
    @h<                         S r SSKJr  SSKJr  SSKJr  SSKJrJ	r	J
r
  SSKJr  SSKJr  SSKJr  SS	KJr  \" S
SSS9 " S S\5      5       rg)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                       \ rS rSr% Sr\" \S9r\\	S'    \
r\\	S'    Sr\\	S'   Sr\\	S	'   \" S
SS9r\S\4S j5       r\S\\   4S j5       r\" SS9S\4S j5       rSrg)ConversationChain   a  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                     g)NF )clss    Z/var/www/html/shao/venv/lib/python3.13/site-packages/langchain/chains/conversation/base.pyis_lc_serializable$ConversationChain.is_lc_serializabler   s        c                     U R                   /$ )z5Use this since so some prompt vars come from history.)r   )selfs    r!   
input_keysConversationChain.input_keysv   s     r$   after)modec                    U R                   R                  nU R                  nX!;   a  SU SU S3n[        U5      eU R                  R
                  n/ UQUPn[        U5      [        U5      :w  a  SU SU SU S3n[        U5      eU $ )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_variables1ConversationChain.validate_prompt_input_variables{   s     kk22NN	#  ,= KM  S/!;;661+1y1}%5!66L#$J{m <(k)BD 
 S/!r$   r   N)__name__
__module____qualname____firstlineno____doc__r   r   r   r   __annotations__r
   r   r   r   strr   r   model_configclassmethodboolr"   propertylistr'   r   r	   r4   __static_attributes__r   r$   r!   r   r      s    Ob /GHFJH!'F'-Is J  $L
 4    DI     '"  #r$   r   N)r:   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>rK      sO    < * , 5 7 7 " 7 ) < 
M
| |
|r$   