json = tool_call.input
        model_json["tool_call"] = tool_call.model_dump()  # type: ignore
        return cls.model_validate(model_json)

    @classmethod
    def from_model(cls, model: Type[BaseModel]) -> Type[AnthropicTool]:
        """Constructs a `AnthropicTool` type from a `BaseModel` type."""
        return convert_base_model_to_tool(model, AnthropicTool)

    @classmethod
    def from_fn(cls, fn: Callable) -> Type[AnthropicTool]:
        """Constructs a `AnthropicTool` type from a function."""
        return convert_function_to_tool(fn, AnthropicTool)

    @classmethod
    def from_base_type(cls, base_type: Type[BaseType]) -> Type[AnthropicTool]:
        """Constructs a `AnthropicTool` type from a `BaseType` type."""
        return convert_base_type_to_tool(base_type, AnthropicTool)

from_base_type(base_type) classmethod

Constructs a AnthropicTool type from a BaseType type.

Source code in mirascope/anthropic/tools.py 
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@classmethod
def from_base_type(cls, base_type: Type[BaseType]) -> Type[AnthropicTool]:
    """Constructs a `AnthropicTool` type from a `BaseType` type."""
    return convert_base_type_to_tool(base_type, AnthropicTool)

from_fn(fn) classmethod

Constructs a AnthropicTool type from a function.

Source code in mirascope/anthropic/tools.py 
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@classmethod
def from_fn(cls, fn: Callable) -> Type[AnthropicTool]:
    """Constructs a `AnthropicTool` type from a function."""
    return convert_function_to_tool(fn, AnthropicTool)

from_model(model) classmethod

Constructs a AnthropicTool type from a BaseModel type.

Source code in mirascope/anthropic/tools.py 
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@classmethod
def from_model(cls, model: Type[BaseModel]) -> Type[AnthropicTool]:
    """Constructs a `AnthropicTool` type from a `BaseModel` type."""
    return convert_base_model_to_tool(model, AnthropicTool)

from_tool_call(tool_call) classmethod

Extracts an instance of the tool constructed from a tool call response.

Given the tool call contents in a Message from an Anthropic call response, this method parses out the arguments of the tool call and creates an AnthropicTool instance from them.

Parameters:

Name Type Description Default
tool_call ToolUseBlock

The list of TextBlock contents.

 required

Returns:

Type Description
AnthropicTool

An instance of the tool constructed from the tool call.

Raises:

Type Description
ValidationError

if the tool call doesn't match the tool schema.
Source code in mirascope/anthropic/tools.py 
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@classmethod
def from_tool_call(cls, tool_call: ToolUseBlock) -> AnthropicTool:
    """Extracts an instance of the tool constructed from a tool call response.

    Given the tool call contents in a `Message` from an Anthropic call response,
    this method parses out the arguments of the tool call and creates an
    `AnthropicTool` instance from them.

    Args:
        tool_call: The list of `TextBlock` contents.

    Returns:
        An instance of the tool constructed from the tool call.

    Raises:
        ValidationError: if the tool call doesn't match the tool schema.
    """
    model_json = tool_call.input
    model_json["tool_call"] = tool_call.model_dump()  # type: ignore
    return cls.model_validate(model_json)

tool_schema() classmethod

Constructs JSON tool schema for use with Anthropic's Claude API.

Source code in mirascope/anthropic/tools.py 
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@classmethod
def tool_schema(cls) -> ToolParam:
    """Constructs JSON tool schema for use with Anthropic's Claude API."""
    schema = super().tool_schema()
    return ToolParam(
        input_schema=schema["parameters"],
        name=schema["name"],
        description=schema["description"],
    )

BaseCall

Bases: BasePrompt, Generic[BaseCallResponseT, BaseCallResponseChunkT, BaseToolT, MessageParamT], ABC

The base class abstract interface for calling LLMs.

Source code in mirascope/base/calls.py 
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class BaseCall(
    BasePrompt,
    Generic[BaseCallResponseT, BaseCallResponseChunkT, BaseToolT, MessageParamT],
    ABC,
):
    """The base class abstract interface for calling LLMs."""

    api_key: ClassVar[Optional[str]] = None
    base_url: ClassVar[Optional[str]] = None
    call_params: ClassVar[BaseCallParams] = BaseCallParams[BaseToolT](
        model="gpt-3.5-turbo-0125"
    )
    configuration: ClassVar[BaseConfig] = BaseConfig(llm_ops=[], client_wrappers=[])
    _provider: ClassVar[str] = "base"

    @abstractmethod
    def call(
        self, retries: Union[int, Retrying] = 0, **kwargs: Any
    ) -> BaseCallResponseT:
        """A call to an LLM.

        An implementation of this function must return a response that extends
        `BaseCallResponse`. This ensures a consistent API and convenience across e.g.
        different model providers.
        """
        ...  # pragma: no cover

    @abstractmethod
    async def call_async(
        self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
    ) -> BaseCallResponseT:
        """An asynchronous call to an LLM.

        An implementation of this function must return a response that extends
        `BaseCallResponse`. This ensures a consistent API and convenience across e.g.
        different model providers.
        """
        ...  # pragma: no cover

    @abstractmethod
    def stream(
        self, retries: Union[int, Retrying] = 0, **kwargs: Any
    ) -> Generator[BaseCallResponseChunkT, None, None]:
        """A call to an LLM that streams the response in chunks.

        An implementation of this function must yield response chunks that extend
        `BaseCallResponseChunk`. This ensures a consistent API and convenience across
        e.g. different model providers.
        """
        ...  # pragma: no cover

    @abstractmethod
    async def stream_async(
        self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
    ) -> AsyncGenerator[BaseCallResponseChunkT, None]:
        """A asynchronous call to an LLM that streams the response in chunks.

        An implementation of this function must yield response chunks that extend
        `BaseCallResponseChunk`. This ensures a consistent API and convenience across
        e.g. different model providers."""
        yield ...  # type: ignore # pragma: no cover

    @classmethod
    def from_prompt(
        cls, prompt_type: type[BasePromptT], call_params: BaseCallParams
    ) -> type[BasePromptT]:
        """Returns a call_type generated dynamically from this base call.

        Args:
            prompt_type: The prompt class to use for the call. Properties and class
                variables of this class will be used to create the new call class. Must
                be a class that can be instantiated.
            call_params: The call params to use for the call.

        Returns:
            A new call class with new call_type.
        """

        fields: dict[str, Any] = {
            name: (field.annotation, field.default)
            for name, field in prompt_type.model_fields.items()
        }

        class_vars = {
            name: value
            for name, value in prompt_type.__dict__.items()
            if name not in prompt_type.model_fields
        }
        new_call = create_model(prompt_type.__name__, __base__=cls, **fields)

        for var_name, var_value in class_vars.items():
            setattr(new_call, var_name, var_value)
        setattr(new_call, "call_params", call_params)

        return cast(type[BasePromptT], new_call)

    ############################## PRIVATE METHODS ###################################

    def _setup(
        self,
        kwargs: dict[str, Any],
        base_tool_type: Optional[Type[BaseToolT]] = None,
    ) -> tuple[dict[str, Any], Optional[list[Type[BaseToolT]]]]:
        """Returns the call params kwargs and tool types.

        The tools in the call params first get converted into BaseToolT types. We then
        need both the converted tools for the response (so it can construct actual tool
        instances if present in the response) as well as the actual schemas injected
        through kwargs. This function handles that setup.
        """
        call_params = self.call_params.model_copy(update=kwargs)
        kwargs = call_params.kwargs(tool_type=base_tool_type)
        tool_types = None
        if "tools" in kwargs and base_tool_type is not None:
            tool_types = kwargs.pop("tools")
            kwargs["tools"] = [tool_type.tool_schema() for tool_type in tool_types]
        return kwargs, tool_types

    def _get_possible_user_message(
        self, messages: list[Any]
    ) -> Optional[MessageParamT]:
        """Returns the most recent message if it's a user message, otherwise `None`."""
        return messages[-1] if messages[-1]["role"] == "user" else None

call(retries=0, **kwargs) abstractmethod

A call to an LLM.

An implementation of this function must return a response that extends BaseCallResponse. This ensures a consistent API and convenience across e.g. different model providers.

Source code in mirascope/base/calls.py 
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@abstractmethod
def call(
    self, retries: Union[int, Retrying] = 0, **kwargs: Any
) -> BaseCallResponseT:
    """A call to an LLM.

    An implementation of this function must return a response that extends
    `BaseCallResponse`. This ensures a consistent API and convenience across e.g.
    different model providers.
    """
    ...  # pragma: no cover

call_async(retries=0, **kwargs) abstractmethod async

An asynchronous call to an LLM.

An implementation of this function must return a response that extends BaseCallResponse. This ensures a consistent API and convenience across e.g. different model providers.

Source code in mirascope/base/calls.py 
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@abstractmethod
async def call_async(
    self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
) -> BaseCallResponseT:
    """An asynchronous call to an LLM.

    An implementation of this function must return a response that extends
    `BaseCallResponse`. This ensures a consistent API and convenience across e.g.
    different model providers.
    """
    ...  # pragma: no cover

from_prompt(prompt_type, call_params) classmethod

Returns a call_type generated dynamically from this base call.

Parameters:

Name Type Description Default
prompt_type type[BasePromptT]

The prompt class to use for the call. Properties and class variables of this class will be used to create the new call class. Must be a class that can be instantiated.

 required
call_params BaseCallParams

The call params to use for the call.

 required

Returns:

Type Description
type[BasePromptT]

A new call class with new call_type.
Source code in mirascope/base/calls.py 
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@classmethod
def from_prompt(
    cls, prompt_type: type[BasePromptT], call_params: BaseCallParams
) -> type[BasePromptT]:
    """Returns a call_type generated dynamically from this base call.

    Args:
        prompt_type: The prompt class to use for the call. Properties and class
            variables of this class will be used to create the new call class. Must
            be a class that can be instantiated.
        call_params: The call params to use for the call.

    Returns:
        A new call class with new call_type.
    """

    fields: dict[str, Any] = {
        name: (field.annotation, field.default)
        for name, field in prompt_type.model_fields.items()
    }

    class_vars = {
        name: value
        for name, value in prompt_type.__dict__.items()
        if name not in prompt_type.model_fields
    }
    new_call = create_model(prompt_type.__name__, __base__=cls, **fields)

    for var_name, var_value in class_vars.items():
        setattr(new_call, var_name, var_value)
    setattr(new_call, "call_params", call_params)

    return cast(type[BasePromptT], new_call)

stream(retries=0, **kwargs) abstractmethod

A call to an LLM that streams the response in chunks.

An implementation of this function must yield response chunks that extend BaseCallResponseChunk. This ensures a consistent API and convenience across e.g. different model providers.

Source code in mirascope/base/calls.py 
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@abstractmethod
def stream(
    self, retries: Union[int, Retrying] = 0, **kwargs: Any
) -> Generator[BaseCallResponseChunkT, None, None]:
    """A call to an LLM that streams the response in chunks.

    An implementation of this function must yield response chunks that extend
    `BaseCallResponseChunk`. This ensures a consistent API and convenience across
    e.g. different model providers.
    """
    ...  # pragma: no cover

stream_async(retries=0, **kwargs) abstractmethod async

A asynchronous call to an LLM that streams the response in chunks.

An implementation of this function must yield response chunks that extend BaseCallResponseChunk. This ensures a consistent API and convenience across e.g. different model providers.

Source code in mirascope/base/calls.py 
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@abstractmethod
async def stream_async(
    self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
) -> AsyncGenerator[BaseCallResponseChunkT, None]:
    """A asynchronous call to an LLM that streams the response in chunks.

    An implementation of this function must yield response chunks that extend
    `BaseCallResponseChunk`. This ensures a consistent API and convenience across
    e.g. different model providers."""
    yield ...  # type: ignore # pragma: no cover

MessageRole

Bases: _Enum

Roles that the BasePrompt messages parser can parse from the template.

SYSTEM: A system message. USER: A user message. ASSISTANT: A message response from the assistant or chat client. MODEL: A message response from the assistant or chat client. Model is used by Google's Gemini instead of assistant, which doesn't have system messages. CHATBOT: A message response from the chat client. Chatbot is used by Cohere instead of assistant. TOOL: A message representing the output of calling a tool.

Source code in mirascope/enums.py 
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class MessageRole(_Enum):
    """Roles that the `BasePrompt` messages parser can parse from the template.

    SYSTEM: A system message.
    USER: A user message.
    ASSISTANT: A message response from the assistant or chat client.
    MODEL: A message response from the assistant or chat client. Model is used by
        Google's Gemini instead of assistant, which doesn't have system messages.
    CHATBOT: A message response from the chat client. Chatbot is used by Cohere instead
        of assistant.
    TOOL: A message representing the output of calling a tool.
    """

    SYSTEM = "system"
    USER = "user"
    ASSISTANT = "assistant"
    MODEL = "model"
    CHATBOT = "chatbot"
    TOOL = "tool"

anthropic_api_calculate_cost(usage, model='claude-3-haiku-20240229')

Calculate the cost of a completion using the Anthropic API.

https://www.anthropic.com/api

claude-instant-1.2 $0.80 / 1M tokens $2.40 / 1M tokens claude-2.0 $8.00 / 1M tokens $24.00 / 1M tokens claude-2.1 $8.00 / 1M tokens $24.00 / 1M tokens claude-3-haiku $0.25 / 1M tokens $1.25 / 1M tokens claude-3-sonnet $3.00 / 1M tokens $15.00 / 1M tokens claude-3-opus $15.00 / 1M tokens $75.00 / 1M tokens

Source code in mirascope/anthropic/utils.py 
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def anthropic_api_calculate_cost(
    usage: Usage, model="claude-3-haiku-20240229"
) -> Optional[float]:
    """Calculate the cost of a completion using the Anthropic API.

    https://www.anthropic.com/api

    claude-instant-1.2        $0.80 / 1M tokens   $2.40 / 1M tokens
    claude-2.0                $8.00 / 1M tokens   $24.00 / 1M tokens
    claude-2.1                $8.00 / 1M tokens   $24.00 / 1M tokens
    claude-3-haiku            $0.25 / 1M tokens   $1.25 / 1M tokens
    claude-3-sonnet           $3.00 / 1M tokens   $15.00 / 1M tokens
    claude-3-opus             $15.00 / 1M tokens   $75.00 / 1M tokens
    """
    pricing = {
        "claude-instant-1.2": {
            "prompt": 0.000_000_8,
            "completion": 0.000_002_4,
        },
        "claude-2.0": {
            "prompt": 0.000_008,
            "completion": 0.000_024,
        },
        "claude-2.1": {
            "prompt": 0.000_008,
            "completion": 0.000_024,
        },
        "claude-3-haiku-20240307": {
            "prompt": 0.000_002_5,
            "completion": 0.000_012_5,
        },
        "claude-3-sonnet-20240229": {
            "prompt": 0.000_003,
            "completion": 0.000_015,
        },
        "claude-3-opus-20240229": {
            "prompt": 0.000_015,
            "completion": 0.000_075,
        },
    }

    try:
        model_pricing = pricing[model]
    except KeyError:
        return None

    prompt_cost = usage.input_tokens * model_pricing["prompt"]
    completion_cost = usage.output_tokens * model_pricing["completion"]
    total_cost = prompt_cost + completion_cost

    return total_cost

get_wrapped_async_client(client, self)

Get a wrapped async client.

Source code in mirascope/base/ops_utils.py 
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def get_wrapped_async_client(client: T, self: Union[BaseCall, BaseEmbedder]) -> T:
    """Get a wrapped async client."""
    if self.configuration.client_wrappers:
        for op in self.configuration.client_wrappers:
            if op == "langfuse":  # pragma: no cover
                from langfuse.openai import AsyncOpenAI as LangfuseAsyncOpenAI

                client = LangfuseAsyncOpenAI(
                    api_key=self.api_key, base_url=self.base_url
                )
            elif op == "logfire":  # pragma: no cover
                import logfire

                if self._provider == "openai":
                    logfire.instrument_openai(client)  # type: ignore
                elif self._provider == "anthropic":
                    logfire.instrument_anthropic(client)  # type: ignore
            elif callable(op):
                client = op(client)
    return client

get_wrapped_call(call, self, **kwargs)

Wrap a call to add the llm_ops parameter if it exists.

Source code in mirascope/base/ops_utils.py 
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def get_wrapped_call(call: C, self: Union[BaseCall, BaseEmbedder], **kwargs) -> C:
    """Wrap a call to add the `llm_ops` parameter if it exists."""
    if self.configuration.llm_ops:
        wrapped_call = call
        for op in self.configuration.llm_ops:
            if op == "weave":  # pragma: no cover
                import weave

                wrapped_call = weave.op()(wrapped_call)
            elif callable(op):
                wrapped_call = op(
                    wrapped_call,
                    self._provider,
                    **kwargs,
                )
        return wrapped_call
    return call

get_wrapped_client(client, self)

Get a wrapped client.

Source code in mirascope/base/ops_utils.py 
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def get_wrapped_client(client: T, self: Union[BaseCall, BaseEmbedder]) -> T:
    """Get a wrapped client."""
    if self.configuration.client_wrappers:
        for op in self.configuration.client_wrappers:  # pragma: no cover
            if op == "langfuse":
                from langfuse.openai import OpenAI as LangfuseOpenAI

                client = LangfuseOpenAI(api_key=self.api_key, base_url=self.base_url)
            elif op == "logfire":  # pragma: no cover
                import logfire

                if self._provider == "openai":
                    logfire.instrument_openai(client)  # type: ignore
                elif self._provider == "anthropic":
                    logfire.instrument_anthropic(client)  # type: ignore
            elif callable(op):
                client = op(client)
    return client

retry(fn)

Decorator for retrying a function.

Source code in mirascope/base/utils.py 
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def retry(fn: F) -> F:
    """Decorator for retrying a function."""

    @wraps(fn)
    def wrapper(*args, **kwargs):
        """Wrapper for retrying a function."""
        retries = kwargs.pop("retries", 0)
        if isinstance(retries, int):
            if retries > 0:
                retries = Retrying(stop=stop_after_attempt(retries))
            else:
                return fn(*args, **kwargs)
        try:
            for attempt in retries:
                with attempt:
                    result = fn(*args, **kwargs)
                if (
                    attempt.retry_state.outcome
                    and not attempt.retry_state.outcome.failed
                ):
                    attempt.retry_state.set_result(result)
            return result
        except RetryError:
            raise

    @wraps(fn)
    async def wrapper_async(*args, **kwargs):
        """Wrapper for retrying an async function."""
        retries = kwargs.pop("retries", 0)
        if isinstance(retries, int):
            if retries > 0:
                retries = AsyncRetrying(stop=stop_after_attempt(retries))
            else:
                return await fn(*args, **kwargs)
        try:
            async for attempt in retries:
                with attempt:
                    result = await fn(*args, **kwargs)
                if (
                    attempt.retry_state.outcome
                    and not attempt.retry_state.outcome.failed
                ):
                    attempt.retry_state.set_result(result)
            return result
        except RetryError:
            raise

    @wraps(fn)
    def wrapper_generator(*args, **kwargs):
        """Wrapper for retrying a generator function."""
        retries = kwargs.pop("retries", 0)
        if isinstance(retries, int):
            if retries > 0:
                retries = Retrying(stop=stop_after_attempt(retries))
            else:
                for value in fn(*args, **kwargs):
                    yield value
                return
        try:
            for attempt in retries:
                with attempt:
                    for value in fn(*args, **kwargs):
                        yield value
        except RetryError:
            raise

    @wraps(fn)
    async def wrapper_generator_async(*args, **kwargs):
        """Wrapper for retrying an async generator function."""
        retries = kwargs.pop("retries", 0)
        if isinstance(retries, int):
            if retries > 0:
                retries = AsyncRetrying(stop=stop_after_attempt(retries))
            else:
                async for value in fn(*args, **kwargs):
                    yield value
                return
        try:
            async for attempt in retries:
                with attempt:
                    async for value in fn(*args, **kwargs):
                        yield value
        except RetryError:
            raise

    if inspect.iscoroutinefunction(fn):
        return cast(F, wrapper_async)
    elif inspect.isgeneratorfunction(fn):
        return cast(F, wrapper_generator)
    elif inspect.isasyncgenfunction(fn):
        return cast(F, wrapper_generator_async)
    else:
        return cast(F, wrapper)