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

[docs]class KayAiRetriever(BaseRetriever): """ Retriever for datasets. To work properly, expects you to have KAY_API_KEY env variable set. You can get one for free at """ client: Any num_contexts: int
[docs] @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