LangChain in Action- Develop LLM-Powered Applications

LangChain in Action: Develop LLM-Powered Applications, available at $69.99, has an average rating of 4.51, with 63 lectures, based on 365 reviews, and has 2846 subscribers.

You will learn about 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 This course is ideal for individuals who are Python Developers, AI Enthusiats It is particularly useful for Python Developers, AI Enthusiats.

Enroll now: LangChain in Action: Develop LLM-Powered Applications

Summary

Title: LangChain in Action: Develop LLM-Powered Applications

Price: $69.99

Average Rating: 4.51

Number of Lectures: 63

Number of Published Lectures: 63

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 63

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Python Developers, AI Enthusiats

Target Audiences

Python Developers, AI Enthusiats

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.

Course Curriculum

Chapter 1: Before we start

Lecture 1: What to expect from this course and how to get all ressources

Lecture 2: Why this course is different

Lecture 3: Prerequisites

Lecture 4: Essential topics and terms (theory)

Lecture 5: Why this course does not cover Open Source models like LLama2

Lecture 6: Optional: Install Visual Studio Code

Lecture 7: Get the source files with Git from Github

Lecture 8: Create OpenAI Account and create API Key

Chapter 2: Preparation

Lecture 1: What we have to do before delving into LangChain

Lecture 2: Setup of a virtual environment

Lecture 3: Setup OpenAI Api-Key as environment variable

Lecture 4: Exploring the vanilla OpenAI package

Chapter 3: LangChain Basics

Lecture 1: IMPORTANT NOTE – LANGCHAIN 0.1 Codechanges

Lecture 2: LLM Basics

Lecture 3: Prompting Basics

Lecture 4: Theory: Prompt Engineering Basics

Lecture 5: Few Shot Prompting

Lecture 6: Chain of thought prompting

Lecture 7: Pipeline-Prompts

Lecture 8: Prompt Serialisation

Chapter 4: Chains – From basic to advanced chains

Lecture 1: Introduction to chains

Lecture 2: Basic chains – the LLMChain

Lecture 3: Response Schemas and OutputParsers

Lecture 4: LLMChain with multiple inputs

Lecture 5: SequentialChains

Lecture 6: RouterChains

Chapter 5: Callbacks

Lecture 1: Callbacks

Chapter 6: Memory

Lecture 1: Memory basics – ConversationBufferMemory

Lecture 2: ConversationSummaryMemory

Lecture 3: EXERCISE: Use Memory to build a streamlit Chatbot

Lecture 4: SOLUTION: Chatbot with Streamlit

Chapter 7: OpenAI Function Calling

Lecture 1: OpenAI Function Calling – Vanilla OpenAI Package

Lecture 2: Function Calling with LangChain [DEPRECATED]

Lecture 3: Limits and issues of the langchain Implementation [DEPRECATED]

Lecture 4: Tool/Function Calling with LangChain – The new way

Chapter 8: Retrieval Augmented Generation (RAG)

Lecture 1: RAG – Theory and building blocks

Lecture 2: Loaders and Splitters

Lecture 3: Embeddings – Theory and practice

Lecture 4: VectorStores and Retrievers

Lecture 5: RAG Service with FastAPI

Chapter 9: Agents

Lecture 1: Agents Basics – LLMs learn to use tools

Lecture 2: Agents with a custom RAG-Tool

Lecture 3: ChatAgents

Chapter 10: Indexing API

Lecture 1: Indexing API – keep your documents in sync

Lecture 2: PREREQUISITE: Docker Installation

Lecture 3: Setup of PgVector and RecordManager

Lecture 4: Indexing Documents in practice

Lecture 5: Document Retrieval with PgVector

Chapter 11: LangSmith

Lecture 1: Introduction to LangSmith (User Interface and Hub)

Lecture 2: LangSmith Projects

Lecture 3: LangSmith Datasets and Evaluation

Chapter 12: Microservice Architecture for LLM Applications

Lecture 1: Before you watch this section

Lecture 2: Introduction to Microservice Architecture

Lecture 3: How our Chatbot works in a Microservice Architecture

Lecture 4: Introduction to Docker

Lecture 5: Introduction to Kubernetes

Lecture 6: Deployment of the LLM Microservices to Kubernetes

Chapter 13: LangChain Expression Language (LCEL)

Lecture 1: LangChain Expression Language

Lecture 2: Intro to LangChain Expression Language

Lecture 3: LCEL Part 1 – Pipes and OpenAI Function Calling

Lecture 4: LCEL – Part 2 – VectorStores, ItemGetter, Tools

Lecture 5: LCEL – Part 3 – Arbitrary Functions, Runnable Interface, Fallbacks

Chapter 14: Congratulations!

Lecture 1: Thank you for participating in this course

Instructors

Markus Lang
Software Engineer – Python Developer – LLM Expert

Rating Distribution

1 stars: 0 votes

2 stars: 4 votes

3 stars: 30 votes

4 stars: 135 votes

5 stars: 196 votes

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