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

Deploy Langchain v1 AI App at AWS, Local LLM Projects, Ollama, DeepSeek, LLAMA, Qwen3, Gemma3, GPT-OSS, Text to MySQL

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.