through an example, again setting verbose=True so we can see the prompt.from langchain.llms import OpenAIfrom langchain.chains import ConversationChainconversation_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.
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.
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. ->: through an example, again setting verbose=True so we can see the prompt.from langchain.llms import OpenAIfrom langchain.chains import ConversationChainconversation_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.