Mô tả

Welcome to the Introduction to LangChain course! Very recently, we saw a revolution with the advent of Large Language Models. It is rare that something changes the world of Machine Learning that much, and the hype around LLM is real! That's something that very few experts predicted, and it's essential to be prepared for the future.


LangChain is an amazing tool that democratizes machine learning for everybody. With LangChain, every software engineer can use machine learning and build applications with it. Prior to LangChain and LLMs, you needed to be an expert in the field. Now, you can build an application with a couple of lines of code. Think about language models as a layer between humans and software. LangChain is a tool that allows the integration of LLMs within a larger software.


Topics covered in that course:

  • LangChain Basics

  • Loading and Summarizing Data

  • Prompt Engineering Fundamentals

  • Vector Database Basics

  • Retrieval Augmented Generation

  • RAG Optimization and Multimodal RAG

  • Augmenting LLMs with a Graph Database

  • Augmenting LLMs with tools

  • How to Build a Smart Voice Assistant

  • How to Automate Writing Novels

  • How to Automate Writing Software


The course is very hands-on! We will work on many examples to build your intuition on the different concepts we will address in this course. By the end of the course, you will be able to build complex software applications powered by Large Language Models!


Warning: during the course, I used a lot of the OpenAI models through their API. If you choose to use the OpenAI API as well, be aware that this will generate additional costs. I expect that reproducing all the examples in the course should not require more than $50 in OpenAI credits. However, all the examples can be reproduced for free if you choose to use open-source LLMs.

Bạn sẽ học được gì

Build software applications with Large Language models

Learn how to augment LLMs with tools and databases

Learn how to connect LLMs to external data

Learn the fundamentals of Prompt Engineering

Learn the fundamentals of Vector Databases

Learn the fundamentals of Retrieval Augmented Generation

LangChain: Models, Chains, Prompts, Memory, Vector stores, Agents!

Yêu cầu

  • Python
  • Jupyter notebooks
  • VS Code

Nội dung khoá học

13 sections

Introduction

3 lectures
Introduction to the course
02:50
Course structure
01:23
Setting up your Jupyter Notebook (optional)
04:07

LangChain Basics

10 lectures
Introduction
00:45
What is LangChain - OpenAI API Key - Installing the Python Packages
03:24
OpenAI package (temporary fix)
03:00
LLMs
01:57
Chains
02:58
Prompt Templates
05:09
Output parsers
03:25
Simple Sequence
03:26
Written material
03:22
Outro
00:14

Loading and Summarizing Data

6 lectures
Introduction
00:35
Loading Data
09:20
Summary strategies
01:59
Summarization examples
10:33
Written material
04:10
Outro
00:17

Prompt Engineering Fundamentals

12 lectures
Introduction
01:26
Elements of a Prompt
01:38
Few-Shot Learning
03:09
Memetic Proxy
01:54
Chain of Thought
05:17
Self-Consistency
03:01
Inception
02:10
Self-Ask
04:20
ReAct
04:28
Plan and Execute
05:20
Written material
11:24
Outro
00:18

Vector Database Basics

11 lectures
Intro
00:47
Why Vector Databases?
02:00
Similarity Metrics
02:48
Why do we need Indexing?
00:55
Product Quantization
01:58
Locality Sensitive-Hashing
01:30
Navigable Small World
02:05
Hierarchical Navigable Small World
02:09
Maximum Marginal Relevance
01:18
Written material
08:47
Outro
01:00

Retrieval augmented generation

12 lectures
Introduction
00:53
Indexing data
08:09
Loading data into a vector database
03:12
Providing sources
04:44
Indexing a website
06:03
Indexing a GitHub repository
06:29
The Stuff Strategy
04:21
The Map-Reduce Strategy
04:13
The Refine strategy
04:53
The Map-Rerank strategy
05:28
Written material
04:11
Outro
00:28

RAG optimization and Multimodal RAG

10 lectures
Introduction
00:47
Multi-Vector Retriever
13:00
Hypothetical Queries
07:45
Parsing a Multimodal Document
06:01
Summarizing the Data
02:45
Describing Images with LlaVA
11:21
Index the Data into a Database
08:55
Finalizing the RAG Pipeline
02:14
Written material
03:55
Outro
00:31

Augmenting LLMs with a Graph Database

10 lectures
Intro
01:03
What is a Knowledge Base
02:01
Getting the Data
02:06
Create the Graph Representation
07:28
Augmenting LLMs with a Knowledge Base
03:13
Using the Diffbot Graph Transformer
03:43
Creating a Local Graph Database
02:16
Augmenting an LLM with the Graph Database
05:10
Written material
02:23
Outro
00:39

Augmenting LLMs with Tools

8 lectures
Intro
00:45
What is an Agent?
00:57
Agent Example
10:38
Dissecting the Iterative Process
07:12
The Different Tools
01:19
Building Custom Tools
07:53
Written material
01:53
Outro
00:28

How to build a Smart Voice Assistant

9 lectures
Introduction
02:13
What are we building
01:00
Setting up the Project
02:26
From Speech to Text
05:14
From Text to Speech
02:37
Building a Conversational Agent
03:59
Augmenting the Agent with Tools
07:05
Written material
02:16
Outro
00:20

How to Automate Writing Books

14 lectures
Introduction
00:57
Formalizing the Book Writing Process
02:33
Setting up the Project
03:05
The Main Character
06:42
The Title
05:53
The Plot
05:19
The Chapters List
08:12
The Chapters' Plots
14:31
The Events List
12:08
Writing the Book
09:20
Writing to File
04:10
Reading the Book
00:49
Written material
06:12
Outro
00:21

Automating Writing Software

11 lectures
Introduction
00:49
The Strategy
02:07
Setting up the Project
01:49
The Technical Requirements
03:52
The Class Structure
02:38
The File Structure
02:56
The File Paths
02:59
The Code
05:08
Iterate
09:08
Written material
02:55
Outro
00:41

Thank you!

1 lectures
Parting words
00:40

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