Source code for langchain_community.retrievers.cohere_rag_retriever

from __future__ import annotations

from typing import TYPE_CHECKING, Any, Dict, List

from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import (
    AsyncCallbackManagerForRetrieverRun,
    CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import HumanMessage
from langchain_core.pydantic_v1 import Field
from langchain_core.retrievers import BaseRetriever

if TYPE_CHECKING:
    from langchain_core.messages import BaseMessage


def _get_docs(response: Any) -> List[Document]:
    docs = (
        []
        if "documents" not in response.generation_info
        else [
            Document(page_content=doc["snippet"], metadata=doc)
            for doc in response.generation_info["documents"]
        ]
    )
    docs.append(
        Document(
            page_content=response.message.content,
            metadata={
                "type": "model_response",
                "citations": response.generation_info["citations"],
                "search_results": response.generation_info["search_results"],
                "search_queries": response.generation_info["search_queries"],
                "token_count": response.generation_info["token_count"],
            },
        )
    )
    return docs


[docs]@deprecated( since="0.0.30", removal="0.3.0", alternative_import="langchain_cohere.CohereRagRetriever", ) class CohereRagRetriever(BaseRetriever): """Cohere Chat API with RAG.""" connectors: List[Dict] = Field(default_factory=lambda: [{"id": "web-search"}]) """ When specified, the model's reply will be enriched with information found by querying each of the connectors (RAG). These will be returned as langchain documents. Currently only accepts {"id": "web-search"}. """ llm: BaseChatModel """Cohere ChatModel to use.""" class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True """Allow arbitrary types.""" def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any ) -> List[Document]: messages: List[List[BaseMessage]] = [[HumanMessage(content=query)]] res = self.llm.generate( messages, connectors=self.connectors, callbacks=run_manager.get_child(), **kwargs, ).generations[0][0] return _get_docs(res) async def _aget_relevant_documents( self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun, **kwargs: Any, ) -> List[Document]: messages: List[List[BaseMessage]] = [[HumanMessage(content=query)]] res = ( await self.llm.agenerate( messages, connectors=self.connectors, callbacks=run_manager.get_child(), **kwargs, ) ).generations[0][0] return _get_docs(res)