2025 Master Langchain and Ollama - Chatbot, RAG and Agents from Udemy

This course is a practical guide to integrating Langchain and Ollama to build, automate, and deploy AI applications. Learn to set up these tools, create prompt templates, automate workflows, manage data retrieval, and deploy real-world applications on AWS. Each section is designed to provide you with hands-on skills and experience.

What You Will Learn

Ollama & Langchain Setup
- Complete setup and installation of Ollama and Langchain.
- Configure base URLs and handle direct API calls.
- Establish the environment for efficient integration.
Prompt Engineering
- Understand AI, human, and system message prompts.
- Use AIPromptTemplate, Human, System, and ChatMessagePromptTemplate to shape responses.
- Explore the invoke method to control the model's behavior.
Chains for Workflow Automation
- Learn Sequential, Parallel, and Router Chains to build flexible workflows.
- Work with custom chains and explore Chain Runnables for added automation.
- Implement real-world workflows using Langchain's chaining capabilities.
Output Parsing
- Format data with parsers like
- Parse structured output and use date-time output handling for organized data.
Chat Message Memory
- Use BaseChatMessageHistory and InMemoryChatMessageHistory for managing chat sessions.
- Create chat applications with memory to improve user experience.
Build and Deploy Chatbots
- Build a chatbot application using Streamlit.
- Maintain chat history and handle user inputs efficiently.
Document Loaders and Retrievals
- Work with loaders for web pages, PDFs, HTML data.
- Retrieve and summarize documents, convert text data, and use vector stores.
Vector Stores and Retrievals
- Integrate vector stores for document retrieval using FAISS and Chroma.
- Reload retrievers, index documents, and enhance retrieval accuracy.
Tool Calling and Custom Agents
- Set up tools for Tavily Search, PubMed, Wikipedia, and more.
- Design custom tools that can be used with the Agents and execute step-by-step instructions.
Real-World Integrations

Execute text-based queries on MySQL.
Parse LinkedIn Profile with LLM
Parse Job Resume with LLM
Deploy LLAMA with OLLAMA on AWS

Who This Course Is For

Developers and data scientists who want to use Langchain and Ollama for AI applications.
AI enthusiasts looking to automate workflows and create document retrieval systems.
Professionals needing to build end-to-end chatbots or deploy applications on AWS.
Learners with basic Python knowledge who want practical experience with real-world AI tools.

By the end of this course, you’ll have the skills to build, deploy, and manage AI-powered applications, from chatbots to document retrievers, ready for production.

What's inside

Learning objectives

Set up and integrate ollama with langchain: students will learn how to install, configure, and operate ollama alongside langchain.
Build custom chatbots: learners will develop skills to create chat applications with memory, history, advanced chatbot features using streamlit and langchain.

Use prompt templates, chains, and output parsers: students will master prompt templates and chaining methods (sequential, parallel, and router chains).
Deploy real-world applications: the course will guide students through deploying applications on aws ec2

Set up and integrate ollama with langchain: students will learn how to install, configure, and operate ollama alongside langchain.
Build custom chatbots: learners will develop skills to create chat applications with memory, history, advanced chatbot features using streamlit and langchain.
Use prompt templates, chains, and output parsers: students will master prompt templates and chaining methods (sequential, parallel, and router chains).
Deploy real-world applications: the course will guide students through deploying applications on aws ec2

Syllabus

Introduction

Hi,

Please install attached requirements.txt file to make this course work without any errors.

pip install -r requirements.txt

Download requirements.txt file then run above commands to install it. Make sure you are in right directory.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Langchain and Ollama, which are valuable tools for developers looking to build AI-powered applications and automate complex workflows

Teaches deployment on AWS, which is a popular cloud platform for hosting and scaling applications, making it highly relevant for professionals

Explores prompt engineering, which is a core skill for anyone looking to effectively interact with and control large language models

Emphasizes hands-on skills and experience, which is crucial for mastering the practical aspects of AI application development and deployment

Requires installing a requirements.txt file, which may pose a challenge for learners unfamiliar with Python package management

Reviews summary

Hands-on langchain & ollama projects

According to learners, this course offers a strong practical focus on integrating Langchain and Ollama to build AI applications. Many appreciate the hands-on projects, particularly those covering RAG (Retrieval-Augmented Generation) and agents, finding them very useful for real-world application. Students highlight that the course provides a clear roadmap from setup to deployment. However, some mention that navigating environment setup and dependencies can sometimes be tricky, and the fast pace might be a challenge for absolute beginners without some prior Python knowledge. Overall, it's seen as a valuable resource for developers looking to implement LLM concepts.

Suitable for those with Python basics.

"The pace is good if you have some existing Python and development background."

"Might be challenging for complete beginners in coding or AI."

"Assumes a certain level of familiarity with coding environments and concepts."

"Moved a bit fast through some of the initial setup steps."

"Felt the pace was just right for someone looking to quickly get up to speed."

Addresses rapidly changing libraries.

"Given how fast Langchain evolves, the content feels reasonably up-to-date for a course."

"Some minor code adjustments were needed due to library updates, but generally followed along."

"It's a challenge to keep pace with Langchain changes, but the core concepts taught here are sound."

"Hope the instructor keeps the code examples updated as the libraries progress."

"Found the material current enough to be immediately useful."

Effectively teaches Langchain, Ollama, RAG, Agents.

"The course does a great job explaining Langchain concepts like chains, parsers, and memory."

"Really appreciate the deep dive into Ollama integration and using local models."

"The sections on RAG and agents were particularly well-explained and practical."

"Covered all the essential building blocks I needed to start working with these libraries."

"Provided a solid foundation in the key components mentioned in the title."

Hands-on exercises and projects are a highlight.

"The hands-on coding and projects are the strongest part of the course for me, especially the RAG implementation."

"Building the chatbot and agent projects really helped solidify my understanding and apply the concepts."

"I enjoyed the practical examples and the real-world focus of the final projects."

"The project work makes the theory much easier to grasp and apply."

"Learned a lot by actually building things covered in the syllabus."

Environment and dependency setup can be tricky.

"Getting the local environment set up with Ollama and Langchain dependencies was a bit frustrating initially."

"Encountered a few versioning conflicts that took some time to resolve."

"The setup section is crucial but requires careful attention to avoid errors."

"Had some issues getting Ollama running smoothly on my machine."

"Wish there was a bit more troubleshooting detail for common setup problems."

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 Langchain and Ollama - Chatbot, RAG and Agents with these activities:

Review Python Fundamentals

Show steps

Strengthen your Python foundation to better understand the Langchain and Ollama integrations, as the course assumes a basic understanding of Python.

Browse courses on Python Basics

Show steps

Review data types, loops, and functions.
Practice writing simple Python scripts.

Read 'Building LLM Applications with LangChain'

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Supplement your learning with a dedicated book on Langchain to gain a deeper understanding of the framework and its capabilities.

View Putting Knowledge to Work on Amazon

Show steps

Read the chapters relevant to the current course modules.
Experiment with the code examples provided in the book.

Read 'Generative AI with LangChain'

Show steps

Supplement your learning with a dedicated book on Langchain to gain a deeper understanding of the framework and its capabilities.

View Putting Knowledge to Work on Amazon

Show steps

Read the chapters relevant to the current course modules.
Experiment with the code examples provided in the book.

Four other activities

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Show all seven activities

Build a Simple Question Answering Chatbot

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Apply your knowledge by building a chatbot that answers questions based on a given document, reinforcing your understanding of document loaders, retrievals, and prompt engineering.

Show steps

Choose a document to use as the knowledge base.
Implement document loading and chunking.
Create a retrieval chain using Langchain and Ollama.
Build a user interface using Streamlit.

Write a Blog Post on Langchain and Ollama

Show steps

Solidify your understanding by explaining the concepts of Langchain and Ollama in a blog post, targeting an audience of developers new to these tools.

Show steps

Choose a specific topic within Langchain and Ollama.
Research the topic thoroughly.
Write a clear and concise explanation of the topic.
Include code examples and practical use cases.

Contribute to a Langchain or Ollama Project

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Deepen your expertise by contributing to open-source projects related to Langchain or Ollama, gaining practical experience and collaborating with other developers.

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Find a suitable open-source project on GitHub.
Identify a bug or feature to work on.
Submit a pull request with your changes.

Create a Langchain Agent Demo

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Showcase your skills by building a demo of a Langchain agent that utilizes various tools and integrations, demonstrating your ability to create complex AI workflows.

Show steps

Define the purpose and functionality of the agent.
Select appropriate tools for the agent to use.
Implement the agent using Langchain and Ollama.
Create a presentation or video to showcase the demo.

Career center

Learners who complete 2025 Master Langchain and Ollama - Chatbot, RAG and Agents will develop knowledge and skills that may be useful to these careers:

Chatbot Developer

A chatbot developer specializes in creating conversational AI agents for various applications. This course is extremely relevant, as it focuses on developing chatbots using Langchain and Ollama. The coursework includes building chatbots with memory, handling user inputs, and managing chat history, which are fundamental to creating effective conversational interfaces. By learning to use Streamlit for application development and focusing on prompt engineering, chat message memory, and output parsing, students of this course will build the core skill set needed to excel as a chatbot developer. Moreover, the course provides instruction on how to deploy these chatbots, providing a full pipeline from development to deployment.

See salaries and explore the career path for Chatbot Developer

AI Application Developer

An AI application developer designs, develops, and deploys AI-powered applications. This course is directly relevant because it provides hands-on experience with Langchain and Ollama, which are essential tools for creating sophisticated AI applications. The course's focus on building chatbots, managing data retrieval, and automating workflows directly aligns with the daily responsibilities of an AI application developer, and the skills learned will help build a foundation for tackling diverse AI projects. The ability to integrate vector stores and work with various document loaders, as covered in this course, will further enable an AI application developer to build more robust and user-friendly applications. Furthermore, the practical exercises on deploying on AWS directly relate to the needs of this role.

See salaries and explore the career path for AI Application Developer

Machine Learning Engineer

A machine learning engineer builds and maintains the infrastructure and tools needed for machine learning models. While this course does not provide deep coverage of machine learning itself, it does provide critical skills in deploying and automating AI workflows. This course in particular helps machine learning engineers build a practical understanding of using Langchain and Ollama for creating pipelines that can incorporate machine learning models. The focus on real-world integrations and the deployment of applications on AWS helps build foundations for tackling the automation and deployment aspects of machine learning projects. The skills learned in this course, while not directly teaching about machine learning, will help create and deploy sophisticated AI systems.

See salaries and explore the career path for Machine Learning Engineer

AI Solutions Architect

An AI solutions architect designs and implements comprehensive AI solutions, often integrating multiple AI tools and technologies. This course may be useful, as it introduces key components used in many AI projects, such as Langchain and Ollama. The course's emphasis on building applications, automating workflows, and deploying on AWS provides a practical understanding of the challenges involved in AI implementation. The skills in prompt engineering, chain creation, and data retrieval taught in this course provide a useful overview of how AI tools work together. An AI solutions architect will benefit from a practical knowledge of how these tools function to design robust solutions for clients or stakeholders.

See salaries and explore the career path for AI Solutions Architect

Natural Language Processing Engineer

A natural language processing engineer develops algorithms and models to enable computers to understand and process human language. This course, while not directly covering fundamental NLP algorithm design, provides practical experience in using tools like Langchain and Ollama, which are often used in building NLP applications. The skills learned in this course, such as using prompt templates, output parsers, and document loaders, provide a practical understanding of how NLP models can interact with textual data to output structured results. The course's focus on chatbot development also gives a natural language processing engineer hands-on experience with a real-world NLP use case. Therefore, this course may be useful for a natural language processing engineer.

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

Automation Engineer

An automation engineer designs and implements automated systems and processes. This course may be useful for automation engineers due to its focus on using Langchain and Ollama to create automated workflows. The skills in creating chains for automation, managing data retrieval, and deploying applications on AWS can help an automation engineer build AI-powered automation tools. Furthermore, the concepts of custom agents and tool calling are relevant to complex automation scenarios. This course provides practical experience that an automation engineer may leverage to optimize processes using artificial intelligence.

See salaries and explore the career path for Automation Engineer

Data Scientist

A data scientist analyzes data to extract meaningful insights, often using machine learning and other analytics techniques. This course may be helpful for a data scientist interested in leveraging large language models for data interpretation and automation. While not directly discussing statistical analysis or machine learning model selection, the course teaches important skills in using Langchain and Ollama to process data and automate workflows. The ability to retrieve and summarize documents, parse structured output, and integrate vector stores can greatly enhance a data scientist's toolkit. This course may be useful in particular for building applications that leverage the power of LLMs to process textual data.

See salaries and explore the career path for Data Scientist

Software Engineer

A software engineer designs, develops, and maintains software applications. This course may be useful for software engineers working on projects that involve AI or natural language processing. The course content, which includes building chatbots, managing data retrieval, and automating workflows, helps a software engineer gain proficiency in integrating AI technologies into their projects and products. Skills such as output parsing, document loading, and vector store integration help enhance a software engineer's ability to create more feature-rich and intelligent applications. This course is helpful for software engineers seeking to broaden their knowledge of AI tools.

See salaries and explore the career path for Software Engineer

Research Scientist

A research scientist conducts research to develop new knowledge or technologies. This course may help a research scientist involved in areas like natural language processing or AI. This course may be helpful for a research scientist by providing a practical hands-on experience in deploying large language models via tools such as Langchain and Ollama. A research scientist may be interested in exploring the application of these tools and frameworks for various research applications. The course may help build a skill set that can be deployed in research contexts.

See salaries and explore the career path for Research Scientist

AI Consultant

An AI consultant provides expert advice to organizations on how to leverage AI solutions to solve business problems. This course may help an AI consultant understand the practical aspects of building and deploying AI applications. The skills in prompt engineering, building chatbots, and automating workflows using tools like Langchain and Ollama may give a consultant a stronger foundation in the technology and help them make informed recommendations. The course's practical, hands on approach may help an AI consultant better understand the process and challenges of AI project delivery.

See salaries and explore the career path for AI Consultant

Technical Project Manager

A technical project manager oversees the planning, execution, and completion of technical projects. While not directly a technical role, a technical project manager may benefit from gaining an understanding of tools like Langchain and Ollama. This course may help a project manager better understand the aspects of AI project development. Familiarity with the topics covered and the skills taught in this course can allow a project manager to interface with their technical teams more effectively. The hands-on aspect of the course may help a technical project manager better understand the needs of a successful AI project.

See salaries and explore the career path for Technical Project Manager

Data Analyst

A data analyst interprets data and provides insights to help inform business decisions. This course may be useful for a data analyst seeking to utilize large language models to analyze and extract insights from unstructured data. The course focus on document loading, data retrieval, and output parsing may assist a data analyst in handling diverse data formats and sources. The skills in generating reports and summaries, as taught in this course, may also be directly useful for a data analyst who seeks to use AI to augment their work. This course may be useful to level up a data analyst's abilities.

See salaries and explore the career path for Data Analyst

Technical Writer

A technical writer creates documentation and user guides for technical products or services. This course may be useful for technical writers who need to create documentation for AI tools or applications, since the course deals with working with AI tools. The hands-on experience in the course may give a technical writer a practical understanding of how these tools function, which can help improve the accuracy of documentation. The focus on real world use cases may also help a technical writer better understand the applications of the technology.

See salaries and explore the career path for Technical Writer

IT Support Specialist

An IT support specialist assists users with technical issues and maintains IT systems. While this course does not directly teach system administration, it may be helpful for an IT support specialist who needs to support systems that use AI tools like Langchain and Ollama. The course provides a practical understanding of these specific tools and their integration, so that an IT support specialist can identify and resolve issues more effectively. An IT specialist might also use the chatbots developed in this course to improve the quality of their service. This course may be useful to expand their skill set.

See salaries and explore the career path for IT Support Specialist

Business Intelligence Analyst

A business intelligence analyst analyzes business data to identify trends and insights to help inform business decisions. This course may be useful for a business intelligence analyst who wishes to use AI tools to enhance their analyses. The course instruction on document loaders, data retrieval, and output parsing may help a business intelligence analyst handle unstructured data. The opportunity to work with real world integrations like MySQL may help a business intelligence analyst integrate traditional databases with AI technologies. This course may be useful to stay on the cutting edge of technology.

See salaries and explore the career path for Business Intelligence Analyst

2025 Master Langchain and Ollama - Chatbot, RAG and Agents

What's inside

Learning objectives

Syllabus

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Reviews summary

Hands-on langchain & ollama projects

Activities

Career center

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