会话摘要记忆 summary

现在让我们来看一下使用稍微复杂的记忆类型 - ConversationSummaryMemory。这种记忆类型会随着时间的推移对对话进行摘要。这对于从对话中压缩信息非常有用。 会话摘要记忆会即时总结对话并将当前摘要存储在记忆中。然后可以将这个记忆用于将到目前为止的对话摘要注入到提示/链中。这种记忆对于较长的对话非常有用，因为将过去的消息历史原样保留在提示中会占用太多的标记。

让我们首先探索这种记忆类型的基本功能。

from langchain.memory import ConversationSummaryMemory, ChatMessageHistory
from langchain.llms import OpenAI

memory = ConversationSummaryMemory(llm=OpenAI(temperature=0))
memory.save_context({"input": "hi"}, {"output": "whats up"})

memory.load_memory_variables({})

    {'history': '\nThe human greets the AI, to which the AI responds.'}

我们还可以将历史记录作为消息列表获取（如果您将其与聊天模型一起使用，这将非常有用）。

memory = ConversationSummaryMemory(llm=OpenAI(temperature=0), return_messages=True)
memory.save_context({"input": "hi"}, {"output": "whats up"})

memory.load_memory_variables({})

    {'history': [SystemMessage(content='\nThe human greets the AI, to which the AI responds.', additional_kwargs={})]}

我们还可以直接利用 predict_new_summary 方法。

messages = memory.chat_memory.messages
previous_summary = ""
memory.predict_new_summary(messages, previous_summary)

    '\nThe human greets the AI, to which the AI responds.'

使用消息初始化

如果您有类外的消息，可以轻松使用 ChatMessageHistory 初始化类。在加载过程中，将计算摘要。

history = ChatMessageHistory()
history.add_user_message("hi")
history.add_ai_message("hi there!")

memory = ConversationSummaryMemory.from_messages(llm=OpenAI(temperature=0), chat_memory=history, return_messages=True)

memory.buffer

    '\nThe human greets the AI, to which the AI responds with a friendly greeting.'

在链中使用

让我们通过一个在链中使用的示例来演示，再次设置 verbose=True 以便我们可以看到提示信息。

from langchain.llms import OpenAI
from langchain.chains import ConversationChain
llm = 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 connecting to the internet. I'm helping them troubleshoot the issue and figure out what the problem is. So far, we've tried resetting the router and checking the network settings, but the issue still persists. We're currently looking into other possible solutions."

conversation_with_summary.predict(input="Very cool -- what is the scope of the project?")

    
    
    > 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 where their computer was not connecting to the internet. The AI was troubleshooting the issue and had already tried resetting the router and checking the network settings, but the issue still persisted and they were looking into other possible solutions.
    Human: Very cool -- what is the scope of the project?
    AI:
    
    > Finished chain.





    " The scope of the project is to troubleshoot the customer's computer issue and find a solution that will allow them to connect to the internet. We are currently exploring different possibilities and have already tried resetting the router and checking the network settings, but the issue still persists."