Source code for langchain_community.retrievers.kay

from __future__ import annotations

from typing import Any, List

from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever




class KayAiRetriever(BaseRetriever):
    """
    Retriever for Kay.ai datasets.

    To work properly, expects you to have KAY_API_KEY env variable set.
    You can get one for free at https://kay.ai/.
    """

    client: Any
    num_contexts: int



    @classmethod
    def create(
        cls,
        dataset_id: str,
        data_types: List[str],
        num_contexts: int = 6,
    ) -> KayAiRetriever:
        """
        Create a KayRetriever given a Kay dataset id and a list of datasources.

        Args:
            dataset_id: A dataset id category in Kay, like "company"
            data_types: A list of datasources present within a dataset. For
                "company" the corresponding datasources could be
                ["10-K", "10-Q", "8-K", "PressRelease"].
            num_contexts: The number of documents to retrieve on each query.
                Defaults to 6.
        """
        try:
            from kay.rag.retrievers import KayRetriever
        except ImportError:
            raise ImportError(
                "Could not import kay python package. Please install it with "
                "`pip install kay`.",
            )

        client = KayRetriever(dataset_id, data_types)
        return cls(client=client, num_contexts=num_contexts)


    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:
        ctxs = self.client.query(query=query, num_context=self.num_contexts)
        docs = []
        for ctx in ctxs:
            page_content = ctx.pop("chunk_embed_text", None)
            if page_content is None:
                continue
            docs.append(Document(page_content=page_content, metadata={**ctx}))
        return docs