We may earn an affiliate commission when you visit our partners.

Build a Knowledge Based System with Vertex AI Vector Search, LangChain and Gemini

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use Vertex AI Vector Search to index documents and create a knowledge base. The knowledge base is utilized to retrieve relevant search results to supply with a query submitted to a large language model (LLM), in this case, Gemini, as context. This technique is known as retrieval augmentation generation (RAG).

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Two deals to help you save

Save money when you learn. Here are two deals and offers that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until December 2

Annual Subscription

Invest in your career growth and save. Start your annual subscription to Coursera Plus and access 10,000+ programs.

Valid until March 12, 2027

Save 40% Off 3 Months

Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.

What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches a practical, in-demand skill that can be applied in many areas

Provides hands-on practice with industry-standard tools

Taught by Google Cloud Training, who have a strong reputation in the field

Focuses on a specific area of knowledge, allowing learners to develop expertise

Offers a comprehensive study of a particular topic

Requires some prior knowledge or experience, which may be a barrier for some learners

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Practical rag with vertex ai and gemini

According to learners, this course offers a highly practical and hands-on experience in building a knowledge base system using Vertex AI Vector Search, LangChain, and Gemini. Students particularly praise the clear and concise instructions for navigating the Google Cloud environment, making the self-paced lab a smooth learning journey. It provides a strong foundational understanding of Retrieval Augmented Generation (RAG) concepts and showcases the application of cutting-edge AI technologies. While it excels in practical application, some note it's best suited for those with some prior knowledge of Python and core AI/ML concepts, as it's a lab and not a deep theoretical dive.

Establishes a solid understanding of Retrieval Augmented Generation (RAG).

"I now have a clear understanding of how RAG works and its components after completing this lab."

"This course provided a good conceptual basis before diving into the practical build of the system."

"It helped solidify my understanding of vector search and its crucial role in modern AI applications."

Instructions are clear and easy to follow within the Google Cloud console.

"The step-by-step guidance in the Google Cloud environment was very helpful for completing the lab."

"I had no issues setting up or running the lab; the instructions were spot on and well-detailed."

"For a self-paced lab, the clarity of instructions made it a smooth and enjoyable learning experience."

Explores current and highly relevant technologies for AI development.

"The use of Gemini and LangChain makes this course highly relevant for today's AI landscape."

"It's great to see a course incorporating the latest developments in LLMs and RAG frameworks."

"I found the integration of Vertex AI Vector Search particularly useful for real-world projects."

Offers valuable practical experience building a RAG system.

"I really appreciated the hands-on nature of this lab; it helped me understand RAG much better."

"Building a functional knowledge base system was a great way to apply these concepts effectively."

"This course is excellent for getting practical experience with Vertex AI Vector Search and Gemini."

Primarily a lab, not an in-depth theoretical course or deployment guide.

"It's a great practical lab, but don't expect deep dives into advanced RAG optimization techniques."

"The course delivers what it promises for a lab, but advanced deployment strategies are not covered in depth."

"I would have liked more on scaling the knowledge base, but it's a very solid starting point for RAG."

Best suited for those with some prior experience in Python and AI/ML.

"As a beginner, I found some parts assumed prior knowledge of LLMs and Python programming."

"While practical, it doesn't delve deep into the theoretical aspects of vector embeddings or advanced ML."

"I recommend having a basic understanding of Google Cloud concepts before starting this specific lab."

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 Build a Knowledge Based System with Vertex AI Vector Search, LangChain and Gemini with these activities:

Review the basics of natural language processing (NLP)

Show steps

Refreshing your NLP knowledge will provide a strong foundation for understanding RAG.

Browse courses on NLP

Show steps

Review NLP concepts
Practice NLP techniques

Follow a tutorial on how to build an end-to-end RAG system

Show steps

Following a tutorial will provide you with practical experience in setting up and using a RAG system.

Show steps

Identify a suitable tutorial
Follow the tutorial step-by-step
Troubleshoot any issues encountered

Deploy a Vertex AI Vector Search Knowledge Base

Show steps

Deploying a Vertex AI Vector Search Knowledge Base will reinforce your understanding of the core concepts.

Browse courses on Knowledge Base

Show steps

Create a Vertex AI Vector Search Index
Configure the Knowledge Base
Deploy the Knowledge Base

Three other activities

Expand to see all activities and additional details

Show all six activities

Write a step-by-step guide to using Retrieval Augmentation Generation (RAG)

Show steps

Creating a detailed guide on how to use RAG will solidify your understanding of the technique.

Show steps

Gather the necessary resources
Outline the steps
Write the guide
Edit and revise

Attend a workshop on advanced applications of Vector Search

Show steps

Attending a workshop will provide in-depth knowledge and hands-on experience in using Vector Search for advanced applications.

Browse courses on Vector Search

Show steps

Research and identify relevant workshops
Register for a workshop
Attend the workshop

Contribute to a Vector Search or RAG open-source project

Show steps

Contributing to an open-source project will enhance your practical skills and deepen your understanding of the underlying technologies.

Browse courses on Vector Search

Show steps

Identify a suitable project
Review the project's documentation and guidelines
Make a meaningful contribution

Career center

Learners who complete Build a Knowledge Based System with Vertex AI Vector Search, LangChain and Gemini will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 25 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 Build a Knowledge Based System with Vertex AI Vector Search, LangChain and Gemini.

AI Engineering

Save

Serves as the definitive engineering blueprint for building production applications on multimodal foundation models like Google Gemini, covering long-context prompting, retrieval-augmented generation, agentic tool calling, evaluation pipelines, and latency optimization.

Build a Knowledge Based System with Vertex AI Vector Search, LangChain and Gemini

Two deals to help you save

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Practical rag with vertex ai and gemini

Activities

Career center

Reading list

Share

Similar courses

Coursera Buyer Protection & Guarantee

7-Day Free Trial & Free Audit

14-Day Refund Guarantee

Verified Academic & Industry Partner