Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents | Udemy [Update 04/2026]
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Deploy Langchain v1 AI App at AWS, Local LLM Projects, Ollama, DeepSeek, LLAMA, Qwen3, Gemma3, GPT-OSS, Text to MySQL

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

Who this course is for:
Developers who want to build AI-powered applications, chatbots, and intelligent automation tools.
Data Scientists & ML Engineers who want hands-on experience with LangChain v1, LangGraph workflows, and real-world RAG systems.
AI enthusiasts and students who want to go beyond theory and build practical GenAI projects using open-source LLMs.
Professionals who want practical experience with tool calling, AI agents, retrieval systems, document processing, and production deployments.
Anyone with basic Python knowledge looking to build end-to-end AI applications that run locally using Ollama.

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