Skip to main content

How to add message history

Prerequisites

This guide assumes familiarity with the following concepts:

Passing conversation state into and out a chain is vital when building a chatbot. The RunnableWithMessageHistory class lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it. Specifically, it loads previous messages in the conversation BEFORE passing it to the Runnable, and it saves the generated response as a message AFTER calling the runnable. This class also enables multiple conversations by saving each conversation with a session_id - it then expects a session_id to be passed in the config when calling the runnable, and uses that to look up the relevant conversation history.

index_diagram

In practice this looks something like:

from langchain_core.runnables.history import RunnableWithMessageHistory


with_message_history = RunnableWithMessageHistory(
    # The underlying runnable
    runnable,  
    # A function that takes in a session id and returns a memory object
    get_session_history,  
    # Other parameters that may be needed to align the inputs/outputs
    # of the Runnable with the memory object
    ...  
)

with_message_history.invoke(
    # The same input as before
    {"ability": "math", "input": "What does cosine mean?"},
    # Configuration specifying the `session_id`,
    # which controls which conversation to load
    config={"configurable": {"session_id": "abc123"}},
)

In order to properly set this up there are two main things to consider:

  1. How to store and load messages? (this is get_session_history in the example above)
  2. What is the underlying Runnable you are wrapping and what are its inputs/outputs? (this is runnable in the example above, as well any additional parameters you pass to RunnableWithMessageHistory to align the inputs/outputs)

Let's walk through these pieces (and more) below.

How to store and load messages​

A key part of this is storing and loading messages. When constructing RunnableWithMessageHistory you need to pass in a get_session_history function. This function should take in a session_id and return a BaseChatMessageHistory object.

What is session_id?

session_id is an identifier for the session (conversation) thread that these input messages correspond to. This allows you to maintain several conversations/threads with the same chain at the same time.

What is BaseChatMessageHistory?

BaseChatMessageHistory is a class that can load and save message objects. It will be called by RunnableWithMessageHistory to do exactly that. These classes are usually initialized with a session id.

Let's create a get_session_history object to use for this example. To keep things simple, we will use a simple SQLiteMessage

! rm memory.db
from langchain_community.chat_message_histories import SQLChatMessageHistory


def get_session_history(session_id):
    return SQLChatMessageHistory(session_id, "sqlite:///memory.db")
API Reference:SQLChatMessageHistory

Check out the memory integrations page for implementations of chat message histories using other providers (Redis, Postgres, etc).

What is the runnable you are trying to wrap?​

RunnableWithMessageHistory can only wrap certain types of Runnables. Specifically, it can be used for any Runnable that takes as input one of:

  • a sequence of BaseMessages
  • a dict with a key that takes a sequence of BaseMessages
  • a dict with a key that takes the latest message(s) as a string or sequence of BaseMessages, and a separate key that takes historical messages

And returns as output one of

  • a string that can be treated as the contents of an AIMessage
  • a sequence of BaseMessage
  • a dict with a key that contains a sequence of BaseMessage

Let's take a look at some examples to see how it works.

Setup​

First we construct a runnable (which here accepts a dict as input and returns a message as output):

pip install -qU langchain-openai
import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
from langchain_core.messages import HumanMessage
from langchain_core.runnables.history import RunnableWithMessageHistory

Messages input, message(s) output​

The simplest form is just adding memory to a ChatModel. ChatModels accept a list of messages as input and output a message. This makes it very easy to use RunnableWithMessageHistory - no additional configuration is needed!

runnable_with_history = RunnableWithMessageHistory(
    model,
    get_session_history,
)
runnable_with_history.invoke(
    [HumanMessage(content="hi - im bob!")],
    config={"configurable": {"session_id": "1"}},
)
AIMessage(content="It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I help you today?", response_metadata={'id': 'msg_01UHCCMiZz9yNYjt41xUJrtk', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-55f6a451-606b-4e04-9e39-e03b81035c1f-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44})
runnable_with_history.invoke(
    [HumanMessage(content="whats my name?")],
    config={"configurable": {"session_id": "1"}},
)
AIMessage(content='I\'m afraid I don\'t actually know your name - you introduced yourself as Bob, but I don\'t have any other information about your identity. As an AI assistant, I don\'t have a way to independently verify people\'s names or identities. I\'m happy to continue our conversation, but I\'ll just refer to you as "Bob" since that\'s the name you provided.', response_metadata={'id': 'msg_018L96tAxiexMKsHBQz22CcE', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 80}}, id='run-7399ddb5-bb06-444b-bfb2-2f65674105dd-0', usage_metadata={'input_tokens': 52, 'output_tokens': 80, 'total_tokens': 132})
info

Note that in this case the context is preserved via the chat history for the provided session_id, so the model knows the users name.

We can now try this with a new session id and see that it does not remember.

runnable_with_history.invoke(
    [HumanMessage(content="whats my name?")],
    config={"configurable": {"session_id": "1a"}},
)
AIMessage(content="I'm afraid I don't actually know your name. As an AI assistant, I don't have personal information about you unless you provide it to me directly.", response_metadata={'id': 'msg_01LhbWu7mSKTvKAx7iQpMPzd', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 35}}, id='run-cf86cad2-21f2-4525-afc8-09bfd1e8af70-0', usage_metadata={'input_tokens': 12, 'output_tokens': 35, 'total_tokens': 47})
info

When we pass a different session_id, we start a new chat history, so the model does not know what the user's name is.

Dictionary input, message(s) output​

Besides just wrapping a raw model, the next step up is wrapping a prompt + LLM. This now changes the input to be a dictionary (because the input to a prompt is a dictionary). This adds two bits of complication.

First: a dictionary can have multiple keys, but we only want to save ONE as input. In order to do this, we now now need to specify a key to save as the input.

Second: once we load the messages, we need to know how to save them to the dictionary. That equates to know which key in the dictionary to save them in. Therefore, we need to specify a key to save the loaded messages in.

Putting it all together, that ends up looking something like:

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You're an assistant who speaks in {language}. Respond in 20 words or fewer",
        ),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{input}"),
    ]
)

runnable = prompt | model

runnable_with_history = RunnableWithMessageHistory(
    runnable,
    get_session_history,
    input_messages_key="input",
    history_messages_key="history",
)
info

Note that we've specified input_messages_key (the key to be treated as the latest input message) and history_messages_key (the key to add historical messages to).

runnable_with_history.invoke(
    {"language": "italian", "input": "hi im bob!"},
    config={"configurable": {"session_id": "2"}},
)
AIMessage(content='Ciao Bob! È un piacere conoscerti. Come stai oggi?', response_metadata={'id': 'msg_0121ADUEe4G1hMC6zbqFWofr', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 23}}, id='run-246a70df-aad6-43d6-a7e8-166d96e0d67e-0', usage_metadata={'input_tokens': 29, 'output_tokens': 23, 'total_tokens': 52})
runnable_with_history.invoke(
    {"language": "italian", "input": "whats my name?"},
    config={"configurable": {"session_id": "2"}},
)
AIMessage(content='Bob, il tuo nome è Bob.', response_metadata={'id': 'msg_01EDUZG6nRLGeti9KhFN5cek', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 60, 'output_tokens': 12}}, id='run-294b4a72-81bc-4c43-b199-3aafdff87cb3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 12, 'total_tokens': 72})
info

Note that in this case the context is preserved via the chat history for the provided session_id, so the model knows the users name.

We can now try this with a new session id and see that it does not remember.

runnable_with_history.invoke(
    {"language": "italian", "input": "whats my name?"},
    config={"configurable": {"session_id": "2a"}},
)
AIMessage(content='Mi dispiace, non so il tuo nome. Come posso aiutarti?', response_metadata={'id': 'msg_01Lyd9FAGQJTxxAZoFi3sQpQ', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 30, 'output_tokens': 23}}, id='run-19a82197-3b1c-4b5f-a68d-f91f4a2ba523-0', usage_metadata={'input_tokens': 30, 'output_tokens': 23, 'total_tokens': 53})
info

When we pass a different session_id, we start a new chat history, so the model does not know what the user's name is.

Messages input, dict output​

This format is useful when you are using a model to generate one key in a dictionary.

from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableParallel

chain = RunnableParallel({"output_message": model})


runnable_with_history = RunnableWithMessageHistory(
    chain,
    get_session_history,
    output_messages_key="output_message",
)
info

Note that we've specified output_messages_key (the key to be treated as the output to save).

runnable_with_history.invoke(
    [HumanMessage(content="hi - im bob!")],
    config={"configurable": {"session_id": "3"}},
)
{'output_message': AIMessage(content="It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I help you today?", response_metadata={'id': 'msg_01WWJSyUyGGKuBqTs3h18ZMM', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-0f50cb43-a734-447c-b535-07c615a0984c-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44})}
runnable_with_history.invoke(
    [HumanMessage(content="whats my name?")],
    config={"configurable": {"session_id": "3"}},
)
{'output_message': AIMessage(content='I\'m afraid I don\'t actually know your name - you introduced yourself as Bob, but I don\'t have any other information about your identity. As an AI assistant, I don\'t have a way to independently verify people\'s names or identities. I\'m happy to continue our conversation, but I\'ll just refer to you as "Bob" since that\'s the name you provided.', response_metadata={'id': 'msg_01TEGrhfLXTwo36rC7svdTy4', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 80}}, id='run-178e8f3f-da21-430d-9edc-ef07797a5e2d-0', usage_metadata={'input_tokens': 52, 'output_tokens': 80, 'total_tokens': 132})}
info

Note that in this case the context is preserved via the chat history for the provided session_id, so the model knows the users name.

We can now try this with a new session id and see that it does not remember.

runnable_with_history.invoke(
    [HumanMessage(content="whats my name?")],
    config={"configurable": {"session_id": "3a"}},
)
{'output_message': AIMessage(content="I'm afraid I don't actually know your name. As an AI assistant, I don't have personal information about you unless you provide it to me directly.", response_metadata={'id': 'msg_0118ZBudDXAC9P6smf91NhCX', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 35}}, id='run-deb14a3a-0336-42b4-8ace-ad1e52ca5910-0', usage_metadata={'input_tokens': 12, 'output_tokens': 35, 'total_tokens': 47})}
info

When we pass a different session_id, we start a new chat history, so the model does not know what the user's name is.

Dict with single key for all messages input, messages output​

This is a specific case of "Dictionary input, message(s) output". In this situation, because there is only a single key we don't need to specify as much - we only need to specify the input_messages_key.

from operator import itemgetter

runnable_with_history = RunnableWithMessageHistory(
    itemgetter("input_messages") | model,
    get_session_history,
    input_messages_key="input_messages",
)
info

Note that we've specified input_messages_key (the key to be treated as the latest input message).

runnable_with_history.invoke(
    {"input_messages": [HumanMessage(content="hi - im bob!")]},
    config={"configurable": {"session_id": "4"}},
)
AIMessage(content="It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I help you today?", response_metadata={'id': 'msg_01UdD5wz1J5xwoz5D94onaQC', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-91bee6eb-0814-4557-ad71-fef9b0270358-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44})
runnable_with_history.invoke(
    {"input_messages": [HumanMessage(content="whats my name?")]},
    config={"configurable": {"session_id": "4"}},
)
AIMessage(content='I\'m afraid I don\'t actually know your name - you introduced yourself as Bob, but I don\'t have any other information about your identity. As an AI assistant, I don\'t have a way to independently verify people\'s names or identities. I\'m happy to continue our conversation, but I\'ll just refer to you as "Bob" since that\'s the name you provided.', response_metadata={'id': 'msg_012WUygxBKXcVJPeTW14LNrc', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 80}}, id='run-fcbaaa1a-8c33-4eec-b0b0-5b800a47bddd-0', usage_metadata={'input_tokens': 52, 'output_tokens': 80, 'total_tokens': 132})
info

Note that in this case the context is preserved via the chat history for the provided session_id, so the model knows the users name.

We can now try this with a new session id and see that it does not remember.

runnable_with_history.invoke(
    {"input_messages": [HumanMessage(content="whats my name?")]},
    config={"configurable": {"session_id": "4a"}},
)
AIMessage(content="I'm afraid I don't actually know your name. As an AI assistant, I don't have personal information about you unless you provide it to me directly.", response_metadata={'id': 'msg_017xW3Ki5y4UBYzCU9Mf1pgM', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 35}}, id='run-d2f372f7-3679-4a5c-9331-a55b820ec03e-0', usage_metadata={'input_tokens': 12, 'output_tokens': 35, 'total_tokens': 47})
info

When we pass a different session_id, we start a new chat history, so the model does not know what the user's name is.

Customization​

The configuration parameters by which we track message histories can be customized by passing in a list of ConfigurableFieldSpec objects to the history_factory_config parameter. Below, we use two parameters: a user_id and conversation_id.

from langchain_core.runnables import ConfigurableFieldSpec


def get_session_history(user_id: str, conversation_id: str):
    return SQLChatMessageHistory(f"{user_id}--{conversation_id}", "sqlite:///memory.db")


with_message_history = RunnableWithMessageHistory(
    runnable,
    get_session_history,
    input_messages_key="input",
    history_messages_key="history",
    history_factory_config=[
        ConfigurableFieldSpec(
            id="user_id",
            annotation=str,
            name="User ID",
            description="Unique identifier for the user.",
            default="",
            is_shared=True,
        ),
        ConfigurableFieldSpec(
            id="conversation_id",
            annotation=str,
            name="Conversation ID",
            description="Unique identifier for the conversation.",
            default="",
            is_shared=True,
        ),
    ],
)

with_message_history.invoke(
    {"language": "italian", "input": "hi im bob!"},
    config={"configurable": {"user_id": "123", "conversation_id": "1"}},
)
API Reference:ConfigurableFieldSpec
AIMessage(content='Ciao Bob! È un piacere conoscerti. Come stai oggi?', response_metadata={'id': 'msg_016RJebCoiAgWaNcbv9wrMNW', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 23}}, id='run-40425414-8f72-47d4-bf1d-a84175d8b3f8-0', usage_metadata={'input_tokens': 29, 'output_tokens': 23, 'total_tokens': 52})
# remembers
with_message_history.invoke(
    {"language": "italian", "input": "whats my name?"},
    config={"configurable": {"user_id": "123", "conversation_id": "1"}},
)
AIMessage(content='Bob, il tuo nome è Bob.', response_metadata={'id': 'msg_01Kktiy3auFDKESY54KtTWPX', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 60, 'output_tokens': 12}}, id='run-c7768420-3f30-43f5-8834-74b1979630dd-0', usage_metadata={'input_tokens': 60, 'output_tokens': 12, 'total_tokens': 72})
# New user_id --> does not remember
with_message_history.invoke(
    {"language": "italian", "input": "whats my name?"},
    config={"configurable": {"user_id": "456", "conversation_id": "1"}},
)
AIMessage(content='Mi dispiace, non so il tuo nome. Come posso aiutarti?', response_metadata={'id': 'msg_0178FpbpPNioB7kqvyHk7rjD', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 30, 'output_tokens': 23}}, id='run-df1f1768-aab6-4aec-8bba-e33fc9e90b8d-0', usage_metadata={'input_tokens': 30, 'output_tokens': 23, 'total_tokens': 53})

Note that in this case the context was preserved for the same user_id, but once we changed it, the new chat history was started, even though the conversation_id was the same.

Was this page helpful?


You can also leave detailed feedback on GitHub.