NLP&ChatGPT&LLMs技术、源码、案例实战210课

超过12.5万行NLP/ChatGPT/LLMs代码的AI课程

讲师介绍
现任职于硅谷一家对话机器人CTO，专精于Conversational AI
在美国曾先后工作于硅谷最顶级的机器学习和人工智能实验室
CTO、杰出AI工程师、首席机器学习工程师
美国一家Talents Sourcing公司的Founder及CTO
21本人工智能及数据科学相关书籍作者。

NLP&ChatGPT&LLMs课程备注

1、本课程为高端技术型知识付费，需要进行源码交付及一年课程技术答疑指导，请添加Gavin导师微信：NLP_Matrix_Space获得相关的视频、代码、资料等。
2、课程面向Developers及Researchers，学习课程需要有基本的Python代码编程经验。课程针对没有基础的学员录制了Python及Transformer免费赠送的基础课，以帮助零基础学员平滑过渡到正式课程的学习。
3、课程深入、系统、使用的讲解Transformers、ChatGPT、LLMs等NLP生产级代码实战及科学研究。核心围绕模型、数据、工具三大维度展开。
4、课程购买者在享有自购买之日起一年的课程技术答疑服务同时，均免费享有5个小时企业级及科研技术咨询服务，由Gavin导师提供生产级技术咨询服务及科研论文指导等，该咨询服务需要提前一周预约Zoom会议。
5、购买本课后，课件及相关的资料、源码等，仅用于个人学习和技术交流，不能作为其他和商业用途，为了保护知识产权，购买后不退款不更换课程。

In this comprehensive course, we will explore diverse applications that harness the capabilities of NLP&ChatGPT&LLMs. Our focus is not only on leveraging language models but also on creating applications that are data-aware and agentic, ensuring that they go beyond mere model integration.

Throughout this course, we firmly believe that the true power and distinctiveness of language model applications lie in their ability to connect with external data sources and actively interact with their environments. By incorporating data-awareness, we can enhance the depth and breadth of information available to the language models, enabling them to deliver more accurate and insightful responses. Additionally, by embracing an agentic approach, we empower language models to engage and respond dynamically to their surroundings, creating richer user experiences. Further, the course will help learners to explore the top best papers and their implementations in NLP&ChatGPT&LLMs.

Join us on this exciting journey as we delve into various practical applications that bring language models to life. By the end of this course, you will have gained valuable insights and skills to develop cutting-edge applications that are not only powered by language models but also possess the qualities of being data-aware and agentic. Get ready to unlock the true potential of language models in the realm of application development!

面向人群
1，计算机相关专业的本科/硕士/博士生
2，Transformer、ChatGPT、LLMs技术爱好者
3，智能对话机器人爱好者
4，想从推荐系统、知识图谱转行到NLP的人员
5，已经有NLP从业经验想升级技能体系的开发者
6，企业中NLP高级科研人员
7，想在短期内系统全面深入的掌握NLP的IT人员
备注：课程针对没有基础的学员录制了Python及Transformer基础课。

面向资料及答疑
购买后联系授课导师Gavin获得代码、资料及完整的课程视频（包含额外的根据学员学习反馈而补充的视频及助教录制的视频）。
课程提供1年的技术答疑服务，Gavin老师负责所有课程技术问题的答疑服务。

课程收获：
1， 通过近13小时掌握基于Transformer的新一代NLP架构、算法、论文、源码及案例，轻松应对Transformer面试及新一代NLP架构及开发工作。
2， 通过近21小时学习导师从自己阅读的超过3000篇NLP论文中的精选出的10篇质量最高的论文的架构、算法、实现等讲解，对新一代的NLP技术了然于胸，极大的加速NLP科研及项目开发进度。
3， 通过近65小时彻底掌握NLP技术的集大成者Rasa这一全球最成功的的智能业务对话机器人架构、算法、源码及实战案例，成为NLP技术公司或者团队NLP技术的引领者。
4， 通过10大企业级NLP实战项目，不仅能够贯通NLP整个领域的核心技术，同时能够学习企业级NLP成熟项目代码的思想和精华，帮助学员轻松应对任意难度的NLP项目面试的同时还能够把这些项目稍加修改应用于企业NLP产品开发中。
5， 该课程尤其是对短期内想对NLP领域有系统全面深入认识特别有帮助，所有的内容都是基于企业开发中用到的内容由浅入深环环相扣展开，可以大大节省学习时间。同时对具有NLP或者知识图谱或者推荐系统工作经验的同学而言，会在1-3月的时间内完成技术的更新换代，成为最新人工智能技术的引领者。
6， ChatGPT技术和OpenAI API的基础应用和进阶应用：学习了ChatGPT技术和OpenAI API的基础和进阶应用，如大模型概述、API接口概述、向量检索、文本生成、嵌入式向量检索实现问答系统、使用LangChain等，可以在不同的场景中灵活应用ChatGPT技术和OpenAI API解决问题。
7， ChatGPT提示工程的构建和应用：学习了如何构建ChatGPT提示工程以及如何应用它来实现概括总结、推断任务、文本转换和扩展功能等，可以自己构建ChatGPT提示工程并应用于实际场景中。
8， 聊天机器人的构建：学习了如何使用ChatGPT提示工程构建聊天机器人和订餐机器人，可以自己构建聊天机器人并应用于实际场景中，提高自己的实际能力和竞争力。
9， 类ChatGPT开源大模型的概述和进阶项目实践：学习了类ChatGPT开源大模型的概述和发展历程，以及基于LoRA SFT+RM+RAFT技术进行模型微调、P-Tuning等技术对特定领域数据进行模型微调、LLama Index和Langchain技术的全面实践，以及基于向量检索技术对特定领域数据进行模型微调等进阶项目实践，可以更好地应用类ChatGPT开源大模型解决实际问题。
10， 案例实践和应用场景：通过多个案例实践，了解ChatGPT技术和OpenAI API的应用场景，并掌握如何在实际场景中解决问题。这些案例涉及多个领域，包括聊天机器人、订餐机器人、问答系统等，使得学生可以将所学知识应用于实际工作中，提高自己的实际能力和竞争力。同时，学习了企业级NLP实战项目的代码和思想，可以将这些项目稍加修改应用于企业NLP产品开发中，提高自己的实际应用能力。

Change log：
2023-5-29: 新增ChatGPT&LLMs十大经典工业界案例及背后超过5万行代码逐行解密

课程大纲(基于直播持续更新)：
第1课 Bayesian Transformer思想及数学原理完整论证
1，线性回归及神经网络AI技术底层通用的贝叶斯数学原理及其有效性证明
2，人工智能算法底层真相之MLE和MAP完整的数学推导过程概率、对数、求导等以及MLE和MAP关系详解
3，语言模型Language Model原理机制、数学推导及神经网络实现
4，图解Transformer精髓之架构设计、数据训练时候全生命周期、数据在推理中的全生命周期、矩阵运算、多头注意力机制可视化等
5，什么叫Bayesian Transformer，Bayesian Transformer和传统的Transformer的核心区别是什么？
6，Bayesian Transformer这种新型思考模型在学术和工业界的意义是什么，为什么说Transformer中到处都是Bayesian的实现？
7，贝叶斯Bayesian Transformer数学推导论证过程全生命周期详解及底层神经网络物理机制剖析

第2课Transformer论文源码完整实现
1，Transformer架构内部的等级化结构及其在NLP中的应用内幕
2，数学内幕、注意力机制代码实现、及Transformer可视化
3，以对话机器人的流式架构为例阐述Transformer学习的第三境界
4，以智能对话机器人为例阐述Transformer的自编码autoencoding和自回归autoregressive语言模型内幕机制

第3课：Transformer语言模型架构、数学原理及内幕机制
1，语言模型的链式法则、运行机制及为何说LM是一个Classifier？
2，基于概率统计Statistical Language Models语言模型内部机制、数学公式、及完整的示例
3，基于神经网络Neural Language Models语言模型内部机制、数学公式、及完整的示例
4，使用困惑度及Cross Entropy来衡量语言模型的质量具体实现及数学公式推导分
5，Language Model底层的数学原理之最大 似然估计MLE及最大后验概率MAP内部机制与关系详解
6，语言模型底层的数学原理之Bayesian模型原理与实现

第4课 GPT自回归语言模型架构、数学原理及内幕机制
1，语言模型的运行机制、架构内部及数学实现回顾
2，GPT可视化、Masking等工作机制解析
3，GPT中的Decoder-Only模式内部运行机制解析
4，数据在GPT模型中的流动生命周期Input Encoding、Self-Attention、及Model Output详解
5，GPT中的Masked多头注意力机制及全连接神经网络内部运行机制解析

第5课 BERT下的自编码语言模型架构、数学原理及内幕机制
1，双向Masking机制数学原理剖析
2，BERT语言模型架构内幕详解
3，BERT训练任务和调优

第6课 BERT Pre-taining模型源码完整实现
1，BERT神经网络的完整源码实现
2，预训练任务MLM完整源码实现
3，预训练任务NSP完整源码实现

第7课 使用BERT 进行Document分类任务案例实战
1，BERT Fine-tuning 数字原理剖析
2，模型及数据处理代码
3，BERT Fine-tuning训练完整代码

第8课 使用BERT进行NER案例实战
1，BERT Fine-tuning 进行NER原理解析
2，Tokenization及Input端代码
3，BERT Fine-tuning训练及优化

第9课 使用BERT进行多任务Fine-Tuning解密
1，Fine-Tuning最佳策略
2，Pre-Training深度优化
3，Multi-Task微调解密及案例

第10课 使用BERT对影评数据分析(数据处理、模型代码、线上部署)
1，Sogou New数据处理
2，模型Input源码
3，模型训练及优化
….
第36课 基于Transformer的Rasa Internals解密之Retrieval Model剖析
1，什么是One Graph to Rule them All
2，为什么工业级对话机器人都是Stateful Computations？
3，Rasa引入Retrieval Model内幕解密及问题解析

第37课 基于Transformer的Rasa Internals解密之去掉对话系统的Intent内幕剖析
1，从inform intent的角度解析为何要去掉intent
2，从Retrieval Intent的角度说明为何要去掉intent
3，从Multi intents的角度说明为何要去掉intent
4，为何有些intent是无法定义的？

第38课 基于Transformer的Rasa Internals解密之去掉对话系统的End2End Learning内幕剖析
1，How end-to-end learning in Rasa works
2，Contextual NLU解析
3，Fully end-to-end assistants

第39课 基于Transformer的Rasa Internals解密之全新一代可伸缩DAG图架构内幕
1，传统的NLU/Policies架构问题剖析
2，面向业务对话机器人的DAG图架构
3，DAGs with Caches解密
4，Example及Migration注意点

第40课 基于Transformer的Rasa Internals解密之定制Graph NLU及Policies组件内幕
1，基于Rasa定制Graph Component的四大要求分析
2，Graph Components解析
3，Graph Components源代码示范

第41课 基于Transformer的Rasa Internals解密之自定义GraphComponent内幕
1，从Python角度分析GraphComponent接口
2，自定义模型的create和load内幕详解
3，自定义模型的languages及Packages支持

第42课 基于Transformer的Rasa Internals解密之自定义组件Persistence源码解析
1，自定义对话机器人组件代码示例分析
2，Rasa中Resource源码逐行解析
3，Rasa中ModelStorage、ModelMetadata等逐行解析

第43课 基于Transformer的Rasa Internals解密之自定义组件Registering源码解析
1，采用Decorator进行Graph Component注册内幕源码分析
2，不同NLU和Policies组件Registering源码解析
3，手工实现类似于Rasa注册机制的Python Decorator全流程实现

第44课 基于Transformer的Rasa Internals解密之自定义组件及常见组件源码解析
1，自定义Dense Message Featurizer和Sparse Message Featurizer源码解析
2，Rasa的Tokenizer及WhitespaceTokenizer源码解析
3，CountVectorsFeaturizer及SpacyFeaturizer源码解析

第45课 基于Transformer的Rasa Internals解密之框架核心graph.py源码完整解析及测试
1，GraphNode源码逐行解析及Testing分析
2，GraphModelConfiguration、ExecutionContext、GraphNodeHook源码解析
3，GraphComponent源码回顾及其应用源码

第46课 基于Transformer的Rasa Internals解密之框架DIETClassifier及TED
1，作为GraphComponent的DIETClassifier和TED实现了All-in-one的Rasa架构
2，DIETClassifier内部工作机制解析及源码注解分析
3，TED内部工作机制解析及源码注解分析

第47课 基于Transformer的Rasa 3.x Internals解密之DIET近1825行源码剖析
1，DIETClassifier代码解析
2，EntityExtractorMixin代码解析
3，DIET代码解析

第48课 基于Transformer的Rasa 3.x Internals解密之TED Policy近2130行源码剖析
1，TEDPolicy父类Policy代码解析
2，TEDPolicy完整解析
3，继承自TransformerRasaModel的TED代码解析
….
第75课 Rasa Interactive Learning运行原理、运行流程及案例实战
1，为什么说Rasa Interactive Learning是解决Rasa对话机器人Bug最容易的途径？
2，Rasa Interactive与Rasa Visualize的联合使用：Stories、Rules、NLU、Policies
3，项目案例Microservices源码逐行解析
4，使用Rasa Interactive Learning逐行调试nlu及prediction案例的三大用例场景
5，使用Rasa Interactive Learning生产数据示例实战

第76课 通过Rasa Interactive Learning发现及解决对话机器人的Bugs案例实战
1，动态的Rasa Visualization http://localhost:5006/visualization.html
2，Rasa Interactive Learning定位Slot的Bug及解决方案现场实战
3，Rasa Interactive Learning定位微服务Bug及其分析

第77课 基于ElasticSearch的Knowledge Base与Rasa对话机器人的整合在对话机器人开发中巨大价值分析
1，通过Rasa Visualize分析Pizza项目的三大运行流程
2，Pizza项目的NLU、Stories及Rules内容详解
3，项目的微服务代码详解
4，通过Rasa Interactive Learning测试Pizza form的运行及validation运行机制
5，通过Rasa Interactive Learning实战围绕Pizza form的错误对话路径及改造方式
6，通过Rasa Interactive Learning生成新的Pizza form训练数据及其训练

第78课 基于ElasticSearch的Rasa项目实战之Movie及Book Knowledge Base整合
1，基于ElasticSearch的Knowledge Base与Rasa对话机器人的整合在对话机器人开发中巨大价值分析
2，基于ElasticSearch的Rasa项目核心运行流程分析：Movies及Books操作功能详情
3，打通Rasa、微服务及ElasticSearch功能演示及运行机制分析
4，通过Rasa Shell演示项目案例的核心功能
5，通过Rasa Interactive Learning演示项目案例的内幕运行机制及流程深度剖析

第79课 Rasa与ElasticSearch整合项目案例数据及配置作机制、最佳实践、及源码剖析
1，domain.yml中的config及session_config工作机制、最佳实践、内幕自定义源码剖析
2，项目的entities及slots、Responses和actions的关系解析
4，config.yml中Pipeline及Policies详解及其背后的Rasa Graph Architecture剖析
5，NLU及Policies训练数据详解
6，通过Rasa Interactive动手实战演示join movie and rating的功能

第80课 基于ElasticSearch的Rasa项目实战之微服务源码逐行解析
1，Rasa微服务和ElasticSearch整合中代码架构分析
2，KnowledgeBase源码解析
3，MovieDocumentType、BookDocumentType、RatingDocumentType源码解析
4，ElasticsearchKnowledgeBase源码解析
5，ActionElasticsearchKnowledgeBase源码解析

第81课 通过Rasa Interactive对Rasa对话机器人项目实战之ConcertBot源码、流程及对话过程内幕解密
1，通过Rasa Visualize从全局分析ConcertBot执行流程
2，ConcertBot中的Data剖析
3，定制Slot的Mapping的三种方式剖析及具体实现
4，Rasa Interactive全程解密ConcertBot内部机制
5，自定义的Slot Mapping的Action行为透视

第82课 Rasa项目实战之Helpdesk Assistant运行流程、交互过程及源码剖析
1，通过Rasa shell演示Helpdesk Assistant的项目功能
2，现场解决DucklingEntityExtractor在Docker中使用问题
3，通过Rasa Visualize透视Helpdesk Assistant核心运行流程
4，action_check_incident_status源码解析及Slot操作深度剖析

第83课：Rasa项目实战之Helpdesk Assistant中Bug调试过程全程再现及各类现象内幕解密
1，通过Rasa Shell交互式命令复现案例中的Bug问题
2，逐词阅读Bug信息定位错误来源
3，关于payload中KeyError内幕剖析
4，配置文件分析及源码解析
5，使用rasa data validate进行数据校验
6，使用Debug模式透视问题内幕
7，Helpdesk Assistant中Bug的解决及过程总结

第84课：Rasa项目实战之Helpdesk Assistant中Domain、Action逐行解密及Rasa Interactive运行过程剖析
1，对Helpdesk Assistant中的Domain内容逐行解密
2，Helpdesk Assistant中的Action微服务代码逐行解密
3，通过Rasa Interactive纠正Helpdesk Assistant中的NLU错误全程演示
4，通过Rasa Interactive纠正Helpdesk Assistant中的Prediction错误全程演示
5，通过Rasa Interactive纠正Helpdesk Assistant中的两大核心场景全程交互解密
…….

《ChatGPT 技术：从基础应用到进阶实践课》是一个涵盖了ChatGPT技术和OpenAI API的基础和应用的课程，实现近10小时的内容讲解，课程内容分为8个部分，从ChatGPT技术概述到类ChatGPT开源大模型技术的进阶项目实践。

1. ChatGPT技术概述：第一节课程主要介绍了GPT-1、GPT-2、GPT-3、GPT-3.5和GPT-4的发展历程和技术特点，以及ChatGPT技术的基本原理和项目案例实战。
2. OpenAI API基础应用实践：第二节课程主要介绍了OpenAI API模型及接口概述，以及如何使用OpenAI API进行向量检索和文本生成。
3. OpenAI API进阶应用实践：第三节课程主要介绍了如何使用OpenAI API基于嵌入式向量检索实现问答系统，如何使用OpenAI API对特定领域模型进行微调。
4. ChatGPT提示工程基础知识：第四节课程主要介绍了如何构建优质提示的两个关键原则，以及如何迭代快速开发构建优质提示。
5. ChatGPT提示工程实现多功能应用：第五节课程主要介绍了如何使用ChatGPT提示工程实现概括总结、推断任务、文本转换和扩展功能。
6. ChatGPT提示工程构建聊天机器人：第六节课程主要介绍了聊天机器人的应用场景，以及如何使用ChatGPT提示工程构建聊天机器人和订餐机器人。
7. 类ChatGPT开源大模型技术概述：第七节课程主要介绍了类ChatGPT开源大模型的发展历程和技术特点，以及ChatGLM项目案例实践和LMFlow项目案例实践。
8. 类ChatGPT开源大模型进阶项目实践：第八节课程主要介绍了类ChatGPT开源大模型的进阶项目实践，包括基于LoRA SFT+RM+RAFT技术进行模型微调、基于P-Tuning等技术对特定领域数据进行模型微调、基于LLama Index和Langchain技术的全面实践，以及使用向量检索技术对特定领域数据进行模型微调。

第132课 ChatGPT 技术概述

1.   GPT-1、GPT-2、GPT-3、GPT-3.5 、GPT4的发展历程与技术特点
   

2.   ChatGPT 技术的基本原理简介：
   

InstructGPT数据集、InstructGPT技术原理、RLHF技术内幕详解)
3. ChatGPT 项目案例实战

第133课 OpenAI API基础应用实践

1.   OpenAI API 模型及接口概述
   

2.   使用 OpenAI API 进行向量检索
   

3.   使用 OpenAI API 进行文本生成
   

第134课 OpenAI API进阶应用实践

1.   OpenAI API 基于嵌入式向量检索实现问答系统
   

2.   OpenAI API 使用LangChain构建工具
   

3.   OpenAI API 对特定领域模型进行微调
   

第135课 ChatGPT 提示工程基础知识

1.   构建优质提示的两个关键原则
   

2.   迭代式快速开发构建优质提示
   

第135课 ChatGPT提示工程实现多功能应用

1.   使用ChatGPT提示工程实现概括总结
   

2.   使用ChatGPT提示工程实现推断任务
   

3.   使用ChatGPT提示工程实现文本转换
   

4.   使用ChatGPT提示工程实现扩展功能
   

第137课 ChatGPT提示工程构建聊天机器人

1.   聊天机器人的应用场景
   

2.   使用ChatGPT提示工程构建聊天机器人
   

3.   使用ChatGPT提示工程构建订餐机器人
   

第138课 类ChatGPT开源大模型技术概述

1.   类ChatGPT开源大模型的发展历程与技术特点
   

2.   类ChatGPT开源大模型之ChatGLM项目案例实践
   

3.   类ChatGPT开源大模型之LMFlow项目案例实践
   

第139课 类ChatGPT开源大模型进阶项目实践

1.   类ChatGPT开源大模型基于LoRA SFT+RM+RAFT技术进行模型微调
   

2.   类ChatGPT开源大模型基于P-Tuning等技术对特定领域数据进行模型微调
   

3.   类Chat GPT 技术基于 LLama Index 和 Langchain 技术的全面实践
   

4.   类 ChatGPT大型语言模型基于向量检索技术对特定领域数据进行模型微调
   

Application: Unleashing Conversational AI: Mastering Voice Chatbot Development with ChatGPT
What You Will Learn:
Upon completing this comprehensive course, you’ll have the know-how to create remarkably human-like voice chatbots utilizing ChatGPT and Eleven Labs. You’ll become skilled in fusing React and FastAPI for dynamic full-stack development and introducing voice generation functionality. Moreover, you’ll acquire a robust understanding of how to tailor AI models through prompt engineering and the ins and outs of deployment and scalability in voice chatbot applications.
Outline:
第140课 Intro to Voice Chatbot Development
• Course overview, highlighting the creation of advanced voice chatbots using ChatGPT and Eleven Labs.
• Grasping the growing importance of voice assistants in today’s AI revolution.
• An introduction to the OpenAI and Eleven Labs APIs.
• A detailed, step-by-step guide to setting up your development environment for smooth React and FastAPI integration.
第141课 Crafting the Chatbot Architecture
• Designing an effective voice chatbot structure with ChatGPT and Eleven Labs.
• Exploring diverse applications and use cases for the chatbot, including areas like sales, language teaching, and more.
第142课 Implementing Voice Generation
• Mastering the techniques for developing a human-like voice assistant using ChatGPT and Eleven Labs.
• Integrating voice generation features, including the unique option to use your own voice.
第143课 Effective Full-Stack Development
• Harnessing React and FastAPI for robust and efficient full-stack application development.
• Adopting best practices for blending front-end and back-end components of the voice chatbot.
第144课 Prompt Engineering for Tailored AI
• Understanding the role of prompt engineering in customizing and maximizing AI large language models.
• Techniques for optimizing prompts to steer conversational outcomes.
第145课 Deployment and Scaling of the Voice Chatbot
• Proven strategies for deploying your voice chatbot application.
• Exploring scalability options to meet growing user demands and ensure peak performance.
第146课 Real-World Applications and Future Visions
• Examining real-life applications of voice chatbots and their transformative impact.
• Discussing the exciting future possibilities and the vital role AI technologies will play in shaping our world.

Application: Building a Responsive Q&A Chatbot with LangChain and FastAPI
What You Will Learn:
By the culmination of this course, you’ll be armed with the proficiency required to create a responsive, locally-hosted chatbot tailored for question answering using LangChain and FastAPI. You’ll gain expertise in the ingestion process, including fetching HTML, engaging with ReadTheDocs Loader, and document partitioning. Additionally, you’ll learn to craft embeddings and a vectorstore utilizing LangChain’s vectorstore wrapper. You’ll grasp how to implement a Q&A feature, leveraging GPT-3 to generate precise answers based on standalone questions and relevant documents. Moreover, you’ll be skilled in using LangChain’s streaming support and async API for real-time chat updates in a bustling multi-user scenario.
Outline:
第147课 Intro to Local Chatbot Development
• Course overview with a focus on developing a Q&A chatbot using LangChain and FastAPI.
• Grasping the importance and potential applications of locally hosted chatbots.
第148课 Mastering Ingestion Component
• A detailed guide to extracting HTML from the documentation site.
• Learning to utilize LangChain’s ReadTheDocs Loader for HTML loading.
• Dividing documents effectively with LangChain’s TextSplitter.
第149课 Creating Embeddings and Vectorstore
• Learning to use LangChain’s vectorstore wrapper for creating a versatile vectorstore of embeddings.
• Integrating OpenAI’s embeddings and FAISS vectorstore for superior performance.
第150课 Q&A Component Implementation
• Deciphering standalone questions based on chat history and user input utilizing GPT-3.
• Searching for relevant documents from the vectorstore based on the deduced question.
• Generating a precise answer using GPT-3, taking into account the standalone question and relevant documents.
第151课 Utilizing Streaming Support and Async API
• An introduction to LangChain’s advanced streaming support and async API.
• Implementing real-time updates to cater to multiple users in the chatbot application.
第152课 Efficient Testing and Debugging
• Adopting proven strategies for testing and debugging your locally hosted chatbot.
• Troubleshooting common challenges and optimizing performance for smooth operations.
第153课 Deployment and Scalability
• Discussing diverse deployment options for your locally hosted chatbot application.
• Exploring scalability considerations to seamlessly handle an increase in user traffic.
第154课 Exploring Enhancements and Advanced Features
• Delving into potential enhancements and advanced features for refining your chatbot application.
• Discussing opportunities for further customization and improvements to enrich user experience.

Application: Constructing Autonomous Agents and Advanced Applications
What You Will Learn:
By the end of this enlightening course, you’ll possess a profound understanding of autonomous agents and the mechanisms of their long-term goal pursuits. You’ll master the art of designing, developing, and optimizing autonomous agents that perfectly blend tool usage with long-term memory. Additionally, you’ll be proficient in creating a Notion question-answering application, amalgamating it with Python scripts, and deploying it via StreamLit. Above all, you’ll gain a broad understanding of advanced AI techniques, ethical implications, and anticipate future trends in AI application development.
Outline:
第155课 Intro to Autonomous Agents
• Understanding the concept and role of autonomous agents in long-running applications.
• Assessing the pros and cons of designing autonomous agents.
• Evaluating real-world autonomous agent applications.
第156课 Crafting Long-Term Goals for Autonomous Agents
• Strategies for outlining single or multiple long-term goals for autonomous agents.
• Understanding the decision-making and task execution in line with the pursuit of set goals.
• Techniques to ensure autonomous agents maintain their autonomy and adaptability.
第157课 Tool Usage and Integration in Autonomous Agents
• Enhancing autonomous agent capabilities with appropriate tool usage.
• Analyzing different tool usage scenarios and their effects on agent performance.
• Integration techniques for flawless tool interaction within the autonomous agent architectures.
第158课 Long-Term Memory in Autonomous Agents
• Understanding the role of long-term memory in autonomous agent operations.
• Techniques for effectively storing, retrieving, and utilizing long-term memory in agent decision-making processes.
• Optimal memory management strategies for efficient agent performance.
第159课 Building Notion Question-Answering Application
• Overview and analysis of the Notion question-answering application and Blendle’s example data.
• A detailed guide on querying Notion with Python script for retrieving answers.
• Deploying the application on StreamLit using the provided code and instructions.
• Instructions for ingesting custom datasets into the Notion question-answering application.
• Setting up environment variables, including the addition of OPENAI_API_KEY as a secret variable.
第160课 Exploring Advanced Techniques in AI Application Development
• Delving into advanced methodologies in AI application development.
• Optimization strategies for enhancing the performance and efficiency of AI applications.
• Incorporating advanced AI models and algorithms for superior functionality.
第161课 Ethical Considerations in AI Application Development
• Comprehending the ethical implications of AI application development.
• Analyzing potential biases, privacy issues, and societal impacts.
• Strategies for ensuring fairness, transparency, and responsible AI application deployment.
第162课 Insight into Future Trends and Emerging Technologies
• Discussing the latest trends and advancements in AI application development.
• Exploring the emerging technologies that are shaping the future of AI applications.
• Predicting the future of AI applications and their potential impact on various industries.

Application: Building an LLM-Powered Chat Application: Mastering Django, React TypeScript, and LangChain
What You Can Learn:
• Understanding of LLMs and their role in powering chat applications.
• Setting up a development environment for Django, React TypeScript, and LangChain.
• Building a backend using Django for a chat application.
• Developing a frontend using React TypeScript for a chat application.
• Understanding LangChain Agents and how to integrate them with LLMs.
• Integration of Django backend and React TypeScript frontend.
• Incorporation of LangChain Agents and LLMs into a full-fledged chat application.
• Techniques for testing, debugging, and improving your LLM-powered chat application.
Outline:
第163课 Introduction to LLM-Powered Chat Applications
• Understanding LLMs and their role in chat applications.
• Overview of the course and the technology stack: Django, React TypeScript, and LangChain.
• How to install and configure the necessary software and libraries.
• Understanding the structure of the provided repository and its content.
第164课 Getting Started with Django
• Introduction to Django and its role in building the backend of a chat application.
• How to run a Django backend on a local machine.
• Exercise: Setting up and testing a basic Django backend.
第165课 Delving into React TypeScript
• Understanding React TypeScript and its advantages for frontend development.
• How to implement a React TypeScript frontend for your chat application.
• Exercise: Building a basic React TypeScript frontend.
第166课 Understanding LangChain Agents and LLMs
• What are LangChain Agents and how do they interact with LLMs?
• How to incorporate LangChain Agents and LLMs into your application.
第167课 Integrating Backend and Frontend
• Strategies for connecting Django backend with React TypeScript frontend.
• Exercise: Establishing communication between your backend and frontend.
第168课 Incorporating LangChain Agents into the Application
• How to integrate LangChain Agents and LLMs into your Django-React TypeScript application.
• Ensuring seamless interaction and information flow.
第169课 Testing and Debugging Your Chat Application
• Techniques for testing your LLM-powered chat application.
• How to troubleshoot common issues and bugs.

Application: Mastering Memory Management in AI Apps: Building and Enriching Chatbot Histories
What You Can Learn:
• The significance of memory management in AI apps and chatbots.
• How to use a specialized application for memory persistence, search, and enrichment.
• Understanding and implementing the Extractor model for enrichment functionality.
• Implementing features like vector search and auto-token counting in your application.
• Using Python and JavaScript SDKs for memory management in AI apps.
• Understanding the role of LangChain in memory persistence and retrieval.
• Skills in extending the Extractor model and creating new enrichment functionality.
• Techniques for testing, debugging, and improving your AI application’s memory management.

Outline:
第170课 Introduction to AI Apps and Chatbots
• Overview of AI apps and chatbots and their significance.
• The importance of memory management in these applications.
第171课 Understanding the Application
• Features and benefits of the application.
• The role of the application in memory persistence, search, and enrichment.
第172课 Memory Persistence and Summarization
• The concept of long-term memory persistence in AI apps.
• How to use the application for auto-summarization of memory messages.
第173课 Extractor Model
• The role and functionality of the Extractor model.
• How to extend the Extractor model for new enrichment functionality.
第174课 Implementing Vector Search and Auto-Token Counting
• The concept of vector search in memory management.
• The importance and method of auto-token counting of memories and summaries.
第175课 Working with Python and JavaScript SDKs
• Introduction to the provided Python and JavaScript SDKs.
• How to use the SDKs for memory management in AI apps.
第176课 LangChain Memory and Retriever Support
• Introduction to LangChain and its role in memory management.
• Utilizing LangChain for memory persistence and retrieval in your application.
第177课 Building New Enrichment Functionality
• Designing and implementing new features like summarizers, entity extractors, embedders, and more.
• Exercise: Extend the Extractor model and add new functionality.
第178课 Testing and Improving Your Application
• Techniques for testing and refining the performance of your AI app.
• How to troubleshoot common issues in memory management.

Application: Mastering LangChain Coder: Generating, Executing, and Saving Code with Streamlit and GPT
What You Can Learn:
• Understanding the concept of LangChain Coder and its uses.
• Setting up and navigating the LangChain Coder app.
• Skills in generating, executing, and saving code using LangChain Coder.
• Working with multiple programming languages within the app.
• Using both offline and online compilers for code execution.
• Advanced usage of LangChain Coder, including tips and best practices.
• Troubleshooting common issues and improving the efficiency of your usage.

Outline:
第179课 LangChain Coder
• What is LangChain Coder and its significance.
• An overview of Streamlit apps and OpenAI’s GPT.
• Installation and setup of the LangChain Coder app.
• Understanding the application interface and functionality.
第180课 Generating Code with LangChain Coder
• How LangChain Coder utilizes LangChain and GPT-3 for code generation.
• Hands-on exercise: Prompting for code description and generating code.
第181课 Executing Code Locally
• The process of executing the generated code locally.
• Displaying the output of the code execution.
• Hands-on exercise: Generating and running code in the app.
第182课 Saving Code for Later Use
• How to save the generated code to a file.
• Exercise: Generate, execute, and save code using LangChain Coder.
第183课 Working with Multiple Programming Languages
• Overview of the supported programming languages: Python, C, C++, and Javascript.
• Generating, running, and saving code in different languages.
第184课 Understanding Offline and Online Compiler Support
• Introduction to JDoodle Web Widget for online compiling.
• Comparing and contrasting offline and online compilers.
• Hands-on exercise: Utilizing both offline and online compilers.
第185课 Advanced Features and Usage of LangChain Coder
• Exploring additional features and capabilities of LangChain Coder.
• Best practices and tips for efficient usage.
第186课 Troubleshooting and Improving Your Usage
• Common issues and solutions when using LangChain Coder.
• How to maximize the benefits of the tool for your coding tasks.

Application: Getting Started with LangChain SQL Agent: Building and Testing an LLM to SQL App
What You Can Learn:
• Understanding of the LangChain SQL Agent and its use in converting LLM to SQL commands.
• Setting up and navigating the application.
• Programming with co-pilot GPT-4 and improving code snippets.
• Understanding and implementing backend endpoints.
• Adding file upload functionality and other enhancements to the application.
• Working with the LangChain SQL Agent to execute commands.
• Advanced usage and customization of the application.
• Testing, debugging, and improving the performance of your application.
Course Outline:
第187课 Introduction to LangChain SQL Agent
• Overview of LangChain SQL Agent and its significance in LLM to SQL commands.
• Brief introduction to sqlite databases and their role in the application.
第188课 Setting Up the Application
• How to setup the application and load a sqlite database with sample data.
• Understanding the structure and content of the provided repository.
第189课 Getting Started with GPT-4 Co-pilot
• Introduction to GPT-4 co-pilot and its role in programming tasks.
• Using GPT-4 for explanations and improvements of code snippets.
第190课 Understanding the Backend
• Understanding different endpoints provided in the backend.
• Role and functionality of each endpoint.
第191课 Implementing File Upload Functionality
• Overview of file upload functionality and its current status in the application.
• How to implement the file upload functionality in the UI.
第192课 Enhancing the Application
• How to clean the output of the agent after each execution.
• Adding auto-refresh or refresh button to the tables.
第193课 Working with the LangChain SQL Agent
• Understanding the role of the langchain sql agent in the ‘run-command’ endpoint.
• Example and hands-on exercise of using the ‘run-command’ endpoint.
第194课 Exploring Advanced Features
• Advanced capabilities of the application and how to utilize them.
• How to extend and customize the application for different needs.
第195课 Testing and Debugging Your Application
• Techniques for testing and refining the performance of your application.
• How to troubleshoot common issues and bugs.