LLMs on Google Cloud: Retrieval-Augmented Generation (RAGs) on Vertex AI
Google Cloud, AI
| Expert
- 14 videos | 1h 57m 20s
- Earns a Badge
Retrieval-Augmented Generation (RAG) on Vertex AI combines the power of information retrieval with generative AI to produce highly accurate and contextually enriched outputs. In this course, learn the principles of Retrieval-Augmented Generation (RAG) and how the Vertex AI RAG Engine combines retrieval techniques with generative models. Next, discover how to integrate the RAG Engine with RagManagedDb, import files from Cloud Storage and Google Drive, and create and deploy a Vector Search index and use it as the vector database. Finally, learn about fine-tuning models and the Vertex AI Model Garden, the Gen AI evaluation service, and LangChain and LangGraph. By the end of this course, you will be able to design advanced, customized AI systems using RAG and cutting-edge tools.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Describe how retrieval-augmented generation (rag) can help llms generate contextually relevant responses
List the steps involved in retrieval-augmented generation (rag)
Perform retrieval-augmented generation using ragmanageddb
Add documents to the corpus from cloud storage and google drive
Create a vector search index and endpoint and deploy the index
Use vector search as the vector database for the rag engine -
Differentiate between fine-tuning models with rag for custom data
Identify the different types of models available in the vertex ai model garden
Outline the google models for text generation, code completion, image generation, and healthcare
Recognize the features of the gen ai evaluation service
Describe the use of langchain and langgraph in ai workflows
Identify how to review the apis to interact and work with models in vertex ai
Summarize the key concepts covered in this course
IN THIS COURSE
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2m 1sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
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11m 46sUpon completion of this video, you will be able to describe how retrieval-augmented generation (RAG) can help LLMs generate contextually relevant responses. FREE ACCESS
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3. The Retrieval-Augmented Generation (RAG) Process10m 18sAfter completing this video, you will be able to list the steps involved in Retrieval-Augmented Generation (RAG). FREE ACCESS
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4. Integrating RAG Engine with RagManagedDb8m 16sDuring this video, discover how to perform Retrieval-Augmented Generation using RagManagedDb. FREE ACCESS
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5. Importing Files from Cloud Storage and Google Drive to the RAG Corpus11m 34sLearn how to add documents to the corpus from Cloud Storage and Google Drive. FREE ACCESS
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6. Creating and Deploying a Vector Search Index8m 23sIn this video, find out how to create a Vector Search index and endpoint and deploy the index. FREE ACCESS
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7. Using Vector Search as the Vector Database for RAG Engine10m 56sDiscover how to use Vector Search as the vector database for the RAG Engine. FREE ACCESS
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8. Fine-Tuning Models5m 21sIn this video, we will differentiate between fine-tuning models with RAG for custom data. FREE ACCESS
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9. The Vertex AI Model Garden9m 30sUpon completion of this video, you will be able to identify the different types of models available in the Vertex AI Model Garden. FREE ACCESS
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10. Google Models10m 21sAfter completing this video, you will be able to outline the Google models for text generation, code completion, image generation, and healthcare. FREE ACCESS
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11. The Gen AI Evaluation Service11m 44sThrough this video, you will be able to recognize the features of the Gen AI Evaluation Service. FREE ACCESS
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12. LangChain and LangGraph with Vertex AI7m 26sUpon completion of this video, you will be able to describe the use of LangChain and LangGraph in AI workflows. FREE ACCESS
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13. Exploring APIs in Vertex AI7m 39sAfter completing this video, you will be able to identify how to review the APIs to interact and work with models in Vertex AI. FREE ACCESS
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14. Course Summary2m 6sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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