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230 | class LLMTextCompletionProgram(BasePydanticProgram[Model]):
"""
LLM Text Completion Program.
Uses generic LLM text completion + an output parser to generate a structured output.
"""
def __init__(
self,
output_parser: BaseOutputParser,
output_cls: Type[Model],
prompt: BasePromptTemplate,
llm: LLM,
verbose: bool = False,
) -> None:
self._output_parser = output_parser
self._output_cls = output_cls
self._llm = llm
self._prompt = prompt
self._verbose = verbose
self._prompt.output_parser = output_parser
@classmethod
def from_defaults(
cls,
output_parser: Optional[BaseOutputParser] = None,
output_cls: Optional[Type[Model]] = None,
prompt_template_str: Optional[str] = None,
prompt: Optional[BasePromptTemplate] = None,
llm: Optional[LLM] = None,
verbose: bool = False,
**kwargs: Any,
) -> "LLMTextCompletionProgram[Model]":
llm = llm or Settings.llm
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)
# decide default output class if not set
if output_cls is None:
if not isinstance(output_parser, PydanticOutputParser):
raise ValueError("Output parser must be PydanticOutputParser.")
output_cls = output_parser.output_cls
else:
if output_parser is None:
output_parser = PydanticOutputParser(output_cls=output_cls)
return cls(
output_parser,
output_cls,
prompt=cast(PromptTemplate, prompt),
llm=llm,
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,
) -> Model:
llm_kwargs = llm_kwargs or {}
if self._llm.metadata.is_chat_model:
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
messages = self._llm._extend_messages(messages)
chat_response = self._llm.chat(messages, **llm_kwargs)
raw_output = chat_response.message.content or ""
else:
formatted_prompt = self._prompt.format(llm=self._llm, **kwargs)
response = self._llm.complete(formatted_prompt, **llm_kwargs)
raw_output = response.text
output = self._output_parser.parse(raw_output)
if not isinstance(output, self._output_cls):
raise ValueError(
f"Output parser returned {type(output)} but expected {self._output_cls}"
)
return output
async def acall(
self,
llm_kwargs: Optional[Dict[str, Any]] = None,
*args: Any,
**kwargs: Any,
) -> Model:
llm_kwargs = llm_kwargs or {}
if self._llm.metadata.is_chat_model:
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
messages = self._llm._extend_messages(messages)
chat_response = await self._llm.achat(messages, **llm_kwargs)
raw_output = chat_response.message.content or ""
else:
formatted_prompt = self._prompt.format(llm=self._llm, **kwargs)
response = await self._llm.acomplete(formatted_prompt, **llm_kwargs)
raw_output = response.text
output = self._output_parser.parse(raw_output)
if not isinstance(output, self._output_cls):
raise ValueError(
f"Output parser returned {type(output)} but expected {self._output_cls}"
)
return output
def stream_call(
self, *args: Any, llm_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any
) -> Generator[
Union[Model, List[Model], FlexibleModel, List[FlexibleModel]], None, None
]:
"""
Stream object.
Returns a generator returning partials of the same object
or a list of objects until it returns.
"""
response_gen: Generator[CompletionResponse | ChatResponse, None, None]
llm_kwargs = llm_kwargs or {}
if self._llm.metadata.is_chat_model:
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
messages = self._llm._extend_messages(messages)
response_gen = self._llm.stream_chat(messages, **llm_kwargs)
else:
formatted_prompt = self._prompt.format(llm=self._llm, **kwargs)
response_gen = self._llm.stream_complete(formatted_prompt, **llm_kwargs)
cur_objects = None
for partial_resp in response_gen:
try:
objects = process_streaming_objects(
partial_resp,
self._output_cls,
cur_objects=cur_objects,
flexible_mode=True,
llm=self._llm,
)
cur_objects = objects if isinstance(objects, list) else [objects]
yield objects
except Exception as e:
_logger.warning(f"Failed to parse streaming response: {e}")
continue
async def astream_call(
self, *args: Any, llm_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any
) -> AsyncGenerator[
Union[Model, List[Model], FlexibleModel, List[FlexibleModel]], None
]:
"""
Stream objects.
Returns a generator returning partials of the same object
or a list of objects until it returns.
"""
response_gen: AsyncGenerator[CompletionResponse | ChatResponse, None]
llm_kwargs = llm_kwargs or {}
if self._llm.metadata.is_chat_model:
messages = self._prompt.format_messages(llm=self._llm, **kwargs)
messages = self._llm._extend_messages(messages)
response_gen = await self._llm.astream_chat(messages, **llm_kwargs)
else:
formatted_prompt = self._prompt.format(llm=self._llm, **kwargs)
response_gen = await self._llm.astream_complete(
formatted_prompt, **llm_kwargs
)
async def gen() -> AsyncGenerator[
Union[Model, List[Model], FlexibleModel, List[FlexibleModel]], None
]:
cur_objects = None
async for partial_resp in response_gen:
try:
objects = process_streaming_objects(
partial_resp,
self._output_cls,
cur_objects=cur_objects,
flexible_mode=True,
llm=self._llm,
)
cur_objects = objects if isinstance(objects, list) else [objects]
yield objects
except Exception as e:
_logger.warning(f"Failed to parse streaming response: {e}")
continue
return gen()
|