Udemy (2025) Master Langchain and Ollama Chatbot RAG and Agents

[0dayddl]

7.16 GB | 00:08:54 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 - Install Ollama (18.06 MB)
10 - Ollama Model Commands show (112.57 MB)
11 - Ollama Model Commands set clear savemodel and loadmodel (151.99 MB)
12 - Ollama Raw API Requests (162.5 MB)
13 - Load Uncesored Models for Banned Content Generation Only Educational Purpose (131.33 MB)
2 - Touch Base with Ollama (73.99 MB)
3 - Inspecting LLAMA 32 Model (34.5 MB)
4 - LLAMA 32 Benchmarking Overview (16.03 MB)
5 - What Type of Models are Available on Ollama (68 MB)
6 - Ollama Commands ollama server ollama show (51.64 MB)
7 - Ollama Commands ollama pull ollama list ollama rm (38.11 MB)
8 - Ollama Commands ollama cp ollama run ollama ps ollama stop (67.8 MB)
9 - Create and Run Ollama Model with Predefined Settings (132.81 MB)
76 - Introduction to Unstructured Data Loader (100.6 MB)
77 - Load PPTX Data with DataLoader (88.88 MB)
78 - Process PPTX data for LLM (92.4 MB)
79 - Generate Speaker Script for Your PPTX Presentation (84.7 MB)
80 - Loading and Parsing Excel Data for LLM (25.14 MB)
81 - Ask Questions from LLM for given Excel Data (15.53 MB)
82 - Load DOCX Document and Write Personalized Job Email (59.07 MB)
83 - Load YouTube Video Subtitles (67.45 MB)
84 - Load YouTube Video Subtitles in 10 Mins Chunks (22.45 MB)
85 - Generate YouTube Keywords from the Transcripts (53.32 MB)
86 - Introduction to RAG Project (27.37 MB)
87 - Introduction to FAISS and Chroma Vector Database (36.25 MB)
88 - Load All PDF Documents (45.58 MB)
89 - Recursive Text Splitter to Create Documents Chunk (99.25 MB)
90 - How Important Chunk Size Selection is (62.31 MB)
91 - Get OllamaEmbeddings (47.48 MB)
92 - Document Indexing in Vector Database (32.43 MB)
93 - How to Save and Search Vector Database (23.92 MB)
94 - Load Vector Database for RAG (82.14 MB)
95 - Get Vector Store as Retriever (44.26 MB)
96 - Exploring Similarity Search Types with Retriever (83.79 MB)
97 - Design RAG Prompt Template (53.59 MB)
98 - Build LLM RAG Chain (49.57 MB)
99 - Prompt Tuning and Generate Response from RAG Chain (31.26 MB)
100 - What is Tool Calling (67.55 MB)
101 - Available Search Tools at Langchain (27.98 MB)
102 - Create Your Custom Tools (23.61 MB)
103 - Bind tools with LLM (31.4 MB)
104 - Working with Tavily and DuckDuckGo Search Tools (50.83 MB)
105 - Working with Wikipedia and PubMed Tools (69.32 MB)
106 - Creating Tool Functions for InBuilt Tools (35.64 MB)
107 - Calling Tools with LLM (21.48 MB)
108 - Passing Tool Calling Result to LLM Part 1 (43.92 MB)
109 - Passing Tool Calling Result to LLM Part 2 (86.2 MB)
110 - How Agent Works (27.72 MB)
111 - Tools Preparation for Agent (71.08 MB)
112 - More About the Agent Working Process (31.77 MB)
113 - Selection of Prompt for Agent (61.03 MB)
114 - Agent in Action (83.38 MB)
115 - Create MySQL Connection with Local Server (56.83 MB)
116 - Get MySQL Execution Chain (64.12 MB)
117 - Correct Malformed MySQL Queries Using LLM (67.79 MB)
118 - MySQL Query Chain Execution (33.09 MB)
119 - MySQL Query Execution with Agents in LangGraph (126.95 MB)
14 - Langchain Introduction (93.9 MB)
15 - Lanchain Installation (105.98 MB)
16 - Langsmith Setup of LLM Observability (84.84 MB)
17 - Calling Your First Langchain Ollama API (75.04 MB)
18 - Generating Uncensored Content in Langchain Educational Purpose (91.89 MB)
19 - Trace LLM Input Output at Langsmith (87.78 MB)
20 - Going a lot Deeper in the Langchain (135.35 MB)
21 - Why We Need Prompt Template (31.13 MB)
22 - Type of Messages Needed for LLM (47.86 MB)
23 - Circle Back to ChatOllama (84.3 MB)
24 - Use Langchain Message Types with ChatOllama (80.41 MB)
25 - Langchain Prompt Templates (105.49 MB)
26 - Prompt Templates with ChatOllama (118.91 MB)
27 - Introduction to LCEL (72.65 MB)
28 - Create Your First LCEL Chain (106.24 MB)
29 - Adding StrOutputParser with Your Chain (115.16 MB)
30 - Chaining Runnables Chain Multiple Runnables (39.3 MB)
31 - Run Chains in Parallel Part 1 (76.49 MB)
32 - Run Chains in Parallel Part 2 (62.11 MB)
33 - How Chain Router Works (39.11 MB)
34 - Creating Independent Chains for Positive and Negative Reviews (37.75 MB)
35 - Route Your Answer Generation to Correct Chain (49.17 MB)
36 - What is RunnableLambda and RunnablePassthrough (33.7 MB)
37 - Make Your Custom Runnable Chain (53.32 MB)
38 - Create Custom Chain with chain Decorator (19.53 MB)
39 - What is Output Parsing (66.04 MB)
40 - What is Pydantic Parser (51.83 MB)
41 - Get Pydantic Parser Instruction (55.3 MB)
42 - Parse LLM Output Using Pydantic Parser (63.37 MB)
43 - Parsing with withstructuredoutput method (34.44 MB)
44 - JSON Output Parser (17.81 MB)
45 - CSV Output Parsing CommaSeparatedListOutputParser (28.39 MB)
46 - Datetime Output Parsing (41.62 MB)
47 - How to Save and Load Chat Message History Concept (75.5 MB)
48 - Simple Chain Setup (65.91 MB)
49 - Chat Message with History Part 1 (49.17 MB)
50 - Chat Message with History Part 2 (65.33 MB)
51 - Chat Message with History using MessagesPlaceholder (100.73 MB)
52 - Introduction (37.23 MB)
53 - Introduction To Streamlit and Our Chat Application (38.19 MB)
54 - Chat Bot Basic Code Setup (68.87 MB)
55 - Create Chat History in Streamlit Session State (48.71 MB)
56 - Create LLM Chat Input Area with Streamlit (71.97 MB)
57 - Update Historical Chat on Streamlit UI (50.88 MB)
58 - Complete Your Own Chat Bot Application (22.66 MB)
59 - Stream Output of Your Chat Bot like ChatGPT (33.84 MB)
60 - Introduction to PDF Document Loaders (86.61 MB)
61 - Load Single PDF Document with PyMuPDFLoader (67.06 MB)
62 - Load All PDFs from a Directory (64.46 MB)
63 - Combine All PDFs Data as Context Text (50.26 MB)
64 - How Many Tokens are There in Contex Data (42.96 MB)
65 - Make Question Answer Prompt Templates and Chain (31.02 MB)
66 - Ask Questions from Your PDF Documents (63.07 MB)
67 - Summarize Your PDF Documents (40.72 MB)
68 - Project 3 Generate Detailed Structured Report from the PDF Documents (66.21 MB)
69 - Introduction to Webpage Loaders (94.53 MB)
70 - Load Unstructured Stock Market Data (46.07 MB)
71 - Make LLM QnA Script (27.44 MB)
72 - Catastrophic Forgetting of LLM (22.92 MB)
73 - Break Down Large Text Data Into Chunks (42.48 MB)
74 - Create Stock Market News Summary for Each Chunks (94.53 MB)
75 - Generate Final Stock Market Report (82.87 MB)

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[0dayddl]

2025 Master Langchain and Ollama - Chatbot, RAG and Agents
Last updated 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.51 GB | Duration: 14h 13m​

Master Langchain v0.3, Local LLM Projects, Ollama, LLAMA 3.2 (Lama 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

Requirements
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

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 LearnOllama & Langchain SetupComplete setup and installation of Ollama and Langchain.Configure base URLs and handle direct API calls.Establish the environment for efficient integration.Prompt EngineeringUnderstand 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 AutomationLearn 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 ParsingFormat data with parsers like JSON, CSV, Markdown, and Pydantic.Parse structured output and use date-time output handling for organized data.Chat Message MemoryUse BaseChatMessageHistory and InMemoryChatMessageHistory for managing chat sessions.Create chat applications with memory to improve user experience.Build and Deploy ChatbotsBuild a chatbot application using Streamlit.Maintain chat history and handle user inputs efficiently.Document Loaders and RetrievalsWork with loaders for web pages, PDFs, HTML data.Retrieve and summarize documents, convert text data, and use vector stores.Vector Stores and RetrievalsIntegrate vector stores for document retrieval using FAISS and Chroma.Reload retrievers, index documents, and enhance retrieval accuracy.Tool Calling and Custom AgentsSet 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 IntegrationsExecute text-based queries on MySQL.Parse LinkedIn Profile with LLMParse Job Resume with LLMDeploy LLAMA with OLLAMA on AWS Who This Course Is ForDevelopers 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, you'll have the skills to build, deploy, and manage AI-powered applications, from chatbots to document retrievers, ready for production.

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.

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