Skip to main content

ConversationTokenBufferMemory

ConversationTokenBufferMemory 在内存中保留最近交互的缓冲区，并使用令牌长度而不是交互次数来确定何时刷新交互。

首先让我们看一下如何使用这些工具

from langchain.memory import ConversationTokenBufferMemory  
from langchain.llms import OpenAI  

llm = OpenAI()

API 参考:​

memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=10)  
memory.save_context({"input": "hi"}, {"output": "whats up"})  
memory.save_context({"input": "not much you"}, {"output": "not much"})  
memory.load_memory_variables({})  

{'history': 'Human: not much you\nAI: not much'}

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

memory = ConversationTokenBufferMemory(  
    llm=llm, max_token_limit=10, return_messages=True  
)  
memory.save_context({"input": "hi"}, {"output": "whats up"})  
memory.save_context({"input": "not much you"}, {"output": "not much"})

在链中使用​

让我们通过一个示例来说明，再次设置 verbose=True 以便我们可以看到提示。

from langchain.chains import ConversationChain  

conversation_with_summary = ConversationChain(  
    llm=llm,  
    # We set a very low max_token_limit for the purposes of testing.  
    memory=ConversationTokenBufferMemory(llm=OpenAI(), max_token_limit=60),  
    verbose=True,  
)  
conversation_with_summary.predict(input="Hi, what's up?")

API 参考:​

> 进入新的 ConversationChain 链...
格式化后的提示：
以下是人类和 AI 之间友好对话的示例。AI 健谈并提供了来自其上下文的许多具体细节。如果 AI 不知道问题的答案，它会真实地说出自己不知道。
当前对话：
Human: Hi, what's up?
AI:

> 链结束。
" Hi there! I'm doing great, just enjoying the day. How about you?"
conversation_with_summary.predict(input="Just working on writing some documentation!")  
> 进入新的 ConversationChain 链...
格式化后的提示：
以下是人类和 AI 之间友好对话的示例。AI 健谈并提供了来自其上下文的许多具体细节。如果 AI 不知道问题的答案，它会真实地说出自己不知道。
当前对话：
Human: Hi, what's up?
AI:  Hi there! I'm doing great, just enjoying the day. How about you?
Human: Just working on writing some documentation!
AI:

> 链结束。
' Sounds like a productive day! What kind of documentation are you writing?'
conversation_with_summary.predict(input="For LangChain! Have you heard of it?")  
> 进入新的 ConversationChain 链...
格式化后的提示：
以下是人类和 AI 之间友好对话的示例。AI 健谈并提供了来自其上下文的许多具体细节。如果 AI 不知道问题的答案，它会真实地说出自己不知道。
当前对话：
Human: Hi, what's up?
AI:  Hi there! I'm doing great, just enjoying the day. How about you?
Human: Just working on writing some documentation!
AI:  Sounds like a productive day! What kind of documentation are you writing?
Human: For LangChain! Have you heard of it?
AI:

> 链结束。
" Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're writing about?"
# We can see here that the buffer is updated  
conversation_with_summary.predict(  
    input="Haha nope, although a lot of people confuse it for that"  
)  
> 进入新的 ConversationChain 链...
格式化后的提示：
以下是人类和 AI 之间友好对话的示例。AI 健谈并提供了来自其上下文的许多具体细节。如果 AI 不知道问题的答案，它会真实地说出自己不知道。
当前对话：
Human: For LangChain! Have you heard of it?
AI:  Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're writing about?
Human: Haha nope, although a lot of people confuse it for that
AI:

> 链结束。
" Oh, I see. Is there another language learning platform you're referring to?"