Master LangChain & Ollama: Build AI Chatbots & RAG Apps 2025
Focused View
9:22:13
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
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
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
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
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
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
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
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
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
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
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
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
More details
Course Overview
This comprehensive course teaches you to build advanced AI applications using LangChain and Ollama, from custom chatbots to document-powered RAG systems with local LLMs.
What You'll Learn
- Build and deploy custom chatbots with memory and history
- Create document-powered AI applications using RAG
- Master prompt engineering and output parsing techniques
Who This Is For
- Developers integrating AI into applications
- Data scientists building document-powered workflows
- AI enthusiasts creating local LLM projects
Key Benefits
- Hands-on projects with real-world applications
- Learn to work with local LLMs like LLAMA 3.2
- Deploy production-ready AI solutions
Curriculum Highlights
- LangChain fundamentals and Ollama setup
- Advanced prompt engineering and chaining
- Building document-powered RAG applications
Focused display
Category
- language english
- Training sessions 93
- duration 9:22:13
- Release Date 2025/04/19
