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Google Cloud Training
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).
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OpenCourser.com/course/n0ao6l/build
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What's inside
Syllabus
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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
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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
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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:
For more career information including salaries, visit:
OpenCourser.com/course/n0ao6l/build
Reading list
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