Conversation Buffer Window

ConversationBufferWindowMemory keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large.

Let's first explore the basic functionality of this type of memory.

from langchain.memory import ConversationBufferWindowMemory

memory = ConversationBufferWindowMemory( k=1)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})

memory.load_memory_variables({})

    {'history': 'Human: not much you\nAI: not much'}

We can also get the history as a list of messages (this is useful if you are using this with a chat model).

memory = ConversationBufferWindowMemory( k=1, return_messages=True)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})

memory.load_memory_variables({})

    {'history': [HumanMessage(content='not much you', additional_kwargs={}),
      AIMessage(content='not much', additional_kwargs={})]}

Using in a chain

Let's walk through an example, again setting verbose=True so we can see the prompt.

from langchain.llms import OpenAI
from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
    llm=OpenAI(temperature=0),
    # We set a low k=2, to only keep the last 2 interactions in memory
    memory=ConversationBufferWindowMemory(k=2),
    verbose=True
)
conversation_with_summary.predict(input="Hi, what's up?")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:

    Human: Hi, what's up?
    AI:

    > Finished chain.

    " Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?"

conversation_with_summary.predict(input="What's their issues?")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:
    Human: Hi, what's up?
    AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?
    Human: What's their issues?
    AI:

    > Finished chain.

    " The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected."

conversation_with_summary.predict(input="Is it going well?")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:
    Human: Hi, what's up?
    AI:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?
    Human: What's their issues?
    AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.
    Human: Is it going well?
    AI:

    > Finished chain.





    " Yes, it's going well so far. We've already identified the problem and are now working on a solution."

# Notice here that the first interaction does not appear.
conversation_with_summary.predict(input="What's the solution?")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:
    Human: What's their issues?
    AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.
    Human: Is it going well?
    AI:  Yes, it's going well so far. We've already identified the problem and are now working on a solution.
    Human: What's the solution?
    AI:

    > Finished chain.





    " The solution is to reset the router and reconfigure the settings. We're currently in the process of doing that."

Conversation Buffer Window

Using in a chain​

Using in a chain