LangChain学习记录（二）Memory_langchain 实现多轮对话

        刚接触语言大模型时，对话过程中大模型表现出的“记忆力”令人惊叹。这种记忆力原理是什么，在langchain中是如何实现的，就是本篇笔记的内容了。

一、对话Memory的实现

        与大模型进行多轮对话时，并不是前面的对话真的被大模型记住了，其实还是通过prompt来实现的。也就是在进行多轮对话时，在没有令牌限制或其他限制的情况下，会将前面的对话内容放进目前对话的prompt中。看一下langchain中的实现。

import  os
import openai
from  langchain_community.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import  ConversationBufferMemory
 
os.environ["OPENAI_API_KEY"] = ''
openai.api_key = os.environ.get("OPENAI_API_KEY")
 
llm = ChatOpenAI(model_name = 'gpt-3.5-turbo',temperature = 0.0)
memory = ConversationBufferMemory() # 全部存储
 
conversation = ConversationChain(
    llm = llm,
    memory = memory,
    verbose = True # 开启查看每次prompt内容
)
 
while 1:
    content = input('user：')
    print(conversation.predict(input=content))

        langchain当中通过memory模块当中的一系列方法来实现多轮对话的记忆存储。其中的ConversationBufferMemory方法，会保存历史对话的所有内容。而启用的方式则是构建一个对话的ConversationChain，将memory的方法作为参数传进去。还可以指定大模型、以及显示prompt的内容等。

user：Hi，my name is Rain.
 
> 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，my name is Rain.
AI:
 
> Finished chain.
Hello Rain! It's nice to meet you. How can I assist you today?
user：what is 1+1?
> 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，my name is Rain.
AI: Hello Rain! It's nice to meet you. How can I assist you today?
Human: what is 1+1?
AI:
 
> Finished chain.
1 + 1 equals 2. Is there anything else you would like to know?
 
user：what is my name?
 
> 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，my name is Rain.
AI: Hello Rain! It's nice to meet you. How can I assist you today?
Human: what is 1+1?
AI: 1 + 1 equals 2. Is there anything else you would like to know?
Human: what is my name?
AI:
> Finished chain.
Your name is Rain.

        另外，我们还可以利用memory.save_context方法在对话开始前就主动的预置一些对话内容到记忆中去

memory.save_context({'input':'Hi,my name is Rain'},{'output':'Hello Rain! It\'s nice to meet you. How can I assist you today?'})
 
 
>>
user: what is my name?
 
> 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,my name is Rain
AI: Hello Rain! It's nice to meet you. How can I assist you today?
Human: what is my name?
AI:
> Finished chain.
Your name is Rain.

二、几种其他的memory

1.ConversationBufferWindowMemory

        这个方法不同于上文提到的方法，可以通过参数设置需要记忆的对话轮数

from langchain.memory import  ConversationBufferWindowMemory
 
memory = ConversationBufferWindowMemory(k = 1) # K参数设置记忆的对话轮数

如设置k=1，就表示多轮对话中当前对话只保存上一轮对话的内容记忆

2.ConversationTokenBufferMemory

        这个方法可以设置最大令牌上限，其实也是限制记忆的数量，毕竟大模型都是按Token数量收费的，当对话轮数很多时，每次对话记忆耗费的Token数量也会变得很大。指定llm参数是因为每种模型的token计算方式不同。

from langchain.memory import  ConversationTokenBufferMemory
 
memory = ConversationTokenBufferMemory(llm=llm,max_token_limit=30) # 设置令牌上限

3.ConversationSummaryBufferMemory

        这个方法会将之前所有的对话轮数根据你设置的令牌上限进行总结，保证新对话的prompt不会超过这个令牌上限，同时又能最大程度保存一些历史对话信息。

from langchain.memory import  ConversationSummaryBufferMemory
 
memory = ConversationSummaryBufferMemory(llm=llm,max_token_limit=30) 

...

三、小结

        通过对langchain实现对话记忆方法的学习，了解了大模型获得记忆能力的方法，对大模型的了解又进了一小步。。