Source code for langchain_google_genai.genai_aqa

"""Google GenerativeAI Attributed Question and Answering (AQA) service.

The GenAI Semantic AQA API is a managed end to end service that allows
developers to create responses grounded on specified passages based on
a user query. For more information visit:

from typing import Any, List, Optional

import as genai
from langchain_core.pydantic_v1 import BaseModel, PrivateAttr
from langchain_core.runnables import RunnableSerializable
from langchain_core.runnables.config import RunnableConfig

from . import _genai_extension as genaix

[docs]class AqaInput(BaseModel): """Input to `GenAIAqa.invoke`. Attributes: prompt: The user's inquiry. source_passages: A list of passage that the LLM should use only to answer the user's inquiry. """ prompt: str source_passages: List[str]
[docs]class AqaOutput(BaseModel): """Output from `GenAIAqa.invoke`. Attributes: answer: The answer to the user's inquiry. attributed_passages: A list of passages that the LLM used to construct the answer. answerable_probability: The probability of the question being answered from the provided passages. """ answer: str attributed_passages: List[str] answerable_probability: float
class _AqaModel(BaseModel): """Wrapper for Google's internal AQA model.""" _client: genai.GenerativeServiceClient = PrivateAttr() _answer_style: int = PrivateAttr() _safety_settings: List[genai.SafetySetting] = PrivateAttr() _temperature: Optional[float] = PrivateAttr() def __init__( self, answer_style: int = genai.GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE, safety_settings: List[genai.SafetySetting] = [], temperature: Optional[float] = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) self._client = genaix.build_generative_service() self._answer_style = answer_style self._safety_settings = safety_settings self._temperature = temperature def generate_answer( self, prompt: str, passages: List[str], ) -> genaix.GroundedAnswer: return genaix.generate_answer( prompt=prompt, passages=passages, client=self._client, answer_style=self._answer_style, safety_settings=self._safety_settings, temperature=self._temperature, )
[docs]class GenAIAqa(RunnableSerializable[AqaInput, AqaOutput]): """Google's Attributed Question and Answering service. Given a user's query and a list of passages, Google's server will return a response that is grounded to the provided list of passages. It will not base the response on parametric memory. Attributes: answer_style: keyword-only argument. See `` for details. """ # Actual type is .aqa_model.AqaModel. _client: _AqaModel = PrivateAttr() # Actual type is genai.AnswerStyle. # 1 = ABSTRACTIVE. # Cannot use the actual type here because user may not have # google.generativeai installed. answer_style: int = 1 def __init__(self, **kwargs: Any) -> None: """Construct a Google Generative AI AQA model. All arguments are optional. Args: answer_style: See ``. safety_settings: See ``. temperature: 0.0 to 1.0. """ super().__init__(**kwargs) self._client = _AqaModel(**kwargs)
[docs] def invoke( self, input: AqaInput, config: Optional[RunnableConfig] = None ) -> AqaOutput: """Generates a grounded response using the provided passages.""" response = self._client.generate_answer( prompt=input.prompt, passages=input.source_passages ) return AqaOutput( answer=response.answer, attributed_passages=[ passage.text for passage in response.attributed_passages ], answerable_probability=response.answerable_probability or 0.0, )