2025 Master LangGraph and LangChain with Ollama- Agentic RAG

Requirements

• Basic programming knowledge (preferably in Python).
• A computer with internet access and the ability to install software.
• Familiarity with databases and basic query writing (optional but helpful).
• Basic Langchain experience is needed

Description

Take a deep dive into the world of cutting-edge AI development with this comprehensive course on LangGraph, Ollama, and Retrieval-Augmented Generation (RAG). Designed for beginners and professionals alike, this course equips you with the skills to build chatbots, manage LLMs locally, and integrate powerful database query capabilities seamlessly into your projects.

With step-by-step guidance, you’ll explore:

• Setting up and benchmarking local LLMs with Ollama.

• Building state-of-the-art chatbots using LangGraph and LangChain.

• Advanced type hinting, data validation, and OOPs principles for clean and efficient coding.

• Designing intelligent agents for MySQL queries and RAG workflows.

Unlock your potential and learn how to create dynamic, memory-enabled chatbots, work with private datasets, and master graph-based programming for AI applications.

Ollama Setup for Local LLM

Learn how to install and configure Ollama to work with local LLMs. Explore available models, run benchmarks, and use powerful Ollama commands to manage and interact with AI models efficiently.

Getting Started with LangChain

Discover LangChain and its capabilities for integrating LLMs into applications. From installation to API calls, this section provides foundational knowledge to leverage LangChain for building intelligent systems.

LangGraph Basics

Gain a clear understanding of LangGraph, a state-machine-inspired tool for designing AI systems. Learn to navigate its Graph and ToolNode modules, and create interactive chatbots that use graph-based programming for enhanced functionality.

Type Hinting and Data Validation for LangGraph

Explore the importance of type hinting, data validation, and OOP principles in AI development. Master tools like TypedDict and Pydantic to write clean, efficient, and reliable code for your projects.

Graph Definitions in LangGraph

Delve into the concept of graph definitions within LangGraph to build complex systems. Learn how these definitions bring clarity and structure to your AI workflows.

Chatbot Development with LangGraph and Ollama

Combine the power of LangGraph and Ollama to build feature-rich chatbots. Implement tool nodes, design robust system architectures, and add memory for interactive and intelligent user conversations.

Agentic Text-to-MySQL Query Execution

Learn to integrate LLMs with MySQL for seamless query execution. Build agents that generate and execute database queries, connect results to AI systems, and create intelligent database-driven workflows.

Agentic RAG with Private Datasets

Master Retrieval-Augmented Generation (RAG) for private datasets. This section teaches you to prepare datasets, create embeddings, store them in vector databases, and implement RAG agents capable of real-time data retrieval and processing.

Who this course is for:

• AI Enthusiasts and Developers: Anyone interested in building chatbots and integrating LLMs into applications.
• Beginner Programmers: Those looking to start their journey in AI with hands-on
• practical examples.
• Database Professionals: Individuals who want to explore how AI can enhance database query automation.
• Tech Innovators: Professionals eager to implement advanced workflows like RAG and graph-based programming.