a ag@sdZddlmZddlmZddlmZddlmZddl m Z m Z m Z ddl mZddlmZdd lmZdd lmZed d d dGdddeZdS)z6Chain that carries on a conversation and calls an LLM.)List) deprecated) BaseMemory)BasePromptTemplate) ConfigDictFieldmodel_validator)Self)PROMPT)LLMChain)ConversationBufferMemoryz0.2.7zRunnableWithMessageHistory: https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.htmlz1.0)Zsince alternativeZremovalc@seZdZUdZeedZeed<e Z e ed<dZ e ed<dZe ed<ed d d Zeed d dZeee d ddZedded ddZdS)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_factorymemorypromptinput input_keyresponse output_keyTZforbid)Zarbitrary_types_allowedextra)returncCsdS)NF)clsrrp/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/conversation/base.pyis_lc_serializablewsz$ConversationChain.is_lc_serializablecCs|jgS)z5Use this since so some prompt vars come from history.)r)selfrrr input_keys{szConversationChain.input_keysafter)modecCsn|jj}|j}||vr,td|d|d|jj}||g}t|t|krjtd|d|d|d|S)z4Validate that prompt input variables are consistent.zThe input key z$ 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.)rZmemory_variablesr ValueErrorrZinput_variablesset)rZ memory_keysrZprompt_variablesZ expected_keysrrrvalidate_prompt_input_variabless* z1ConversationChain.validate_prompt_input_variablesN)__name__ __module__ __qualname____doc__rr rr__annotations__r rrrstrrrZ model_config classmethodboolrpropertyrrrr r"rrrrrs Q   rN)r&typingrZlangchain_core._apirZlangchain_core.memoryrZlangchain_core.promptsrZpydanticrrrZtyping_extensionsr Z$langchain.chains.conversation.promptr Zlangchain.chains.llmr Zlangchain.memory.bufferr rrrrrs