)
DEFAULT_SEARCH_PROMPT = PromptTemplate(
    input_variables=["question"],
    template="""You are an assistant tasked with improving Google search \
results. Generate THREE 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}""",
)
[docs]class LineList(BaseModel):
    """List of questions."""
    lines: List[str] = Field(description="Questions")
[docs]class QuestionListOutputParser(PydanticOutputParser):
    """Output parser for a list of numbered questions."""
    def __init__(self) -> None:
        super().__init__(pydantic_object=LineList)
[docs]    def parse(self, text: str) -> LineList:
        lines = re.findall(r"\d+\..*?(?:\n|$)", text)
        return LineList(lines=lines)
[docs]class WebResearchRetriever(BaseRetriever):
    """`Google Search API` retriever."""
    # Inputs
    vectorstore: VectorStore = Field(
        ..., description="Vector store for storing web pages"
    )
    llm_chain: LLMChain
    search: GoogleSearchAPIWrapper = Field(..., description="Google Search API Wrapper")
    num_search_results: int = Field(1, description="Number of pages per Google search")
    text_splitter: TextSplitter = Field(
        RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50),
        description="Text splitter for splitting web pages into chunks",
    )
    url_database: List[str] = Field(
        default_factory=list, description="List of processed URLs"
    )
[docs]    @classmethod
    def from_llm(