Langchain 的 Conversation buffer window memory

Langchain 的 Conversation buffer window memory

ConversationBufferWindowMemory 保存一段时间内对话交互的列表。它仅使用最后 K 个交互。这对于保持最近交互的滑动窗口非常有用,因此缓冲区不会变得太大。

我们首先来探讨一下这种存储器的基本功能。

示例代码,

复制代码
from langchain.memory import ConversationBufferWindowMemory

memory = ConversationBufferWindowMemory( k=1)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})

memory.load_memory_variables({})

输出结果,

复制代码
    {'history': 'Human: not much you\nAI: not much'}

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

示例代码,

复制代码
memory = ConversationBufferWindowMemory( k=1, return_messages=True)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})

memory.load_memory_variables({})

输出结果,

复制代码
    {'history': [HumanMessage(content='not much you', additional_kwargs={}),
      AIMessage(content='not much', additional_kwargs={})]}

Using in a chain

让我们看一下示例,再次设置 verbose=True 以便我们可以看到提示。

复制代码
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
    llm=OpenAI(temperature=0), 
    # We set a low k=2, to only keep the last 2 interactions in memory
    memory=ConversationBufferWindowMemory(k=2), 
    verbose=True
)
conversation_with_summary.predict(input="Hi, what's up?")

输出结果,

复制代码
    > 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?"

示例代码,

复制代码
conversation_with_summary.predict(input="What's their issues?")

输出结果,

复制代码
    > 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:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?
    Human: What's their issues?
    AI:
    
    > Finished chain.





    " The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected."

示例代码,

复制代码
conversation_with_summary.predict(input="Is it going well?")

输出结果,

复制代码
    > 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:  Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?
    Human: What's their issues?
    AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.
    Human: Is it going well?
    AI:
    
    > Finished chain.





    " Yes, it's going well so far. We've already identified the problem and are now working on a solution."

示例代码,

复制代码
# Notice here that the first interaction does not appear.
conversation_with_summary.predict(input="What's the solution?")

输出结果,

复制代码
    > 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: What's their issues?
    AI:  The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.
    Human: Is it going well?
    AI:  Yes, it's going well so far. We've already identified the problem and are now working on a solution.
    Human: What's the solution?
    AI:
    
    > Finished chain.





    " The solution is to reset the router and reconfigure the settings. We're currently in the process of doing that."

完结!

相关推荐
shut up12 小时前
LangChain - 如何使用阿里云百炼平台的Qwen-plus模型构建一个桌面文件查询AI助手 - 超详细
人工智能·python·langchain·智能体
liliangcsdn13 小时前
如何基于ElasticsearchRetriever构建RAG系统
大数据·elasticsearch·langchain
东方佑13 小时前
基于FastAPI与LangChain的Excel智能数据分析API开发实践
langchain·excel·fastapi
大模型教程2 天前
搞懂 LangChain RAG:检索、召回原理及 docid 的关键意义
程序员·langchain·llm
AI大模型2 天前
别再死磕 Chain 了!想做复杂的 Agent,你必须得上 LangGraph
程序员·langchain·llm
玲小珑2 天前
LangChain.js 完全开发手册(十四)生产环境部署与 DevOps 实践
前端·langchain·ai编程
weixin_438077493 天前
langchain官网翻译:Build a Question/Answering system over SQL data
数据库·sql·langchain·agent·langgraph
老顾聊技术3 天前
【AI课程上线了哦,打造类FastGPT产品】
langchain·rag
行者阿毅3 天前
langchain4j+SpringBoot+DashScope(灵积)整合
spring boot·langchain·ai编程
深度学习机器3 天前
AI Agent上下文工程设计指南|附实用工具推荐
langchain·llm·agent