
    3fir
                    P    d dl mZ d dlmZ d dlmZmZ d dlmZm	Z	 	 	 	 	 	 	 ddZ
y)    )annotations)Any)BaseRetrieverRetrieverOutput)RunnableRunnablePassthroughc                    t        | t              s| }nd | z  }t        j                  |j	                  d            j                  |      j	                  d      S )a\  Create retrieval chain that retrieves documents and then passes them on.

    Args:
        retriever: Retriever-like object that returns list of documents. Should
            either be a subclass of BaseRetriever or a Runnable that returns
            a list of documents. If a subclass of BaseRetriever, then it
            is expected that an `input` key be passed in - this is what
            is will be used to pass into the retriever. If this is NOT a
            subclass of BaseRetriever, then all the inputs will be passed
            into this runnable, meaning that runnable should take a dictionary
            as input.
        combine_docs_chain: Runnable that takes inputs and produces a string output.
            The inputs to this will be any original inputs to this chain, a new
            context key with the retrieved documents, and chat_history (if not present
            in the inputs) with a value of `[]` (to easily enable conversational
            retrieval.

    Returns:
        An LCEL Runnable. The Runnable return is a dictionary containing at the very
        least a `context` and `answer` key.

    Example:
        ```python
        # pip install -U langchain langchain-openai

        from langchain_openai import ChatOpenAI
        from langchain_classic.chains.combine_documents import (
            create_stuff_documents_chain,
        )
        from langchain_classic.chains import create_retrieval_chain
        from langchain_classic import hub

        retrieval_qa_chat_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
        model = ChatOpenAI()
        retriever = ...
        combine_docs_chain = create_stuff_documents_chain(
            model, retrieval_qa_chat_prompt
        )
        retrieval_chain = create_retrieval_chain(retriever, combine_docs_chain)

        retrieval_chain.invoke({"input": "..."})
        ```
    c                    | d   S )Ninput )xs    `/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langchain_classic/chains/retrieval.py<lambda>z(create_retrieval_chain.<locals>.<lambda>>   s
    AgJ     retrieve_documents)run_name)context)answerretrieval_chain)
isinstancer   r   assignwith_config)	retrievercombine_docs_chainretrieval_docss      r   create_retrieval_chainr      s_    ^ i/:C.); 	"""..8L.M	

&*&
+k,k-	.r   N)r   z/BaseRetriever | Runnable[dict, RetrieverOutput]r   zRunnable[dict[str, Any], str]returnr   )
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