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152 | class Accumulate(BaseSynthesizer):
"""Accumulate responses from multiple text chunks."""
def __init__(
self,
llm: Optional[LLMPredictorType] = None,
callback_manager: Optional[CallbackManager] = None,
prompt_helper: Optional[PromptHelper] = None,
text_qa_template: Optional[BasePromptTemplate] = None,
output_cls: Optional[Any] = None,
streaming: bool = False,
use_async: bool = False,
# deprecated
service_context: Optional[ServiceContext] = None,
) -> None:
super().__init__(
llm=llm,
callback_manager=callback_manager,
prompt_helper=prompt_helper,
service_context=service_context,
streaming=streaming,
)
self._text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT_SEL
self._use_async = use_async
self._output_cls = output_cls
def _get_prompts(self) -> PromptDictType:
"""Get prompts."""
return {"text_qa_template": self._text_qa_template}
def _update_prompts(self, prompts: PromptDictType) -> None:
"""Update prompts."""
if "text_qa_template" in prompts:
self._text_qa_template = prompts["text_qa_template"]
def flatten_list(self, md_array: List[List[Any]]) -> List[Any]:
return [item for sublist in md_array for item in sublist]
def _format_response(self, outputs: List[Any], separator: str) -> str:
responses: List[str] = []
for response in outputs:
responses.append(response or "Empty Response")
return separator.join(
[f"Response {index + 1}: {item}" for index, item in enumerate(responses)]
)
async def aget_response(
self,
query_str: str,
text_chunks: Sequence[str],
separator: str = "\n---------------------\n",
**response_kwargs: Any,
) -> RESPONSE_TEXT_TYPE:
"""Apply the same prompt to text chunks and return async responses."""
if self._streaming:
raise ValueError("Unable to stream in Accumulate response mode")
tasks = [
self._give_responses(
query_str, text_chunk, use_async=True, **response_kwargs
)
for text_chunk in text_chunks
]
flattened_tasks = self.flatten_list(tasks)
outputs = await asyncio.gather(*flattened_tasks)
return self._format_response(outputs, separator)
def get_response(
self,
query_str: str,
text_chunks: Sequence[str],
separator: str = "\n---------------------\n",
**response_kwargs: Any,
) -> RESPONSE_TEXT_TYPE:
"""Apply the same prompt to text chunks and return responses."""
if self._streaming:
raise ValueError("Unable to stream in Accumulate response mode")
tasks = [
self._give_responses(
query_str, text_chunk, use_async=self._use_async, **response_kwargs
)
for text_chunk in text_chunks
]
outputs = self.flatten_list(tasks)
if self._use_async:
outputs = run_async_tasks(outputs)
return self._format_response(outputs, separator)
def _give_responses(
self,
query_str: str,
text_chunk: str,
use_async: bool = False,
**response_kwargs: Any,
) -> List[Any]:
"""Give responses given a query and a corresponding text chunk."""
text_qa_template = self._text_qa_template.partial_format(query_str=query_str)
text_chunks = self._prompt_helper.repack(text_qa_template, [text_chunk])
predictor: Callable
if self._output_cls is None:
predictor = self._llm.apredict if use_async else self._llm.predict
return [
predictor(
text_qa_template,
context_str=cur_text_chunk,
**response_kwargs,
)
for cur_text_chunk in text_chunks
]
else:
predictor = (
self._llm.astructured_predict
if use_async
else self._llm.structured_predict
)
return [
predictor(
self._output_cls,
text_qa_template,
context_str=cur_text_chunk,
**response_kwargs,
)
for cur_text_chunk in text_chunks
]
|