Source code for langchain.chains.conversation.base

"""Chain that carries on a conversation and calls an LLM."""

from typing import Dict, List

from langchain_core._api import deprecated
from langchain_core.memory import BaseMemory
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Extra, Field, root_validator

from langchain.chains.conversation.prompt import PROMPT
from langchain.chains.llm import LLMChain
from langchain.memory.buffer import ConversationBufferMemory


[docs] @deprecated( since="0.2.7", alternative=( "RunnableWithMessageHistory: " "https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html" # noqa: E501 ), removal="1.0", ) class ConversationChain(LLMChain): """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/v0.2/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()) """ memory: BaseMemory = Field(default_factory=ConversationBufferMemory) """Default memory store.""" prompt: BasePromptTemplate = PROMPT """Default conversation prompt to use.""" input_key: str = "input" #: :meta private: output_key: str = "response" #: :meta private: class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @classmethod def is_lc_serializable(cls) -> bool: return False @property def input_keys(self) -> List[str]: """Use this since so some prompt vars come from history.""" return [self.input_key] @root_validator(pre=False, skip_on_failure=True) def validate_prompt_input_variables(cls, values: Dict) -> Dict: """Validate that prompt input variables are consistent.""" memory_keys = values["memory"].memory_variables input_key = values["input_key"] if input_key in memory_keys: raise ValueError( f"The input key {input_key} was also found in the memory keys " f"({memory_keys}) - please provide keys that don't overlap." ) prompt_variables = values["prompt"].input_variables expected_keys = memory_keys + [input_key] if set(expected_keys) != set(prompt_variables): raise ValueError( "Got unexpected prompt input variables. The prompt expects " f"{prompt_variables}, but got {memory_keys} as inputs from " f"memory, and {input_key} as the normal input key." ) return values