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

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

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.