using a previously generated summary, and avoid regenerating the summary by just initializing directly.memory = ConversationSummaryMemory(    llm=OpenAI(temperature=0),    buffer="The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.",    chat_memory=history,    return_messages=True)Using in a chain‚ÄãLet's walk through an example of using this in a chain, again setting verbose=True so we can see the prompt.from langchain.llms import OpenAIfrom langchain.chains import ConversationChainllm = OpenAI(temperature=0)conversation_with_summary = ConversationChain(    llm=llm,    memory=ConversationSummaryMemory(llm=OpenAI()),    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="Tell me more about it!")    > 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:    The human greeted the AI and asked how it was doing. The AI replied that it was doing great and was currently helping a customer with a technical issue.    Human: Tell me more about it!    AI:    > Finished chain.    " Sure! The customer is having trouble with their computer not
Now let's take a look at using a slightly more complex type of memory - ConversationSummaryMemory. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.
Now let's take a look at using a slightly more complex type of memory - ConversationSummaryMemory. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time. ->: using a previously generated summary, and avoid regenerating the summary by just initializing directly.memory = ConversationSummaryMemory(    llm=OpenAI(temperature=0),    buffer="The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential.",    chat_memory=history,    return_messages=True)Using in a chain‚ÄãLet's walk through an example of using this in a chain, again setting verbose=True so we can see the prompt.from langchain.llms import OpenAIfrom langchain.chains import ConversationChainllm = OpenAI(temperature=0)conversation_with_summary = ConversationChain(    llm=llm,    memory=ConversationSummaryMemory(llm=OpenAI()),    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="Tell me more about it!")    > 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:    The human greeted the AI and asked how it was doing. The AI replied that it was doing great and was currently helping a customer with a technical issue.    Human: Tell me more about it!    AI:    > Finished chain.    " Sure! The customer is having trouble with their computer not