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."PreviousConversation BufferNextEntityUsing in a chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
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. ->: 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."PreviousConversation BufferNextEntityUsing in a chainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.