Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents

2026 Upgrade: Course completely re-recorded with LangChain v1 and LangGraph v1. All projects, agents, tools, and RAG pipelines rebuilt from scratch. **Perfect for developers, AI engineers, and serious learners who want production-grade GenAI skills.** This course is a comprehensive, practical guide to integrating Langchain v1 (latest release) and Ollama to build, automate, and deploy production-ready AI applications. Updated with the newest technologies and frameworks, you'll learn to set up these cutting-edge tools, create advanced prompt templates, build autonomous AI agents, implement RAG (Retrieval-Augmented Generation) systems, and deploy real-world applications on AWS. Each section is designed to provide you with hands-on skills and real-world experience with the latest AI development practices. What You Will Learn 1. Ollama & Langchain Setup - Complete installation and configuration of Ollama and Langchain - Work with the latest models: GPT-OSS, Gemma3, Qwen3, DeepSeek R1, and LLAMA 3.2 - Master Ollama commands, custom model creation, and raw API integration - Configure local LLM environments for optimal performance 2. Advanced Prompt Engineering - Design effective AI, human, and system message prompts - Use ChatPromptTemplate and MessagesPlaceholder for dynamic conversations - Master the invoke method and structured prompt patterns - Implement best practices for prompt tuning and optimization 3. LCEL Chains for Workflow Automation - Build Sequential, Parallel, and Router Chains with Langchain Expression Language (LCEL) - Create custom chains using RunnableLambda and RunnablePassthrough - Implement chain decorators for simplified workflow automation - Design conditional logic and dynamic chain routing for complex applications 4. Structured Output Parsing - Parse LLM outputs using Pydantic, JSON, CSV, and custom parsers - Use with_structured_output method for type-safe responses - Handle date-time parsing and structured data extraction - Format data for downstream processing and integration 5. Chat Memory and Conversation Management - Implement chat history with BaseChatMessageHistory and InMemoryChatMessageHistory - Use MessagesPlaceholder for dynamic conversation flow - Build stateful conversational AI applications - Manage long-term chat sessions efficiently 6. Build Production-Ready Chatbots - Create interactive chatbot applications using Streamlit - Implement streaming responses like ChatGPT - Maintain persistent chat history and session state - Deploy user-friendly chat interfaces with real-time updates 7. Document Processing with Multiple Loaders - Process PDFs using PyMuPDF and create QA systems - Work with Microsoft Office files (PPTX, DOCX, Excel) - Use Microsoft's MarkItDown for universal document conversion - Implement IBM's Docling for advanced OCR and document processing - Extract tables, images, and figures from any document type 8. Vector Stores and RAG Implementation - Build Retrieval-Augmented Generation (RAG) systems with FAISS and Chroma - Create and manage vector embeddings using OllamaEmbeddings - Implement document chunking strategies with RecursiveTextSplitter - Optimize chunk sizes for better retrieval performance - Design RAG prompt templates for context-aware responses 9. Agentic RAG Systems - Build autonomous RAG agents that retrieve and reason - Create custom tool decorators for agent capabilities - Implement real-time streaming for agent responses - Integrate vector stores with intelligent agent workflows 10. Tool Calling and Function Execution - Set up built-in tools: Tavily Search, DuckDuckGo, PubMed, Wikipedia - Create custom tools and bind them to LLMs - Implement tool calling loops for multi-step reasoning - Pass tool results back to LLMs for informed responses 11. AI Agents with Langchain - Master the create_agent API for building intelligent agents - Build web search agents with DuckDuckGo integration - Implement agent state management and middleware - Create dynamic model selection for intelligent agent routing - Stream agent responses in real-time using values, updates, and messages 12. Text-to-SQL Agent (MySQL Integration) - Build natural language to SQL query systems - Create schema inspection, query generation, and validation tools - Implement automatic SQL error correction with LLMs - Execute complex database queries from natural language 13. Real-World AI Projects - Stock Market News Analysis: Scrape web data and generate comprehensive reports - LinkedIn Profile Scraper: Extract and parse profile data with LLMs - Resume Parser: Build AI-powered CV analysis and JSON extraction system - Health Supplements QA: Create domain-specific RAG question-answering systems 14. Production Deployment on AWS - Launch and configure AWS EC2 instances for LLM applications - Install Ollama and Langchain on cloud servers - Deploy Streamlit applications in production environments - Connect VS Code to remote servers for seamless development By the end of this course, you'll have the expertise to build, deploy, and manage production-grade AI-powered applications using Langchain and Ollama. You'll be able to create intelligent chatbots, RAG systems, autonomous agents, and document processors that are ready for real-world deployment. Start building the future of AI applications today.

What you'll learn:

• Install and integrate LangChain v1 and Ollama to run Qwen3, Gemma3, DeepSeek R1, GPT-OSS, LLAMA, and custom GGUF models locally.
• Build complete chatbots with memory, history, streaming responses, and a Streamlit UI.
• Use prompt templates, LCEL chains, chain routing, parallel chains, custom chains, and runnable pipelines to structure LLM workflows.
• Parse structured output using Pydantic, JSON, CSV parsers, and .with_structured_output() methods.
• Implement advanced retrieval systems including similarity search, MMR search, threshold search, and optimized chunking.
• Use tool calling and function calling with DuckDuckGo, Tavily, Wikipedia, PubMed, and custom tools.
• Build production-ready AI agents using LangChain v1 agent API, dynamic model selection, middleware, state management, and real-time streaming.
• Create Agentic RAG systems including autonomous retrieval, context citation, custom FAISS tools, and streamed agentic responses.
• Build a complete Text-to-SQL Agent for MySQL with schema extraction, SQL generation, validation, execution, and automated error correction.
• Build LinkedIn scraper, resume parser, and data extraction workflows using Selenium, BeautifulSoup, LLM parsing, and Streamlit apps.
• Deploy LangChain v1 + Ollama applications to AWS EC2, configure remote servers, and run production-level AI apps.