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Valyu

ValyuToolSpec #

Bases: BaseToolSpec

Valyu tool spec.

Source code in llama-index-integrations/tools/llama-index-tools-valyu/llama_index/tools/valyu/base.py 
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class ValyuToolSpec(BaseToolSpec):
    """Valyu tool spec."""

    spec_functions = [
        "search",
    ]

    def __init__(
        self,
        api_key: str,
        verbose: bool = False,
        max_price: Optional[float] = 100,
    ) -> None:
        """Initialize with parameters."""
        from valyu import Valyu

        self.client = Valyu(api_key=api_key)
        self._verbose = verbose
        self._max_price = max_price

    def search(
        self,
        query: str,
        search_type: str = "all",
        max_num_results: int = 5,
        relevance_threshold: float = 0.5,
        max_price: Optional[float] = None,
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> List[Document]:
        """
        Search and retrieve relevant content from proprietary and public sources using Valyu's deep search.

        Args:
            query (str): The input query to be processed
            search_type (str): Type of search - "all" (both proprietary and web), "proprietary" (Valyu indices only), or "web" (web search only). Defaults to "all"
            max_num_results (int): Maximum number of results to return (1-20). Defaults to 5
            relevance_threshold (float): Minimum relevance score required for results (0.0-1.0). Defaults to 0.5
            max_price (Optional[float]): Maximum cost in dollars for this search. Defaults to 20.0
            start_date (Optional[str]): Start date for time filtering in YYYY-MM-DD format
            end_date (Optional[str]): End date for time filtering in YYYY-MM-DD format

        Returns:
            List[Document]: List of Document objects containing the search results

        """
        if max_price is None:
            max_price = self._max_price

        response = self.client.search(
            query=query,
            search_type=search_type,
            max_num_results=max_num_results,
            relevance_threshold=relevance_threshold,
            max_price=max_price,
            start_date=start_date,
            end_date=end_date,
        )

        if self._verbose:
            print(f"[Valyu Tool] Response: {response}")

        documents = []
        for result in response.results:
            metadata = {
                "title": result.title,
                "url": result.url,
                "source": result.source,
                "price": result.price,
                "length": result.length,
                "data_type": result.data_type,
                "relevance_score": result.relevance_score,
            }

            documents.append(
                Document(
                    text=result.content,
                    metadata=metadata,
                )
            )

        return documents

search #

search(query: str, search_type: str = 'all', max_num_results: int = 5, relevance_threshold: float = 0.5, max_price: Optional[float] = None, start_date: Optional[str] = None, end_date: Optional[str] = None) -> List[Document]

Search and retrieve relevant content from proprietary and public sources using Valyu's deep search.

Parameters:

Name Type Description Default
query str

The input query to be processed

 required
search_type str

Type of search - "all" (both proprietary and web), "proprietary" (Valyu indices only), or "web" (web search only). Defaults to "all"

 'all'
max_num_results int

Maximum number of results to return (1-20). Defaults to 5

 5
relevance_threshold float

Minimum relevance score required for results (0.0-1.0). Defaults to 0.5

 0.5
max_price Optional[float]

Maximum cost in dollars for this search. Defaults to 20.0

 None
start_date Optional[str]

Start date for time filtering in YYYY-MM-DD format

 None
end_date Optional[str]

End date for time filtering in YYYY-MM-DD format

 None

Returns:

Type Description
List[Document]

List[Document]: List of Document objects containing the search results
Source code in llama-index-integrations/tools/llama-index-tools-valyu/llama_index/tools/valyu/base.py 
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def search(
    self,
    query: str,
    search_type: str = "all",
    max_num_results: int = 5,
    relevance_threshold: float = 0.5,
    max_price: Optional[float] = None,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
) -> List[Document]:
    """
    Search and retrieve relevant content from proprietary and public sources using Valyu's deep search.

    Args:
        query (str): The input query to be processed
        search_type (str): Type of search - "all" (both proprietary and web), "proprietary" (Valyu indices only), or "web" (web search only). Defaults to "all"
        max_num_results (int): Maximum number of results to return (1-20). Defaults to 5
        relevance_threshold (float): Minimum relevance score required for results (0.0-1.0). Defaults to 0.5
        max_price (Optional[float]): Maximum cost in dollars for this search. Defaults to 20.0
        start_date (Optional[str]): Start date for time filtering in YYYY-MM-DD format
        end_date (Optional[str]): End date for time filtering in YYYY-MM-DD format

    Returns:
        List[Document]: List of Document objects containing the search results

    """
    if max_price is None:
        max_price = self._max_price

    response = self.client.search(
        query=query,
        search_type=search_type,
        max_num_results=max_num_results,
        relevance_threshold=relevance_threshold,
        max_price=max_price,
        start_date=start_date,
        end_date=end_date,
    )

    if self._verbose:
        print(f"[Valyu Tool] Response: {response}")

    documents = []
    for result in response.results:
        metadata = {
            "title": result.title,
            "url": result.url,
            "source": result.source,
            "price": result.price,
            "length": result.length,
            "data_type": result.data_type,
            "relevance_score": result.relevance_score,
        }

        documents.append(
            Document(
                text=result.content,
                metadata=metadata,
            )
        )

    return documents