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338 | @deprecated("Please use `FunctionCallingProgram` instead.")
class OpenAIPydanticProgram(BaseLLMFunctionProgram[LLM]):
"""
An OpenAI-based function that returns a pydantic model.
Note: this interface is not yet stable.
"""
def __init__(
self,
output_cls: Type[Model],
llm: LLM,
prompt: BasePromptTemplate,
tool_choice: Union[str, Dict[str, Any]],
allow_multiple: bool = False,
verbose: bool = False,
) -> None:
"""Init params."""
self._output_cls = output_cls
self._llm = llm
self._prompt = prompt
self._verbose = verbose
self._allow_multiple = allow_multiple
self._tool_choice = tool_choice
@classmethod
def from_defaults(
cls,
output_cls: Type[Model],
prompt_template_str: Optional[str] = None,
prompt: Optional[PromptTemplate] = None,
llm: Optional[LLM] = None,
verbose: bool = False,
allow_multiple: bool = False,
tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
**kwargs: Any,
) -> "OpenAIPydanticProgram":
llm = llm or Settings.llm
if not isinstance(llm, OpenAI):
raise ValueError(
"OpenAIPydanticProgram only supports OpenAI LLMs. " f"Got: {type(llm)}"
)
if not llm.metadata.is_function_calling_model:
raise ValueError(
f"Model name {llm.metadata.model_name} does not support "
"function calling API. "
)
if prompt is None and prompt_template_str is None:
raise ValueError("Must provide either prompt or prompt_template_str.")
if prompt is not None and prompt_template_str is not None:
raise ValueError("Must provide either prompt or prompt_template_str.")
if prompt_template_str is not None:
prompt = PromptTemplate(prompt_template_str)
tool_choice = tool_choice or _default_tool_choice(output_cls, allow_multiple)
return cls(
output_cls=output_cls,
llm=llm,
prompt=cast(PromptTemplate, prompt),
tool_choice=tool_choice,
allow_multiple=allow_multiple,
verbose=verbose,
)
@property
def output_cls(self) -> Type[Model]:
return self._output_cls
@property
def prompt(self) -> BasePromptTemplate:
return self._prompt
@prompt.setter
def prompt(self, prompt: BasePromptTemplate) -> None:
self._prompt = prompt
def __call__(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
*args: Any,
**kwargs: Any,
) -> Union[Model, List[Model]]:
llm_kwargs = llm_kwargs or {}
description = self._description_eval(**kwargs)
openai_fn_spec = to_openai_tool(self._output_cls, description=description)
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
if "tool_choice" not in llm_kwargs:
llm_kwargs["tool_choice"] = self._tool_choice
chat_response = self._llm.chat(
messages=messages,
tools=[openai_fn_spec],
**llm_kwargs,
)
message = chat_response.message
if "tool_calls" not in message.additional_kwargs:
raise ValueError(
"Expected tool_calls in ai_message.additional_kwargs, "
"but none found."
)
tool_calls = message.additional_kwargs["tool_calls"]
return _parse_tool_calls(
tool_calls,
output_cls=self.output_cls,
allow_multiple=self._allow_multiple,
verbose=self._verbose,
)
async def acall(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
*args: Any,
**kwargs: Any,
) -> Union[Model, List[Model]]:
llm_kwargs = llm_kwargs or {}
description = self._description_eval(**kwargs)
openai_fn_spec = to_openai_tool(self._output_cls, description=description)
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
if "tool_choice" not in llm_kwargs:
llm_kwargs["tool_choice"] = self._tool_choice
chat_response = await self._llm.achat(
messages=messages,
tools=[openai_fn_spec],
**llm_kwargs,
)
message = chat_response.message
if "tool_calls" not in message.additional_kwargs:
raise ValueError(
"Expected function call in ai_message.additional_kwargs, "
"but none found."
)
tool_calls = message.additional_kwargs["tool_calls"]
return _parse_tool_calls(
tool_calls,
output_cls=self.output_cls,
allow_multiple=self._allow_multiple,
verbose=self._verbose,
)
def stream_list(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
*args: Any,
**kwargs: Any,
) -> Generator[Model, None, None]:
"""Streams a list of objects."""
llm_kwargs = llm_kwargs or {}
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
description = self._description_eval(**kwargs)
list_output_cls = create_list_model(self._output_cls)
openai_fn_spec = to_openai_tool(list_output_cls, description=description)
if "tool_choice" not in llm_kwargs:
llm_kwargs["tool_choice"] = _default_tool_choice(list_output_cls)
chat_response_gen = self._llm.stream_chat(
messages=messages,
tools=[openai_fn_spec],
**llm_kwargs,
)
# extract function call arguments
# obj_start_idx finds start position (before a new "{" in JSON)
obj_start_idx: int = -1 # NOTE: uninitialized
for stream_resp in chat_response_gen:
kwargs = stream_resp.message.additional_kwargs
tool_calls = kwargs["tool_calls"]
if len(tool_calls) == 0:
continue
# NOTE: right now assume only one tool call
# TODO: handle parallel tool calls in streaming setting
fn_args = kwargs["tool_calls"][0].function.arguments
# this is inspired by `get_object` from `MultiTaskBase` in
# the openai_function_call repo
if fn_args.find("[") != -1:
if obj_start_idx == -1:
obj_start_idx = fn_args.find("[") + 1
else:
# keep going until we find the start position
continue
new_obj_json_str, obj_start_idx = _get_json_str(fn_args, obj_start_idx)
if new_obj_json_str is not None:
obj_json_str = new_obj_json_str
obj = self._output_cls.parse_raw(obj_json_str)
if self._verbose:
print(f"Extracted object: {obj.json()}")
yield obj
def stream_partial_objects(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
*args: Any,
**kwargs: Any,
) -> Generator[Model, None, None]:
"""Streams the intermediate partial object."""
llm_kwargs = llm_kwargs or {}
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
description = self._description_eval(**kwargs)
openai_fn_spec = to_openai_tool(self._output_cls, description=description)
if "tool_choice" not in llm_kwargs:
llm_kwargs["tool_choice"] = _default_tool_choice(self._output_cls)
chat_response_gen = self._llm.stream_chat(
messages=messages,
tools=[openai_fn_spec],
**llm_kwargs,
)
for partial_resp in chat_response_gen:
kwargs = partial_resp.message.additional_kwargs
tool_calls = kwargs["tool_calls"]
if len(tool_calls) == 0:
continue
fn_args = kwargs["tool_calls"][0].function.arguments
try:
partial_object = parse_partial_json(fn_args)
yield self._output_cls.parse_obj(partial_object)
except (ValidationError, ValueError):
continue
def _description_eval(self, **kwargs: Any) -> Optional[str]:
description = kwargs.get("description", None)
## __doc__ checks if docstring is provided in the Pydantic Model
if not (self._output_cls.__doc__ or description):
raise ValueError(
"Must provide description for your Pydantic Model. Either provide a docstring or add `description=<your_description>` to the method. Required to convert Pydantic Model to OpenAI Function."
)
## If both docstring and description are provided, raise error
if self._output_cls.__doc__ and description:
raise ValueError(
"Must provide either a docstring or a description, not both."
)
return description
|
