ChatGPT and LangChain: The Complete Developer's Masterclass

12h 18s
English
Paid
July 19, 2024

You've found the most advanced, most complete, and most intensive masterclass online for learning how to integrate LangChain and ChatGPT into production-ready applications!

Thousands of engineers have learned how to build amazing applications using ChatGPT, and you can too. This course uses a time-tested, battle-proven method to make sure you understand exactly how ChatGPT works, and is the perfect pathway to help you get a new job as a software engineer working on AI-enabled apps.

The difference between this course and all the others: you will go far beyond the basics of simple ChatGPT prompts, and understand how companies are integrating text generation into their apps today.

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What will you build?

This course focuses on creating a series of different projects of increasing complexity. You'll start from the very basics, understanding how to access ChatGPT 4 programatically. From there, we will quickly increase in complexity, building more complex projects with many more features. By the end, you will make a fully-featured web app that implements a "Chat-with-a-PDF" feature. Note: no previous web development experience is required.

Here's a partial list of some of the topics you'll cover:

  • Understand how complex text-generation pipelines work
  • Write reusable code using chains provided by LangChain
  • Connect chains together in different ways to dramatically change your apps behavior with ease
  • Store, retrieve, and summarize chat messages using conversational memory
  • Implement semantic search for Retrieval-Augmented Generation using embeddings
  • Generate and store embeddings in vector databases like ChromaDB and Pinecone
  • Use retrievers to refine, reduce, and rank context documents, teaching ChatGPT new information
  • Create agents to automatically accomplish tasks for you using goals you define
  • Write tools and plugins to allow ChatGPT to access the outside world
  • Maintain a consistent focus on performance through distributed processing using Celery and Redis
  • Extend LangChain to implement server-to-browser text streaming
  • Improve ChatGPT's output quality through user-generated feedback mechanisms
  • Get visibility into how users interact with your text generation features by using tracing

There are a ton of courses that show how to use ChatGPT at a very basic level. This is one of the very few courses online that goes far beyond the basics to teach you advanced techniques that top companies are using today. I have a passion for teaching topics the right way - the way that you'll actually use technology in the real world. Sign up today and join me!


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# Title Duration
1 How to Get Help 01:15
2 What is LangChain? 03:58
3 How a Typical AI-Enabled App Works 10:00
4 Here It Is, This is Why We Use LangChain 05:29
5 Project Overview and Setup 03:21
6 Using LangChain the Simple Way 02:56
7 Introducing Chains 10:09
8 Adding a Chain 04:11
9 Parsing Command Line Arguments 02:30
10 Securing the API Key 04:45
11 Connecting Chains Together 02:57
12 Chains in Series with SequentialChain 07:01
13 App Overview 02:12
14 Receiving User Input 02:00
15 Chat vs Completion Style Models 10:10
16 Representing Messages with ChatPromptTemplates 06:02
17 Implementing a Chat Chain 04:38
18 Understanding Memory 09:26
19 Using ChatBufferMemory to Store Conversations 07:28
20 Saving and Extending Conversations 04:44
21 Summarizations Conversation Summary Memory 09:43
22 Project Overview 03:29
23 Project Setup 01:54
24 Loading Files with Document Loaders 06:15
25 Search Criteria 04:37
26 Introducing Embeddings 10:32
27 The Entire Embedding Flow 02:10
28 Chunking Text 07:16
29 Generating Embeddings 04:22
30 Introducing ChromaDB 10:02
31 Building a Retrieval Chain 10:33
32 What is a Retriever? 05:21
33 [Optional] Understanding Refine, MapReduce, and MapRerank 28:09
34 Removing Duplicate Documents 07:54
35 Creating a Custom Retriever 11:13
36 Custom Retriever in Action 06:02
37 Visualizing Embeddings 04:35
38 App Overview 04:14
39 Understanding Tools 08:13
40 Understanding ChatGPT Functions 10:55
41 Defining a Tool 06:36
42 Defining an Agent and AgentExecutor 05:52
43 Understanding Agents and AgentExecutors 09:14
44 Shortcomings in ChatGPT's Assumptions 04:45
45 Recovering from Errors in Tools 04:28
46 Adding Table Context 09:29
47 Adding a Table Description Tool 05:21
48 Being Direct with System Messages 02:52
49 Adding Better Descriptions for Tool Arguments 06:59
50 Tools with Multiple Arguments 07:13
51 Memory vs Agent Scratchpad 09:25
52 Preserving Messages with Agent Executor 02:38
53 Understanding Callbacks 04:47
54 Implementing a Basic Callback Handler 05:04
55 More Handler Implementaion 11:23
56 App Overview 02:27
57 Taking a Look at Mockups 03:24
58 Boilerplate Setup 04:44
59 How This App is Designed 06:10
60 Outlining the First Feature 04:29
61 Loading and Splitting From a PDF 03:41
62 Testing the PDF Upload 02:17
63 Introducing Pinecone 06:31
64 Initializing the Pinecone Client 05:54
65 Adding Documents to the Vector Store 03:52
66 Why is Processing Taking Forever? 06:11
67 Introducing Background Jobs 07:45
68 Redis Setup 01:56
69 Adding in the Worker 04:09
70 Queuing Up Jobs 04:04
71 Updating Document Metadata 07:08
72 Understanding the Apps Requirements 07:59
73 Persistent Message Storage 12:09
74 Introducing the Conversational Retrieval Chain 10:36
75 Building the Retriever 04:57
76 Custom History Objects 04:44
77 Building a Custom SQL History 08:53
78 Testing the Chain 04:59
79 Streaming Text Generation 03:58
80 Creating a Working Playground 05:12
81 Experimenting with a Streaming Language Model 09:11
82 Chains Don't Want to Stream 06:53
83 Receiving Chunks with a Callback 04:34
84 Extending a LLM Chain 08:50
85 Adding a Queue for Communication 07:28
86 The Chain Really Wants to Wait 04:14
87 Solving the Slow Chain 02:45
88 It Works! 02:41
89 Ending the Loop 04:59
90 Isolating the Queue and Handler 03:37
91 Using a Mixin Approach 04:47
92 Integrating the Streaming Code 06:59
93 Testing the Streaming Setup 07:07
94 Here's the Issue 04:38
95 Isolating the Handler 07:50
96 Streaming Complete! 10:34
97 Random Component Parts 04:17
98 Component Part Flow 05:19
99 Partial KWArg Application 06:14
100 Building Component Maps 04:35
101 Randomly Picking a Component 08:02
102 Generalizing Component Picking 10:09
103 Collecting User Feedback 05:16
104 Redis Connection Setup 06:52
105 Storing Votes in Redis 07:35
106 Weighted Randomness 03:03
107 Extracting Scores 06:31
108 Calculating the Average Score 07:33
109 Selecting Components By Score 04:38
110 Adding Score Observability 02:45
111 Building the Score Aggregate 03:50
112 Adding Another Form of Memory 02:37
113 Window Memory Implementation 06:10
114 Text Generation Tracing 04:32
115 Langfuse Signup 03:28
116 Adding in Tracing 06:50
117 Understanding the Trace 05:27
118 Automatic Trace Creation 10:32

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