openai.types

Types for interacting with OpenAI models using Mirascope.

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)

OpenAICallResponse

Bases: BaseCallResponse[ChatCompletion, OpenAITool]

A convenience wrapper around the OpenAI ChatCompletion response.

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

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)
#> The Name of the Wind

print(response.message)
#> ChatCompletionMessage(content='The Name of the Wind', role='assistant',
#  function_call=None, tool_calls=None)

print(response.choices)
#> [Choice(finish_reason='stop', index=0, logprobs=None,
#  message=ChatCompletionMessage(content='The Name of the Wind', role='assistant',
#  function_call=None, tool_calls=None))]

Source code in mirascope/openai/types.py

class OpenAICallResponse(BaseCallResponse[ChatCompletion, OpenAITool]):
    """A convenience wrapper around the OpenAI `ChatCompletion` response.

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

    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)
    #> The Name of the Wind

    print(response.message)
    #> ChatCompletionMessage(content='The Name of the Wind', role='assistant',
    #  function_call=None, tool_calls=None)

    print(response.choices)
    #> [Choice(finish_reason='stop', index=0, logprobs=None,
    #  message=ChatCompletionMessage(content='The Name of the Wind', role='assistant',
    #  function_call=None, tool_calls=None))]
    ```
    """

    response_format: Optional[ResponseFormat] = None

    @property
    def choices(self) -> list[Choice]:
        """Returns the array of chat completion choices."""
        return self.response.choices

    @property
    def choice(self) -> Choice:
        """Returns the 0th choice."""
        return self.choices[0]

    @property
    def message(self) -> ChatCompletionMessage:
        """Returns the message of the chat completion for the 0th choice."""
        return self.choice.message

    @property
    def content(self) -> str:
        """Returns the content of the chat completion for the 0th choice."""
        return self.message.content if self.message.content is not None else ""

    @property
    def tool_calls(self) -> Optional[list[ChatCompletionMessageToolCall]]:
        """Returns the tool calls for the 0th choice message."""
        return self.message.tool_calls

    @property
    def tools(self) -> Optional[list[OpenAITool]]:
        """Returns the tools for the 0th choice message.

        Raises:
            ValidationError: if a tool call doesn't match the tool's schema.
        """
        if not self.tool_types:
            return None

        if self.choice.finish_reason == "length":
            raise RuntimeError(
                "Finish reason was `length`, indicating the model ran out of tokens "
                "(and could not complete the tool call if trying to)"
            )

        def reconstruct_tools_from_content() -> list[OpenAITool]:
            # Note: we only handle single tool calls in this case
            tool_type = self.tool_types[0]  # type: ignore
            return [
                tool_type.from_tool_call(
                    ChatCompletionMessageToolCall(
                        id="id",
                        function=Function(
                            name=tool_type.__name__, arguments=self.content
                        ),
                        type="function",
                    )
                )
            ]

        if self.response_format == ResponseFormat(type="json_object"):
            return reconstruct_tools_from_content()

        if not self.tool_calls:
            # Let's see if we got an assistant message back instead and try to
            # reconstruct a tool call in this case. We'll assume if it starts with
            # an open curly bracket that we got a tool call assistant message.
            if "{" == self.content[0]:
                # Note: we only handle single tool calls in JSON mode.
                return reconstruct_tools_from_content()
            return None

        extracted_tools = []
        for tool_call in self.tool_calls:
            for tool_type in self.tool_types:
                if tool_call.function.name == tool_type.__name__:
                    extracted_tools.append(tool_type.from_tool_call(tool_call))
                    break

        return extracted_tools

    @property
    def tool(self) -> Optional[OpenAITool]:
        """Returns the 0th tool for the 0th choice message.

        Raises:
            ValidationError: if the tool call doesn't match the tool's schema.
        """
        tools = self.tools
        if tools:
            return tools[0]
        return None

    @property
    def usage(self) -> Optional[CompletionUsage]:
        """Returns the usage of the chat completion."""
        if self.response.usage:
            return self.response.usage
        return None

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

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

    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(),
            "cost": self.cost,
        }

choice: Choice property

Returns the 0th choice.

choices: list[Choice] property

Returns the array of chat completion choices.

content: str property

Returns the content of the chat completion for the 0th choice.

input_tokens: Optional[int] property

Returns the number of input tokens.

message: ChatCompletionMessage property

Returns the message of the chat completion for the 0th choice.

output_tokens: Optional[int] property

Returns the number of output tokens.

tool: Optional[OpenAITool] property

Returns the 0th tool for the 0th choice message.

Raises:

Type	Description
ValidationError	if the tool call doesn't match the tool's schema.

tool_calls: Optional[list[ChatCompletionMessageToolCall]] property

Returns the tool calls for the 0th choice message.

tools: Optional[list[OpenAITool]] property

Returns the tools for the 0th choice message.

Raises:

Type	Description
ValidationError	if a tool call doesn't match the tool's schema.

usage: Optional[CompletionUsage] property

Returns the usage of the chat completion.

dump()

Dumps the response to a dictionary.

Source code in mirascope/openai/types.py

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(),
        "cost": self.cost,
    }

OpenAICallResponseChunk

Bases: BaseCallResponseChunk[ChatCompletionChunk, OpenAITool]

Convenience wrapper around chat completion streaming chunks.

When using Mirascope's convenience wrappers to interact with OpenAI models via OpenAICall.stream, responses will return an OpenAICallResponseChunk, whereby the implemented properties allow for simpler syntax and a convenient developer experience.

Example:

from mirascope.openai import OpenAICall


class Math(OpenAICall):
    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/openai/types.py

class OpenAICallResponseChunk(BaseCallResponseChunk[ChatCompletionChunk, OpenAITool]):
    """Convenience wrapper around chat completion streaming chunks.

    When using Mirascope's convenience wrappers to interact with OpenAI models via
    `OpenAICall.stream`, responses will return an `OpenAICallResponseChunk`, whereby
    the implemented properties allow for simpler syntax and a convenient developer
    experience.

    Example:

    ```python
    from mirascope.openai import OpenAICall


    class Math(OpenAICall):
        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[ResponseFormat] = None

    @property
    def choices(self) -> list[ChunkChoice]:
        """Returns the array of chat completion choices."""
        return self.chunk.choices

    @property
    def choice(self) -> ChunkChoice:
        """Returns the 0th choice."""
        return self.chunk.choices[0]

    @property
    def delta(self) -> Optional[ChoiceDelta]:
        """Returns the delta for the 0th choice."""
        if self.chunk.choices:
            return self.chunk.choices[0].delta
        return None

    @property
    def content(self) -> str:
        """Returns the content for the 0th choice delta."""
        return (
            self.delta.content if self.delta is not None and self.delta.content else ""
        )

    @property
    def tool_calls(self) -> Optional[list[ChoiceDeltaToolCall]]:
        """Returns the partial tool calls for the 0th choice message.

        The first `list[ChoiceDeltaToolCall]` will contain the name of the tool and
        index, and subsequent `list[ChoiceDeltaToolCall]`s will contain the arguments
        which will be strings that need to be concatenated with future
        `list[ChoiceDeltaToolCall]`s to form a complete JSON tool calls. The last
        `list[ChoiceDeltaToolCall]` will be None indicating end of stream.
        """
        if self.delta:
            return self.delta.tool_calls
        return None

choice: ChunkChoice property

Returns the 0th choice.

choices: list[ChunkChoice] property

Returns the array of chat completion choices.

content: str property

Returns the content for the 0th choice delta.

delta: Optional[ChoiceDelta] property

Returns the delta for the 0th choice.

tool_calls: Optional[list[ChoiceDeltaToolCall]] property

Returns the partial tool calls for the 0th choice message.

The first list[ChoiceDeltaToolCall] will contain the name of the tool and index, and subsequent list[ChoiceDeltaToolCall]s will contain the arguments which will be strings that need to be concatenated with future list[ChoiceDeltaToolCall]s to form a complete JSON tool calls. The last list[ChoiceDeltaToolCall] will be None indicating end of stream.

OpenAIEmbeddingResponse

Bases: BaseEmbeddingResponse[CreateEmbeddingResponse]

A convenience wrapper around the OpenAI CreateEmbeddingResponse response.

Source code in mirascope/openai/types.py

class OpenAIEmbeddingResponse(BaseEmbeddingResponse[CreateEmbeddingResponse]):
    """A convenience wrapper around the OpenAI `CreateEmbeddingResponse` response."""

    @property
    def embeddings(self) -> list[list[float]]:
        """Returns the raw embeddings."""
        embeddings_model: list[Embedding] = [
            embedding for embedding in self.response.data
        ]
        return [embedding.embedding for embedding in embeddings_model]

embeddings: list[list[float]] property

Returns the raw embeddings.