Deploy LLM App with Ollama and Langchain in Production

Free Download Deploy LLM App with Ollama and Langchain in Production
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.16 GB | Duration: 12h 0m
Build AI chatbots, automate workflows, deploy on AWS. Master Langchain, Ollama, prompt engineering and RAG

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, Google Drive, and WhatsApp 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 agents that can use these tools and execute step-by-step instructions.Real-World IntegrationsExecute text-based queries on MySQL .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.
Overview
Section 1: Introduction
Lecture 1 Install Ollama
Lecture 2 Touch Base with Ollama
Lecture 3 Inspecting LLAMA 3.2 Model
Lecture 4 LLAMA 3.2 Benchmarking Overview
Lecture 5 What Type of Models are Available on Ollama
Lecture 6 Ollama Commands - ollama server, ollama show
Lecture 7 Ollama Commands - ollama pull, ollama list, ollama rm
Lecture 8 Ollama Commands - ollama cp, ollama run, ollama ps, ollama stop
Lecture 9 Create and Run Ollama Model with Predefined Settings
Lecture 10 Ollama Model Commands - /show
Lecture 11 Ollama Model Commands - /set, /clear, /save_model and /load_model
Lecture 12 Ollama Raw API Requests
Lecture 13 Load Uncesored Models for Banned Content Generation[Only Educational Purpose]
Section 2: Getting Started with Langchain
Lecture 14 Langchain Introduction
Lecture 15 Lanchain Installation
Lecture 16 Langsmith Setup of LLM Observability
Lecture 17 Calling Your First Langchain Ollama API
Lecture 18 Generating Uncensored Content in Langchain[Educational Purpose]
Lecture 19 Trace LLM Input Output at Langsmith
Lecture 20 Going a lot Deeper in the Langchain
Section 3: Chat Prompt Templates
Lecture 21 Why We Need Prompt Template
Lecture 22 Type of Messages Needed for LLM
Lecture 23 Circle Back to ChatOllama
Lecture 24 Use Langchain Message Types with ChatOllama
Lecture 25 Langchain Prompt Templates
Lecture 26 Prompt Templates with ChatOllama
Section 4: Chains
Lecture 27 Introduction to LCEL
Lecture 28 Create Your First LCEL Chain
Lecture 29 Adding StrOutputParser with Your Chain
Lecture 30 Chaining Runnables (Chain Multiple Runnables)
Lecture 31 Run Chains in Parallel Part 1
Lecture 32 Run Chains in Parallel Part 2
Lecture 33 How Chain Router Works
Lecture 34 Creating Independent Chains for Positive and Negative Reviews
Lecture 35 Route Your Answer Generation to Correct Chain
Lecture 36 What is RunnableLambda and RunnablePassthrough
Lecture 37 Make Your Custom Runnable Chain
Lecture 38 Create Custom Chain with chain Decorator
Section 5: Output Parsing
Lecture 39 What is Output Parsing
Lecture 40 What is Pydantic Parser
Lecture 41 Get Pydantic Parser Instruction
Lecture 42 Parse LLM Output Using Pydantic Parser
Lecture 43 Parsing with `.with_structured_output()` method
Lecture 44 JSON Output Parser
Lecture 45 CSV Output Parsing - CommaSeparatedListOutputParser
Lecture 46 Datetime Output Parsing
Section 6: Chat Message Memory | How to Keep Chat History
Lecture 47 How to Save and Load Chat Message History (Concept)
Lecture 48 Simple Chain Setup
Lecture 49 Chat Message with History Part 1
Lecture 50 Chat Message with History Part 2
Lecture 51 Chat Message with History using MessagesPlaceholder
Section 7: Make Your Own Chatbot Application
Lecture 52 Introduction
Lecture 53 Introduction To Streamlit and Our Chat Application
Lecture 54 Chat Bot Basic Code Setup
Lecture 55 Create Chat History in Streamlit Session State
Lecture 56 Create LLM Chat Input Area with Streamlit
Lecture 57 Update Historical Chat on Streamlit UI
Lecture 58 Complete Your Own Chat Bot Application
Lecture 59 Stream Output of Your Chat Bot like ChatGPT
Section 8: Document Loaders | Projects on PDF Documents
Lecture 60 Introduction to PDF Document Loaders
Lecture 61 Load Single PDF Document with PyMuPDFLoader
Lecture 62 Load All PDFs from a Directory
Lecture 63 Combine All PDFs Data as Context Text
Lecture 64 How Many Tokens are There in Contex Data.
Lecture 65 Make Question Answer Prompt Templates and Chain
Lecture 66 Ask Questions from Your PDF Documents
Lecture 67 Summarize Your PDF Documents
Lecture 68 Project 3 - Generate Detailed Structured Report from the PDF Documents
Section 9: Document Loaders | Stock Market News Report Generation
Lecture 69 Introduction to Webpage Loaders
Lecture 70 Load Unstructured Stock Market Data
Lecture 71 Make LLM QnA Script
Lecture 72 Catastrophic Forgetting of LLM
Lecture 73 Break Down Large Text Data Into Chunks
Lecture 74 Create Stock Market News Summary for Each Chunks
Lecture 75 Generate Final Stock Market Report
Section 10: Document Loaders | Microsoft Office Files Reader and Projects
Lecture 76 Introduction to Unstructured Data Loader
Lecture 77 Load .PPTX Data with DataLoader
Lecture 78 Process .PPTX data for LLM
Lecture 79 Generate Speaker Script for Your .PPTX Presentation
Lecture 80 Loading and Parsing Excel Data for LLM
Lecture 81 Ask Questions from LLM for given Excel Data
Lecture 82 Load .DOCX Document and Write Personalized Job Email
Section 11: Document Loaders | YouTube Video Transcripts and SEO Keywords Generator
Lecture 83 Load YouTube Video Subtitles
Lecture 84 Load YouTube Video Subtitles in 10 Mins Chunks
Lecture 85 Generate YouTube Keywords from the Transcripts
Section 12: Vector Stores and Retrievals
Lecture 86 Introduction to RAG Project
Lecture 87 Introduction to FAISS and Chroma Vector Database
Lecture 88 Load All PDF Documents
Lecture 89 Recursive Text Splitter to Create Documents Chunk
Lecture 90 How Important Chunk Size Selection is?
Lecture 91 Get OllamaEmbeddings
Lecture 92 Document Indexing in Vector Database
Lecture 93 How to Save and Search Vector Database
Section 13: RAG | Question Answer Over the Health Supplements Data
Lecture 94 Load Vector Database for RAG
Lecture 95 Get Vector Store as Retriever
Lecture 96 Exploring Similarity Search Types with Retriever
Lecture 97 Design RAG Prompt Template
Lecture 98 Build LLM RAG Chain
Lecture 99 Prompt Tuning and Generate Response from RAG Chain
Section 14: Tool and Function Calling
Lecture 100 What is Tool Calling
Lecture 101 Available Search Tools at Langchain
Lecture 102 Create Your Custom Tools
Lecture 103 Bind tools with LLM
Lecture 104 Working with Tavily and DuckDuckGo Search Tools
Lecture 105 Working with Wikipedia and PubMed Tools
Lecture 106 Creating Tool Functions for In-Built Tools
Lecture 107 Calling Tools with LLM
Lecture 108 Passing Tool Calling Result to LLM Part 1
Lecture 109 Passing Tool Calling Result to LLM Part 2
Section 15: Agents
Lecture 110 How Agent Works
Lecture 111 Tools Preparation for Agent
Lecture 112 More About the Agent Working Process
Lecture 113 Selection of Prompt for Agent
Lecture 114 Agent in Action
Section 16: Text to MySQL Queries | With and Without Agents
Lecture 115 Create MySQL Connection with Local Server
Lecture 116 Get MySQL Execution Chain
Lecture 117 Correct Malformed MySQL Queries Using LLM
Lecture 118 MySQL Query Chain Execution
Lecture 119 MySQL Query Execution with Agents in LangGraph
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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