Tips
Go
(18条消息) Go语言自学系列 | golang包_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang并发编程之channel的遍历_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang并发编程之select switch_COCOgsta的博客-CSDN博客_golang select switch
(18条消息) Go语言自学系列 | golang并发编程之runtime包_COCOgsta的博客-CSDN博客_golang runtime包
(18条消息) Go语言自学系列 | golang接口值类型接收者和指针类型接收者_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang并发编程之Timer_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang方法_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang并发编程之WaitGroup实现同步_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang构造函数_COCOgsta的博客-CSDN博客_golang 构造函数
(18条消息) Go语言自学系列 | golang方法接收者类型_COCOgsta的博客-CSDN博客_golang 方法接收者
(18条消息) Go语言自学系列 | golang接口_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang接口和类型的关系_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang结构体_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang结构体_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang标准库os模块 - File文件读操作_COCOgsta的博客-CSDN博客_golang os.file
(18条消息) Go语言自学系列 | golang继承_COCOgsta的博客-CSDN博客_golang 继承
(18条消息) Go语言自学系列 | golang嵌套结构体_COCOgsta的博客-CSDN博客_golang 结构体嵌套
(18条消息) Go语言自学系列 | golang并发编程之Mutex互斥锁实现同步_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang并发变成之通道channel_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang并发编程之原子操作详解_COCOgsta的博客-CSDN博客_golang 原子操作
(18条消息) Go语言自学系列 | golang并发编程之原子变量的引入_COCOgsta的博客-CSDN博客_go 原子变量
(18条消息) Go语言自学系列 | golang并发编程之协程_COCOgsta的博客-CSDN博客_golang 协程 并发
(18条消息) Go语言自学系列 | golang接口嵌套_COCOgsta的博客-CSDN博客_golang 接口嵌套
(18条消息) Go语言自学系列 | golang包管理工具go module_COCOgsta的博客-CSDN博客_golang 包管理器
(18条消息) Go语言自学系列 | golang标准库os模块 - File文件写操作_COCOgsta的博客-CSDN博客_go os模块
(18条消息) Go语言自学系列 | golang结构体的初始化_COCOgsta的博客-CSDN博客_golang 结构体初始化
(18条消息) Go语言自学系列 | golang通过接口实现OCP设计原则_COCOgsta的博客-CSDN博客
(18条消息) Go语言自学系列 | golang标准库os包进程相关操作_COCOgsta的博客-CSDN博客_golang os包
(18条消息) Go语言自学系列 | golang标准库ioutil包_COCOgsta的博客-CSDN博客_golang ioutil
(18条消息) Go语言自学系列 | golang标准库os模块 - 文件目录相关_COCOgsta的博客-CSDN博客_go语言os库
Golang技术栈,Golang文章、教程、视频分享!
(18条消息) Go语言自学系列 | golang结构体指针_COCOgsta的博客-CSDN博客_golang 结构体指针
Ansible
太厉害了,终于有人能把Ansible讲的明明白白了,建议收藏_互联网老辛
ansible.cfg配置详解
Docker
Docker部署
linux安装docker和Docker Compose
linux 安装 docker
Docker中安装Docker遇到的问题处理
Docker常用命令
docker常用命令小结
docker 彻底卸载
Docker pull 时报错:Get https://registry-1.docker.io/v2/library/mysql: net/http: TLS handshake timeout
Docker 拉镜像无法访问 registry-x.docker.io 问题(Centos7)
docker 容器内没有权限
Linux中关闭selinux的方法是什么?
docker run 生成 docker-compose
Docker覆盖网络部署
docker pull后台拉取镜像
docker hub
Redis
Redis 集群别乱搭,这才是正确的姿势
linux_离线_redis安装
怎么实现Redis的高可用?(主从、哨兵、集群) - 雨点的名字 - 博客园
redis集群离线安装
always-show-logo yes
Redis集群搭建及原理
[ERR] Node 172.168.63.202:7001 is not empty. Either the nodealready knows other nodes (check with CLUSTER NODES) or contains some - 亲爱的不二999 - 博客园
Redis daemonize介绍
redis 下载地址
Redis的redis.conf配置注释详解(三) - 云+社区 - 腾讯云
Redis的redis.conf配置注释详解(一) - 云+社区 - 腾讯云
Redis的redis.conf配置注释详解(二) - 云+社区 - 腾讯云
Redis的redis.conf配置注释详解(四) - 云+社区 - 腾讯云
Linux
在终端连接ssh的断开关闭退出的方法
漏洞扫描 - 灰信网(软件开发博客聚合)
find 命令的参数详解
vim 编辑器搜索功能
非root安装rpm时,mockbuild does not exist
Using a SSH password instead of a key is not possible because Host Key checking
(9条消息) 安全扫描5353端口mDNS服务漏洞问题_NamiJava的博客-CSDN博客_5353端口
Linux中使用rpm命令安装rpm包
ssh-copy-id非22端口的使用方法
How To Resolve SSH Weak Key Exchange Algorithms on CentOS7 or RHEL7 - infotechys.com
Linux cp 命令
yum 下载全量依赖 rpm 包及离线安装(终极解决方案) - 叨叨软件测试 - 博客园
How To Resolve SSH Weak Key Exchange Algorithms on CentOS7 or RHEL7 - infotechys.com
RPM zlib 下载地址
运维架构网站
欢迎来到 Jinja2
/usr/local/bin/ss-server -uv -c /etc/shadowsocks-libev/config.json -f /var/run/s
ruby 安装Openssl 默认安装位置
Linux 常用命令学习 | 菜鸟教程
linux 重命名文件和文件夹
linux命令快速指南
ipvsadm
Linux 下查找日志中的关键字
Linux 切割大 log 日志
CentOS7 关于网络的设置
rsync 命令_Linux rsync 命令用法详解:远程数据同步工具
linux 可视化界面安装
[问题已处理]-执行yum卡住无响应
GCC/G++升级高版本
ELK
Docker部署ELK
ELK+kafka+filebeat+Prometheus+Grafana - SegmentFault 思否
(9条消息) Elasticsearch设置账号密码_huas_xq的博客-CSDN博客_elasticsearch设置密码
Elasticsearch 7.X 性能优化
Elasticsearch-滚动更新
Elasticsearch 的内存优化_大数据系统
Elasticsearch之yml配置文件
ES 索引为Yellow状态
Logstash:Grok filter 入门
logstash grok 多项匹配
Mysql
Mysql相关Tip
基于ShardingJDBC实现数据库读写分离 - 墨天轮
MySQL-MHA高可用方案
京东三面:我要查询千万级数据量的表,怎么操作?
OpenStack
(16条消息) openstack项目中遇到的各种问题总结 其二(云主机迁移、ceph及扩展分区)_weixin_34104341的博客-CSDN博客
OpenStack组件介绍
百度大佬OpenStack流程
openstack各组件介绍
OpenStack生产实际问题总结(一)
OpenStack Train版离线部署
使用Packstack搭建OpenStack
K8S
K8S部署
K8S 集群部署
kubeadm 重新 init 和 join-pudn.com
Kubernetes 实战总结 - 阿里云 ECS 自建 K8S 集群 Kubernetes 实战总结 - 自定义 Prometheus
【K8S实战系列-清理篇1】k8s docker 删除没用的资源
Flannel Pod Bug汇总
Java
Jdk 部署
JDK部署
java线程池ThreadPoolExecutor类使用详解 - bigfan - 博客园
ShardingJDBC实现多数据库节点分库分表 - 墨天轮
Maven Repository: Search/Browse/Explore
其他
Git在阿里,我们如何管理代码分支?
chrome F12调试网页出现Paused in debugger
体验IntelliJ IDEA的远程开发(Remote Development) - 掘金
Idea远程调试
PDF转MD
强哥分享干货
优秀开源项目集合
vercel 配合Github 搭建项目Doc门户
如何用 Github Issues 写技术博客?
Idea 2021.3 Maven 3.8.1 报错 Blocked mirror for repositories 解决
列出maven依赖
[2022-09 持续更新] 谷歌 google 镜像 / Sci-Hub 可用网址 / Github 镜像可用网址总结
阿里云ECS迁移
linux访问github
一文教你使用 Docker 启动并安装 Nacos-腾讯云开发者社区-腾讯云
Nginx
Nginx 部署
Nginx 部署安装
Nginx反向代理cookie丢失的问题_longzhoufeng的博客-CSDN博客_nginx 代理后cookie丢失
Linux 系统 Https 证书生成与Nginx配置 https
数据仓库
实时数仓
松果出行 x StarRocks:实时数仓新范式的实践之路
实时数据仓库的一些分层和分层需要处理的事情,以及数据流向
湖仓一体电商项目
湖仓一体电商项目(一):项目背景和架构介绍
湖仓一体电商项目(二):项目使用技术及版本和基础环境准备
湖仓一体电商项目(三):3万字带你从头开始搭建12个大数据项目基础组件
数仓笔记
数仓学习总结
数仓常用平台和框架
数仓学习笔记
数仓技术选型
尚硅谷教程
尚硅谷学习笔记
尚硅谷所有已知的课件资料
尚硅谷大数据项目之尚品汇(11数据质量管理V4.0)
尚硅谷大数据项目之尚品汇(10元数据管理AtlasV4.0)
尚硅谷大数据项目之尚品汇(9权限管理RangerV4.0)
尚硅谷大数据项目之尚品汇(8安全环境实战V4.0)
尚硅谷大数据项目之尚品汇(7用户认证KerberosV4.1)
尚硅谷大数据项目之尚品汇(6集群监控ZabbixV4.1)
尚硅谷大数据项目之尚品汇(5即席查询PrestoKylinV4.0)
尚硅谷大数据项目之尚品汇(4可视化报表SupersetV4.0)
尚硅谷大数据项目之尚品汇(3数据仓库系统)V4.2.0
尚硅谷大数据项目之尚品汇(2业务数据采集平台)V4.1.0
尚硅谷大数据项目之尚品汇(1用户行为采集平台)V4.1.0
数仓治理
数据中台 元数据规范
数据中台的那些 “经验与陷阱”
2万字详解数据仓库数据指标数据治理体系建设方法论
数据仓库,为什么需要分层建设和管理? | 人人都是产品经理
网易数帆数据治理演进
数仓技术
一文看懂大数据生态圈完整知识体系
阿里云—升舱 - 数据仓库升级白皮书
最全企业级数仓建设迭代版(4W字建议收藏)
基于Hue,Dolphinscheduler,HIVE分析数据仓库层级实现及项目需求案例实践分析
详解数据仓库分层架构
数据仓库技术细节
大数据平台组件介绍
总览 2016-2021 年全球机器学习、人工智能和大数据行业技术地图
Apache DolphinScheduler 3.0.0 正式版发布!
数据仓库面试题——介绍下数据仓库
数据仓库为什么要分层,各层的作用是什么
Databend v0.8 发布,基于 Rust 开发的现代化云数据仓库 - OSCHINA - 中文开源技术交流社区
数据中台
数据中台设计
大数据同步工具之 FlinkCDC/Canal/Debezium 对比
有数数据开发平台文档
Shell
Linux Shell 命令参数
shell 脚本编程
一篇教会你写 90% 的 Shell 脚本
Kibana
Kibana 查询语言(KQL)
Kibana:在 Kibana 中的四种表格制作方式
Kafka
Kafka部署
canal 动态监控 Mysql,将 binlog 日志解析后,把采集到的数据发送到 Kafka
OpenApi
OpenAPI 标准规范,了解一下?
OpenApi学术论文
贵阳市政府数据开放平台设计与实现
OpenAPI简介
开放平台:运营模式与技术架构研究综述
管理
技术部门Leader是不是一定要技术大牛担任?
华为管理体系流程介绍
DevOps
*Ops
XOps 已经成为一个流行的术语 - 它是什么?
Practical Linux DevOps
Jenkins 2.x实践指南 (翟志军)
Jenkins 2权威指南 ((美)布伦特·莱斯特(Brent Laster)
DevOps组件高可用的思路
KeepAlived
VIP + KEEPALIVED + LVS 遇到Connection Peer的问题的解决
MinIO
MinIO部署
Minio 分布式集群搭建部署
Minio 入门系列【16】Minio 分片上传文件 putObject 接口流程源码分析
MinioAPI 浅入及问题
部署 minio 兼容 aws S3 模式
超详细分布式对象存储 MinIO 实战教程
Hadoop
Hadoop 部署
Hadoop集群部署
windows 搭建 hadoop 环境(解决 HADOOP_HOME and hadoop.home.dir are unset
Hadoop 集群搭建和简单应用(参考下文)
Hadoop 启动 NameNode 报错 ERROR: Cannot set priority of namenode process 2639
jps 命令查看 DataNode 进程不见了 (hadoop3.0 亲测可用)
hadoop 报错: Operation category READ is not supported in state standby
Spark
Spark 部署
Spark 集群部署
spark 心跳超时分析 Cannot receive any reply in 120 seconds
Spark学习笔记
apache spark - Failed to find data source: parquet, when building with sbt assembly
Spark Thrift Server 架构和原理介绍
InLong
InLong 部署
Apache InLong部署文档
安装部署 - Docker 部署 - 《Apache InLong v1.2 中文文档》 - 书栈网 · BookStack
基于 Apache Flink SQL 的 InLong Sort ETL 方案解析
关于 Apache Pulsar 在 Apache InLong 接入数据
zookeeper
zookeeper 部署
使用 Docker 搭建 Zookeeper 集群
美团技术团队
StarRocks
StarRocks技术白皮书(在线版)
JuiceFS
AI 场景存储优化:云知声超算平台基于 JuiceFS 的存储实践
JuiceFS 在 Elasticsearch/ClickHouse 温冷数据存储中的实践
JuiceFS format
元数据备份和恢复 | JuiceFS Document Center
JuiceFS 元数据引擎选型指南
Apache Hudi 使用文件聚类功能 (Clustering) 解决小文件过多的问题
普罗米修斯
k8s 之 Prometheus(普罗米修斯)监控,简单梳理下 K8S 监控流程
k8s 部署 - 使用helm3部署监控prometheus(普罗米修斯),从零到有,一文搞定
k8s 部署 - 使用 helm3 部署监控 prometheus(普罗米修斯),从零到有,一文搞定
k8s 部署 - 如何完善 k8s 中 Prometheus(普罗米修斯)监控项目呢?
k8s 部署 - k8s 中 Prometheus(普罗米修斯)的大屏展示 Grafana + 监控报警
zabbix
一文带你掌握 Zabbix 监控系统
Stream Collectors
Nvidia
Nvidia API
CUDA Nvidia驱动安装
NVIDIA驱动失效简单解决方案:NVIDIA-SMI has failed because it couldn‘t communicate with the NVIDIA driver.
ubuntu 20 CUDA12.1安装流程
nvidia开启持久化模式
nvidia-smi 开启持久化
Harbor
Harbor部署文档
Docker 爆出 it doesn't contain any IP SANs
pandoc
其他知识
大模型
COS 597G (Fall 2022): Understanding Large Language Models
如何优雅的使用各类LLM
ChatGLM3在线搜索功能升级
当ChatGLM3能用搜索引擎时
OCR神器,PDF、数学公式都能转
Stable Diffusion 动画animatediff-cli-prompt-travel
基于ERNIE Bot自定义虚拟数字人生成
pika负面提示词
开通GPT4的方式
GPT4网站
低价开通GPT Plus
大模型应用场景分享
AppAgent AutoGPT变体
机器学习
最大似然估计
权衡偏差(Bias)和方差(Variance)以最小化均方误差(Mean Squared Error, MSE)
伯努利分布
方差计算公式
均值的高斯分布估计
没有免费午餐定理
贝叶斯误差
非参数模型
最近邻回归
表示容量
最优容量
权重衰减
正则化项
Sora
Sora官方提示词
看完32篇论文,你大概就知道Sora如何炼成? |【经纬低调出品】
Sora论文
Sora 物理悖谬的几何解释
Sora 技术栈讨论
RAG垂直落地
DB-GPT与TeleChat-7B搭建相关RAG知识库
ChatWithRTX
ChatRTX安装教程
ChatWithRTX 踩坑记录
ChatWithRTX 使用其他量化模型
ChatWithRTX介绍
RAG 相关资料
英伟达—大模型结合 RAG 构建客服场景自动问答
又一大模型技术开源!有道自研RAG引擎QAnything正式开放下载
收藏!RAG入门参考资料开源大总结:RAG综述、介绍、比较、预处理、RAG Embedding等
RAG调研
解决现代RAG实际生产问题
解决现代 RAG 系统中的生产问题-II
Modular RAG and RAG Flow: Part Ⅰ
Modular RAG and RAG Flow: Part II
先进的Retriever技术来增强你的RAGs
高级RAG — 使用假设文档嵌入 (HyDE) 改进检索
提升 RAG:选择最佳嵌入和 Reranker 模型
LangGraph
增强型RAG:re-rank
LightRAG:使用 PyTorch 为 LLM 应用程序提供支持
模型训练
GPU相关资料
[教程] conda安装简明教程(基于miniconda和Windows)
PyTorch CUDA对应版本 | PyTorch
资料
李一舟课程全集
零碎资料
苹果各服共享ID
数据中心网络技术概览
华为大模型训练学习笔记
百度AIGC工程师认证考试答案(可换取工信部证书)
百度智能云生成式AI认证工程师 考试和证书查询指南
深入理解 Megatron-LM(1)基础知识
QAnything
接入QAnything的AI问答知识库,可私有化部署的企业级WIKI知识库
wsl --update失效Error code: Wsl/UpdatePackage/0x80240438的解决办法
Docker Desktop 启动docker engine一直转圈解决方法
win10开启了hyper-v,docker 启动还是报错 docker desktop windows hypervisor is not present
WSL虚拟磁盘过大,ext4迁移 Windows 中创建软链接和硬链接
WSL2切换默认的Linux子系统
Windows的WSL子系统,自动开启sshd服务
新版docker desktop设置wsl(使用windown的子系统)
WSL 开启ssh
Windows安装网易开源QAnything打造智能客服系统
芯片
国内互联网大厂自研芯片梳理
超算平台—算力供应商
Linux 磁盘扩容
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LangGraph
![img](https://yg9538.kmgy.top/202405051406845.png) 来源:作者使用 MidJourney 创建的图像 ## **介绍** LangChain的LangGraph Agents为制定智能工作流程提供了一个强大的平台,集成Retriever-Augmented Generator(RAG)模型将电子邮件通信的效率提升到一个新的水平。本文探讨了 RAG 如何专门用于回复客户电子邮件的 LangChain 代理。 **座席目标在行动:** 1. **检索电子邮件:**LangChain代理通过获取传入的客户电子邮件来启动。 2. **电子邮件分类:**利用预先训练的模型或基于规则的系统,代理将电子邮件分为“销售查询”、“自定义查询”、“偏离主题”或“客户投诉”等类别。这种分类是制定相关对策的基础。 3. **RAG搜索的问题表述:**根据电子邮件类别和初始内容,代理为 RAG 模型生成特定问题。以下是每个类别的展开方式: **i) 销售咨询:** - 问题可能针对产品详细信息、定价信息或在知识库中发现的类似过去查询中提出的常见销售异议。 - 示例问题:“客户最常询问的产品 X 的功能是什么?”或“产品 Y 最有效的推销是什么?” **ii) 海关查询:** - 在这里,问题更深入地了解客户的具体需求。 - 示例包括:“对于与客户描述的问题类似的挑战,提供了哪些常见的解决方案?”或“是否有任何案例研究展示了此类场景的成功实施? **iii) 题外话:** - 座席可能会提出问题,礼貌地将客户重定向到适当的渠道或资源。 - 示例:“客户询问的最常见的支持主题是什么? **iv) 客户投诉:** - 问题旨在确定潜在的解决方案或故障排除步骤。 - 例如:“对于此类投诉,最常见的解决方案是什么?”或“是否有任何知识库文章可以解决与客户类似的问题? **4. 回复草稿生成:** - LangChain 代理中的 LLM 利用从 RAG 中检索到的信息(产品详细信息、解决方案、故障排除步骤等)来创建初始草稿响应。 - 此草案考虑了电子邮件类别、客户的特定查询以及检索到的知识库信息。 **5. RAG验证和回复细化:** - 将答复草稿与原始 RAG 答复进行比较,以确保准确纳入检索到的信息。 - 如果出现不一致,代理会优化草稿以与检索到的知识保持一致。 **6. 最终回复输出:** - LangChain代理向客户提供最终的、精炼的电子邮件回复。这种回答是针对他们的具体询问量身定制的,利用知识库中的相关知识,并保持专业和信息丰富的语气。 ![img](https://yg9538.kmgy.top/202405051406846.png) 资料来源:[Sam-Witteveen-course](https://www.youtube.com/watch?v=WyIWaopiUEo) ## **什么是RAG?** RAG 是一种基于神经检索的抽象摘要方法。它由两个组件组成:猎犬和发电机。检索器从知识库中检索相关段落以响应查询。然后,生成器使用检索到的段落来生成一个既有信息又有抽象性的摘要,这意味着它不仅仅是简单地从源文本中复制句子。 ## **将RAG与LangChain的LangGraph代理集成的好处** 将RAG与LangChain的LangGraph Agents集成,为电子邮件起草提供了几个优势: - **改进了上下文理解:**RAG 可以利用外部知识库来了解初始电子邮件的上下文。这允许LangChain代理生成更相关和信息量更大的草稿响应。 - **增强的问答:**RAG检索相关信息的能力可用于识别电子邮件草稿中需要解决的关键问题。这可以简化电子邮件写作过程,并确保涵盖所有要点。 - **更自然的语言生成:**通过整合从知识库中检索到的信息,RAG 可以帮助 LLM 生成更自然、更连贯的电子邮件草稿。 ![img](https://yg9538.kmgy.top/202405051406847.png) 资料来源: [LangGraph-Llama3](https://twitter.com/LangChainAI/status/1781349425337729176/photo/1) ## **代码实现** 以下是将 RAG 添加到 LangChain 的 LangGraph Agents 以进行电子邮件起草的代码实现的高级概述: 1. **定义 LangGraph 代理:**第一步涉及定义负责电子邮件起草的 LangGraph 代理。此代理将收到初始电子邮件作为输入。 2. **RAG 集成:**在 LangGraph 代理中,集成 RAG 模型。RAG 模型将初始电子邮件作为查询,并从知识库中检索相关段落。 3. **问答:**使用检索到的段落来确定电子邮件草稿中需要解决的关键问题。这可以通过信息提取或问题生成等技术来实现。 4. **电子邮件生成草稿:**利用 LLM 生成草稿电子邮件内容。LLM应该考虑最初的电子邮件,从RAG中检索到的段落,以及确定的关键问题。 5. **精炼和输出:**生成的电子邮件草稿可以进行进一步的优化步骤,例如语法检查和情感分析。最后,LangGraph 代理输出最终的电子邮件草稿。 **步骤一:安装库并下载数据** ``` # Download Data !wget -q -O westworld_resort_facts.csv https://www.dropbox.com/scl/fi/qhzosgi21sqsymv5j4o1b/westworld_resort_facts.csv?rlkey=d81cez1bxck2y3lw53phar8nk&st=70bbkq9n&dl=1 # Install Libraries !pip -q install langchain-groq !pip -q install -U langchain_community tiktoken langchainhub !pip -q install -U langchain langgraph # for RAG Only !pip -q install -U langchain langchain-community langchainhub !pip -q install langchain-chroma bs4 !pip -q install huggingface_hub unstructured sentence_transformers ``` **第二步:初始 HF 和 Groq API** ``` import os from pprint import pprint from google.colab import userdata os.environ["GROQ_API_KEY"] = userdata.get('GROQ_API_KEY') os.environ["HF_TOKEN"] = userdata.get('HF_TOKEN') ``` **第三步:构建 RAG** ``` # Load Sheet base from langchain_community.document_loaders.csv_loader import CSVLoader from langchain_community.document_loaders.merge import MergedDataLoader from langchain.embeddings import HuggingFaceBgeEmbeddings from langchain_groq import ChatGroq from langchain_core.prompts import ChatPromptTemplate from langchain.prompts import PromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_core.output_parsers import StrOutputParser from langchain_core.output_parsers import JsonOutputParser from langchain_chroma import Chroma loader_csv = CSVLoader(file_path="/content/westworld_resort_facts.csv") # Merge All Loader loader_all = MergedDataLoader(loaders=[loader_csv]) docs_all = loader_all.load() len(docs_all) # Output 148 # Text splitting if you have long documents from langchain.text_splitter import RecursiveCharacterTextSplitter #splitting the text into text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) texts = text_splitter.split_documents(docs_all) # Embeddings model_name = "BAAI/bge-base-en" encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity bge_embeddings = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs={'device': 'cuda'}, encode_kwargs=encode_kwargs ) #Vector Store persist_directory = 'db' ## Here you can change the embeddings etc embedding = bge_embeddings vectordb = Chroma.from_documents(documents=texts, embedding=embedding, persist_directory=persist_directory) # Retreiever retriever = vectordb.as_retriever(search_kwargs={"k": 5}) #RAG Chain GROQ_LLM = ChatGroq( model="llama3-70b-8192", ) rag_prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n <|eot_id|><|start_header_id|>user<|end_header_id|> QUESTION: {question} \n CONTEXT: {context} \n Answer: <|eot_id|> <|start_header_id|>assistant<|end_header_id|> """, input_variables=["question","context"], ) rag_prompt_chain = rag_prompt | GROQ_LLM | StrOutputParser() QUESTION = """What can I do in the Westworld Park?""" CONTEXT = retriever.invoke(QUESTION) rag_chain = ( {"context": retriever , "question": RunnablePassthrough()} | rag_prompt | GROQ_LLM | StrOutputParser() ) rag_chain.invoke("What is the westworld park all about?") # Output Westworld is an immersive, high-tech theme park that allows guests to experience the Wild West in a realistic and interactive way, populated by advanced androids called "hosts." The park offers a level of raw, unscripted excitement that cannot be replicated by traditional amusement park rides. It immerses guests in a living, breathing world where they become active participants in their own stories. ``` **第四步:基本链** 1. 对电子邮件进行分类 2. 研究路由器 3. RAG 问题 4. 撰写草稿电子邮件 5. 重写路由器 6. 电子邮件草稿分析 7. 重写电子邮件 8. RAG Chain — 在上面的 RAG 部分创建 ``` #Categorize EMAIL prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are the Email Categorizer Agent for the theme park Westworld,You are a master at \ understanding what a customer wants when they write an email and are able to categorize \ it in a useful way. Remember people maybe asking about experiences they can have in westworld. \ <|eot_id|><|start_header_id|>user<|end_header_id|> Conduct a comprehensive analysis of the email provided and categorize into one of the following categories: price_equiry - used when someone is asking for information about pricing \ customer_complaint - used when someone is complaining about something \ product_enquiry - used when someone is asking for information about a product feature, benefit or service but not about pricing \\ customer_feedback - used when someone is giving feedback about a product \ off_topic when it doesnt relate to any other category \ Output a single cetgory only from the types ('price_equiry', 'customer_complaint', 'product_enquiry', 'customer_feedback', 'off_topic') \ eg: 'price_enquiry' \ EMAIL CONTENT:\n\n {initial_email} \n\n <|eot_id|> <|start_header_id|>assistant<|end_header_id|> """, input_variables=["initial_email"], ) email_category_generator = prompt | GROQ_LLM | StrOutputParser() EMAIL = """HI there, \n I am emailing to find out info about your them park and what I can do there. \n I am looking for new experiences. Thanks, Paul """ result = email_category_generator.invoke({"initial_email": EMAIL}) print(result) #Output 'product_enquiry' ## Research Router research_router_prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert at reading the initial email and routing to our internal knowledge system\ or directly to a draft email. \n Use the following criteria to decide how to route the email: \n\n If the initial email only requires a simple response Just choose 'draft_email' for questions you can easily answer, prompt engineering, and adversarial attacks. If the email is just saying thank you etc then choose 'draft_email' If you are unsure or the person is asking a question you don't understand then choose 'research_info' You do not need to be stringent with the keywords in the question related to these topics. Otherwise, use research-info. Give a binary choice 'research_info' or 'draft_email' based on the question. Return the a JSON with a single key 'router_decision' and no premable or explaination. use both the initial email and the email category to make your decision <|eot_id|><|start_header_id|>user<|end_header_id|> Email to route INITIAL_EMAIL : {initial_email} \n EMAIL_CATEGORY: {email_category} \n <|eot_id|><|start_header_id|>assistant<|end_header_id|>""", input_variables=["initial_email","email_category"], ) research_router = research_router_prompt | GROQ_LLM | JsonOutputParser() email_category = 'product_enquiry' print(research_router.invoke({"initial_email": EMAIL, "email_category":email_category})) # Output {'router_decision': 'research_info'} ## RAG QUESTIONS search_rag_prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a master at working out the best questions to ask our knowledge agent to get the best info for the customer. given the INITIAL_EMAIL and EMAIL_CATEGORY. Work out the best questions that will find the best \ info for helping to write the final email. Remember when people ask about a generic park they are \ probably reffering to the park WestWorld. Write the questions to our knowledge system not to the customer. Return a JSON with a single key 'questions' with no more than 3 strings of and no premable or explaination. <|eot_id|><|start_header_id|>user<|end_header_id|> INITIAL_EMAIL: {initial_email} \n EMAIL_CATEGORY: {email_category} \n <|eot_id|><|start_header_id|>assistant<|end_header_id|>""", input_variables=["initial_email","email_category"], ) question_rag_chain = search_rag_prompt | GROQ_LLM | JsonOutputParser() email_category = 'product_enquiry' research_info = None print(question_rag_chain.invoke({"initial_email": EMAIL, "email_category":email_category})) #Output {'questions': ['What are the main attractions and experiences offered at WestWorld theme park?', 'What are some new or unique experiences that WestWorld theme park has to offer?', 'What are the most popular activities or rides at WestWorld theme park?']} ## Write Draft Email draft_writer_prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are the Email Writer Agent for the theme park Westworld, take the INITIAL_EMAIL below \ from a human that has emailed our company email address, the email_category \ that the categorizer agent gave it and the research from the research agent and \ write a helpful email in a thoughtful and friendly way. Remember people maybe asking \ about experiences they can have in westworld. If the customer email is 'off_topic' then ask them questions to get more information. If the customer email is 'customer_complaint' then try to assure we value them and that we are addressing their issues. If the customer email is 'customer_feedback' then try to assure we value them and that we are addressing their issues. If the customer email is 'product_enquiry' then try to give them the info the researcher provided in a succinct and friendly way. If the customer email is 'price_equiry' then try to give the pricing info they requested. You never make up information that hasn't been provided by the research_info or in the initial_email. Always sign off the emails in appropriate manner and from Sarah the Resident Manager. Return the email a JSON with a single key 'email_draft' and no premable or explaination. <|eot_id|><|start_header_id|>user<|end_header_id|> INITIAL_EMAIL: {initial_email} \n EMAIL_CATEGORY: {email_category} \n RESEARCH_INFO: {research_info} \n <|eot_id|><|start_header_id|>assistant<|end_header_id|>""", input_variables=["initial_email","email_category","research_info"], ) draft_writer_chain = draft_writer_prompt | GROQ_LLM | JsonOutputParser() email_category = 'customer_feedback' research_info = None print(draft_writer_chain.invoke({"initial_email": EMAIL, "email_category":email_category,"research_info":research_info})) ## Rewrite Router rewrite_router_prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert at evaluating the emails that are draft emails for the customer and deciding if they need to be rewritten to be better. \n Use the following criteria to decide if the DRAFT_EMAIL needs to be rewritten: \n\n If the INITIAL_EMAIL only requires a simple response which the DRAFT_EMAIL contains then it doesn't need to be rewritten. If the DRAFT_EMAIL addresses all the concerns of the INITIAL_EMAIL then it doesn't need to be rewritten. If the DRAFT_EMAIL is missing information that the INITIAL_EMAIL requires then it needs to be rewritten. Give a binary choice 'rewrite' (for needs to be rewritten) or 'no_rewrite' (for doesn't need to be rewritten) based on the DRAFT_EMAIL and the criteria. Return the a JSON with a single key 'router_decision' and no premable or explaination. \ <|eot_id|><|start_header_id|>user<|end_header_id|> INITIAL_EMAIL: {initial_email} \n EMAIL_CATEGORY: {email_category} \n DRAFT_EMAIL: {draft_email} \n <|eot_id|><|start_header_id|>assistant<|end_header_id|>""", input_variables=["initial_email","email_category","draft_email"], ) rewrite_router = rewrite_router_prompt | GROQ_LLM | JsonOutputParser() email_category = 'customer_feedback' draft_email = "Yo we can't help you, best regards Sarah" print(rewrite_router.invoke({"initial_email": EMAIL, "email_category":email_category, "draft_email":draft_email})) ## Draft Email Analysis draft_analysis_prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are the Quality Control Agent read the INITIAL_EMAIL below from a human that has emailed \ our company email address, the email_category that the categorizer agent gave it and the \ research from the research agent and write an analysis of how the email. Check if the DRAFT_EMAIL addresses the customer's issued based on the email category and the \ content of the initial email.\n Give feedback of how the email can be improved and what specific things can be added or change\ to make the email more effective at addressing the customer's issues. You never make up or add information that hasn't been provided by the research_info or in the initial_email. Return the analysis a JSON with a single key 'draft_analysis' and no premable or explaination. <|eot_id|><|start_header_id|>user<|end_header_id|> INITIAL_EMAIL: {initial_email} \n\n EMAIL_CATEGORY: {email_category} \n\n RESEARCH_INFO: {research_info} \n\n DRAFT_EMAIL: {draft_email} \n\n <|eot_id|><|start_header_id|>assistant<|end_header_id|>""", input_variables=["initial_email","email_category","research_info"], ) draft_analysis_chain = draft_analysis_prompt | GROQ_LLM | JsonOutputParser() email_category = 'customer_feedback' research_info = None draft_email = "Yo we can't help you, best regards Sarah" email_analysis = draft_analysis_chain.invoke({"initial_email": EMAIL, "email_category":email_category, "research_info":research_info, "draft_email": draft_email}) pprint(email_analysis) # Rewrite Email with Analysis rewrite_email_prompt = PromptTemplate( template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are the Final Email Agent read the email analysis below from the QC Agent \ and use it to rewrite and improve the draft_email to create a final email. You never make up or add information that hasn't been provided by the research_info or in the initial_email. Return the final email as JSON with a single key 'final_email' which is a string and no premable or explaination. <|eot_id|><|start_header_id|>user<|end_header_id|> EMAIL_CATEGORY: {email_category} \n\n RESEARCH_INFO: {research_info} \n\n DRAFT_EMAIL: {draft_email} \n\n DRAFT_EMAIL_FEEDBACK: {email_analysis} \n\n <|eot_id|><|start_header_id|>assistant<|end_header_id|>""", input_variables=["initial_email", "email_category", "research_info", "email_analysis", "draft_email", ], ) rewrite_chain = rewrite_email_prompt | GROQ_LLM | JsonOutputParser() email_category = 'customer_feedback' research_info = None draft_email = "Yo we can't help you, best regards Sarah" final_email = rewrite_chain.invoke({"initial_email": EMAIL, "email_category":email_category, "research_info":research_info, "draft_email": draft_email, "email_analysis":email_analysis}) final_email['final_email'] ``` **步骤 V:LangGraph 状态** ``` from langchain.schema import Document from langgraph.graph import END, StateGraph from typing_extensions import TypedDict from typing import List ### State class GraphState(TypedDict): """ Represents the state of our graph. Attributes: initial_email: email email_category: email category draft_email: LLM generation final_email: LLM generation research_info: list of documents info_needed: whether to add search info num_steps: number of steps """ initial_email : str email_category : str draft_email : str final_email : str research_info : List[str] # this will now be the RAG results info_needed : bool num_steps : int draft_email_feedback : dict rag_questions : List[str] ``` **第六步:节点** 1. categorize_email 2. research_info_search # 现在使用 RAG 完成 3. draft_email_writer 4. analyze_draft_email 5. rewrite_email 6. no_rewrite 7. state_printer ``` def categorize_email(state): """take the initial email and categorize it""" print("---CATEGORIZING INITIAL EMAIL---") initial_email = state['initial_email'] num_steps = int(state['num_steps']) num_steps += 1 email_category = email_category_generator.invoke({"initial_email": initial_email}) print(email_category) # save to local disk write_markdown_file(email_category, "email_category") return {"email_category": email_category, "num_steps":num_steps} def research_info_search(state): print("---RESEARCH INFO RAG---") initial_email = state["initial_email"] email_category = state["email_category"] num_steps = state['num_steps'] num_steps += 1 # Web search questions = question_rag_chain.invoke({"initial_email": initial_email, "email_category": email_category }) questions = questions['questions'] # print(questions) rag_results = [] for question in questions: print(question) temp_docs = rag_chain.invoke(question) print(temp_docs) question_results = question + '\n\n' + temp_docs + "\n\n\n" if rag_results is not None: rag_results.append(question_results) else: rag_results = [question_results] print(rag_results) print(type(rag_results)) write_markdown_file(rag_results, "research_info") write_markdown_file(questions, "rag_questions") return {"research_info": rag_results,"rag_questions":questions, "num_steps":num_steps} def draft_email_writer(state): print("---DRAFT EMAIL WRITER---") ## Get the state initial_email = state["initial_email"] email_category = state["email_category"] research_info = state["research_info"] num_steps = state['num_steps'] num_steps += 1 # Generate draft email draft_email = draft_writer_chain.invoke({"initial_email": initial_email, "email_category": email_category, "research_info":research_info}) print(draft_email) # print(type(draft_email)) email_draft = draft_email['email_draft'] write_markdown_file(email_draft, "draft_email") return {"draft_email": email_draft, "num_steps":num_steps} def analyze_draft_email(state): print("---DRAFT EMAIL ANALYZER---") ## Get the state initial_email = state["initial_email"] email_category = state["email_category"] draft_email = state["draft_email"] research_info = state["research_info"] num_steps = state['num_steps'] num_steps += 1 # Generate draft email draft_email_feedback = draft_analysis_chain.invoke({"initial_email": initial_email, "email_category": email_category, "research_info":research_info, "draft_email":draft_email} ) # print(draft_email) # print(type(draft_email)) write_markdown_file(str(draft_email_feedback), "draft_email_feedback") return {"draft_email_feedback": draft_email_feedback, "num_steps":num_steps} def rewrite_email(state): print("---ReWRITE EMAIL ---") ## Get the state initial_email = state["initial_email"] email_category = state["email_category"] draft_email = state["draft_email"] research_info = state["research_info"] draft_email_feedback = state["draft_email_feedback"] num_steps = state['num_steps'] num_steps += 1 # Generate draft email final_email = rewrite_chain.invoke({"initial_email": initial_email, "email_category": email_category, "research_info":research_info, "draft_email":draft_email, "email_analysis": draft_email_feedback} ) write_markdown_file(str(final_email), "final_email") return {"final_email": final_email['final_email'], "num_steps":num_steps} def no_rewrite(state): print("---NO REWRITE EMAIL ---") ## Get the state draft_email = state["draft_email"] num_steps = state['num_steps'] num_steps += 1 write_markdown_file(str(draft_email), "final_email") return {"final_email": draft_email, "num_steps":num_steps} def state_printer(state): """print the state""" print("---STATE PRINTER---") print(f"Initial Email: {state['initial_email']} \n" ) print(f"Email Category: {state['email_category']} \n") print(f"Draft Email: {state['draft_email']} \n" ) print(f"Final Email: {state['final_email']} \n" ) print(f"Research Info: {state['research_info']} \n") print(f"RAG Questions: {state['rag_questions']} \n") print(f"Num Steps: {state['num_steps']} \n") return ``` **步骤 VII:条件边** ``` def route_to_research(state): """ Route email to web search or not. Args: state (dict): The current graph state Returns: str: Next node to call """ print("---ROUTE TO RESEARCH---") initial_email = state["initial_email"] email_category = state["email_category"] router = research_router.invoke({"initial_email": initial_email,"email_category":email_category }) print(router) # print(type(router)) print(router['router_decision']) if router['router_decision'] == 'research_info': print("---ROUTE EMAIL TO RESEARCH INFO---") return "research_info" elif router['router_decision'] == 'draft_email': print("---ROUTE EMAIL TO DRAFT EMAIL---") return "draft_email" def route_to_rewrite(state): print("---ROUTE TO REWRITE---") initial_email = state["initial_email"] email_category = state["email_category"] draft_email = state["draft_email"] # research_info = state["research_info"] # draft_email = "Yo we can't help you, best regards Sarah" router = rewrite_router.invoke({"initial_email": initial_email, "email_category":email_category, "draft_email":draft_email} ) print(router) print(router['router_decision']) if router['router_decision'] == 'rewrite': print("---ROUTE TO ANALYSIS - REWRITE---") return "rewrite" elif router['router_decision'] == 'no_rewrite': print("---ROUTE EMAIL TO FINAL EMAIL---") return "no_rewrite" ``` **步骤 VIII:构建图形** ``` # Add nodes workflow = StateGraph(GraphState) # Define the nodes workflow.add_node("categorize_email", categorize_email) # categorize email workflow.add_node("research_info_search", research_info_search) # web search workflow.add_node("state_printer", state_printer) workflow.add_node("draft_email_writer", draft_email_writer) workflow.add_node("analyze_draft_email", analyze_draft_email) workflow.add_node("rewrite_email", rewrite_email) workflow.add_node("no_rewrite", no_rewrite) # Add Edges workflow.set_entry_point("categorize_email") # workflow.add_conditional_edges( # "categorize_email", # route_to_research, # { # "research_info": "research_info_search", # "draft_email": "draft_email_writer", # }, # ) workflow.add_edge("categorize_email", "research_info_search") workflow.add_edge("research_info_search", "draft_email_writer") workflow.add_conditional_edges( "draft_email_writer", route_to_rewrite, { "rewrite": "analyze_draft_email", "no_rewrite": "no_rewrite", }, ) workflow.add_edge("analyze_draft_email", "rewrite_email") workflow.add_edge("no_rewrite", "state_printer") workflow.add_edge("rewrite_email", "state_printer") workflow.add_edge("state_printer", END) # Compile app = workflow.compile() # EMAIL = """HI there, \n # I am emailing to find out the current price of Bitcoin. \n # Can you please help me/ # Thanks, # John # """ EMAIL = """HI there, \n I am emailing to find out info about your them park and what I can do there. \n I am looking for new experiences. Thanks, Paul """ EMAIL = """HI there, \n I am a big fan of westworld. can I meet Maeve in the park? Really want to chat with her. Thanks, Ringo """ # run the agent inputs = {"initial_email": EMAIL, "num_steps":0} for output in app.stream(inputs): for key, value in output.items(): pprint(f"Finished running: {key}:") # Output ---CATEGORIZING INITIAL EMAIL--- product_enquiry 'Finished running: categorize_email:' ---RESEARCH INFO RAG--- What are the character meet and greet options available in WestWorld? In WestWorld, guests can meet and interact with hosts modeled after Dolores, Maeve, Clementine, Logan, and Teddy. Each character offers unique experiences, such as going on an adventure with Dolores, engaging in conversations with Maeve, or training with Teddy. Are there any special experiences or tours that allow interaction with WestWorld characters like Maeve? Yes, guests can meet and interact with a host based on Maeve, engaging in conversations and storylines that explore her wit, intelligence, and growing self-awareness. You can also embark on a thrilling heist storyline with a host modeled after Hector, which involves working with Maeve. Additionally, you can explore romantic storylines with Maeve or other hosts of your choice. Can guests have personalized conversations with WestWorld characters like Maeve? Yes, guests can have personalized conversations with Westworld characters like Maeve, engaging in conversations and storylines that explore her wit, intelligence, and growing self-awareness. ['What are the character meet and greet options available in WestWorld?\n\nIn WestWorld, guests can meet and interact with hosts modeled after Dolores, Maeve, Clementine, Logan, and Teddy. Each character offers unique experiences, such as going on an adventure with Dolores, engaging in conversations with Maeve, or training with Teddy.\n\n\n', 'Are there any special experiences or tours that allow interaction with WestWorld characters like Maeve?\n\nYes, guests can meet and interact with a host based on Maeve, engaging in conversations and storylines that explore her wit, intelligence, and growing self-awareness. You can also embark on a thrilling heist storyline with a host modeled after Hector, which involves working with Maeve. Additionally, you can explore romantic storylines with Maeve or other hosts of your choice.\n\n\n', 'Can guests have personalized conversations with WestWorld characters like Maeve?\n\nYes, guests can have personalized conversations with Westworld characters like Maeve, engaging in conversations and storylines that explore her wit, intelligence, and growing self-awareness.\n\n\n'] <class 'list'> 'Finished running: research_info_search:' ---DRAFT EMAIL WRITER--- {'email_draft': "Dear Ringo,\n\nThank you for your enthusiasm for Westworld! We're thrilled to hear that you're a big fan of the park. \n\nRegarding your question, I'm delighted to inform you that yes, you can meet Maeve in the park! You can engage in conversations with her, exploring her wit, intelligence, and growing self-awareness. You can also embark on a thrilling heist storyline with a host modeled after Hector, which involves working with Maeve. If you're interested, you can even explore romantic storylines with Maeve or other hosts of your choice.\n\nWe offer various character meet and greet options, including interactions with Dolores, Maeve, Clementine, Logan, and Teddy. Each character offers unique experiences, and we're confident you'll have a fantastic time interacting with Maeve.\n\nIf you have any more questions or would like to know more about our character meet and greet options, please don't hesitate to ask. We're always here to help.\n\nThank you for choosing Westworld, and we look forward to welcoming you to the park!\n\nBest regards,\nSarah\nResident Manager"} ---ROUTE TO REWRITE--- {'router_decision': 'no_rewrite'} no_rewrite ---ROUTE EMAIL TO FINAL EMAIL--- 'Finished running: draft_email_writer:' ---NO REWRITE EMAIL --- 'Finished running: no_rewrite:' ---STATE PRINTER--- Initial Email: HI there, I am a big fan of westworld. can I meet Maeve in the park? Really want to chat with her. Thanks, Ringo Email Category: product_enquiry Draft Email: Dear Ringo, Thank you for your enthusiasm for Westworld! We're thrilled to hear that you're a big fan of the park. Regarding your question, I'm delighted to inform you that yes, you can meet Maeve in the park! You can engage in conversations with her, exploring her wit, intelligence, and growing self-awareness. You can also embark on a thrilling heist storyline with a host modeled after Hector, which involves working with Maeve. If you're interested, you can even explore romantic storylines with Maeve or other hosts of your choice. We offer various character meet and greet options, including interactions with Dolores, Maeve, Clementine, Logan, and Teddy. Each character offers unique experiences, and we're confident you'll have a fantastic time interacting with Maeve. If you have any more questions or would like to know more about our character meet and greet options, please don't hesitate to ask. We're always here to help. Thank you for choosing Westworld, and we look forward to welcoming you to the park! Best regards, Sarah Resident Manager Final Email: Dear Ringo, Thank you for your enthusiasm for Westworld! We're thrilled to hear that you're a big fan of the park. Regarding your question, I'm delighted to inform you that yes, you can meet Maeve in the park! You can engage in conversations with her, exploring her wit, intelligence, and growing self-awareness. You can also embark on a thrilling heist storyline with a host modeled after Hector, which involves working with Maeve. If you're interested, you can even explore romantic storylines with Maeve or other hosts of your choice. We offer various character meet and greet options, including interactions with Dolores, Maeve, Clementine, Logan, and Teddy. Each character offers unique experiences, and we're confident you'll have a fantastic time interacting with Maeve. If you have any more questions or would like to know more about our character meet and greet options, please don't hesitate to ask. We're always here to help. Thank you for choosing Westworld, and we look forward to welcoming you to the park! Best regards, Sarah Resident Manager Research Info: ['What are the character meet and greet options available in WestWorld?\n\nIn WestWorld, guests can meet and interact with hosts modeled after Dolores, Maeve, Clementine, Logan, and Teddy. Each character offers unique experiences, such as going on an adventure with Dolores, engaging in conversations with Maeve, or training with Teddy.\n\n\n', 'Are there any special experiences or tours that allow interaction with WestWorld characters like Maeve?\n\nYes, guests can meet and interact with a host based on Maeve, engaging in conversations and storylines that explore her wit, intelligence, and growing self-awareness. You can also embark on a thrilling heist storyline with a host modeled after Hector, which involves working with Maeve. Additionally, you can explore romantic storylines with Maeve or other hosts of your choice.\n\n\n', 'Can guests have personalized conversations with WestWorld characters like Maeve?\n\nYes, guests can have personalized conversations with Westworld characters like Maeve, engaging in conversations and storylines that explore her wit, intelligence, and growing self-awareness.\n\n\n'] RAG Questions: ['What are the character meet and greet options available in WestWorld?', 'Are there any special experiences or tours that allow interaction with WestWorld characters like Maeve?', 'Can guests have personalized conversations with WestWorld characters like Maeve?'] Num Steps: 4 ``` ## **结论** 通过将RAG与LangChain的LangGraph代理集成,我们可以创建一个强大的系统来制作对客户的智能电子邮件回复。该系统可自动执行电子邮件分类、问题生成和知识检索等任务,从而简化沟通并使企业能够提供卓越的客户服务。效率的提高使座席能够专注于更复杂的问题,而上下文感知的回复草稿确保所有客户查询都得到全面解决。 此外,随着LangChain代理随着时间的推移收到用户反馈,它可以不断提高其性能,确保电子邮件回复变得更加自然和有用。RAG 和 LangChain 代理的这种集成代表了人工智能驱动的通信的重大飞跃,促进了更高效和积极的客户体验。
yg9538
2024年5月5日 14:07
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