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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.
