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.

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