会话摘要

现在让我们来看一下使用稍微复杂的记忆类型 - ConversationSummaryMemory。这种类型的记忆会随着时间的推移创建一份对话摘要。这对于从对话中压缩信息非常有用。会话摘要记忆将对话进行摘要并将当前摘要存储在记忆中。然后可以将此记忆用于将迄今为止的对话摘要注入到提示/链中。此记忆对于较长的对话非常有用，如果在提示中完全保留过去的消息历史将占用太多的标记。

让我们首先探索一下这种类型记忆的基本功能。

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.'}

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

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={})]}

We can also utilize the predict_new_summary method directly.

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

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

Initializing with messages/existing summary

If you have messages outside this class, you can easily initialize the class with ChatMessageHistory. During loading, a summary will be calculated.

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.'

Optionally you can speed up initialization 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

Let'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 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."

会话摘要

Initializing with messages/existing summary​

Using in a chain​

Initializing with messages/existing summary

Using in a chain