
    h<                         d Z ddlmZ ddlmZ ddlmZ ddlmZm	Z	m
Z
 ddlmZ ddlmZ ddlmZ dd	lmZ  ed
dd       G d de             Zy)z6Chain that carries on a conversation and calls an LLM.    )
deprecated)
BaseMemory)BasePromptTemplate)
ConfigDictFieldmodel_validator)Self)PROMPT)LLMChain)ConversationBufferMemoryz0.2.7z;langchain_core.runnables.history.RunnableWithMessageHistoryz1.0)sincealternativeremovalc                       e Zd ZU dZ ee      Zeed<   	 e	Z
eed<   	 dZeed<   dZeed<    ed	d
      Zedefd       Zedee   fd       Z ed      defd       Zy)ConversationChaina}  Chain to have a conversation and load context from memory.

    This class is deprecated in favor of ``RunnableWithMessageHistory``. Please refer
    to this tutorial for more detail: https://python.langchain.com/docs/tutorials/chatbot/

    ``RunnableWithMessageHistory`` offers several benefits, including:

    - Stream, batch, and async support;
    - More flexible memory handling, including the ability to manage memory
      outside the chain;
    - Support for multiple threads.

    Below is a minimal implementation, analogous to using ``ConversationChain`` with
    the default ``ConversationBufferMemory``:

        .. code-block:: python

            from langchain_core.chat_history import InMemoryChatMessageHistory
            from langchain_core.runnables.history import RunnableWithMessageHistory
            from langchain_openai import ChatOpenAI


            store = {}  # memory is maintained outside the chain

            def get_session_history(session_id: str) -> InMemoryChatMessageHistory:
                if session_id not in store:
                    store[session_id] = InMemoryChatMessageHistory()
                return store[session_id]

            llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

            chain = RunnableWithMessageHistory(llm, get_session_history)
            chain.invoke(
                "Hi I'm Bob.",
                config={"configurable": {"session_id": "1"}},
            )  # session_id determines thread
    Memory objects can also be incorporated into the ``get_session_history`` callable:

        .. code-block:: python

            from langchain.memory import ConversationBufferWindowMemory
            from langchain_core.chat_history import InMemoryChatMessageHistory
            from langchain_core.runnables.history import RunnableWithMessageHistory
            from langchain_openai import ChatOpenAI


            store = {}  # memory is maintained outside the chain

            def get_session_history(session_id: str) -> InMemoryChatMessageHistory:
                if session_id not in store:
                    store[session_id] = InMemoryChatMessageHistory()
                    return store[session_id]

                memory = ConversationBufferWindowMemory(
                    chat_memory=store[session_id],
                    k=3,
                    return_messages=True,
                )
                assert len(memory.memory_variables) == 1
                key = memory.memory_variables[0]
                messages = memory.load_memory_variables({})[key]
                store[session_id] = InMemoryChatMessageHistory(messages=messages)
                return store[session_id]

            llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

            chain = RunnableWithMessageHistory(llm, get_session_history)
            chain.invoke(
                "Hi I'm Bob.",
                config={"configurable": {"session_id": "1"}},
            )  # session_id determines thread

    Example:
        .. code-block:: python

            from langchain.chains import ConversationChain
            from langchain_community.llms import OpenAI

            conversation = ConversationChain(llm=OpenAI())
    )default_factorymemorypromptinput	input_keyresponse
output_keyTforbid)arbitrary_types_allowedextrareturnc                      y)NF )clss    `/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/langchain/chains/conversation/base.pyis_lc_serializablez$ConversationChain.is_lc_serializabler   s        c                     | j                   gS )z5Use this since so some prompt vars come from history.)r   )selfs    r    
input_keyszConversationChain.input_keysv   s     r"   after)modec                    | j                   j                  }| j                  }||v rd| d| d}t        |      | j                  j
                  }g ||}t        |      t        |      k7  rd| d| d| d}t        |      | S )z4Validate that prompt input variables are consistent.zThe input key z$ was also found in the memory keys (z+) - please provide keys that don't overlap.z:Got unexpected prompt input variables. The prompt expects z
, but got z as inputs from memory, and z as the normal input key.)r   memory_variablesr   
ValueErrorr   input_variablesset)r$   memory_keysr   msgprompt_variablesexpected_keyss         r    validate_prompt_input_variablesz1ConversationChain.validate_prompt_input_variables{   s     kk22NN	#  ,= KM  S/!;;661+1y1}%5!66L#$J{m <(k)BD 
 S/!r"   N)__name__
__module____qualname____doc__r   r   r   r   __annotations__r
   r   r   r   strr   r   model_configclassmethodboolr!   propertylistr%   r   r	   r1   r   r"   r    r   r      s    Ob /GHFJH!'F'-Is J  $L
 4    DI     '"  #r"   r   N)r5   langchain_core._apir   langchain_core.memoryr   langchain_core.promptsr   pydanticr   r   r   typing_extensionsr	   $langchain.chains.conversation.promptr
   langchain.chains.llmr   langchain.memory.bufferr   r   r   r"   r    <module>rE      sO    < * , 5 7 7 " 7 ) < 
M
| |
|r"   