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).
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Activities
Review the basics of natural language processing (NLP)
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Refreshing your NLP knowledge will provide a strong foundation for understanding RAG.
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NLP
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Review NLP concepts
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Practice NLP techniques
Follow a tutorial on how to build an end-to-end RAG system
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Following a tutorial will provide you with practical experience in setting up and using a RAG system.
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Identify a suitable tutorial
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Follow the tutorial step-by-step
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Troubleshoot any issues encountered
Deploy a Vertex AI Vector Search Knowledge Base
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Deploying a Vertex AI Vector Search Knowledge Base will reinforce your understanding of the core concepts.
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Knowledge Base
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Create a Vertex AI Vector Search Index
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Configure the Knowledge Base
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Deploy the Knowledge Base
Three other activities
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Write a step-by-step guide to using Retrieval Augmentation Generation (RAG)
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Creating a detailed guide on how to use RAG will solidify your understanding of the technique.
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Gather the necessary resources
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Outline the steps
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Write the guide
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Edit and revise
Attend a workshop on advanced applications of Vector Search
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Attending a workshop will provide in-depth knowledge and hands-on experience in using Vector Search for advanced applications.
Browse courses on
Vector Search
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Research and identify relevant workshops
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Register for a workshop
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Attend the workshop
Contribute to a Vector Search or RAG open-source project
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Contributing to an open-source project will enhance your practical skills and deepen your understanding of the underlying technologies.
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Vector Search
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Identify a suitable project
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Review the project's documentation and guidelines
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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
- 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
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
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