Master LangChain: Build AI Chat Apps with OpenAI (2024)
Focused View
4:58:20
1 - Introduction to the Course.mp4
04:53
2 - Business Applications of LangChain.mp4
05:22
3 - What Makes LangChain Powerful.mp4
04:32
4 - What Does the Course Cover.mp4
05:32
5 - Tokens.html
6 - Models and Prices.html
7 - Setting Up a Custom Anaconda Environment for Jupyter Integration.mp4
03:42
8 - Obtaining an OpenAI API Key.mp4
02:04
9 - Setting the API Key as an Environment Variable.html
Files.zip
10 - First Steps.html
11 - System User and Assistant Roles.html
12 - Creating a Sarcastic Chatbot.html
13 - Temperature Max Tokens and Streaming.html
Files.zip
14 - The LangChain Framework.html
15 - ChatOpenAI.mp4
06:24
16 - System and Human Messages.mp4
04:29
17 - AI Messages.mp4
05:07
18 - Prompt Templates and Prompt Values.mp4
05:22
19 - Chat Prompt Templates and Chat Prompt Values.mp4
06:06
20 - FewShot Chat Message Prompt Templates.mp4
06:22
21 - LLMChain.mp4
02:38
Files.zip
22 - Chat Message History.mp4
06:00
23 - Conversation Buffer Memory Implementing the Setup.mp4
03:49
24 - Conversation Buffer Memory Configuring the Chain.mp4
06:37
25 - Conversation Buffer Window Memory.mp4
04:02
26 - Conversation Summary Memory.mp4
06:55
27 - Combined Memory.mp4
05:12
Files.zip
28 - String Output Parser.mp4
02:44
29 - CommaSeparated List Output Parser.mp4
03:15
30 - Datetime Output Parser.mp4
02:47
Files.zip
31 - Piping a Prompt Model and an Output Parser.mp4
06:51
32 - Batching.mp4
04:35
33 - Streaming.mp4
04:18
34 - The Runnable and RunnableSequence Classes.mp4
04:52
35 - Piping Chains and the RunnablePassthrough Class.mp4
07:32
36 - Graphing Runnables.mp4
02:15
37 - RunnableParallel.mp4
06:23
38 - Piping a RunnableParallel with Other Runnables.mp4
05:32
39 - RunnableLambda.mp4
05:23
40 - The chain Decorator.mp4
04:21
41 - Adding Memory to a Chain Part 1 Implementing the Setup.mp4
04:02
42 - RunnablePassthrough with Additional Keys.mp4
05:24
43 - Itemgetter.mp4
03:25
44 - Adding Memory to a Chain Part 2 Creating the Chain.mp4
08:05
Files.zip
45 - How to Integrate Custom Data into an LLM.mp4
04:02
46 - Introduction to RAG.mp4
03:40
47 - Introduction to Document Loading and Splitting.mp4
03:56
48 - Introduction to Document Embedding.mp4
06:46
49 - Introduction to Document Storing Retrieval and Generation.mp4
03:49
50 - Indexing Document Loading with PyPDFLoader.mp4
07:10
50 - Introduction-to-Data-and-Data-Science.pdf
51 - Indexing Document Loading with Docx2txtLoader.mp4
02:25
51 - Introduction-to-Data-and-Data-Science.docx
52 - Indexing Document Splitting with Character Text Splitter Theory.mp4
02:46
53 - Indexing Document Splitting with Character Text Splitter Code Along.mp4
05:20
54 - Indexing Document Splitting with Markdown Header Text Splitter.mp4
05:53
54 - Introduction-to-Data-and-Data-Science-2.docx
55 - Indexing Text Embedding with OpenAI.mp4
06:00
56 - Indexing Creating a Chroma Vector Store.mp4
05:42
57 - Indexing Inspecting and Managing Documents in a Vector Store.mp4
04:22
58 - Retrieval Similarity Search.mp4
05:29
59 - Retrieval Maximal Marginal Relevance Search.mp4
06:47
60 - Retrieval Vector StoreBacked Retriever.mp4
03:30
61 - Generation Stuffing Documents.mp4
04:22
62 - Generation Generating a Response.mp4
03:41
Files.zip
63 - Introduction to Reasoning Chatbots.mp4
03:05
64 - Tools Toolkits Agents and Agent Executors.mp4
06:41
65 - Fixing the GuessedAtParserWarning.html
66 - Creating a Wikipedia Tool and Piping It to a Chain.mp4
06:03
67 - Creating a Retriever and a Custom Tool.mp4
05:37
68 - LangChain Hub.mp4
04:06
69 - Creating a Tool Calling Agent and an Agent Executor.mp4
05:39
70 - AgentAction and AgentFinish.mp4
04:37
Files.zip
More details
Course Overview
This comprehensive course teaches you to build cutting-edge AI chat applications using LangChain and OpenAI. Gain hands-on experience integrating large language models into real-world products through practical coding exercises and industry-relevant projects.
What You'll Learn
- Master LangChain framework for seamless LLM integration
- Develop advanced prompt engineering skills for better AI responses
- Implement Retrieval Augmented Generation (RAG) with custom knowledge bases
Who This Is For
- Aspiring AI engineers looking to specialize in LLM applications
- Developers serious about integrating AI into their products
- Python programmers wanting to expand into AI engineering
Key Benefits
- Acquire rare, in-demand AI engineering skills
- Learn to leverage OpenAI's powerful language models
- Build chatbots with memory and advanced conversation capabilities
Curriculum Highlights
- OpenAI API integration and chatbot development
- LangChain Expression Language and memory systems
- Retrieval Augmented Generation with custom data
Focused display
Category
- language english
- Training sessions 61
- duration 4:58:20
- Release Date 2025/06/08
