当前位置: 首页 > news >正文

自然语言处理从入门到应用——LangChain:记忆(Memory)-[记忆的类型Ⅲ]

分类目录:《自然语言处理从入门到应用》总目录


对话令牌缓冲存储器ConversationTokenBufferMemory

ConversationTokenBufferMemory在内存中保留了最近的一些对话交互,并使用标记长度来确定何时刷新交互,而不是交互数量。

from langchain.memory import ConversationTokenBufferMemory
from langchain.llms import OpenAI
llm = OpenAI()
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({})

输出:

{‘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,如果我们正在使用聊天模型,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"})
在链式模型中的应用

让我们通过一个例子来演示如何在链式模型中使用它,同样设置verbose=True,以便我们可以看到提示信息。

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?")

日志输出:

> 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.

输出:

" Hi there! I'm doing great, just enjoying the day. How about you?"

输入:

conversation_with_summary.predict(input="Just working on writing some documentation!")

日志输出:

> 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, just enjoying the day. How about you?
Human: Just working on writing some documentation!
AI:> Finished chain.

输出:

    ' Sounds like a productive day! What kind of documentation are you writing?'

输入:

conversation_with_summary.predict(input="For LangChain! Have you heard of it?")

日志输出:

> 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, 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:> Finished chain.

输出:

    " 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?"

输入:

# 我们可以看到这里缓冲区被更新了
conversation_with_summary.predict(input="Haha nope, although a lot of people confuse it for that")

日志输出:

> 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: 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:> Finished chain.

输出:

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

基于向量存储的记忆VectorStoreRetrieverMemory

VectorStoreRetrieverMemory将内存存储在VectorDB中,并在每次调用时查询最重要的前 K K K个文档。与大多数其他Memory类不同,它不明确跟踪交互的顺序。在这种情况下,“文档”是先前的对话片段。这对于提及AI在对话中早些时候得知的相关信息非常有用。

from datetime import datetime
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.memory import VectorStoreRetrieverMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate
初始化VectorStore

根据我们选择的存储方式,此步骤可能会有所不同,我们可以查阅相关的VectorStore文档以获取更多详细信息。

import faissfrom langchain.docstore import InMemoryDocstore
from langchain.vectorstores import FAISSembedding_size = 1536 # Dimensions of the OpenAIEmbeddings
index = faiss.IndexFlatL2(embedding_size)
embedding_fn = OpenAIEmbeddings().embed_query
vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {})
创建VectorStoreRetrieverMemory

记忆对象是从VectorStoreRetriever实例化的。

# In actual usage, you would set `k` to be a higher value, but we use k=1 to show that the vector lookup still returns the semantically relevant information
retriever = vectorstore.as_retriever(search_kwargs=dict(k=1))
memory = VectorStoreRetrieverMemory(retriever=retriever)# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"})
memory.save_context({"input": "My favorite sport is soccer"}, {"output": "..."})
memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"}) # 
# Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant
# to a 1099 than the other documents, despite them both containing numbers.
print(memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"])

输出:

input: My favorite sport is soccer
output: ...
在对话链中使用

让我们通过一个示例来演示,在此示例中我们继续设置verbose=True以便查看提示。

llm = OpenAI(temperature=0) # Can be any valid LLM
_DEFAULT_TEMPLATE = """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.Relevant pieces of previous conversation:
{history}(You do not need to use these pieces of information if not relevant)Current conversation:
Human: {input}
AI:"""
PROMPT = PromptTemplate(input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
)
conversation_with_summary = ConversationChain(llm=llm, prompt=PROMPT,# We set a very low max_token_limit for the purposes of testing.memory=memory,verbose=True
)
conversation_with_summary.predict(input="Hi, my name is Perry, what's up?")

日志输出:

> 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.Relevant pieces of previous conversation:
input: My favorite food is pizza
output: thats good to know(You do not need to use these pieces of information if not relevant)Current conversation:
Human: Hi, my name is Perry, what's up?
AI:> Finished chain.

输出:

" Hi Perry, I'm doing well. How about you?"

输入:

# Here, the basketball related content is surfaced
conversation_with_summary.predict(input="what's my favorite sport?")

日志输出:

> 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.Relevant pieces of previous conversation:
input: My favorite sport is soccer
output: ...(You do not need to use these pieces of information if not relevant)Current conversation:
Human: what's my favorite sport?
AI:> Finished chain.

输出:

  ' You told me earlier that your favorite sport is soccer.'

输入:

# Even though the language model is stateless, since relavent memory is fetched, it can "reason" about the time.
# Timestamping memories and data is useful in general to let the agent determine temporal relevance
conversation_with_summary.predict(input="Whats my favorite food")

日志输出:

> 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.Relevant pieces of previous conversation:
input: My favorite food is pizza
output: thats good to know(You do not need to use these pieces of information if not relevant)Current conversation:
Human: Whats my favorite food
AI:> Finished chain.

输出:

  ' You said your favorite food is pizza.'

输入:

# The memories from the conversation are automatically stored,
# since this query best matches the introduction chat above,
# the agent is able to 'remember' the user's name.
conversation_with_summary.predict(input="What's my name?")

日志输出:

> 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.Relevant pieces of previous conversation:
input: Hi, my name is Perry, what's up?
response:  Hi Perry, I'm doing well. How about you?(You do not need to use these pieces of information if not relevant)Current conversation:
Human: What's my name?
AI:> Finished chain.

输出:

' Your name is Perry.'

参考文献:
[1] LangChain官方网站:https://www.langchain.com/
[2] LangChain 🦜️🔗 中文网,跟着LangChain一起学LLM/GPT开发:https://www.langchain.com.cn/
[3] LangChain中文网 - LangChain 是一个用于开发由语言模型驱动的应用程序的框架:http://www.cnlangchain.com/

相关文章:

自然语言处理从入门到应用——LangChain:记忆(Memory)-[记忆的类型Ⅲ]

分类目录:《自然语言处理从入门到应用》总目录 对话令牌缓冲存储器ConversationTokenBufferMemory ConversationTokenBufferMemory在内存中保留了最近的一些对话交互,并使用标记长度来确定何时刷新交互,而不是交互数量。 from langchain.me…...

【ARM 嵌入式 编译系列 10.3 -- GNU elfutils 工具小结】

文章目录 什么是 GNU elfutils?GNU elfutils 常用工具有哪些?objcopy 常用参数有哪些?GNU binutils和GNU elfutils区别是什么? 上篇文章:ARM 嵌入式 编译系列 10.2 – 符号表与可执行程序分离详细讲解 什么是 GNU elfu…...

黑马项目一阶段面试 项目介绍篇

我完成了一个外卖项目,名叫苍穹外卖,是跟着黑马程序员的课程来自己动手写的。 项目基本实现了外卖客户端、商家端的后端完整业务。 商家端分为员工管理、文件上传、菜品管理、分类管理、套餐管理、店铺营业状态、订单下单派送等的管理、数据统计等&…...

重构内置类Function原型上的call方法

重构内置类Function原型上的call方法 // > 重构内置类Function原型上的call方法 ~(function () {/*** call: 改变函数中的this指向* params* context 可以不传递,传递必须是引用类型的值,因为后面要给它加 fn 属性**/function myCall(context) {/…...

Nginx之lnmp架构

目录 一.什么是LNMP二.LNMP环境搭建1.Nginx的搭建2.安装php3.安装数据库4.测试Nginx与PHP的连接5.测试PHP连接数据库 一.什么是LNMP LNMP是一套技术的组合,Llinux,Nnginx,Mmysql,Pphp 首先Nginx服务是不能处理动态资源请求&…...

C# 使用FFmpeg.Autogen对byte[]进行编解码

C# 使用FFmpeg.Autogen对byte[]进行编解码,参考:https://github.com/vanjoge/CSharpVideoDemo 入口调用类: using System; using System.IO; using System.Drawing; using System.Runtime.InteropServices; using FFmpeg.AutoGen;namespace F…...

websocket是多线程的嘛

经过测试, onOpen事件的threadId和onMessage的threadId是不一样的,但是onMessage的threadId一直是同一个,就是说收消息的部分是单线程的,收到第一个Message后如果给它sleep较长时间,期间收到第二个,效果是它在排队&am…...

CentOS7.9 禁用22端口,使用其他端口替代

文章目录 业务场景操作步骤修改sshd配置文件修改SELinux开放给ssh使用的端口修改防火墙,开放新端口重启sshd生效 相关知识点介绍sshd服务SELinux服务firewall.service服务 业务场景 我们在某市实施交通信控平台项目,我们申请了一台服务器,用…...

2023国赛 高教社杯数学建模ABCDE题思路汇总分析

文章目录 0 赛题思路1 竞赛信息2 竞赛时间3 建模常见问题类型3.1 分类问题3.2 优化问题3.3 预测问题3.4 评价问题 4 建模资料 0 赛题思路 (赛题出来以后第一时间在CSDN分享) https://blog.csdn.net/dc_sinor?typeblog 1 竞赛信息 全国大学生数学建模…...

【网络层+数据链路层】深入理解IP协议和MAC帧协议的基本原理

文章目录 前言一、IP协议二、MAC帧协议 1.以太网2.以太网帧(MAC帧)格式报头3.基于协议讲解局域网转发的原理总结 前言 为什么经常将TCP/IP放在一起呢?这是因为IP层的核心工作就是通过IP地址来定位主机的,具有将一个数据报从A主机…...

银行家算法【学习算法】

银行家算法【学习算法】 前言版权推荐银行家算法7.避免死锁7.1 系统安全状态7.2 利用银行家算法避免死锁 Java算法实现代码结果 最后 前言 2023-8-14 18:18:01 以下内容源自《【学习算法】》 仅供学习交流使用 版权 禁止其他平台发布时删除以下此话 本文首次发布于CSDN平台…...

萤石直播以及回放的接入和销毁

以下基于vue项目 1.安装 npm i ezuikit-js 2、导入 main.js中 import EZUIKit from "ezuikit-js"; //导入萤石Vue.use(EZUIKit); 3、创建容器 <div class"video"><div id"video-container"></div><!-- <iframe :src…...

C语言易错知识点总结2

函数 第 1 题&#xff08;单选题&#xff09; 题目名称&#xff1a; 能把函数处理结果的二个数据返回给主调函数&#xff0c;在下面的方法中不正确的是&#xff1a;&#xff08; &#xff09; 题目内容&#xff1a; A .return 这二个数 B .形参用数组 C .形参用二个指针 D .用…...

Go学习-Day1

Go学习-Day1 个人博客&#xff1a;CSDN博客 打卡。 Go语言的核心开发团队&#xff1a; Ken Thompson (C语言&#xff0c;B语言&#xff0c;Unix的发明者&#xff0c;牛人)Rob Pike(UTF-8发明人)Robert Griesemer(协助HotSpot编译器&#xff0c;Js引擎V8) Go语言有静态语言的…...

冠达管理:机构密集调研医药生物股 反腐政策影响受关注

进入8月&#xff0c;跟着反腐事件发酵&#xff0c;医药生物板块呈现震荡。与此一起&#xff0c;组织出资者对该板块上市公司也展开了密集调研。 到昨日&#xff0c;8月以来就有包含南微医学、百济神州、维力医疗、方盛制药等12家医药生物板块的上市公司接受组织调研&#xff0c…...

安装Tomac服务器——安装步骤以及易出现问题的解决方法

文章目录 前言 一、下载Tomcat及解压 1、选择下载版本&#xff08;本文选择tomcat 8版本为例&#xff09; 2、解压安装包 二、配置环境 1、在电脑搜索栏里面搜索环境变量即可 2、点击高级系统设置->环境变量->新建系统变量 1) 新建系统变量&#xff0c;变量名为…...

JVM 性能优化思路

点击下方关注我&#xff0c;然后右上角点击...“设为星标”&#xff0c;就能第一时间收到更新推送啦~~~ 一般在系统出现问题的时候&#xff0c;我们会考虑对 JVM 进行性能优化。优化思路就是根据问题的情况&#xff0c;结合工具进行问题排查&#xff0c;针对排查出来的可能问题…...

Labview解决“重置VI:xxx.vi”报错问题

文章目录 前言一、程序框图二、前面板三、问题描述四、解决办法 前言 在程序关闭前面板的时候小概率型出现了 重置VI&#xff1a;xxx.vi 这个报错&#xff0c;并且发现此时只能通过任务管理器杀掉 LabVIEW 进程才能退出&#xff0c;这里介绍一下解决方法。 一、程序框图 程序…...

2023河南萌新联赛第(五)场:郑州轻工业大学C-数位dp

链接&#xff1a;登录—专业IT笔试面试备考平台_牛客网 给定一个正整数 n&#xff0c;你可以对 n 进行任意次&#xff08;包括零次&#xff09;如下操作&#xff1a; 选择 n 上的某一数位&#xff0c;将其删去&#xff0c;剩下的左右部分合并。例如 123&#xff0c;你可以选择…...

找不到mfc140u.dll怎么办?mfc140u.dll丢失怎样修复?简单三招搞定

最近我遇到了一个问题&#xff0c;发现我的电脑上出现了mfc140u.dll文件丢失的错误提示。这个错误导致一些应用程序无法正常运行&#xff0c;让我感到非常困扰。经过一番研究和尝试&#xff0c;我终于成功修复了这个问题&#xff0c;并从中总结出了一些心得。 mfc140u.dll丢失原…...

利用最小二乘法找圆心和半径

#include <iostream> #include <vector> #include <cmath> #include <Eigen/Dense> // 需安装Eigen库用于矩阵运算 // 定义点结构 struct Point { double x, y; Point(double x_, double y_) : x(x_), y(y_) {} }; // 最小二乘法求圆心和半径 …...

springboot 百货中心供应链管理系统小程序

一、前言 随着我国经济迅速发展&#xff0c;人们对手机的需求越来越大&#xff0c;各种手机软件也都在被广泛应用&#xff0c;但是对于手机进行数据信息管理&#xff0c;对于手机的各种软件也是备受用户的喜爱&#xff0c;百货中心供应链管理系统被用户普遍使用&#xff0c;为方…...

Xshell远程连接Kali(默认 | 私钥)Note版

前言:xshell远程连接&#xff0c;私钥连接和常规默认连接 任务一 开启ssh服务 service ssh status //查看ssh服务状态 service ssh start //开启ssh服务 update-rc.d ssh enable //开启自启动ssh服务 任务二 修改配置文件 vi /etc/ssh/ssh_config //第一…...

抖音增长新引擎:品融电商,一站式全案代运营领跑者

抖音增长新引擎&#xff1a;品融电商&#xff0c;一站式全案代运营领跑者 在抖音这个日活超7亿的流量汪洋中&#xff0c;品牌如何破浪前行&#xff1f;自建团队成本高、效果难控&#xff1b;碎片化运营又难成合力——这正是许多企业面临的增长困局。品融电商以「抖音全案代运营…...

STM32标准库-DMA直接存储器存取

文章目录 一、DMA1.1简介1.2存储器映像1.3DMA框图1.4DMA基本结构1.5DMA请求1.6数据宽度与对齐1.7数据转运DMA1.8ADC扫描模式DMA 二、数据转运DMA2.1接线图2.2代码2.3相关API 一、DMA 1.1简介 DMA&#xff08;Direct Memory Access&#xff09;直接存储器存取 DMA可以提供外设…...

论文浅尝 | 基于判别指令微调生成式大语言模型的知识图谱补全方法(ISWC2024)

笔记整理&#xff1a;刘治强&#xff0c;浙江大学硕士生&#xff0c;研究方向为知识图谱表示学习&#xff0c;大语言模型 论文链接&#xff1a;http://arxiv.org/abs/2407.16127 发表会议&#xff1a;ISWC 2024 1. 动机 传统的知识图谱补全&#xff08;KGC&#xff09;模型通过…...

涂鸦T5AI手搓语音、emoji、otto机器人从入门到实战

“&#x1f916;手搓TuyaAI语音指令 &#x1f60d;秒变表情包大师&#xff0c;让萌系Otto机器人&#x1f525;玩出智能新花样&#xff01;开整&#xff01;” &#x1f916; Otto机器人 → 直接点明主体 手搓TuyaAI语音 → 强调 自主编程/自定义 语音控制&#xff08;TuyaAI…...

让AI看见世界:MCP协议与服务器的工作原理

让AI看见世界&#xff1a;MCP协议与服务器的工作原理 MCP&#xff08;Model Context Protocol&#xff09;是一种创新的通信协议&#xff0c;旨在让大型语言模型能够安全、高效地与外部资源进行交互。在AI技术快速发展的今天&#xff0c;MCP正成为连接AI与现实世界的重要桥梁。…...

Java面试专项一-准备篇

一、企业简历筛选规则 一般企业的简历筛选流程&#xff1a;首先由HR先筛选一部分简历后&#xff0c;在将简历给到对应的项目负责人后再进行下一步的操作。 HR如何筛选简历 例如&#xff1a;Boss直聘&#xff08;招聘方平台&#xff09; 直接按照条件进行筛选 例如&#xff1a…...

Git 3天2K星标:Datawhale 的 Happy-LLM 项目介绍(附教程)

引言 在人工智能飞速发展的今天&#xff0c;大语言模型&#xff08;Large Language Models, LLMs&#xff09;已成为技术领域的焦点。从智能写作到代码生成&#xff0c;LLM 的应用场景不断扩展&#xff0c;深刻改变了我们的工作和生活方式。然而&#xff0c;理解这些模型的内部…...