Migrating from ConversationalChain

ConversationChain incorporated a memory of previous messages to sustain a stateful conversation.

Some advantages of switching to the LCEL implementation are:

• Innate support for threads/separate sessions. To make this work with ConversationChain, you'd need to instantiate a separate memory class outside the chain.
• More explicit parameters. ConversationChain contains a hidden default prompt, which can cause confusion.
• Streaming support. ConversationChain only supports streaming via callbacks.

RunnableWithMessageHistory implements sessions via configuration parameters. It should be instantiated with a callable that returns a chat message history. By default, it expects this function to take a single argument session_id.

%pip install --upgrade --quiet langchain langchain-openai

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

Legacy

Details

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

template = """
You are a pirate. Answer the following questions as best you can.
Chat history: {history}
Question: {input}
"""

prompt = ChatPromptTemplate.from_template(template)

memory = ConversationBufferMemory()

chain = ConversationChain(
    llm=ChatOpenAI(),
    memory=memory,
    prompt=prompt,
)

chain({"input": "how are you?"})

API Reference:ConversationChain | ConversationBufferMemory | ChatPromptTemplate | ChatOpenAI

{'input': 'how are you?',
 'history': '',
 'response': "Arr matey, I be doin' well on the high seas, plunderin' and pillagin' as usual. How be ye?"}

LCEL

Details

from langchain_core.chat_history import InMemoryChatMessageHistory
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a pirate. Answer the following questions as best you can."),
        ("placeholder", "{chat_history}"),
        ("human", "{input}"),
    ]
)

history = InMemoryChatMessageHistory()


def get_history():
    return history


chain = prompt | ChatOpenAI() | StrOutputParser()

wrapped_chain = RunnableWithMessageHistory(
    chain,
    get_history,
    history_messages_key="chat_history",
)

wrapped_chain.invoke({"input": "how are you?"})

API Reference:InMemoryChatMessageHistory | StrOutputParser | ChatPromptTemplate | RunnableWithMessageHistory | ChatOpenAI

"Arr, me matey! I be doin' well, sailin' the high seas and searchin' for treasure. How be ye?"

The above example uses the same history for all sessions. The example below shows how to use a different chat history for each session.

from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

store = {}


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


chain = prompt | ChatOpenAI() | StrOutputParser()

wrapped_chain = RunnableWithMessageHistory(
    chain,
    get_session_history,
    history_messages_key="chat_history",
)

wrapped_chain.invoke(
    {"input": "Hello!"},
    config={"configurable": {"session_id": "abc123"}},
)

API Reference:BaseChatMessageHistory | RunnableWithMessageHistory

'Ahoy there, me hearty! What can this old pirate do for ye today?'

Next steps

See this tutorial for a more end-to-end guide on building with RunnableWithMessageHistory.

Check out the LCEL conceptual docs for more background information.