2025 Master Langchain and Ollama - Chatbot, RAG and Agents

Focused View

Laxmi Kant KGP Talkie

9:22:13

0 View

1 - Introduction

1 - Install Ollama.mp4

04:59

2 - Touch Base with Ollama.mp4

05:54

3 - Inspecting LLAMA 32 Model.mp4

06:28

4 - LLAMA 32 Benchmarking Overview.mp4

03:47

5 - What Type of Models are Available on Ollama.mp4

06:36

6 - Ollama Commands ollama server ollama show.mp4

05:26

7 - Ollama Commands ollama pull ollama list ollama rm.mp4

05:53

8 - Ollama Commands ollama cp ollama run ollama ps ollama stop.mp4

06:27

9 - Create and Run Ollama Model with Predefined Settings.mp4

08:57

10 - Ollama Model Commands show.mp4

06:19

11 - Ollama Model Commands set clear savemodel and loadmodel.mp4

09:18

12 - Ollama Raw API Requests.mp4

08:54

13 - Load Uncesored Models for Banned Content Generation Only Educational Purpose.mp4

08:48

2 - Getting Started with Langchain

14 - Langchain Introduction.mp4

05:50

15 - Lanchain Installation.mp4

05:57

16 - Langsmith Setup of LLM Observability.mp4

06:38

17 - Calling Your First Langchain Ollama API.mp4

06:38

18 - Generating Uncensored Content in Langchain Educational Purpose.mp4

06:21

19 - Trace LLM Input Output at Langsmith.mp4

06:23

20 - Going a lot Deeper in the Langchain.mp4

07:54

3 - Chat Prompt Templates

21 - Why We Need Prompt Template.mp4

04:24

22 - Type of Messages Needed for LLM.mp4

04:32

23 - Circle Back to ChatOllama.mp4

06:04

24 - Use Langchain Message Types with ChatOllama.mp4

07:10

25 - Langchain Prompt Templates.mp4

06:19

26 - Prompt Templates with ChatOllama.mp4

09:30

4 - Chains

27 - Introduction to LCEL.mp4

06:24

28 - Create Your First LCEL Chain.mp4

08:49

29 - Adding StrOutputParser with Your Chain.mp4

07:37

30 - Chaining Runnables Chain Multiple Runnables.mp4

09:00

31 - Run Chains in Parallel Part 1.mp4

07:07

32 - Run Chains in Parallel Part 2.mp4

06:33

33 - How Chain Router Works.mp4

05:09

34 - Creating Independent Chains for Positive and Negative Reviews.mp4

07:13

35 - Route Your Answer Generation to Correct Chain.mp4

07:50

36 - What is RunnableLambda and RunnablePassthrough.mp4

06:29

37 - Make Your Custom Runnable Chain.mp4

05:24

38 - Create Custom Chain with chain Decorator.mp4

03:50

5 - Output Parsing

39 - What is Output Parsing.mp4

05:19

40 - What is Pydantic Parser.mp4

05:18

41 - Get Pydantic Parser Instruction.mp4

05:10

42 - Parse LLM Output Using Pydantic Parser.mp4

07:43

43 - Parsing with withstructuredoutput method.mp4

04:14

44 - JSON Output Parser.mp4

04:25

45 - CSV Output Parsing CommaSeparatedListOutputParser.mp4

06:21

46 - Datetime Output Parsing.mp4

08:07

6 - Chat Message Memory How to Keep Chat History

47 - How to Save and Load Chat Message History Concept.mp4

07:08

48 - Simple Chain Setup.mp4

05:20

49 - Chat Message with History Part 1.mp4

05:15

50 - Chat Message with History Part 2.mp4

06:17

51 - Chat Message with History using MessagesPlaceholder.mp4

08:38

7 - Make Your Own Chatbot Application

52 - Introduction.mp4

04:25

53 - Introduction To Streamlit and Our Chat Application.mp4

04:59

54 - Chat Bot Basic Code Setup.mp4

04:22

55 - Create Chat History in Streamlit Session State.mp4

06:17

56 - Create LLM Chat Input Area with Streamlit.mp4

05:05

57 - Update Historical Chat on Streamlit UI.mp4

05:37

58 - Complete Your Own Chat Bot Application.mp4

04:41

59 - Stream Output of Your Chat Bot like ChatGPT.mp4

06:18

8 - Document Loaders Projects on PDF Documents

60 - Introduction to PDF Document Loaders.mp4

07:07

61 - Load Single PDF Document with PyMuPDFLoader.mp4

05:01

62 - Load All PDFs from a Directory.mp4

06:12

63 - Combine All PDFs Data as Context Text.mp4

03:56

64 - How Many Tokens are There in Contex Data.mp4

05:05

65 - Make Question Answer Prompt Templates and Chain.mp4

07:19

66 - Ask Questions from Your PDF Documents.mp4

06:37

67 - Summarize Your PDF Documents.mp4

03:53

68 - Project 3 Generate Detailed Structured Report from the PDF Documents.mp4

04:37

9 - Document Loaders Stock Market News Report Generation

69 - Introduction to Webpage Loaders.mp4

05:51

70 - Load Unstructured Stock Market Data.mp4

05:27

71 - Make LLM QnA Script.mp4

04:57

72 - Catastrophic Forgetting of LLM.mp4

05:01

73 - Break Down Large Text Data Into Chunks.mp4

04:54

74 - Create Stock Market News Summary for Each Chunks.mp4

04:58

75 - Generate Final Stock Market Report.mp4

05:36

10 - Document Loaders Microsoft Office Files Reader and Projects

76 - Introduction to Unstructured Data Loader.mp4

06:02

77 - Load PPTX Data with DataLoader.mp4

06:08

78 - Process PPTX data for LLM.mp4

06:55

79 - Generate Speaker Script for Your PPTX Presentation.mp4

06:39

80 - Loading and Parsing Excel Data for LLM.mp4

04:21

81 - Ask Questions from LLM for given Excel Data.mp4

03:56

82 - Load DOCX Document and Write Personalized Job Email.mp4

06:04

11 - Document Loaders YouTube Video Transcripts and SEO Keywords Generator

83 - Load YouTube Video Subtitles.mp4

07:33

84 - Load YouTube Video Subtitles in 10 Mins Chunks.mp4

04:04

85 - Generate YouTube Keywords from the Transcripts.mp4

07:03

12 - Vector Stores and Retrievals

86 - Introduction to RAG Project.mp4

05:35

87 - Introduction to FAISS and Chroma Vector Database.mp4

05:46

88 - Load All PDF Documents.mp4

04:27

89 - Recursive Text Splitter to Create Documents Chunk.mp4

06:21

90 - How Important Chunk Size Selection is.mp4

04:31

91 - Get OllamaEmbeddings.mp4

06:28

92 - Document Indexing in Vector Database.mp4

06:11

93 - How to Save and Search Vector Database.mp4

03:48

Description

Master Langchain v0.3, Local LLM Projects, Ollama, DeepSeek, LLAMA 3.2, Ollama Chatbot, Ollama and Langchain Tutorial

What You'll Learn?

Set up and Integrate Ollama with Langchain: Students will learn how to install, configure, and operate Ollama alongside Langchain.
Build Custom Chatbots: Learners will develop skills to create chat applications with memory, history, advanced chatbot features using Streamlit and Langchain.
Use Prompt Templates, Chains, and Output Parsers: Students will master prompt templates and chaining methods (Sequential, Parallel, and Router Chains).
Deploy Real-World Applications: The course will guide students through deploying applications on AWS EC2

Who is this for?

Developers aiming to integrate language models into applications.

Data scientists interested in automating workflows and leveraging document retrieval.

AI enthusiasts eager to build custom chatbots and conversational tools.

Professionals seeking skills in deploying applications on AWS and other platforms.

Learners with basic Python and API knowledge who want to create end-to-end AI solutions.

What You Need to Know?

Basic Python programming knowledge

Familiarity with APIs and web requests

Basic understanding of machine learning concepts

Access to a computer with internet for installations and setups

More details

Description
This course is a practical guide to integrating Langchain and Ollama to build, automate, and deploy AI applications. Learn to set up these tools, create prompt templates, automate workflows, manage data retrieval, and deploy real-world applications on AWS. Each section is designed to provide you with hands-on skills and experience.

What You Will Learn
Ollama & Langchain Setup
Complete setup and installation of Ollama and Langchain.
Configure base URLs and handle direct API calls.
Establish the environment for efficient integration.
Prompt Engineering
Understand AI, human, and system message prompts.
Use AIPromptTemplate, Human, System, and ChatMessagePromptTemplate to shape responses.
Explore the invoke method to control the model's behavior.
Chains for Workflow Automation
Learn Sequential, Parallel, and Router Chains to build flexible workflows.
Work with custom chains and explore Chain Runnables for added automation.
Implement real-world workflows using Langchain's chaining capabilities.
Output Parsing
Format data with parsers like JSON, CSV, Markdown, and Pydantic.
Parse structured output and use date-time output handling for organized data.
Chat Message Memory
Use BaseChatMessageHistory and InMemoryChatMessageHistory for managing chat sessions.
Create chat applications with memory to improve user experience.
Build and Deploy Chatbots
Build a chatbot application using Streamlit.
Maintain chat history and handle user inputs efficiently.
Document Loaders and Retrievals
Work with loaders for web pages, PDFs, HTML data.
Retrieve and summarize documents, convert text data, and use vector stores.
Vector Stores and Retrievals
Integrate vector stores for document retrieval using FAISS and Chroma.
Reload retrievers, index documents, and enhance retrieval accuracy.
Tool Calling and Custom Agents
Set up tools for Tavily Search, PubMed, Wikipedia, and more.
Design custom tools that can be used with the Agents and execute step-by-step instructions.
Real-World Integrations
Execute text-based queries on MySQL.
Parse LinkedIn Profile with LLM
Parse Job Resume with LLM
Deploy LLAMA (LAMA) with OLLAMA on AWS
Who This Course Is For
Developers and data scientists who want to use Langchain and Ollama for AI applications.
AI enthusiasts looking to automate workflows and create document retrieval systems.
Professionals needing to build end-to-end chatbots or deploy applications on AWS.
Learners with basic Python knowledge who want practical experience with real-world AI tools.

By the end of this course, youâ€™ll have the skills to build, deploy, and manage AI-powered applications, from chatbots to document retrievers, ready for production.
Who this course is for:
Developers aiming to integrate language models into applications.
Data scientists interested in automating workflows and leveraging document retrieval.
AI enthusiasts eager to build custom chatbots and conversational tools.
Professionals seeking skills in deploying applications on AWS and other platforms.
Learners with basic Python and API knowledge who want to create end-to-end AI solutions.

User Reviews

Rating

average 0

Total votes0

Focused display

Machine Learning

Laxmi Kant KGP Talkie

Instructor's Courses

I am AVP, Data Science at Join Ventures, and have been Ph.D. Scholar at the Indian Institute of Technology (IIT), Kharagpur. I also co-founded a company, mBreath Technologies. I have 8+ years of experience in data science, team management, business development, and customer profiling. I have worked with startups and MNCs. You can join me at my YouTube channel KGP Talkie.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.