Build Chat Applications with OpenAI and LangChain

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4:58:20

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1 - Introduction to the Course

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

2 - Tokens Models and Prices

5 - Tokens.html

6 - Models and Prices.html

3 - Setting Up the Environment

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

4 - The OpenAI API

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

5 - Model Inputs

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

6 - Message History and Chatbot Memory

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

7 - Output Parsers

28 - String Output Parser.mp4

02:44

29 - CommaSeparated List Output Parser.mp4

03:15

30 - Datetime Output Parser.mp4

02:47

Files.zip

8 - LangChain Expression Language LCEL

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

9 - Retrieval Augmented Generation RAG

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

10 - Tools and Agents

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

Description

Gain cutting-edge AI skills: Master the LangChain framework to build and deploy real-world AI applications

What You'll Learn?

Master LangChain to seamlessly integrate existing applications with potent Large Language Models (LLMs)
Learn to connect to OpenAI’s language and embedding models
Develop prompt engineering skills that improve performance and relevance of AI responses
Apply the state-of-the-art Retrieval Augmented Generation (RAG) technique to empower your AI-driven product with a knowledge base
Leverage AI to open up endless opportunities for your organization
Enhance your career prospects with rare and highly sought-after AI Engineering skills

Who is this for?

Aspiring AI engineers

Everyone who is serious about integrating AI into their product

What You Need to Know?

Intermediate Python coding skills are required

You need to have Jupyter Notebook up and running

More details

Description
Are you an aspiring AI engineer excited to integrate AI into your product?
Are you thrilled about the breakthroughs in the field of AI?
Or maybe youâ€™re eager to learn this new and exciting LangChain framework everyoneâ€™s talking about.
If yes, then youâ€™ve come to the right place!
Why should you consider taking this LangChain course?
In this Build Chat Applications with OpenAI and LangChain course, weâ€™ll explore the increasingly popular LangChain Python library to develop engaging chatbot applications.
With detailed, step-by-step guidance, you will use OpenAIâ€™s API key to access their powerful large language models (LLMs). Once we have access to foundational models, we'll utilize LangChain and its integrations to create compelling prompts, add memory, input external data, and link it to third-party tools.
LangChain's integration with third-party tools distinguishes it by enabling connections to various language models and loading documents in multiple formats. It also allows for selecting suitable embedding models, storing embeddings in a vector store, and linking to search engines, code interpreters, and tools like Wikipedia, GitHub, Gmail, and more.
None of this would be possible without mastering the LangChain Expression Language (LCEL)â€”essential for developing stateful, context-aware reasoning chatbots. These chatbots remember past conversations, answer questions about unseen data, and tackle more complex problems.
Additionally, weâ€™ll spend much of our time discussing the state-of-the-art Retrieval Augmented Generation (RAG), both theoretically and practically. This technique allows LLM-powered applications to analyze and answer questions about information outside their training data. Ultimately, weâ€™ll create a chatbot that answers studentsâ€™ questions on courses from the 365 library.
What skills do you gain?
- Integrate existing applications with powerful LLMs.
- Connect to OpenAIâ€™s language and embedding models using an OpenAI API key.
- Develop prompt engineering techniques to enhance AI response performance and relevance.
- Implement RAG to enrich your AI-driven product with a knowledge base.
- Master the LCEL protocolâ€”essential for developing applications with the LangChain Python library.
- Connect external tools to your LLM-powered application.
- Understand the mechanics behind agents and agent executors.
Enhance your career prospects with rare and highly sought-after AI Engineering skills by enrolling in this LangChain and OpenAI course.
Click â€˜Buy Nowâ€™ and acquire real-world AI engineer skills today!
Who this course is for:
Aspiring AI engineers
Everyone who is serious about integrating AI into their product

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