2025 Master LangGraph and LangChain with Ollama- Agentic RAG from Udemy

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.

What's inside

Learning objectives

Set up and manage local llms with ollama.
Build dynamic, memory-enabled chatbots using langgraph.

Integrate ai with databases for intelligent mysql query execution.
Create rag workflows using private datasets and embeddings.

Set up and manage local llms with ollama.
Build dynamic, memory-enabled chatbots using langgraph.
Integrate ai with databases for intelligent mysql query execution.
Create rag workflows using private datasets and embeddings.

Syllabus

Introduction

https://github.com/laxmimerit/LangGraph-and-Ollama

Ollama Setup for Local LLM

Install Ollama

Touch Base with Ollama

Inspecting LLAMA 3.2 Model

LLAMA 3.2 Benchmarking Overview

What Type of Models are Available on Ollama

Ollama Commands - ollama server, ollama show

Ollama Commands - ollama pull, ollama list, ollama rm

Ollama Commands - ollama cp, ollama run, ollama ps, ollama stop

Create and Run Ollama Model with Predefined Settings

Ollama Model Commands - show

Ollama Model Commands - set, clear, save and load

Ollama Raw API Requests

Load Uncesored Models for Banned Content Generation [Only Educational Purpose

Getting Started with LangChain

Langchain Introduction

Lanchain Installation

Langsmith Setup of LLM Observability

Calling Your First Langchain Ollama API

Generating Uncensored Content in Langchain [Only Educational Purpose]

Trace LLM Input Output at Langsmith

Going a lot Deeper in the Langchain

LangGraph Getting Started

LangGraph Introduction and Installation

How State Machine Inspired LangGraph

First Step Toward LangGraph Understanding

Understanding LangGraph Example Code on High Level

Going Deeper in LangGraph Graph Module

Going Deeper in LangGraph ToolNode Module

Your First Chatbot with LangGraph and Ollama Part 1

Your First Chatbot with LangGraph and Ollama Part 2

Your First Chatbot with LangGraph and Ollama Part 3

Type Hinting and Data Validation | OOPs Concept to Work with LangGraph

Why You Need to Know OOPs Concepts

What is Parent and Child Classes

What is Type Hinting

Type Hinting Basic Part 1

Type Hinting Basic Part 2

Type Hinting with TypedDict

Data Validation Using Pydantic Base Model

Data Validation using Annotated

Graph Definitions in LangGraph

Chatbot with LangGraph and Ollama | Baby Step Toward the Agent

LangGraph Basic Chatbot Code Setup

Tavily Search API Setup

Chatbot System Design for Tool Calling

Create Tools for Your Chatbot

Build Your Chatbot with Tools

Chatting with Your Chatbot

Adding Memory to Your Chatbot

Agentic Text to MySQL Query Execution

Text to MySQL Query Execution Background Setup

Download Chinook Database

Explore Chinook DB with MySQL Queries

Getting LLM Connection - LLAMA vs Qwen Models

Write Graph State for MySQL Query and LLM Result

Fetch MySQL Write Query LLM Prompt

Write Structured Output Parser Class for MySQL Query

Prepare MySQL Write Query Node

Prepare MySQL Query Execution Node

Prepare LLM Generate Node based on MySQL Result

Building LangGraph for MySQL Query Execution

LangGraph MySQL Agent System Prompt Preparation

LangGraph MySQL Toolkits Preparation for Agent

Your MySQL AGENT is in Action

Agentic RAG for Private Dataset with Ollama and LangGraph

Introduction to RAG Dataset and Process

Load RAG Dataset

Do RAG Dataset Chunking

Load Embedding Model with Ollama Embeddings

Storing Documents Embedding in the Vector Store

Loading Vector Database as Retriever

Create Retriever as Tool Node

Understanding Agentic RAG Workflow

Implementing Agent State

Write Code for Document Grader Node

Create RAG Agent Node

Implement Rewrite and Generate Node

Building RAG Agent with LangGraph - Agentic RAG

Agentic RAG for Health Supplements Private Dataset is in Action

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers LangGraph, a state-machine-inspired tool, which allows developers to design AI systems with enhanced functionality and clarity

Teaches how to build agents that generate and execute database queries, connecting results to AI systems, which is useful for database-driven workflows

Explores type hinting, data validation, and OOP principles, which are essential for writing clean, efficient, and reliable code in AI projects

Requires installing and configuring Ollama to work with local LLMs, which may require specific system configurations and resources for optimal performance

Uses Langchain, which is a rapidly evolving framework, so learners should be prepared to adapt to potential updates and changes in the API

Focuses on integrating LLMs with MySQL for query execution, which may not be applicable to learners working with other database systems

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 2025 Master LangGraph and LangChain with Ollama- Agentic RAG with these activities:

Review LangChain Fundamentals

Show steps

Solidify your understanding of LangChain basics to better grasp its integration with LangGraph and Ollama.

Browse courses on LangChain

Show steps

Review LangChain documentation and tutorials.
Practice building simple applications with LangChain.
Familiarize yourself with LangChain modules and components.

Review 'Generative AI with LangChain'

Show steps

Enhance your understanding of LangChain concepts and applications by reading this book.

View Putting Knowledge to Work on Amazon

Show steps

Read the book 'Generative AI with LangChain'.
Take notes on key concepts and examples.
Experiment with the code examples provided in the book.

Build a Simple Chatbot with LangChain and Ollama

Show steps

Apply your knowledge by building a basic chatbot using LangChain and Ollama to solidify your understanding of the core concepts.

Show steps

Set up Ollama and LangChain in your development environment.
Create a simple chatbot application using LangChain and Ollama.
Test and refine your chatbot's functionality.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Review 'Building LLM Applications with LangChain'

Show steps

Gain practical insights into building LLM applications with LangChain by reading this book.

View Putting Knowledge to Work on Amazon

Show steps

Read the book 'Building LLM Applications with LangChain'.
Experiment with the code examples provided in the book.
Apply the concepts learned to your own projects.

Write a Blog Post on LangGraph Use Cases

Show steps

Deepen your understanding of LangGraph by researching and writing about its various use cases and applications.

Show steps

Research different use cases for LangGraph.
Write a blog post summarizing your findings.
Publish your blog post online.

Create a Presentation on Agentic RAG

Show steps

Demonstrate your understanding of Agentic RAG by creating a presentation that explains the concept and its implementation.

Show steps

Research Agentic RAG and its components.
Create a presentation outlining the key concepts and implementation details.
Practice delivering your presentation.

Contribute to a LangChain or LangGraph Project

Show steps

Deepen your understanding and contribute to the community by contributing to an open-source LangChain or LangGraph project.

Show steps

Identify an open-source LangChain or LangGraph project.
Find an issue or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete 2025 Master LangGraph and LangChain with Ollama- Agentic RAG will develop knowledge and skills that may be useful to these careers:

Chatbot Developer

A Chatbot Developer specializes in designing, building, and maintaining conversational AI applications. This course specifically delves into using LangGraph and Ollama to build sophisticated chatbots, making it extremely relevant for anyone entering this field. The course covers everything from initial setup to advanced techniques such as adding memory and implementing tool nodes for enhanced functionality. A chatbot developer will be equipped with the ability to create interactive and intelligent user experiences. The step-by-step guidance on graph-based programming and integration of RAG workflows ensures that the chatbot developer is well-versed in the latest AI advancements.

See salaries and explore the career path for Chatbot Developer

AI Engineer

An AI Engineer builds and implements artificial intelligence systems, including designing, developing, and testing machine learning models and AI applications. This course is ideal for aspiring AI Engineers as it provides hands-on experience with LangGraph, Ollama, and Retrieval-Augmented Generation, all of which are crucial for creating advanced AI systems. The course's emphasis on building chatbots and integrating AI with databases will be particularly beneficial. The practical skills taught, such as setting up local LLMs, creating graph-based programs, and managing data, help build a foundation for complex AI projects. The curriculum specifically targets the creation of AI agents and the integration of databases and private datasets into RAG workflows.

See salaries and explore the career path for AI Engineer

Machine Learning Engineer

A Machine Learning Engineer is responsible for developing and deploying machine learning models into real-world applications. This course provides a practical approach to machine learning by focusing on LangGraph, Ollama and Retrieval Augmented Generation. A machine learning engineer will find the modules on local LLM management, graph-based programming, and RAG workflows directly applicable to their work. The course's focus on data validation, type hinting, and object-oriented programming principles helps ensure the delivery of high-quality, reliable machine learning solutions, which is vital for the machine learning engineer. Learning how to integrate with databases and handle private datasets is also essential for a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer focuses on enabling computers to understand, interpret, and generate human language. This course may be useful for a natural language processing engineer because it covers advanced topics in language model integration with LangGraph and Ollama. The practical experience with building chatbots, managing local LLMs, and integrating databases helps natural language processing engineers to expand their skills in developing intelligent language-based systems. The course's detailed exploration of RAG will be particularly relevant for a natural language processing engineer, as this is essential for retrieving and processing data, which is a core component of natural language processing projects.

See salaries and explore the career path for Natural Language Processing Engineer

AI Solutions Architect

An AI Solutions Architect designs and oversees the implementation of AI-driven systems and solutions. This role requires a solid understanding of AI technologies and their applications. This course may be particularly useful for an AI solutions architect as it provides a comprehensive look into the practical applications of LangGraph and Ollama, which are used for managing LLMs and building intelligent agents. The course's focus on creating dynamic chatbots, integrating AI with databases, and implementing RAG workflows offers direct insight into the design of robust AI solutions. The knowledge of graph-based programming and agentic workflows is indispensable for creating complex, high-performing AI systems.

See salaries and explore the career path for AI Solutions Architect

Research Scientist

A Research Scientist conducts advanced research to create new knowledge and technology often in academic or industrial labs. This course may be useful to a research scientist, because it introduces several cutting-edge AI development tools and techniques. The course teaches how to set up and manage local LLMs, build memory-enabled chatbots, and integrate AI with databases. A research scientist will find the information on agentic RAG with private datasets particularly beneficial. The course gives a strong practical foundation for many new research ideas.

See salaries and explore the career path for Research Scientist

Software Developer

A Software Developer designs, develops, and maintains software applications. This course may be helpful for a software developer because it teaches how to incorporate AI capabilities into various software projects. The hands-on experience with LangGraph, Ollama, and RAG provides a software developer with the knowledge to create intelligent systems. The course covers important concepts such as type hinting, data validation, and object oriented programming, which are essential for writing clean and reliable code. A software developer will develop valuable skills in integrating databases and building advanced chatbots.

See salaries and explore the career path for Software Developer

Data Scientist

A Data Scientist analyzes large datasets, develops statistical models, and extracts insights to help inform business decisions. This course provides skills that are useful to a data scientist in the context of advanced AI applications. The course includes building intelligent AI driven applications, handling data validation, and working with private dataset, which is crucial for any data scientist. The course focuses on Retrieval Augmented Generation, which data scientists can apply to improve their modeling. Additionally, learning how to create embeddings and work with vector databases is a valuable skill for a data scientist interested in AI.

See salaries and explore the career path for Data Scientist

AI Product Manager

An AI Product Manager is responsible for the strategy, roadmap, and execution of AI-powered products. This role requires a strong understanding of AI technologies, markets, and user needs. This course may be useful to an AI product manager, because it offers insight into how to build various AI applications using LangGraph, Ollama, and RAG. The course covers crucial aspects of AI development, including the creation of dynamic chatbots and integration with databases. With this information, an AI product manager will have a clearer understanding of the practical challenges and opportunities in creating AI products and be able to more effectively lead development teams.

See salaries and explore the career path for AI Product Manager

Database Administrator

A Database Administrator is responsible for managing and maintaining databases, ensuring data integrity, security, and accessibility. This course offers a unique intersection with the field of database administration by teaching how to integrate AI agents with MySQL databases. A database administrator will learn how to execute database queries via AI, which can help to automate and optimize certain database tasks. The course teaches how to build agents that generate and execute queries and connect results to AI systems. This skill set enhances a database administrator's ability to work with modern, AI-driven systems.

See salaries and explore the career path for Database Administrator

Data Analyst

A Data Analyst interprets data to identify trends, patterns and insights that inform decisions. This course may be helpful for a Data Analyst to expand their knowledge by teaching them about Retrieval-Augmented Generation. This course teaches how to prepare datasets, create embeddings, store them in vector databases, and implement RAG agents. This knowledge will help the data analyst to work with modern AI systems. The course also includes modules on integrating AI with databases, which can help the data analyst to optimize their workflows.

See salaries and explore the career path for Data Analyst

Robotics Engineer

A Robotics Engineer designs, tests, and builds robots and robotic systems. This course may be useful to a robotics engineer as modern robotics often integrates AI for intelligent decision making. This course focuses on techniques of building AI driven agents, which can make the robot smarter. The course also covers managing local large language models and building advanced chatbots, which are often key components of modern robotic systems. The use of LangGraph in particular is applicable to robotic systems, which can be modeled by graph representations.

See salaries and explore the career path for Robotics Engineer

Technical Project Manager

A Technical Project Manager oversees complex technology projects, ensuring they are completed on time and within budget. This course may be useful to a technical project manager working on AI systems, because it provides an understanding of the latest machine learning tools and techniques. The course gives an overview of the practical aspects of using LLMs, LangGraph, and RAG. The course content will help a technical project manager to better understand project requirements, potential challenges, and innovative approaches in AI, which allows for more effective project planning and management.

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Business Intelligence Analyst

A Business Intelligence Analyst uses data to understand market trends, customer behavior, and business performance. This course may be useful to a business intelligence analyst, because it provides an understanding of how AI can be used to process and extract value from data. The course focuses on Retrieval Augmented Generation and teaches how to work with private datasets. The analyst can then use this information to gather more accurate and insightful information for business decisions. The skills learned in the class can help the analyst develop AI tools that can help with their work.

See salaries and explore the career path for Business Intelligence Analyst

Systems Analyst

A Systems Analyst analyzes and designs computer systems to improve efficiency and solve organizational problems. This course may be useful to a systems analyst by showing how to integrate sophisticated AI capabilities into existing systems. The course provides a thorough introduction to LangGraph, Ollama, and RAG workflows, which are used to build intelligent systems. The course has a practical approach that helps to understand the complexities and possibilities when incorporating AI into various organizational systems. This practical approach is especially useful to a systems analyst who is working to implement new technology.

See salaries and explore the career path for Systems Analyst

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in 2025 Master LangGraph and LangChain with Ollama- Agentic RAG.

Putting Knowledge to Work

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Provides a comprehensive guide to building generative AI applications using LangChain. It covers various aspects of LangChain, including prompt engineering, model integration, and application development. It is particularly useful for understanding the practical applications of LangChain in building chatbots and other AI-powered systems. This book adds depth to the course by providing real-world examples and use cases.

Putting Knowledge to Work

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2025 Master LangGraph and LangChain with Ollama- Agentic RAG

What's inside

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