langchain/libs/community/langchain_community/retrievers/cohere_rag_retriever.py

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.retrievers import BaseRetriever
from pydantic import ConfigDict, Field

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


@deprecated(
    since="0.0.30",
    removal="1.0",
    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."""

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    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)