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.
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.
