Skip to content

openai.types

Types for interacting with OpenAI models using Mirascope.

BaseAsyncStream

Bases: Generic[BaseCallResponseChunkT, UserMessageParamT, AssistantMessageParamT, BaseToolT], ABC

A base class for async streaming responses from LLMs.

Source code in mirascope/base/types.py 
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
class BaseAsyncStream(
    Generic[
        BaseCallResponseChunkT,
        UserMessageParamT,
        AssistantMessageParamT,
        BaseToolT,
    ],
    ABC,
):
    """A base class for async streaming responses from LLMs."""

    stream: AsyncGenerator[BaseCallResponseChunkT, None]
    message_param_type: type[AssistantMessageParamT]

    cost: Optional[float] = None
    user_message_param: Optional[UserMessageParamT] = None
    message_param: AssistantMessageParamT

    def __init__(
        self,
        stream: AsyncGenerator[BaseCallResponseChunkT, None],
        message_param_type: type[AssistantMessageParamT],
    ):
        """Initializes an instance of `BaseAsyncStream`."""
        self.stream = stream
        self.message_param_type = message_param_type

    def __aiter__(
        self,
    ) -> AsyncGenerator[tuple[BaseCallResponseChunkT, Optional[BaseToolT]], None]:
        """Iterates over the stream and stores useful information."""

        async def generator():
            content = ""
            async for chunk in self.stream:
                content += chunk.content
                if chunk.cost is not None:
                    self.cost = chunk.cost
                yield chunk, None
                self.user_message_param = chunk.user_message_param
            kwargs = {"role": "assistant"}
            if "message" in self.message_param_type.__annotations__:
                kwargs["message"] = content
            else:
                kwargs["content"] = content
            self.message_param = self.message_param_type(**kwargs)

        return generator()

BaseCallParams

Bases: BaseModel, Generic[BaseToolT]

The parameters with which to make a call.

Source code in mirascope/base/types.py 
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
class BaseCallParams(BaseModel, Generic[BaseToolT]):
    """The parameters with which to make a call."""

    model: str
    tools: Optional[list[Union[Callable, Type[BaseToolT]]]] = None

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

    def kwargs(
        self,
        tool_type: Optional[Type[BaseToolT]] = None,
        exclude: Optional[set[str]] = None,
    ) -> dict[str, Any]:
        """Returns all parameters for the call as a keyword arguments dictionary."""
        extra_exclude = {"tools"}
        exclude = extra_exclude if exclude is None else exclude.union(extra_exclude)
        kwargs = {
            key: value
            for key, value in self.model_dump(exclude=exclude).items()
            if value is not None
        }
        if not self.tools or tool_type is None:
            return kwargs
        kwargs["tools"] = [
            tool if isclass(tool) else convert_function_to_tool(tool, tool_type)
            for tool in self.tools
        ]
        return kwargs

kwargs(tool_type=None, exclude=None)

Returns all parameters for the call as a keyword arguments dictionary.

Source code in mirascope/base/types.py 
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
def kwargs(
    self,
    tool_type: Optional[Type[BaseToolT]] = None,
    exclude: Optional[set[str]] = None,
) -> dict[str, Any]:
    """Returns all parameters for the call as a keyword arguments dictionary."""
    extra_exclude = {"tools"}
    exclude = extra_exclude if exclude is None else exclude.union(extra_exclude)
    kwargs = {
        key: value
        for key, value in self.model_dump(exclude=exclude).items()
        if value is not None
    }
    if not self.tools or tool_type is None:
        return kwargs
    kwargs["tools"] = [
        tool if isclass(tool) else convert_function_to_tool(tool, tool_type)
        for tool in self.tools
    ]
    return kwargs

BaseCallResponse

Bases: BaseModel, Generic[ResponseT, BaseToolT], ABC

A base abstract interface for LLM call responses.

Attributes:

Name Type Description
response ResponseT

The original response from whichever model response this wraps.
Source code in mirascope/base/types.py 
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
class BaseCallResponse(BaseModel, Generic[ResponseT, BaseToolT], ABC):
    """A base abstract interface for LLM call responses.

    Attributes:
        response: The original response from whichever model response this wraps.
    """

    response: ResponseT
    user_message_param: Optional[Any] = None
    tool_types: Optional[list[Type[BaseToolT]]] = None
    start_time: float  # The start time of the completion in ms
    end_time: float  # The end time of the completion in ms
    cost: Optional[float] = None  # The cost of the completion in dollars

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

    @property
    @abstractmethod
    def message_param(self) -> Any:
        """Returns the assistant's response as a message parameter."""
        ...  # pragma: no cover

    @property
    @abstractmethod
    def tools(self) -> Optional[list[BaseToolT]]:
        """Returns the tools for the 0th choice message."""
        ...  # pragma: no cover

    @property
    @abstractmethod
    def tool(self) -> Optional[BaseToolT]:
        """Returns the 0th tool for the 0th choice message."""
        ...  # pragma: no cover

    @classmethod
    @abstractmethod
    def tool_message_params(
        cls, tools_and_outputs: list[tuple[BaseToolT, Any]]
    ) -> list[Any]:
        """Returns the tool message parameters for tool call results."""
        ...  # pragma: no cover

    @property
    @abstractmethod
    def content(self) -> str:
        """Should return the string content of the response.

        If there are multiple choices in a response, this method should select the 0th
        choice and return it's string content.

        If there is no string content (e.g. when using tools), this method must return
        the empty string.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def finish_reasons(self) -> Union[None, list[str]]:
        """Should return the finish reasons of the response.

        If there is no finish reason, this method must return None.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def model(self) -> Optional[str]:
        """Should return the name of the response model."""
        ...  # pragma: no cover

    @property
    @abstractmethod
    def id(self) -> Optional[str]:
        """Should return the id of the response."""
        ...  # pragma: no cover

    @property
    @abstractmethod
    def usage(self) -> Any:
        """Should return the usage of the response.

        If there is no usage, this method must return None.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def input_tokens(self) -> Optional[Union[int, float]]:
        """Should return the number of input tokens.

        If there is no input_tokens, this method must return None.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def output_tokens(self) -> Optional[Union[int, float]]:
        """Should return the number of output tokens.

        If there is no output_tokens, this method must return None.
        """
        ...  # pragma: no cover

content: str abstractmethod property

Should return the string content of the response.

If there are multiple choices in a response, this method should select the 0th choice and return it's string content.

If there is no string content (e.g. when using tools), this method must return the empty string.

finish_reasons: Union[None, list[str]] abstractmethod property

Should return the finish reasons of the response.

If there is no finish reason, this method must return None.

id: Optional[str] abstractmethod property

Should return the id of the response.

input_tokens: Optional[Union[int, float]] abstractmethod property

Should return the number of input tokens.

If there is no input_tokens, this method must return None.

message_param: Any abstractmethod property

Returns the assistant's response as a message parameter.

model: Optional[str] abstractmethod property

Should return the name of the response model.

output_tokens: Optional[Union[int, float]] abstractmethod property

Should return the number of output tokens.

If there is no output_tokens, this method must return None.

tool: Optional[BaseToolT] abstractmethod property

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

tools: Optional[list[BaseToolT]] abstractmethod property

Returns the tools for the 0th choice message.

usage: Any abstractmethod property

Should return the usage of the response.

If there is no usage, this method must return None.

tool_message_params(tools_and_outputs) abstractmethod classmethod

Returns the tool message parameters for tool call results.

Source code in mirascope/base/types.py 
162
163
164
165
166
167
168
@classmethod
@abstractmethod
def tool_message_params(
    cls, tools_and_outputs: list[tuple[BaseToolT, Any]]
) -> list[Any]:
    """Returns the tool message parameters for tool call results."""
    ...  # pragma: no cover

BaseCallResponseChunk

Bases: BaseModel, Generic[ChunkT, BaseToolT], ABC

A base abstract interface for LLM streaming response chunks.

Attributes:

Name Type Description
response

The original response chunk from whichever model response this wraps.
Source code in mirascope/base/types.py 
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
class BaseCallResponseChunk(BaseModel, Generic[ChunkT, BaseToolT], ABC):
    """A base abstract interface for LLM streaming response chunks.

    Attributes:
        response: The original response chunk from whichever model response this wraps.
    """

    chunk: ChunkT
    user_message_param: Optional[Any] = None
    tool_types: Optional[list[Type[BaseToolT]]] = None
    cost: Optional[float] = None  # The cost of the completion in dollars
    model_config = ConfigDict(extra="allow", arbitrary_types_allowed=True)

    @property
    @abstractmethod
    def content(self) -> str:
        """Should return the string content of the response chunk.

        If there are multiple choices in a chunk, this method should select the 0th
        choice and return it's string content.

        If there is no string content (e.g. when using tools), this method must return
        the empty string.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def model(self) -> Optional[str]:
        """Should return the name of the response model."""
        ...  # pragma: no cover

    @property
    @abstractmethod
    def id(self) -> Optional[str]:
        """Should return the id of the response."""
        ...  # pragma: no cover

    @property
    @abstractmethod
    def finish_reasons(self) -> Union[None, list[str]]:
        """Should return the finish reasons of the response.

        If there is no finish reason, this method must return None.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def usage(self) -> Any:
        """Should return the usage of the response.

        If there is no usage, this method must return None.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def input_tokens(self) -> Optional[Union[int, float]]:
        """Should return the number of input tokens.

        If there is no input_tokens, this method must return None.
        """
        ...  # pragma: no cover

    @property
    @abstractmethod
    def output_tokens(self) -> Optional[Union[int, float]]:
        """Should return the number of output tokens.

        If there is no output_tokens, this method must return None.
        """
        ...  # pragma: no cover

content: str abstractmethod property

Should return the string content of the response chunk.

If there are multiple choices in a chunk, this method should select the 0th choice and return it's string content.

If there is no string content (e.g. when using tools), this method must return the empty string.

finish_reasons: Union[None, list[str]] abstractmethod property

Should return the finish reasons of the response.

If there is no finish reason, this method must return None.

id: Optional[str] abstractmethod property

Should return the id of the response.

input_tokens: Optional[Union[int, float]] abstractmethod property

Should return the number of input tokens.

If there is no input_tokens, this method must return None.

model: Optional[str] abstractmethod property

Should return the name of the response model.

output_tokens: Optional[Union[int, float]] abstractmethod property

Should return the number of output tokens.

If there is no output_tokens, this method must return None.

usage: Any abstractmethod property

Should return the usage of the response.

If there is no usage, this method must return None.

BaseEmbeddingParams

Bases: BaseModel

The parameters with which to make an embedding.

Source code in mirascope/rag/types.py 
53
54
55
56
57
58
59
60
61
62
63
64
65
class BaseEmbeddingParams(BaseModel):
    """The parameters with which to make an embedding."""

    model: str

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

    def kwargs(self) -> dict[str, Any]:
        """Returns all parameters for the embedder as a keyword arguments dictionary."""
        kwargs = {
            key: value for key, value in self.model_dump().items() if value is not None
        }
        return kwargs

kwargs()

Returns all parameters for the embedder as a keyword arguments dictionary.

Source code in mirascope/rag/types.py 
60
61
62
63
64
65
def kwargs(self) -> dict[str, Any]:
    """Returns all parameters for the embedder as a keyword arguments dictionary."""
    kwargs = {
        key: value for key, value in self.model_dump().items() if value is not None
    }
    return kwargs

BaseEmbeddingResponse

Bases: BaseModel, Generic[ResponseT], ABC

A base abstract interface for LLM embedding responses.

Attributes:

Name Type Description
response ResponseT

The original response from whichever model response this wraps.
Source code in mirascope/rag/types.py 
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
class BaseEmbeddingResponse(BaseModel, Generic[ResponseT], ABC):
    """A base abstract interface for LLM embedding responses.

    Attributes:
        response: The original response from whichever model response this wraps.
    """

    response: ResponseT
    start_time: float  # The start time of the embedding in ms
    end_time: float  # The end time of the embedding in ms

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

    @property
    @abstractmethod
    def embeddings(self) -> Optional[Union[list[list[float]], list[list[int]]]]:
        """Should return the embedding of the response.

        If there are multiple choices in a response, this method should select the 0th
        choice and return it's embedding.
        """
        ...  # pragma: no cover

embeddings: Optional[Union[list[list[float]], list[list[int]]]] abstractmethod property

Should return the embedding of the response.

If there are multiple choices in a response, this method should select the 0th choice and return it's embedding.

BaseStream

Bases: Generic[BaseCallResponseChunkT, UserMessageParamT, AssistantMessageParamT, BaseToolT], ABC

A base class for streaming responses from LLMs.

Source code in mirascope/base/types.py 
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
class BaseStream(
    Generic[
        BaseCallResponseChunkT,
        UserMessageParamT,
        AssistantMessageParamT,
        BaseToolT,
    ],
    ABC,
):
    """A base class for streaming responses from LLMs."""

    stream: Generator[BaseCallResponseChunkT, None, None]
    message_param_type: type[AssistantMessageParamT]

    cost: Optional[float] = None
    user_message_param: Optional[UserMessageParamT] = None
    message_param: AssistantMessageParamT

    def __init__(
        self,
        stream: Generator[BaseCallResponseChunkT, None, None],
        message_param_type: type[AssistantMessageParamT],
    ):
        """Initializes an instance of `BaseStream`."""
        self.stream = stream
        self.message_param_type = message_param_type

    def __iter__(
        self,
    ) -> Generator[tuple[BaseCallResponseChunkT, Optional[BaseToolT]], None, None]:
        """Iterator over the stream and stores useful information."""
        content = ""
        for chunk in self.stream:
            content += chunk.content
            if chunk.cost is not None:
                self.cost = chunk.cost
            yield chunk, None
            self.user_message_param = chunk.user_message_param
        kwargs = {"role": "assistant"}
        if "message" in self.message_param_type.__annotations__:
            kwargs["message"] = content
        else:
            kwargs["content"] = content
        self.message_param = self.message_param_type(**kwargs)

BaseToolStream

Bases: BaseModel, Generic[BaseCallResponseChunkT, BaseToolT], ABC

A base class for streaming tools from response chunks.

Source code in mirascope/base/types.py 
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
class BaseToolStream(BaseModel, Generic[BaseCallResponseChunkT, BaseToolT], ABC):
    """A base class for streaming tools from response chunks."""

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

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

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

    @classmethod
    @abstractmethod
    def from_stream(cls, stream, allow_partial=False):
        """Yields tools from the given stream of chunks."""
        yield ...  # type: ignore # pragma: no cover

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

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

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

    @classmethod
    @abstractmethod
    async def from_async_stream(cls, async_stream, allow_partial=False):
        """Yields tools asynchronously from the given async stream of chunks."""
        yield ...  # type: ignore # pragma: no cover

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

    @classmethod
    def _check_version_for_partial(cls, partial: bool) -> None:
        """Checks that the correct version of Pydantic is installed to use partial."""
        if partial and int(pydantic.__version__.split(".")[1]) < 7:
            raise ImportError(
                "You must have `pydantic==^2.7.0` to stream tools. "
                f"Current version: {pydantic.__version__}"
            )  # pragma: no cover

from_async_stream(async_stream, allow_partial=False) abstractmethod async classmethod

Yields tools asynchronously from the given async stream of chunks.

Source code in mirascope/base/types.py 
383
384
385
386
387
@classmethod
@abstractmethod
async def from_async_stream(cls, async_stream, allow_partial=False):
    """Yields tools asynchronously from the given async stream of chunks."""
    yield ...  # type: ignore # pragma: no cover

from_stream(stream, allow_partial=False) abstractmethod classmethod

Yields tools from the given stream of chunks.

Source code in mirascope/base/types.py 
347
348
349
350
351
@classmethod
@abstractmethod
def from_stream(cls, stream, allow_partial=False):
    """Yields tools from the given stream of chunks."""
    yield ...  # type: ignore # pragma: no cover

OpenAIAsyncStream

Bases: BaseAsyncStream[OpenAICallResponseChunk, ChatCompletionUserMessageParam, ChatCompletionAssistantMessageParam, OpenAITool]

A class for streaming responses from OpenAI's API.

Source code in mirascope/openai/types.py 
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
class OpenAIAsyncStream(
    BaseAsyncStream[
        OpenAICallResponseChunk,
        ChatCompletionUserMessageParam,
        ChatCompletionAssistantMessageParam,
        OpenAITool,
    ]
):
    """A class for streaming responses from OpenAI's API."""

    def __init__(
        self,
        stream: AsyncGenerator[OpenAICallResponseChunk, None],
        allow_partial: bool = False,
    ):
        """Initializes an instance of `OpenAIAsyncStream`."""
        OpenAIToolStream._check_version_for_partial(allow_partial)
        super().__init__(stream, ChatCompletionAssistantMessageParam)
        self._allow_partial = allow_partial

    def __aiter__(
        self,
    ) -> AsyncGenerator[tuple[OpenAICallResponseChunk, Optional[OpenAITool]], None]:
        """Iterator over the stream and constructs tools as they are streamed."""
        stream = super().__aiter__()

        async def generator():
            current_tool_call = ChatCompletionMessageToolCall(
                id="", function=Function(arguments="", name=""), type="function"
            )
            current_tool_type, tool_calls = None, []
            async for chunk, _ in stream:
                if not chunk.tool_types or not chunk.tool_calls:
                    if current_tool_type:
                        yield chunk, current_tool_type.from_tool_call(current_tool_call)
                        tool_calls.append(current_tool_call)
                        current_tool_type = None
                    else:
                        yield chunk, None
                (
                    tool,
                    current_tool_call,
                    current_tool_type,
                    starting_new,
                ) = _handle_chunk(
                    chunk, current_tool_call, current_tool_type, self._allow_partial
                )
                if tool is not None:
                    yield chunk, tool
                    if starting_new:
                        tool_calls.append(tool.tool_call)
                if starting_new and self._allow_partial:
                    yield chunk, None
            if tool_calls:
                self.message_param["tool_calls"] = tool_calls  # type: ignore

        return generator()

    @classmethod
    def tool_message_params(cls, tools_and_outputs):
        """Returns the tool message parameters for tool call results."""
        return OpenAICallResponse.tool_message_params(tools_and_outputs)

tool_message_params(tools_and_outputs) classmethod

Returns the tool message parameters for tool call results.

Source code in mirascope/openai/types.py 
694
695
696
697
@classmethod
def tool_message_params(cls, tools_and_outputs):
    """Returns the tool message parameters for tool call results."""
    return OpenAICallResponse.tool_message_params(tools_and_outputs)

OpenAICallParams

Bases: BaseCallParams[OpenAITool]

The parameters to use when calling the OpenAI API.

Source code in mirascope/openai/types.py 
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
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 
71
72
73
74
75
76
77
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 
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
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
    user_message_param: Optional[ChatCompletionUserMessageParam] = None

    @property
    def message_param(self) -> ChatCompletionAssistantMessageParam:
        """Returns the assistants's response as a message parameter."""
        return self.message.model_dump(exclude={"function_call"})  # type: ignore

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

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

    @property
    def finish_reasons(self) -> list[str]:
        """Returns the finish reasons of the response."""
        return [str(choice.finish_reason) for choice in self.response.choices]

    @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

    @classmethod
    def tool_message_params(
        self, tools_and_outputs: list[tuple[OpenAITool, str]]
    ) -> list[ChatCompletionToolMessageParam]:
        return [
            ChatCompletionToolMessageParam(
                role="tool",
                content=output,
                tool_call_id=tool.tool_call.id,
                name=tool.name(),  # type: ignore
            )
            for tool, output in tools_and_outputs
        ]

    @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.

finish_reasons: list[str] property

Returns the finish reasons of the response.

id: str property

Returns the id of the response.

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.

message_param: ChatCompletionAssistantMessageParam property

Returns the assistants's response as a message parameter.

model: str property

Returns the name of the response model.

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 
262
263
264
265
266
267
268
269
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 
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
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
    user_message_param: Optional[ChatCompletionUserMessageParam] = 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 model(self) -> str:
        """Returns the name of the response model."""
        return self.chunk.model

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

    @property
    def finish_reasons(self) -> list[str]:
        """Returns the finish reasons of the response."""
        return [str(choice.finish_reason) for choice in self.chunk.choices]

    @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

    @property
    def usage(self) -> Optional[CompletionUsage]:
        """Returns the usage of the chat completion."""
        if self.chunk.usage:
            return self.chunk.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

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.

finish_reasons: list[str] property

Returns the finish reasons of the response.

id: str property

Returns the id of the response.

input_tokens: Optional[int] property

Returns the number of input tokens.

model: str property

Returns the name of the response model.

output_tokens: Optional[int] property

Returns the number of output tokens.

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.

usage: Optional[CompletionUsage] property

Returns the usage of the chat completion.

OpenAIEmbeddingResponse

Bases: BaseEmbeddingResponse[CreateEmbeddingResponse]

A convenience wrapper around the OpenAI CreateEmbeddingResponse response.

Source code in mirascope/openai/types.py 
382
383
384
385
386
387
388
389
390
391
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.

OpenAIStream

Bases: BaseStream[OpenAICallResponseChunk, ChatCompletionUserMessageParam, ChatCompletionAssistantMessageParam, OpenAITool]

A class for streaming responses from OpenAI's API.

Source code in mirascope/openai/types.py 
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
class OpenAIStream(
    BaseStream[
        OpenAICallResponseChunk,
        ChatCompletionUserMessageParam,
        ChatCompletionAssistantMessageParam,
        OpenAITool,
    ]
):
    """A class for streaming responses from OpenAI's API."""

    def __init__(
        self,
        stream: Generator[OpenAICallResponseChunk, None, None],
        allow_partial: bool = False,
    ):
        """Initializes an instance of `OpenAIStream`."""
        OpenAIToolStream._check_version_for_partial(allow_partial)
        super().__init__(stream, ChatCompletionAssistantMessageParam)
        self._allow_partial = allow_partial

    def __iter__(
        self,
    ) -> Generator[tuple[OpenAICallResponseChunk, Optional[OpenAITool]], None, None]:
        """Iterator over the stream and constructs tools as they are streamed."""
        current_tool_call = ChatCompletionMessageToolCall(
            id="", function=Function(arguments="", name=""), type="function"
        )
        current_tool_type, tool_calls = None, []
        for chunk, _ in super().__iter__():
            if not chunk.tool_types or not chunk.tool_calls:
                if current_tool_type:
                    yield chunk, current_tool_type.from_tool_call(current_tool_call)
                    tool_calls.append(current_tool_call)
                    current_tool_type = None
                else:
                    yield chunk, None
            tool, current_tool_call, current_tool_type, starting_new = _handle_chunk(
                chunk, current_tool_call, current_tool_type, self._allow_partial
            )
            if tool is not None:
                yield chunk, tool
                if starting_new:
                    tool_calls.append(tool.tool_call)
            if starting_new and self._allow_partial:
                yield chunk, None
        if tool_calls:
            self.message_param["tool_calls"] = tool_calls  # type: ignore

    @classmethod
    def tool_message_params(cls, tools_and_outputs):
        """Returns the tool message parameters for tool call results."""
        return OpenAICallResponse.tool_message_params(tools_and_outputs)

tool_message_params(tools_and_outputs) classmethod

Returns the tool message parameters for tool call results.

Source code in mirascope/openai/types.py 
630
631
632
633
@classmethod
def tool_message_params(cls, tools_and_outputs):
    """Returns the tool message parameters for tool call results."""
    return OpenAICallResponse.tool_message_params(tools_and_outputs)

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 
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
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 
121
122
123
124
@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 
116
117
118
119
@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 
111
112
113
114
@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 
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
@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 
65
66
67
68
69
70
71
72
73
74
75
76
77
@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 
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
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 
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
@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 
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
@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)

partial(wrapped_class)

Generate a new class with all attributes optionals.

Notes

This will wrap a class inheriting form BaseModel and will recursively convert all its attributes and its children's attributes to optionals.

Example:

@partial
class User(BaseModel):
    name: str

user = User(name="None")
Source code in mirascope/partial.py 
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
def partial(wrapped_class: type[Model]) -> type[Model]:
    """Generate a new class with all attributes optionals.

    Notes:
        This will wrap a class inheriting form BaseModel and will recursively
        convert all its attributes and its children's attributes to optionals.

    Example:

    ```python
    @partial
    class User(BaseModel):
        name: str

    user = User(name="None")
    ```
    """

    def _make_field_optional(
        field: FieldInfo,
    ) -> tuple[object, FieldInfo]:
        tmp_field = deepcopy(field)

        annotation = field.annotation
        # If the field is a BaseModel, then recursively convert it's
        # attributes to optionals.
        if type(annotation) is type(BaseModel):
            tmp_field.annotation = Optional[partial(annotation)]  # type: ignore
            tmp_field.default = {}
        else:
            tmp_field.annotation = Optional[field.annotation]  # type: ignore[assignment]
            tmp_field.default = None
        return tmp_field.annotation, tmp_field

    return create_model(  # type: ignore[no-any-return, call-overload]
        f"Partial{wrapped_class.__name__}",
        __base__=wrapped_class,
        __module__=wrapped_class.__module__,
        __doc__=wrapped_class.__doc__,
        **{
            field_name: _make_field_optional(field_info)
            for field_name, field_info in wrapped_class.model_fields.items()
        },
    )