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Markus Lang

This course provides an in-depth exploration into LangChain, a framework pivotal for developing generative AI applications. Aimed at both beginners and experienced practitioners in the AI world, the course starts with the fundamentals, such as the basic usage of the OpenAI API, progressively delving into the more intricate aspects of LangChain.

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This course provides an in-depth exploration into LangChain, a framework pivotal for developing generative AI applications. Aimed at both beginners and experienced practitioners in the AI world, the course starts with the fundamentals, such as the basic usage of the OpenAI API, progressively delving into the more intricate aspects of LangChain.

You'll learn about the intricacies of input and output mechanisms in LangChain and how to craft effective prompt templates for OpenAI models. The course takes you through the critical components of LangChain, such as Chains, Callbacks, and Memory, teaching you to create interactive and context-aware AI systems.

Midway, the focus shifts to advanced concepts like Retrieval Augmented Generation (RAG) and the creation of Autonomous Agents, enriching your understanding of intelligent system design. Topics like Hybrid Search, Indexing API, and LangSmith will be covered, highlighting their roles in enhancing the efficiency and functionality of AI applications.

Toward the end, the course integrates theory with practical skills, introducing Microservice Architecture in large language model (LLM) applications and the LangChain Expression Language. This ensures not only a theoretical understanding of the concepts but also their practical applications.

This course is tailored for individuals with a foundational knowledge of Python, aiming to build or enhance their expertise in AI. The structured curriculum ensures a comprehensive grasp of LangChain, from basic concepts to complex applications, preparing you for the future of generative AI.

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What's inside

Learning objectives

  • Master langchain from basics to advanced features
  • Understand and implement retrieval augmented generation (rag) using vectorstores
  • Learn about the creation and use of powerful autonomous agents.
  • Grasp the functionalities and applications of the indexing api.
  • Explore the langsmith platform for production ready application
  • Learn about microservice architecture in the context of large language model (llm) applications.
  • Learn about the new langchain expression language with the runnable interface

Syllabus

LLM basics, OpenAI and what to expect from that course
What to expect from this course and how to get all ressources
Why this course is different
Read more
Prerequisites
Essential topics and terms (theory)
Why this course does not cover Open Source models like LLama2
Optional: Install Visual Studio Code
Get the source files with Git from Github
Create OpenAI Account and create API Key
Learn how to setup your IDE, OpenAI Account and everything you need before being able to use LangChain

Before delving into LangChain, it's crucial to set up our workspace for seamless code execution. Additionally, we'll explore the basic OpenAI package, evaluating its strengths and limitations. This will underscore the prowess of LangChain and highlight its significance.

Setup of a virtual environment
Setup OpenAI Api-Key as environment variable
Exploring the vanilla OpenAI package
In this part of the course you learn basic concepts of LangChain - LLMs, PromptTemplate and Chains - The backbone of more complex applications
IMPORTANT NOTE - LANGCHAIN 0.1 Codechanges
LLM Basics
Prompting Basics
Theory: Prompt Engineering Basics
Few Shot Prompting
Chain of thought prompting
Pipeline-Prompts
Prompt Serialisation
Learn how to combine an LLM With a prompt to chains. Chains can be very simple, but Langchain also allows us to compose quite complex chains like RouterChains or SequentialChains
Introduction to chains
Basic chains - the LLMChain
Response Schemas and OutputParsers
LLMChain with multiple inputs
SequentialChains
RouterChains
Learn how to hook into the lifecycle of chains to log tokens, monitor your chain and some other tasks.
Callbacks
Learn how to give your LLM Applications memory
Memory basics - ConversationBufferMemory
ConversationSummaryMemory
EXERCISE: Use Memory to build a streamlit Chatbot
SOLUTION: Chatbot with Streamlit
Learn what OpenAI Function Calling is, learn how to use the OpenAI package for this and do the same with langchain. Also learn the limits of the current langchain implementation
OpenAI Function Calling - Vanilla OpenAI Package
Function Calling with LangChain
Limits and issues of the langchain Implementation
Learn how to take raw data, split it, embed it and put it into a vector store. Also learn how to retrieve it with langchain retrievers
RAG - Theory and building blocks
Loaders and Splitters
Embeddings - Theory and practice
VectorStores and Retrievers
RAG Service with FastAPI
Learn what agents are, how they interact with the "outside" world and already learn about their strenghts and weaknesses
Agents Basics - LLMs learn to use tools
Agents with a custom RAG-Tool
ChatAgents
Learn how to gain more control over creating, updating and deleting vectors from your VectorStore
Indexing API - keep your documents in sync
PREREQUISITE: Docker Installation
Setup of PgVector and RecordManager
Indexing Documents in practice
Document Retrieval with PgVector
Learn everything about LangSmith - the new LLM Development platform from the langchain team
Introduction to LangSmith (User Interface and Hub)
LangSmith Projects
LangSmith Datasets and Evaluation
Learn to go from prototypes with streamlit to a Microservice Architecture with React, Redis, FastAPI and more - all deployed on a kubernetes cluster
Before you watch this section
Introduction to Microservice Architecture
How our Chatbot works in a Microservice Architecture
Introduction to Docker
Introduction to Kubernetes
Deployment of the LLM Microservices to Kubernetes
Learn about the new LangChain Expression Language and how it allows you to compose chains with Runnables
LangChain Expression Language
Intro to LangChain Expression Language
LCEL Part 1 - Pipes and OpenAI Function Calling
LCEL - Part 2 - VectorStores, ItemGetter, Tools
LCEL - Part 3 - Arbitrary Functions, Runnable Interface, Fallbacks
Congratulations!
Thank you for participating in this course

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches LangChain, a powerful framework for generative AI applications
Provides a comprehensive foundation in LangChain, from its basics to advanced features
Covers advanced concepts like RAG and the creation of Autonomous Agents
Offers a practical approach with hands-on exercises and real-world examples
Introduces the latest technologies like Microservice Architecture and the LangChain Expression Language
Suitable for learners with a foundational knowledge of Python

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in LangChain in Action: Develop LLM-Powered Applications with these activities:
Refresh Probability and Statistics In Advance
This course requires a basic understanding of probability and statistics. Completing this activity will allow you to review those basics and ensure your success.
Browse courses on Probability
Show steps
  • Review your lecture notes from your previous probability and statistics course
  • Complete the online refresher course on probability and statistics
  • Work through the practice problems provided in the refresher course
  • Take a practice quiz to test your understanding of the material
Follow a Course on Prompt Engineering
There are many online courses and tutorials that can help you learn more about prompt engineering. Completing a course will give you a structured and comprehensive understanding of the topic.
Browse courses on Prompt Engineering
Show steps
  • Find a course or tutorial that fits your learning style
  • Complete the course or tutorial
  • Apply what you've learned to your own LLM projects
Practice Prompt Engineering
Prompt engineering is a core skill for using LLMs effectively. This activity will help you practice and improve your prompt engineering skills.
Browse courses on Prompt Engineering
Show steps
  • Read a few articles or blog posts about prompt engineering
  • Experiment with different prompting techniques using an LLM
  • Get feedback on your prompts from a mentor or peer
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a LangChain Workshop
Attending a workshop is a great way to learn more about LangChain and connect with other people who are using it.
Browse courses on LangChain
Show steps
  • Find a LangChain workshop that fits your interests
  • Register for the workshop
  • Attend the workshop
Mentor a beginner in LangChain
Reinforce your understanding of LangChain by sharing your knowledge and helping others learn the framework.
Browse courses on Knowledge Transfer
Show steps
  • Find a beginner who is interested in learning LangChain.
  • Provide guidance and support on LangChain concepts and practices.
  • Answer questions and troubleshoot issues.
Create a LangChain Tutorial
Creating a tutorial will help you solidify your understanding of LangChain and its features.
Browse courses on LangChain
Show steps
  • Choose a topic for your tutorial
  • Write a script or outline for your tutorial
  • Create a video or written tutorial
  • Share your tutorial with others
Build a Chatbot Using LangChain
Building a chatbot is a great way to apply your LangChain skills and create something practical and useful.
Browse courses on LangChain
Show steps
  • Design your chatbot
  • Develop your chatbot using LangChain
  • Test and deploy your chatbot
Follow tutorials on RAG and Autonomous Agents
Deepen your understanding of RAG and Autonomous Agents by working through guided tutorials that demonstrate their practical applications.
Show steps
  • Find tutorials on RAG and Autonomous Agents from the course materials or online resources.
  • Follow the tutorials step-by-step, experimenting with the code examples.
  • Apply your learnings to create your own RAG or Autonomous Agent applications.

Career center

Learners who complete LangChain in Action: Develop LLM-Powered Applications will develop knowledge and skills that may be useful to these careers:
AI Engineer
AI Engineers use engineering principles and techniques to design, build, and maintain AI systems. LangChain gives AI Engineers a simplified environment to quickly develop AI prototypes that can later be scaled into production-ready AI applications. This course dives into the fundamentals and advanced features of LangChain and teaches you how to build intelligent AI applications that leverage the latest advancements in AI technology.
Machine Learning Engineer
Machine Learning Engineers have a unique opportunity to design, monitor and improve the performance of models that are used to extract valuable insights from data. This course introduces the fundamentals of Machine Learning Engineering and provides learners with hands-on experience in building and implementing a production ready model with LangChain. The course also caters to professionals who are in a related domain and wish to either transition or advance their current role with a strong foundation in LangChain and its applications.
Software Engineer
Software Engineers apply their knowledge and skills to design, develop, and test software applications. LangChain is a highly effective tool for software engineers who want to learn how to integrate Machine Learning models into their software applications. This course teaches software engineers how to use LangChain to build more efficient and intelligent applications that are capable of sophisticated tasks that were previously impossible.
AI Architect
AI Architects design, develop, and manage AI solutions at scale. With LangChain, they can rapidly build, deploy, and maintain AI applications using a unified platform. This course covers the fundamentals and advanced techniques of LangChain, enabling AI Architects to leverage its capabilities to create scalable, efficient, and reliable AI solutions that meet the demands of complex business environments.
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between software development and operations. LangChain integrates seamlessly into existing DevOps pipelines, enabling DevOps Engineers to automate and streamline the deployment and management of Machine Learning models. This course provides a comprehensive overview of LangChain, with a focus on its integration with DevOps practices and tools, allowing DevOps Engineers to enhance their skillset and drive innovation within their organizations.
Full Stack Engineer
Full Stack Engineers design, develop, implement and maintain software applications. LangChain enables them to seamlessly integrate AI capabilities into their applications utilizing different programming languages and frameworks. This course provides a comprehensive overview of LangChain, from basic concepts to advanced features, while also covering practical implementation and deployment of LangChain in Full Stack applications.
Natural Language Processing Scientist
Natural Language Processing Scientists strive to make computers understand and process human languages. LangChain is a fantastic tool that enables and empowers NLP practitioners to quickly prototype and release NLP models, in their most recent versions and with the latest capabilities, to the end users and customers. This course covers essential NLP components of LangChain that allow developers and engineers to leverage state-of-the-art NLP models like GPT-3, Codex and BLOOM into their applications.
Product Manager
Product Managers are responsible for the development and management of products throughout their lifecycle. Incorporating AI into products can bring significant advantages, and LangChain can simplify and accelerate this process for Product Managers. This course provides a comprehensive overview of LangChain, enabling Product Managers to understand its potential, make informed decisions about its use, and drive the development of innovative AI-powered products.
AI Consultant
AI Consultants provide guidance and expertise on AI-related initiatives. With a solid understanding of LangChain, AI Consultants can effectively assess client needs, recommend solutions, and guide organizations in implementing and managing AI projects. This course provides a comprehensive overview of LangChain and its applications, enabling AI Consultants to expand their service offerings and deliver value to clients.
Data Scientist
Data science is a field that helps organizations make better and more timely decisions by leveraging their data to uncover meaningful trends, influences, and correlations. With this course, you can learn how LangChain automates many of the tasks that data scientists do on a regular basis and allows practitioners to focus on understanding the data and extracting the business value from it. Through a series of demonstrations and hands-on labs, you will learn how to use LangChain to quickly build ML models, prepare and transform data, select features for predictive modeling, evaluate the models, and implement them in web applications.
AI Product Manager
AI Product Managers focus on the development and management of AI products and solutions. This course provides a solid foundation in LangChain and its applications, enabling AI Product Managers to make informed decisions about the integration of LangChain into their products. By understanding the capabilities and limitations of LangChain, Product Managers can ensure that their products are well-aligned with business needs and deliver optimal value to users.
AI Trainer
AI Trainers are responsible for developing and delivering training programs on AI technologies and applications. This course equips AI Trainers with the knowledge and skills to effectively leverage LangChain within their training programs. By understanding the capabilities and best practices of LangChain, AI Trainers can empower learners to build, deploy, and maintain AI applications with greater efficiency and effectiveness.
Data Analyst
Data Analysts collect, analyze, interpret, and present data to inform decision-making. Using LangChain, Data Analysts can automate most of their mundane tasks and focus more on understanding the bigger picture within the data, and how it can be used to inform better decision-making. This course provides hands-on experience with LangChain, helping Data Analysts leverage its capabilities to uncover valuable insights and make meaningful contributions to their organization.
Business Analyst
Business Analysts help organizations improve their processes and performance. LangChain can be a powerful tool for Business Analysts who want to leverage data and AI to identify inefficiencies, optimize operations, and drive better decision-making. This course provides a foundation in LangChain and its applications, enabling Business Analysts to understand how AI can enhance their work and contribute to the success of their organizations.
AI Researcher
AI Researchers are at the forefront of developing new AI technologies and techniques. This course may be helpful for AI Researchers who want to explore the use of LangChain as a tool for prototyping and testing new ideas. LangChain's capabilities in combining multiple AI models and automating tasks can accelerate the research process and allow AI Researchers to focus on developing innovative solutions.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in LangChain in Action: Develop LLM-Powered Applications.
Provides a comprehensive overview of deep learning for natural language processing (NLP). It covers the theoretical foundations of deep learning, as well as practical applications in a variety of NLP tasks.
Provides a thought-provoking look at the future of humanity and the role that AI will play in it. It must-read for anyone who wants to think about the long-term implications of AI.
Focuses on the application of deep learning models in NLP, covering advanced topics such as transformer architectures and attention mechanisms.
Introduces the essential concepts and techniques of NLP using Python, covering topics such as text preprocessing, feature extraction, and machine learning models.
A comprehensive reference work covering a wide range of topics in NLP, providing detailed explanations and advanced research.
Provides a practical introduction to deep learning for coders. It great resource for anyone who wants to learn how to build and train deep learning models.
Provides a comprehensive introduction to reinforcement learning. It great resource for anyone who wants to learn how to build and train reinforcement learning models.
Provides a comprehensive overview of computer vision. It great resource for anyone who wants to learn how to build and train computer vision models.
Provides a comprehensive overview of generative adversarial networks (GANs). It great resource for anyone who wants to learn how to build and train GANs.
Provides a comprehensive overview of the mathematics that is used in machine learning. It great resource for anyone who wants to learn the mathematical foundations of AI.

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