openai.extractors

A class for extracting structured information using OpenAI chat models.

BaseExtractor

Bases: BasePrompt, Generic[BaseCallT, BaseToolT, BaseToolStreamT, ExtractedTypeT], ABC

The base abstract interface for extracting structured information using LLMs.

Source code in mirascope/base/extractors.py

class BaseExtractor(
    BasePrompt, Generic[BaseCallT, BaseToolT, BaseToolStreamT, ExtractedTypeT], ABC
):
    """The base abstract interface for extracting structured information using LLMs."""

    extract_schema: ExtractionType

    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=[])

    @abstractmethod
    def extract(self, retries: int = 0) -> ExtractedTypeT:
        """Extracts the `extraction_schema` from an LLM call."""
        ...  # pragma: no cover

    @abstractmethod
    async def extract_async(self, retries: int = 0) -> ExtractedTypeT:
        """Asynchronously extracts the `extraction_schema` from an LLM call."""
        ...  # pragma: no cover

    # Note: only some model providers support streaming tools, so we only implement
    # streaming for those providers and do not require all extractors to implement
    # the `stream` and `stream_async` methods.
    # @abstractmethod
    # def stream(self, retries: int = 0) -> Generator[ExtractedTypeT, None, None]:
    #     """Streams extracted partial `extraction_schema` instances."""
    #     ...  # pragma: no cover

    # @abstractmethod
    # async def stream_async(
    #     self, retries: int = 0
    # ) -> AsyncGenerator[ExtractedTypeT, None]:
    #     """Asynchronously streams extracted partial `extraction_schema` instances."""
    #     ...  # pragma: no cover

    @classmethod
    def from_prompt(
        cls,
        prompt_type: type[BasePromptT],
        call_params: BaseCallParams,
        *,
        extract_schema: Optional[ExtractedType] = None,
    ) -> type[BasePromptT]:
        """Returns an extractor_type generated dynamically from this base extractor.

        Args:
            prompt_type: The prompt class to use for the extractor. Properties and class
                variables of this class will be used to create the new extractor class.
                Must be a class that can be instantiated.
            call_params: The call params to use for the extractor.
            extract_schema: The extract schema to use for the extractor. If none, the
                extractor will use the class' extract_schema.

        Returns:
            A new extractor class with new extractor type.
        """

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

        if extract_schema is not None:
            fields["extract_schema"] = (type[extract_schema], extract_schema)  # type: ignore
        else:
            extract_schema = fields["extract_schema"][1]

        class_vars = {
            name: value
            for name, value in prompt_type.__dict__.items()
            if name not in prompt_type.model_fields
        }
        new_extractor = create_model(
            prompt_type.__name__,
            __base__=cls[extract_schema],  # type: ignore
            **fields,
        )

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

        return cast(type[BasePromptT], new_extractor)

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

    def _extract(
        self,
        call_type: Type[BaseCallT],
        tool_type: Type[BaseToolT],
        retries: Union[int, Retrying] = 0,
        **kwargs: Any,
    ) -> ExtractedTypeT:
        """Extracts `extract_schema` from the call response.

        The `extract_schema` is converted into a tool, complete with a description of
        the tool, all of the fields, and their types. This allows us to take advantage
        of tools/function calling functionality to extract information from a prompt
        according to the context provided by the `BaseModel` schema.

        Args:
            call_type: The type of call to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            tool_type: The type of tool to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            retries: The number of call attempts to make on `ValidationError` before
                giving up and throwing the error to the user.
            **kwargs: Additional keyword arguments.

        Returns:
            An instance of the `extract_schema` with it's fields populated.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
        """

        def _extract_attempt(
            call_type: Type[BaseCallT],
            tool_type: Type[BaseToolT],
            error_messages: dict[str, Any],
            **kwargs: Any,
        ) -> ExtractedTypeT:
            kwargs, return_tool = self._setup(tool_type, kwargs)

            temp_call = self._generate_temp_call(call_type, error_messages)
            response = temp_call(
                **self.model_dump(exclude={"extract_schema"}),
            ).call(**kwargs)
            try:
                extracted_schema = self._extract_schema(
                    response.tool, self.extract_schema, return_tool, response=response
                )
                if extracted_schema is None:
                    raise AttributeError("No tool found in the completion.")
                return extracted_schema
            except (AttributeError, ValueError, ValidationError):
                raise

        if isinstance(retries, int):
            if retries > 0:
                retries = Retrying(stop=stop_after_attempt(retries))
            else:
                return _extract_attempt(call_type, tool_type, {}, **kwargs)
        try:
            error_messages: dict[str, Any] = {}
            for attempt in retries:
                with attempt:
                    try:
                        extraction = _extract_attempt(
                            call_type, tool_type, error_messages, **kwargs
                        )
                    except (AttributeError, ValueError, ValidationError) as e:
                        error_messages[str(e)] = None
                        if "logfire" in self.configuration.llm_ops:  # pragma: no cover
                            logfire.error(f"Retrying due to exception: {e}")
                        raise
        except RetryError as e:
            raise e
        return extraction

    async def _extract_async(
        self,
        call_type: Type[BaseCallT],
        tool_type: Type[BaseToolT],
        retries: Union[int, AsyncRetrying],
        **kwargs: Any,
    ) -> ExtractedTypeT:
        """Extracts `extract_schema` from the asynchronous call response.

        The `extract_schema` is converted into a tool, complete with a description of
        the tool, all of the fields, and their types. This allows us to take advantage
        of tools/function calling functionality to extract information from a prompt
        according to the context provided by the `BaseModel` schema.

        Args:
            call_type: The type of call to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            tool_type: The type of tool to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            retries: The number of call attempts to make on `ValidationError` before
                giving up and throwing the error to the user.
            **kwargs: Additional keyword arguments.

        Returns:
            An instance of the `extract_schema` with it's fields populated.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
        """

        async def _extract_attempt_async(
            call_type: Type[BaseCallT],
            tool_type: Type[BaseToolT],
            error_messages: dict[str, Any],
            **kwargs: Any,
        ) -> ExtractedTypeT:
            kwargs, return_tool = self._setup(tool_type, kwargs)

            temp_call = self._generate_temp_call(call_type, error_messages)

            response = await temp_call(
                **self.model_dump(exclude={"extract_schema"})
            ).call_async(**kwargs)
            try:
                extracted_schema = self._extract_schema(
                    response.tool, self.extract_schema, return_tool, response=response
                )
                if extracted_schema is None:
                    raise AttributeError("No tool found in the completion.")
                return extracted_schema
            except (AttributeError, ValueError, ValidationError):
                raise

        if isinstance(retries, int):
            if retries > 0:
                retries = AsyncRetrying(stop=stop_after_attempt(retries))
            else:
                return await _extract_attempt_async(call_type, tool_type, {}, **kwargs)
        try:
            error_messages: dict[str, Any] = {}
            async for attempt in retries:
                with attempt:
                    try:
                        extraction = await _extract_attempt_async(
                            call_type, tool_type, error_messages, **kwargs
                        )
                    except (AttributeError, ValueError, ValidationError) as e:
                        error_messages[str(e)] = None
                        if "logfire" in self.configuration.llm_ops:  # pragma: no cover
                            logfire.error(f"Retrying due to exception: {e}")
                        raise
        except RetryError as e:
            raise e
        return extraction

    def _stream(
        self,
        call_type: Type[BaseCallT],
        tool_type: Type[BaseToolT],
        tool_stream_type: Type[BaseToolStreamT],
        retries: Union[int, Retrying],
        **kwargs: Any,
    ) -> Generator[ExtractedTypeT, None, None]:
        """Streams partial `extract_schema` instances from the streamed chunks.

        The `extract_schema` is converted into a partial tool, complete with a
        description of the tool, all of the fields, and their types. This allows us to
        take advantage of tools/function calling functionality to stream information
        extracted from a prompt according to the context provided by the `BaseModel`
        schema.

        Args:
            call_type: The type of call to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            tool_type: The type of tool to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            tool_stream_type: The type of tool stream to use for streaming tools. This
                enables shared code across various model providers that have slight
                variations but the same internal interfaces.
            retries: The number of call attempts to make on `ValidationError` before
                giving up and throwing the error to the user.
            **kwargs: Additional keyword arguments.

        Yields:
            An instance of the partial `extract_schema` with it's available fields
            populated.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
        """

        def _stream_attempt(
            call_type: Type[BaseCallT],
            tool_type: Type[BaseToolT],
            tool_stream_type: Type[BaseToolStreamT],
            error_messages: dict[str, Any],
            **kwargs: Any,
        ) -> Generator[ExtractedTypeT, None, None]:
            kwargs, return_tool = self._setup(tool_type, kwargs)

            temp_call = self._generate_temp_call(call_type, error_messages)

            stream = temp_call(**self.model_dump(exclude={"extract_schema"})).stream(
                **kwargs
            )
            tool_stream = tool_stream_type.from_stream(stream, allow_partial=True)
            try:
                yielded = False
                for partial_tool in tool_stream:
                    extracted_schema = self._extract_schema(
                        partial_tool, self.extract_schema, return_tool, response=None
                    )
                    if extracted_schema is None:
                        break
                    yielded = True
                    yield extracted_schema

                if not yielded:
                    raise AttributeError("No tool found in the completion.")
            except (AttributeError, ValueError, ValidationError):
                raise

        if isinstance(retries, int):
            if retries > 0:
                retries = Retrying(stop=stop_after_attempt(retries))
            else:
                for partial_tool in _stream_attempt(
                    call_type,
                    tool_type,
                    tool_stream_type,
                    {},
                    **kwargs,
                ):
                    yield partial_tool
                return
        try:
            error_messages: dict[str, Any] = {}
            for attempt in retries:
                with attempt:
                    try:
                        for partial_tool in _stream_attempt(
                            call_type,
                            tool_type,
                            tool_stream_type,
                            error_messages,
                            **kwargs,
                        ):
                            yield partial_tool
                    except (AttributeError, ValueError, ValidationError) as e:
                        error_messages[str(e)] = None
                        if "logfire" in self.configuration.llm_ops:  # pragma: no cover
                            logfire.error(f"Retrying due to exception: {e}")
                        raise
        except RetryError as e:
            raise e

    async def _stream_async(
        self,
        call_type: Type[BaseCallT],
        tool_type: Type[BaseToolT],
        tool_stream_type: Type[BaseToolStreamT],
        retries: Union[int, AsyncRetrying],
        **kwargs: Any,
    ) -> AsyncGenerator[ExtractedTypeT, None]:
        """Asynchronously streams partial `extract_schema`s from streamed chunks.

        The `extract_schema` is converted into a partial tool, complete with a
        description of the tool, all of the fields, and their types. This allows us to
        take advantage of tools/function calling functionality to stream information
        extracted from a prompt according to the context provided by the `BaseModel`
        schema.

        Args:
            call_type: The type of call to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            tool_type: The type of tool to use for extraction. This enables shared code
                across various model providers that have slight variations but the same
                internal interfaces.
            tool_stream_type: The type of tool stream to use for streaming tools. This
                enables shared code across various model providers that have slight
                variations but the same internal interfaces.
            retries: The number of call attempts to make on `ValidationError` before
                giving up and throwing the error to the user.
            **kwargs: Additional keyword arguments.

        Yields:
            An instance of the partial `extract_schema` with it's available fields
            populated.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
        """

        async def _stream_attempt_async(
            call_type: Type[BaseCallT],
            tool_type: Type[BaseToolT],
            tool_stream_type: Type[BaseToolStreamT],
            error_messages: dict[str, Any],
            **kwargs: Any,
        ) -> AsyncGenerator[ExtractedTypeT, None]:
            kwargs, return_tool = self._setup(tool_type, kwargs)

            temp_call = self._generate_temp_call(call_type, error_messages)

            stream = temp_call(
                **self.model_dump(exclude={"extract_schema"})
            ).stream_async(**kwargs)
            tool_stream = tool_stream_type.from_async_stream(stream, allow_partial=True)
            try:
                yielded = False
                async for partial_tool in tool_stream:
                    extracted_schema = self._extract_schema(
                        partial_tool, self.extract_schema, return_tool, response=None
                    )
                    if extracted_schema is None:
                        break
                    yielded = True
                    yield extracted_schema

                if not yielded:
                    raise AttributeError("No tool found in the completion.")
            except (AttributeError, ValueError, ValidationError):
                raise

        if isinstance(retries, int):
            if retries > 0:
                retries = AsyncRetrying(stop=stop_after_attempt(retries))
            else:
                async for partial_tool in _stream_attempt_async(
                    call_type, tool_type, tool_stream_type, {}, **kwargs
                ):
                    yield partial_tool
                return
        try:
            error_messages: dict[str, Any] = {}
            async for attempt in retries:
                with attempt:
                    try:
                        async for partial_tool in _stream_attempt_async(
                            call_type,
                            tool_type,
                            tool_stream_type,
                            error_messages,
                            **kwargs,
                        ):
                            yield partial_tool
                    except (AttributeError, ValueError, ValidationError) as e:
                        error_messages[str(e)] = None
                        if "logfire" in self.configuration.llm_ops:  # pragma: no cover
                            logfire.error(f"Retrying due to exception: {e}")
                        raise
        except RetryError as e:
            raise e

    def _generate_temp_call(
        self, call_type: Type[BaseCallT], error_messages: dict[str, Any]
    ) -> Type[BaseCallT]:
        """Returns a `TempCall` generated using the extractors definition."""
        _prompt_template = self.prompt_template
        if error_messages:
            formatted_error_messages = [
                "- " + element for element in error_messages.keys()
            ]
            error_messages_list = "\n".join(formatted_error_messages)
            _prompt_template = (
                f"{_prompt_template}\n"
                "Errors found:\n\n"
                f"{error_messages_list}\n\n"
                "Please fix the errors and try again."
            )

        class TempCall(call_type):  # type: ignore
            prompt_template = _prompt_template

            base_url = self.base_url
            api_key = self.api_key
            call_params = self.call_params
            configuration = self.configuration

            model_config = ConfigDict(extra="allow")

        properties = getmembers(self)
        for name, value in properties:
            if not hasattr(TempCall, name) or (
                name == "messages" and "messages" in self.__class__.__dict__
            ):
                setattr(TempCall, name, value)

        return TempCall

    def _extract_schema(
        self,
        tool: Optional[BaseToolT],
        schema: ExtractedType,
        return_tool: bool,
        response: Optional[Any],
    ) -> Optional[ExtractedTypeT]:
        """Returns the extracted schema extracted depending on it's extraction type.

        Due to mypy issues with all these generics, we have to type ignore a bunch
        of stuff so it doesn't complain, but each conditional properly checks types
        before doing anything specific to that type (it's just that mypy is annoying).
        """
        if tool is None:
            return None
        if return_tool:
            return tool  # type: ignore
        if _is_base_type(schema):
            return tool.value  # type: ignore
        if response:
            model = schema(**tool.model_dump())  # type: ignore
            model._response = response
        else:
            schema = partial(schema)  # type: ignore
            model = schema(**tool.model_dump())
            model._tool_call = tool.tool_call  # type: ignore
        return model

    def _setup(
        self, tool_type: Type[BaseToolT], kwargs: dict[str, Any]
    ) -> tuple[dict[str, Any], bool]:
        """Returns the call params kwargs and whether to return the tool directly."""
        call_params = self.call_params.model_copy(update=kwargs)
        kwargs = call_params.kwargs(tool_type=tool_type)
        if _is_base_type(self.extract_schema):
            tool = tool_type.from_base_type(self.extract_schema)  # type: ignore
            return_tool = False
        elif not isclass(self.extract_schema):
            tool = tool_type.from_fn(self.extract_schema)
            return_tool = True
        elif not issubclass(self.extract_schema, tool_type):
            tool = tool_type.from_model(self.extract_schema)
            return_tool = False
        else:
            tool = self.extract_schema
            return_tool = True
        kwargs["tools"] = [tool]
        return kwargs, return_tool

extract(retries=0) abstractmethod

Extracts the extraction_schema from an LLM call.

Source code in mirascope/base/extractors.py

@abstractmethod
def extract(self, retries: int = 0) -> ExtractedTypeT:
    """Extracts the `extraction_schema` from an LLM call."""
    ...  # pragma: no cover

extract_async(retries=0) abstractmethod async

Asynchronously extracts the extraction_schema from an LLM call.

Source code in mirascope/base/extractors.py

@abstractmethod
async def extract_async(self, retries: int = 0) -> ExtractedTypeT:
    """Asynchronously extracts the `extraction_schema` from an LLM call."""
    ...  # pragma: no cover

from_prompt(prompt_type, call_params, *, extract_schema=None) classmethod

Returns an extractor_type generated dynamically from this base extractor.

Parameters:

Name	Type	Description	Default
prompt_type	type[BasePromptT]	The prompt class to use for the extractor. Properties and class variables of this class will be used to create the new extractor class. Must be a class that can be instantiated.	required
call_params	BaseCallParams	The call params to use for the extractor.	required
extract_schema	Optional[ExtractedType]	The extract schema to use for the extractor. If none, the extractor will use the class' extract_schema.	None

Returns:

Type	Description
type[BasePromptT]	A new extractor class with new extractor type.

Source code in mirascope/base/extractors.py

@classmethod
def from_prompt(
    cls,
    prompt_type: type[BasePromptT],
    call_params: BaseCallParams,
    *,
    extract_schema: Optional[ExtractedType] = None,
) -> type[BasePromptT]:
    """Returns an extractor_type generated dynamically from this base extractor.

    Args:
        prompt_type: The prompt class to use for the extractor. Properties and class
            variables of this class will be used to create the new extractor class.
            Must be a class that can be instantiated.
        call_params: The call params to use for the extractor.
        extract_schema: The extract schema to use for the extractor. If none, the
            extractor will use the class' extract_schema.

    Returns:
        A new extractor class with new extractor type.
    """

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

    if extract_schema is not None:
        fields["extract_schema"] = (type[extract_schema], extract_schema)  # type: ignore
    else:
        extract_schema = fields["extract_schema"][1]

    class_vars = {
        name: value
        for name, value in prompt_type.__dict__.items()
        if name not in prompt_type.model_fields
    }
    new_extractor = create_model(
        prompt_type.__name__,
        __base__=cls[extract_schema],  # type: ignore
        **fields,
    )

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

    return cast(type[BasePromptT], new_extractor)

OpenAICall

Bases: BaseCall[OpenAICallResponse, OpenAICallResponseChunk, OpenAITool, ChatCompletionUserMessageParam]

A base class for calling OpenAI's Chat Completion models.

Example:

from mirascope.openai import OpenAICall


class BookRecommender(OpenAICall):
    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/openai/calls.py

class OpenAICall(
    BaseCall[
        OpenAICallResponse,
        OpenAICallResponseChunk,
        OpenAITool,
        ChatCompletionUserMessageParam,
    ]
):
    """A base class for calling OpenAI's Chat Completion models.

    Example:

    ```python
    from mirascope.openai import OpenAICall


    class BookRecommender(OpenAICall):
        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[OpenAICallParams] = OpenAICallParams()
    _provider: ClassVar[str] = "openai"

    def messages(self) -> list[ChatCompletionMessageParam]:
        """Returns the template as a formatted list of messages."""
        message_type_by_role = {
            MessageRole.SYSTEM: ChatCompletionSystemMessageParam,
            MessageRole.USER: ChatCompletionUserMessageParam,
            MessageRole.ASSISTANT: ChatCompletionAssistantMessageParam,
            MessageRole.TOOL: ChatCompletionToolMessageParam,
        }
        return [
            message_type_by_role[MessageRole(message["role"])](**message)
            for message in self._parse_messages(list(message_type_by_role.keys()))
        ]

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

        Args:
            retries: An integer for the number of times to retry the call or
                a `tenacity.Retrying` instance.
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Returns:
            A `OpenAICallResponse` instance.

        Raises:
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        kwargs, tool_types = self._setup_openai_kwargs(kwargs)
        client = self._setup_openai_client(OpenAI)
        create = get_wrapped_call(
            client.chat.completions.create,
            self,
            response_type=OpenAICallResponse,
            tool_types=tool_types,
        )
        messages = self._update_messages_if_json(self.messages(), tool_types)
        user_message_param = self._get_possible_user_message(messages)
        start_time = datetime.datetime.now().timestamp() * 1000
        completion = create(
            messages=messages,
            stream=False,
            **kwargs,
        )
        return OpenAICallResponse(
            response=completion,
            user_message_param=user_message_param,
            tool_types=tool_types,
            start_time=start_time,
            end_time=datetime.datetime.now().timestamp() * 1000,
            cost=openai_api_calculate_cost(completion.usage, completion.model),
            response_format=self.call_params.response_format,
        )

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

        Args:
            retries: An integer for the number of times to retry the call or
                a `tenacity.AsyncRetrying` instance.
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Returns:
            An `OpenAICallResponse` instance.

        Raises:
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        kwargs, tool_types = self._setup_openai_kwargs(kwargs)
        client = self._setup_openai_client(AsyncOpenAI)
        create = get_wrapped_call(
            client.chat.completions.create,
            self,
            is_async=True,
            response_type=OpenAICallResponse,
            tool_types=tool_types,
        )
        messages = self._update_messages_if_json(self.messages(), tool_types)
        user_message_param = self._get_possible_user_message(messages)
        start_time = datetime.datetime.now().timestamp() * 1000
        completion = await create(
            messages=messages,
            stream=False,
            **kwargs,
        )
        return OpenAICallResponse(
            response=completion,
            user_message_param=user_message_param,
            tool_types=tool_types,
            start_time=start_time,
            end_time=datetime.datetime.now().timestamp() * 1000,
            cost=openai_api_calculate_cost(completion.usage, completion.model),
            response_format=self.call_params.response_format,
        )

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

        Args:
            retries: An integer for the number of times to retry the call or
                a `tenacity.Retrying` instance.
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Yields:
            A `OpenAICallResponseChunk` for each chunk of the response.

        Raises:
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        kwargs, tool_types = self._setup_openai_kwargs(kwargs)
        client = self._setup_openai_client(OpenAI)
        create = get_wrapped_call(
            client.chat.completions.create,
            self,
            response_chunk_type=OpenAICallResponseChunk,
            tool_types=tool_types,
        )
        messages = self._update_messages_if_json(self.messages(), tool_types)
        user_message_param = self._get_possible_user_message(messages)
        if not isinstance(client, AzureOpenAI):
            kwargs["stream_options"] = {"include_usage": True}
        stream = create(
            messages=messages,
            stream=True,
            **kwargs,
        )
        for chunk in stream:
            yield OpenAICallResponseChunk(
                chunk=chunk,
                user_message_param=user_message_param,
                tool_types=tool_types,
                cost=openai_api_calculate_cost(chunk.usage, chunk.model),
                response_format=self.call_params.response_format,
            )

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

        Args:
            retries: An integer for the number of times to retry the call or
                a `tenacity.AsyncRetrying` instance.
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Yields:
            A `OpenAICallResponseChunk` for each chunk of the response.

        Raises:
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        kwargs, tool_types = self._setup_openai_kwargs(kwargs)
        client = self._setup_openai_client(AsyncOpenAI)
        create = get_wrapped_call(
            client.chat.completions.create,
            self,
            is_async=True,
            response_chunk_type=OpenAICallResponseChunk,
            tool_types=tool_types,
        )
        messages = self._update_messages_if_json(self.messages(), tool_types)
        user_message_param = self._get_possible_user_message(messages)
        if not isinstance(client, AsyncAzureOpenAI):
            kwargs["stream_options"] = {"include_usage": True}
        stream = await create(
            messages=messages,
            stream=True,
            **kwargs,
        )
        async for chunk in stream:
            yield OpenAICallResponseChunk(
                chunk=chunk,
                user_message_param=user_message_param,
                tool_types=tool_types,
                cost=openai_api_calculate_cost(chunk.usage, chunk.model),
                response_format=self.call_params.response_format,
            )

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

    def _setup_openai_kwargs(
        self,
        kwargs: dict[str, Any],
    ) -> tuple[
        dict[str, Any],
        Optional[list[Type[OpenAITool]]],
    ]:
        """Overrides the `BaseCall._setup` for Anthropic specific setup."""
        kwargs, tool_types = self._setup(kwargs, OpenAITool)
        if (
            self.call_params.response_format == ResponseFormat(type="json_object")
            and tool_types
        ):
            kwargs.pop("tools")
        return kwargs, tool_types

    @overload
    def _setup_openai_client(self, client_type: type[OpenAI]) -> OpenAI:
        ...  # pragma: no cover

    @overload
    def _setup_openai_client(self, client_type: type[AsyncOpenAI]) -> AsyncOpenAI:
        ...  # pragma: no cover

    def _setup_openai_client(
        self, client_type: Union[type[OpenAI], type[AsyncOpenAI]]
    ) -> Union[OpenAI, AsyncOpenAI]:
        """Returns the proper OpenAI/AsyncOpenAI client, including wrapping it."""
        using_azure = "inner_azure_client_wrapper" in [
            getattr(wrapper, __name__, None)
            for wrapper in self.configuration.client_wrappers
        ]
        client = client_type(
            api_key=self.api_key if not using_azure else "make-azure-not-fail",
            base_url=self.base_url,
        )
        if client_type == OpenAI:
            client = get_wrapped_client(client, self)
        elif client_type == AsyncOpenAI:
            client = get_wrapped_async_client(client, self)
        return client

    def _update_messages_if_json(
        self,
        messages: list[ChatCompletionMessageParam],
        tool_types: Optional[list[type[OpenAITool]]],
    ) -> list[ChatCompletionMessageParam]:
        if (
            self.call_params.response_format == ResponseFormat(type="json_object")
            and tool_types
        ):
            messages.append(
                ChatCompletionUserMessageParam(
                    role="user", content=_json_mode_content(tool_type=tool_types[0])
                )
            )
        return messages

call(retries=0, **kwargs)

Makes a call to the model using this OpenAICall instance.

Parameters:

Name	Type	Description	Default
retries	Union[int, Retrying]	An integer for the number of times to retry the call or a tenacity.Retrying instance.	0
**kwargs	Any	Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.	{}

Returns:

Type	Description
OpenAICallResponse	A OpenAICallResponse instance.

Raises:

Type	Description
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/calls.py

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

    Args:
        retries: An integer for the number of times to retry the call or
            a `tenacity.Retrying` instance.
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Returns:
        A `OpenAICallResponse` instance.

    Raises:
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    kwargs, tool_types = self._setup_openai_kwargs(kwargs)
    client = self._setup_openai_client(OpenAI)
    create = get_wrapped_call(
        client.chat.completions.create,
        self,
        response_type=OpenAICallResponse,
        tool_types=tool_types,
    )
    messages = self._update_messages_if_json(self.messages(), tool_types)
    user_message_param = self._get_possible_user_message(messages)
    start_time = datetime.datetime.now().timestamp() * 1000
    completion = create(
        messages=messages,
        stream=False,
        **kwargs,
    )
    return OpenAICallResponse(
        response=completion,
        user_message_param=user_message_param,
        tool_types=tool_types,
        start_time=start_time,
        end_time=datetime.datetime.now().timestamp() * 1000,
        cost=openai_api_calculate_cost(completion.usage, completion.model),
        response_format=self.call_params.response_format,
    )

call_async(retries=0, **kwargs) async

Makes an asynchronous call to the model using this OpenAICall.

Parameters:

Name	Type	Description	Default
retries	Union[int, AsyncRetrying]	An integer for the number of times to retry the call or a tenacity.AsyncRetrying instance.	0
**kwargs	Any	Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.	{}

Returns:

Type	Description
OpenAICallResponse	An OpenAICallResponse instance.

Raises:

Type	Description
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/calls.py

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

    Args:
        retries: An integer for the number of times to retry the call or
            a `tenacity.AsyncRetrying` instance.
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Returns:
        An `OpenAICallResponse` instance.

    Raises:
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    kwargs, tool_types = self._setup_openai_kwargs(kwargs)
    client = self._setup_openai_client(AsyncOpenAI)
    create = get_wrapped_call(
        client.chat.completions.create,
        self,
        is_async=True,
        response_type=OpenAICallResponse,
        tool_types=tool_types,
    )
    messages = self._update_messages_if_json(self.messages(), tool_types)
    user_message_param = self._get_possible_user_message(messages)
    start_time = datetime.datetime.now().timestamp() * 1000
    completion = await create(
        messages=messages,
        stream=False,
        **kwargs,
    )
    return OpenAICallResponse(
        response=completion,
        user_message_param=user_message_param,
        tool_types=tool_types,
        start_time=start_time,
        end_time=datetime.datetime.now().timestamp() * 1000,
        cost=openai_api_calculate_cost(completion.usage, completion.model),
        response_format=self.call_params.response_format,
    )

messages()

Returns the template as a formatted list of messages.

Source code in mirascope/openai/calls.py

def messages(self) -> list[ChatCompletionMessageParam]:
    """Returns the template as a formatted list of messages."""
    message_type_by_role = {
        MessageRole.SYSTEM: ChatCompletionSystemMessageParam,
        MessageRole.USER: ChatCompletionUserMessageParam,
        MessageRole.ASSISTANT: ChatCompletionAssistantMessageParam,
        MessageRole.TOOL: ChatCompletionToolMessageParam,
    }
    return [
        message_type_by_role[MessageRole(message["role"])](**message)
        for message in self._parse_messages(list(message_type_by_role.keys()))
    ]

stream(retries=0, **kwargs)

Streams the response for a call using this OpenAICall.

Parameters:

Name	Type	Description	Default
retries	Union[int, Retrying]	An integer for the number of times to retry the call or a tenacity.Retrying instance.	0
**kwargs	Any	Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.	{}

Yields:

Type	Description
OpenAICallResponseChunk	A OpenAICallResponseChunk for each chunk of the response.

Raises:

Type	Description
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/calls.py

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

    Args:
        retries: An integer for the number of times to retry the call or
            a `tenacity.Retrying` instance.
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Yields:
        A `OpenAICallResponseChunk` for each chunk of the response.

    Raises:
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    kwargs, tool_types = self._setup_openai_kwargs(kwargs)
    client = self._setup_openai_client(OpenAI)
    create = get_wrapped_call(
        client.chat.completions.create,
        self,
        response_chunk_type=OpenAICallResponseChunk,
        tool_types=tool_types,
    )
    messages = self._update_messages_if_json(self.messages(), tool_types)
    user_message_param = self._get_possible_user_message(messages)
    if not isinstance(client, AzureOpenAI):
        kwargs["stream_options"] = {"include_usage": True}
    stream = create(
        messages=messages,
        stream=True,
        **kwargs,
    )
    for chunk in stream:
        yield OpenAICallResponseChunk(
            chunk=chunk,
            user_message_param=user_message_param,
            tool_types=tool_types,
            cost=openai_api_calculate_cost(chunk.usage, chunk.model),
            response_format=self.call_params.response_format,
        )

stream_async(retries=0, **kwargs) async

Streams the response for an asynchronous call using this OpenAICall.

Parameters:

Name	Type	Description	Default
retries	Union[int, AsyncRetrying]	An integer for the number of times to retry the call or a tenacity.AsyncRetrying instance.	0
**kwargs	Any	Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.	{}

Yields:

Type	Description
AsyncGenerator[OpenAICallResponseChunk, None]	A OpenAICallResponseChunk for each chunk of the response.

Raises:

Type	Description
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/calls.py

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

    Args:
        retries: An integer for the number of times to retry the call or
            a `tenacity.AsyncRetrying` instance.
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Yields:
        A `OpenAICallResponseChunk` for each chunk of the response.

    Raises:
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    kwargs, tool_types = self._setup_openai_kwargs(kwargs)
    client = self._setup_openai_client(AsyncOpenAI)
    create = get_wrapped_call(
        client.chat.completions.create,
        self,
        is_async=True,
        response_chunk_type=OpenAICallResponseChunk,
        tool_types=tool_types,
    )
    messages = self._update_messages_if_json(self.messages(), tool_types)
    user_message_param = self._get_possible_user_message(messages)
    if not isinstance(client, AsyncAzureOpenAI):
        kwargs["stream_options"] = {"include_usage": True}
    stream = await create(
        messages=messages,
        stream=True,
        **kwargs,
    )
    async for chunk in stream:
        yield OpenAICallResponseChunk(
            chunk=chunk,
            user_message_param=user_message_param,
            tool_types=tool_types,
            cost=openai_api_calculate_cost(chunk.usage, chunk.model),
            response_format=self.call_params.response_format,
        )

OpenAICallParams

Bases: BaseCallParams[OpenAITool]

The parameters to use when calling the OpenAI API.

Source code in mirascope/openai/types.py

class OpenAICallParams(BaseCallParams[OpenAITool]):
    """The parameters to use when calling the OpenAI API."""

    model: str = "gpt-4o-2024-05-13"
    frequency_penalty: Optional[float] = None
    logit_bias: Optional[dict[str, int]] = None
    logprobs: Optional[bool] = None
    max_tokens: Optional[int] = None
    n: Optional[int] = None
    presence_penalty: Optional[float] = None
    response_format: Optional[ResponseFormat] = None
    seed: Optional[int] = None
    stop: Union[Optional[str], list[str]] = None
    temperature: Optional[float] = None
    tool_choice: Optional[ChatCompletionToolChoiceOptionParam] = None
    top_logprobs: Optional[int] = None
    top_p: Optional[float] = None
    user: Optional[str] = None
    # Values defined below take precedence over values defined elsewhere. Use these
    # params to pass additional parameters to the API if necessary that aren't already
    # available as params.
    extra_headers: Optional[Headers] = None
    extra_query: Optional[Query] = None
    extra_body: Optional[Body] = None
    timeout: Optional[Union[float, Timeout]] = None

    model_config = ConfigDict(arbitrary_types_allowed=True)

    def kwargs(
        self,
        tool_type: Optional[Type[OpenAITool]] = OpenAITool,
        exclude: Optional[set[str]] = None,
    ) -> dict[str, Any]:
        """Returns the keyword argument call parameters."""
        return super().kwargs(tool_type, exclude)

kwargs(tool_type=OpenAITool, exclude=None)

Returns the keyword argument call parameters.

Source code in mirascope/openai/types.py

def kwargs(
    self,
    tool_type: Optional[Type[OpenAITool]] = OpenAITool,
    exclude: Optional[set[str]] = None,
) -> dict[str, Any]:
    """Returns the keyword argument call parameters."""
    return super().kwargs(tool_type, exclude)

OpenAIExtractor

Bases: BaseExtractor[OpenAICall, OpenAITool, OpenAIToolStream, T], Generic[T]

A class for extracting structured information using OpenAI chat models.

Example:

from typing import Literal, Type

from mirascope.openai import OpenAIExtractor
from pydantic import BaseModel


class TaskDetails(BaseModel):
    title: str
    priority: Literal["low", "normal", "high"]
    due_date: str


class TaskExtractor(OpenAIExtractor[TaskDetails]):
    extract_schema: Type[TaskDetails] = TaskDetails

    prompt_template = """
    Please extract the task details:
    {task}
    """

    task: str


task_description = "Submit quarterly report by next Friday. Task is high priority."
task = TaskExtractor(task=task_description).extract(retries=3)
assert isinstance(task, TaskDetails)
print(task)
#> title='Submit quarterly report' priority='high' due_date='next Friday'

Source code in mirascope/openai/extractors.py

class OpenAIExtractor(
    BaseExtractor[OpenAICall, OpenAITool, OpenAIToolStream, T], Generic[T]
):
    '''A class for extracting structured information using OpenAI chat models.

    Example:

    ```python
    from typing import Literal, Type

    from mirascope.openai import OpenAIExtractor
    from pydantic import BaseModel


    class TaskDetails(BaseModel):
        title: str
        priority: Literal["low", "normal", "high"]
        due_date: str


    class TaskExtractor(OpenAIExtractor[TaskDetails]):
        extract_schema: Type[TaskDetails] = TaskDetails

        prompt_template = """
        Please extract the task details:
        {task}
        """

        task: str


    task_description = "Submit quarterly report by next Friday. Task is high priority."
    task = TaskExtractor(task=task_description).extract(retries=3)
    assert isinstance(task, TaskDetails)
    print(task)
    #> title='Submit quarterly report' priority='high' due_date='next Friday'
    ```
    '''

    call_params: ClassVar[OpenAICallParams] = OpenAICallParams()
    _provider: ClassVar[str] = "openai"

    def extract(self, retries: Union[int, Retrying] = 0, **kwargs: Any) -> T:
        """Extracts `extract_schema` from the OpenAI call response.

        The `extract_schema` is converted into an `OpenAITool`, complete with a
        description of the tool, all of the fields, and their types. This allows us to
        take advantage of OpenAI's tool/function calling functionality to extract
        information from a prompt according to the context provided by the `BaseModel`
        schema.

        Args:
            retries: The maximum number of times to retry the query on validation error.
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Returns:
            The `extract_schema` instance extracted from the completion.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        return self._extract(OpenAICall, OpenAITool, retries, **kwargs)

    async def extract_async(
        self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
    ) -> T:
        """Asynchronously extracts `extract_schema` from the OpenAI call response.

        The `extract_schema` is converted into an `OpenAITool`, complete with a
        description of the tool, all of the fields, and their types. This allows us to
        take advantage of OpenAI's tool/function calling functionality to extract
        information from a prompt according to the context provided by the `BaseModel`
        schema.

        Args:
            retries: The maximum number of times to retry the query on validation error.
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Returns:
            The `extract_schema` instance extracted from the completion.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        return await self._extract_async(OpenAICall, OpenAITool, retries, **kwargs)

    def stream(
        self, retries: Union[int, Retrying] = 0, **kwargs: Any
    ) -> Generator[T, None, None]:
        """Streams partial instances of `extract_schema` as the schema is streamed.

        The `extract_schema` is converted into a `partial(OpenAITool)`, which allows for
        any field (i.e.function argument) in the tool to be `None`. This allows us to
        stream partial results as we construct the tool from the streamed chunks.

        Args:
            retries: The maximum number of times to retry the query on validation error.
            **kwargs: Additional keyword argument parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Yields:
            The partial `extract_schema` instance from the current buffer.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        yield from self._stream(
            OpenAICall, OpenAITool, OpenAIToolStream, retries, **kwargs
        )

    async def stream_async(
        self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
    ) -> AsyncGenerator[T, None]:
        """Asynchronously streams partial instances of `extract_schema` as streamed.

        The `extract_schema` is converted into a `partial(OpenAITool)`, which allows for
        any field (i.e.function argument) in the tool to be `None`. This allows us to
        stream partial results as we construct the tool from the streamed chunks.

        Args:
            retries: The maximum number of times to retry the query on validation error.
            **kwargs: Additional keyword arguments parameters to pass to the call. These
                will override any existing arguments in `call_params`.

        Yields:
            The partial `extract_schema` instance from the current buffer.

        Raises:
            AttributeError: if there is no tool in the call creation.
            ValidationError: if the schema cannot be instantiated from the completion.
            OpenAIError: raises any OpenAI errors, see:
                https://platform.openai.com/docs/guides/error-codes/api-errors
        """
        async for partial_tool in self._stream_async(
            OpenAICall, OpenAITool, OpenAIToolStream, retries, **kwargs
        ):
            yield partial_tool

extract(retries=0, **kwargs)

Extracts extract_schema from the OpenAI call response.

The extract_schema is converted into an OpenAITool, complete with a description of the tool, all of the fields, and their types. This allows us to take advantage of OpenAI's tool/function calling functionality to extract information from a prompt according to the context provided by the BaseModel schema.

Parameters:

Name	Type	Description	Default
retries	Union[int, Retrying]	The maximum number of times to retry the query on validation error.	0
**kwargs	Any	Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.	{}

Returns:

Type	Description
T	The extract_schema instance extracted from the completion.

Raises:

Type	Description
AttributeError	if there is no tool in the call creation.
ValidationError	if the schema cannot be instantiated from the completion.
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/extractors.py

def extract(self, retries: Union[int, Retrying] = 0, **kwargs: Any) -> T:
    """Extracts `extract_schema` from the OpenAI call response.

    The `extract_schema` is converted into an `OpenAITool`, complete with a
    description of the tool, all of the fields, and their types. This allows us to
    take advantage of OpenAI's tool/function calling functionality to extract
    information from a prompt according to the context provided by the `BaseModel`
    schema.

    Args:
        retries: The maximum number of times to retry the query on validation error.
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Returns:
        The `extract_schema` instance extracted from the completion.

    Raises:
        AttributeError: if there is no tool in the call creation.
        ValidationError: if the schema cannot be instantiated from the completion.
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    return self._extract(OpenAICall, OpenAITool, retries, **kwargs)

extract_async(retries=0, **kwargs) async

Asynchronously extracts extract_schema from the OpenAI call response.

The extract_schema is converted into an OpenAITool, complete with a description of the tool, all of the fields, and their types. This allows us to take advantage of OpenAI's tool/function calling functionality to extract information from a prompt according to the context provided by the BaseModel schema.

Parameters:

Name	Type	Description	Default
retries	Union[int, AsyncRetrying]	The maximum number of times to retry the query on validation error.	0
**kwargs	Any	Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.	{}

Returns:

Type	Description
T	The extract_schema instance extracted from the completion.

Raises:

Type	Description
AttributeError	if there is no tool in the call creation.
ValidationError	if the schema cannot be instantiated from the completion.
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/extractors.py

async def extract_async(
    self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
) -> T:
    """Asynchronously extracts `extract_schema` from the OpenAI call response.

    The `extract_schema` is converted into an `OpenAITool`, complete with a
    description of the tool, all of the fields, and their types. This allows us to
    take advantage of OpenAI's tool/function calling functionality to extract
    information from a prompt according to the context provided by the `BaseModel`
    schema.

    Args:
        retries: The maximum number of times to retry the query on validation error.
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Returns:
        The `extract_schema` instance extracted from the completion.

    Raises:
        AttributeError: if there is no tool in the call creation.
        ValidationError: if the schema cannot be instantiated from the completion.
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    return await self._extract_async(OpenAICall, OpenAITool, retries, **kwargs)

stream(retries=0, **kwargs)

Streams partial instances of extract_schema as the schema is streamed.

The extract_schema is converted into a partial(OpenAITool), which allows for any field (i.e.function argument) in the tool to be None. This allows us to stream partial results as we construct the tool from the streamed chunks.

Parameters:

Name	Type	Description	Default
retries	Union[int, Retrying]	The maximum number of times to retry the query on validation error.	0
**kwargs	Any	Additional keyword argument parameters to pass to the call. These will override any existing arguments in call_params.	{}

Yields:

Type	Description
T	The partial extract_schema instance from the current buffer.

Raises:

Type	Description
AttributeError	if there is no tool in the call creation.
ValidationError	if the schema cannot be instantiated from the completion.
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/extractors.py

def stream(
    self, retries: Union[int, Retrying] = 0, **kwargs: Any
) -> Generator[T, None, None]:
    """Streams partial instances of `extract_schema` as the schema is streamed.

    The `extract_schema` is converted into a `partial(OpenAITool)`, which allows for
    any field (i.e.function argument) in the tool to be `None`. This allows us to
    stream partial results as we construct the tool from the streamed chunks.

    Args:
        retries: The maximum number of times to retry the query on validation error.
        **kwargs: Additional keyword argument parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Yields:
        The partial `extract_schema` instance from the current buffer.

    Raises:
        AttributeError: if there is no tool in the call creation.
        ValidationError: if the schema cannot be instantiated from the completion.
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    yield from self._stream(
        OpenAICall, OpenAITool, OpenAIToolStream, retries, **kwargs
    )

stream_async(retries=0, **kwargs) async

Asynchronously streams partial instances of extract_schema as streamed.

The extract_schema is converted into a partial(OpenAITool), which allows for any field (i.e.function argument) in the tool to be None. This allows us to stream partial results as we construct the tool from the streamed chunks.

Parameters:

Name	Type	Description	Default
retries	Union[int, AsyncRetrying]	The maximum number of times to retry the query on validation error.	0
**kwargs	Any	Additional keyword arguments parameters to pass to the call. These will override any existing arguments in call_params.	{}

Yields:

Type	Description
AsyncGenerator[T, None]	The partial extract_schema instance from the current buffer.

Raises:

Type	Description
AttributeError	if there is no tool in the call creation.
ValidationError	if the schema cannot be instantiated from the completion.
OpenAIError	raises any OpenAI errors, see: https://platform.openai.com/docs/guides/error-codes/api-errors

Source code in mirascope/openai/extractors.py

async def stream_async(
    self, retries: Union[int, AsyncRetrying] = 0, **kwargs: Any
) -> AsyncGenerator[T, None]:
    """Asynchronously streams partial instances of `extract_schema` as streamed.

    The `extract_schema` is converted into a `partial(OpenAITool)`, which allows for
    any field (i.e.function argument) in the tool to be `None`. This allows us to
    stream partial results as we construct the tool from the streamed chunks.

    Args:
        retries: The maximum number of times to retry the query on validation error.
        **kwargs: Additional keyword arguments parameters to pass to the call. These
            will override any existing arguments in `call_params`.

    Yields:
        The partial `extract_schema` instance from the current buffer.

    Raises:
        AttributeError: if there is no tool in the call creation.
        ValidationError: if the schema cannot be instantiated from the completion.
        OpenAIError: raises any OpenAI errors, see:
            https://platform.openai.com/docs/guides/error-codes/api-errors
    """
    async for partial_tool in self._stream_async(
        OpenAICall, OpenAITool, OpenAIToolStream, retries, **kwargs
    ):
        yield partial_tool

OpenAITool

Bases: BaseTool[ChatCompletionMessageToolCall]

A base class for easy use of tools with the OpenAI Chat client.

OpenAITool internally handles the logic that allows you to use tools with simple calls such as OpenAICallResponse.tool or OpenAITool.fn, as seen in the examples below.

Example:

from mirascope.openai import OpenAICall, OpenAICallParams


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(OpenAICall):
    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 = OpenAICallParams(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/openai/tools.py

class OpenAITool(BaseTool[ChatCompletionMessageToolCall]):
    '''A base class for easy use of tools with the OpenAI Chat client.

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

    Example:

    ```python
    from mirascope.openai import OpenAICall, OpenAICallParams


    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(OpenAICall):
        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 = OpenAICallParams(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) -> ChatCompletionToolParam:
        """Constructs a tool schema for use with the OpenAI Chat client.

        A Mirascope `OpenAITool` is deconstructed into a JSON schema, and relevant keys
        are renamed to match the OpenAI `ChatCompletionToolParam` schema used to make
        function/tool calls in OpenAI API.

        Returns:
            The constructed `ChatCompletionToolParam` schema.
        """
        fn = super().tool_schema()
        return cast(ChatCompletionToolParam, {"type": "function", "function": fn})

    @classmethod
    def from_tool_call(
        cls,
        tool_call: ChatCompletionMessageToolCall,
        allow_partial: bool = False,
    ) -> OpenAITool:
        """Extracts an instance of the tool constructed from a tool call response.

        Given `ChatCompletionMessageToolCall` from an OpenAI chat completion response,
        takes its function arguments and creates an `OpenAITool` instance from it.

        Args:
            tool_call: The `ChatCompletionMessageToolCall` to extract the tool from.
            allow_partial: Whether to allow partial JSON schemas.

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

        Raises:
            ValidationError: if the tool call doesn't match the tool schema.
        """
        if allow_partial:
            model_json = from_json(tool_call.function.arguments, allow_partial=True)
        else:
            try:
                model_json = json.loads(tool_call.function.arguments)
            except json.JSONDecodeError as e:
                raise ValueError() from e

        model_json["tool_call"] = tool_call.model_dump()
        return cls.model_validate(model_json)

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

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

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

from_base_type(base_type) classmethod

Constructs a OpenAITool type from a BaseType type.

Source code in mirascope/openai/tools.py

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

from_fn(fn) classmethod

Constructs a OpenAITool type from a function.

Source code in mirascope/openai/tools.py

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

from_model(model) classmethod

Constructs a OpenAITool type from a BaseModel type.

Source code in mirascope/openai/tools.py

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

from_tool_call(tool_call, allow_partial=False) classmethod

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

Given ChatCompletionMessageToolCall from an OpenAI chat completion response, takes its function arguments and creates an OpenAITool instance from it.

Parameters:

Name	Type	Description	Default
tool_call	ChatCompletionMessageToolCall	The ChatCompletionMessageToolCall to extract the tool from.	required
allow_partial	bool	Whether to allow partial JSON schemas.	False

Returns:

Type	Description
OpenAITool	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/openai/tools.py

@classmethod
def from_tool_call(
    cls,
    tool_call: ChatCompletionMessageToolCall,
    allow_partial: bool = False,
) -> OpenAITool:
    """Extracts an instance of the tool constructed from a tool call response.

    Given `ChatCompletionMessageToolCall` from an OpenAI chat completion response,
    takes its function arguments and creates an `OpenAITool` instance from it.

    Args:
        tool_call: The `ChatCompletionMessageToolCall` to extract the tool from.
        allow_partial: Whether to allow partial JSON schemas.

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

    Raises:
        ValidationError: if the tool call doesn't match the tool schema.
    """
    if allow_partial:
        model_json = from_json(tool_call.function.arguments, allow_partial=True)
    else:
        try:
            model_json = json.loads(tool_call.function.arguments)
        except json.JSONDecodeError as e:
            raise ValueError() from e

    model_json["tool_call"] = tool_call.model_dump()
    return cls.model_validate(model_json)

tool_schema() classmethod

Constructs a tool schema for use with the OpenAI Chat client.

A Mirascope OpenAITool is deconstructed into a JSON schema, and relevant keys are renamed to match the OpenAI ChatCompletionToolParam schema used to make function/tool calls in OpenAI API.

Returns:

Type	Description
ChatCompletionToolParam	The constructed ChatCompletionToolParam schema.

Source code in mirascope/openai/tools.py

@classmethod
def tool_schema(cls) -> ChatCompletionToolParam:
    """Constructs a tool schema for use with the OpenAI Chat client.

    A Mirascope `OpenAITool` is deconstructed into a JSON schema, and relevant keys
    are renamed to match the OpenAI `ChatCompletionToolParam` schema used to make
    function/tool calls in OpenAI API.

    Returns:
        The constructed `ChatCompletionToolParam` schema.
    """
    fn = super().tool_schema()
    return cast(ChatCompletionToolParam, {"type": "function", "function": fn})

OpenAIToolStream

Bases: BaseToolStream[OpenAICallResponseChunk, OpenAITool]

A base class for streaming tools from response chunks.

Source code in mirascope/openai/types.py

class OpenAIToolStream(BaseToolStream[OpenAICallResponseChunk, OpenAITool]):
    """A base class for streaming tools from response chunks."""

    @classmethod
    @overload
    def from_stream(
        cls,
        stream: Generator[OpenAICallResponseChunk, None, None],
        allow_partial: Literal[True],
    ) -> Generator[Optional[OpenAITool], None, None]:
        yield ...  # type: ignore  # pragma: no cover

    @classmethod
    @overload
    def from_stream(
        cls,
        stream: Generator[OpenAICallResponseChunk, None, None],
        allow_partial: Literal[False],
    ) -> Generator[OpenAITool, None, None]:
        yield ...  # type: ignore  # pragma: no cover

    @classmethod
    @overload
    def from_stream(
        cls,
        stream: Generator[OpenAICallResponseChunk, None, None],
        allow_partial: bool = False,
    ) -> Generator[Optional[OpenAITool], None, None]:
        yield ...  # type: ignore  # pragma: no cover

    @classmethod
    def from_stream(cls, stream, allow_partial=False):
        """Yields partial tools from the given stream of chunks.

        Args:
            stream: The generator of chunks from which to stream tools.
            allow_partial: Whether to allow partial tools.

        Raises:
            RuntimeError: if a tool in the stream is of an unknown type.
        """
        cls._check_version_for_partial(allow_partial)
        current_tool_call = ChatCompletionMessageToolCall(
            id="", function=Function(arguments="", name=""), type="function"
        )
        current_tool_type = None
        for chunk in stream:
            tool, current_tool_call, current_tool_type, starting_new = _handle_chunk(
                chunk, current_tool_call, current_tool_type, allow_partial
            )
            if tool is not None:
                yield tool
            if starting_new and allow_partial:
                yield None
        if current_tool_type:
            yield current_tool_type.from_tool_call(current_tool_call)

    @classmethod
    @overload
    async def from_async_stream(
        cls,
        stream: AsyncGenerator[OpenAICallResponseChunk, None],
        allow_partial: Literal[True],
    ) -> AsyncGenerator[Optional[OpenAITool], None]:
        yield ...  # type: ignore  # pragma: no cover

    @classmethod
    @overload
    async def from_async_stream(
        cls,
        stream: AsyncGenerator[OpenAICallResponseChunk, None],
        allow_partial: Literal[False],
    ) -> AsyncGenerator[OpenAITool, None]:
        yield ...  # type: ignore  # pragma: no cover

    @classmethod
    @overload
    async def from_async_stream(
        cls,
        stream: AsyncGenerator[OpenAICallResponseChunk, None],
        allow_partial: bool = False,
    ) -> AsyncGenerator[Optional[OpenAITool], None]:
        yield ...  # type: ignore  # pragma: no cover

    @classmethod
    async def from_async_stream(cls, async_stream, allow_partial=False):
        """Yields partial tools from the given stream of chunks asynchronously.

        Args:
            stream: The async generator of chunks from which to stream tools.
            allow_partial: Whether to allow partial tools.

        Raises:
            RuntimeError: if a tool in the stream is of an unknown type.
        """
        cls._check_version_for_partial(allow_partial)
        current_tool_call = ChatCompletionMessageToolCall(
            id="", function=Function(arguments="", name=""), type="function"
        )
        current_tool_type = None
        async for chunk in async_stream:
            tool, current_tool_call, current_tool_type, starting_new = _handle_chunk(
                chunk, current_tool_call, current_tool_type, allow_partial
            )
            if tool is not None:
                yield tool
            if starting_new and allow_partial:
                yield None
        if current_tool_type:
            yield current_tool_type.from_tool_call(current_tool_call)

from_async_stream(async_stream, allow_partial=False) async classmethod

Yields partial tools from the given stream of chunks asynchronously.

Parameters:

Name	Type	Description	Default
stream		The async generator of chunks from which to stream tools.	required
allow_partial		Whether to allow partial tools.	False

Raises:

Type	Description
RuntimeError	if a tool in the stream is of an unknown type.

Source code in mirascope/openai/types.py

@classmethod
async def from_async_stream(cls, async_stream, allow_partial=False):
    """Yields partial tools from the given stream of chunks asynchronously.

    Args:
        stream: The async generator of chunks from which to stream tools.
        allow_partial: Whether to allow partial tools.

    Raises:
        RuntimeError: if a tool in the stream is of an unknown type.
    """
    cls._check_version_for_partial(allow_partial)
    current_tool_call = ChatCompletionMessageToolCall(
        id="", function=Function(arguments="", name=""), type="function"
    )
    current_tool_type = None
    async for chunk in async_stream:
        tool, current_tool_call, current_tool_type, starting_new = _handle_chunk(
            chunk, current_tool_call, current_tool_type, allow_partial
        )
        if tool is not None:
            yield tool
        if starting_new and allow_partial:
            yield None
    if current_tool_type:
        yield current_tool_type.from_tool_call(current_tool_call)

from_stream(stream, allow_partial=False) classmethod

Yields partial tools from the given stream of chunks.

Parameters:

Name	Type	Description	Default
stream		The generator of chunks from which to stream tools.	required
allow_partial		Whether to allow partial tools.	False

Raises:

Type	Description
RuntimeError	if a tool in the stream is of an unknown type.

Source code in mirascope/openai/types.py

@classmethod
def from_stream(cls, stream, allow_partial=False):
    """Yields partial tools from the given stream of chunks.

    Args:
        stream: The generator of chunks from which to stream tools.
        allow_partial: Whether to allow partial tools.

    Raises:
        RuntimeError: if a tool in the stream is of an unknown type.
    """
    cls._check_version_for_partial(allow_partial)
    current_tool_call = ChatCompletionMessageToolCall(
        id="", function=Function(arguments="", name=""), type="function"
    )
    current_tool_type = None
    for chunk in stream:
        tool, current_tool_call, current_tool_type, starting_new = _handle_chunk(
            chunk, current_tool_call, current_tool_type, allow_partial
        )
        if tool is not None:
            yield tool
        if starting_new and allow_partial:
            yield None
    if current_tool_type:
        yield current_tool_type.from_tool_call(current_tool_call)