can use load_qa_chain for QA using the retrieved docs.from langchain.chains.question_answering import load_qa_chainchain = load_qa_chain(llm, chain_type="stuff")output = chain({"input_documents": docs, "question": user_input},return_only_outputs=True)output['output_text']    'Task decomposition in LLM-powered autonomous agents refers to the process of breaking down a complex task into smaller, more manageable subgoals. This allows the agent to efficiently handle and execute the individual steps required to complete the overall task. By decomposing the task, the agent can prioritize and organize its actions, making it easier to plan and execute the necessary steps towards achieving the desired outcome.'More flexibility‚ÄãPass an LLM chain with custom prompt and output parsing.import osimport refrom typing import Listfrom langchain.chains import LLMChainfrom pydantic import BaseModel, Fieldfrom langchain.prompts import PromptTemplatefrom langchain.output_parsers.pydantic import PydanticOutputParser# LLMChainsearch_prompt = PromptTemplate(    input_variables=["question"],    template="""You are an assistant tasked with improving Google search     results. Generate FIVE Google search queries that are similar to    this question. The output should be a numbered list of questions and each    should have a question mark at the end: {question}""",)class LineList(BaseModel):    """List of questions."""    lines: List[str] = Field(description="Questions")class QuestionListOutputParser(PydanticOutputParser):    """Output parser for a list of numbered questions."""    def __init__(self) -> None:        super().__init__(pydantic_object=LineList)    def parse(self, text: str) -> LineList:        lines = re.findall(r"\d+\..*?\n", text)        return LineList(lines=lines)    llm_chain = LLMChain(            llm=llm,            prompt=search_prompt,            output_parser=QuestionListOutputParser(),        )# Initializeweb_research_retriever_llm_chain = WebResearchRetriever(
Given a query, this retriever will:
Given a query, this retriever will: ->: can use load_qa_chain for QA using the retrieved docs.from langchain.chains.question_answering import load_qa_chainchain = load_qa_chain(llm, chain_type="stuff")output = chain({"input_documents": docs, "question": user_input},return_only_outputs=True)output['output_text']    'Task decomposition in LLM-powered autonomous agents refers to the process of breaking down a complex task into smaller, more manageable subgoals. This allows the agent to efficiently handle and execute the individual steps required to complete the overall task. By decomposing the task, the agent can prioritize and organize its actions, making it easier to plan and execute the necessary steps towards achieving the desired outcome.'More flexibility‚ÄãPass an LLM chain with custom prompt and output parsing.import osimport refrom typing import Listfrom langchain.chains import LLMChainfrom pydantic import BaseModel, Fieldfrom langchain.prompts import PromptTemplatefrom langchain.output_parsers.pydantic import PydanticOutputParser# LLMChainsearch_prompt = PromptTemplate(    input_variables=["question"],    template="""You are an assistant tasked with improving Google search     results. Generate FIVE Google search queries that are similar to    this question. The output should be a numbered list of questions and each    should have a question mark at the end: {question}""",)class LineList(BaseModel):    """List of questions."""    lines: List[str] = Field(description="Questions")class QuestionListOutputParser(PydanticOutputParser):    """Output parser for a list of numbered questions."""    def __init__(self) -> None:        super().__init__(pydantic_object=LineList)    def parse(self, text: str) -> LineList:        lines = re.findall(r"\d+\..*?\n", text)        return LineList(lines=lines)    llm_chain = LLMChain(            llm=llm,            prompt=search_prompt,            output_parser=QuestionListOutputParser(),        )# Initializeweb_research_retriever_llm_chain = WebResearchRetriever(