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.