Google Cloud
Create Embeddings, Vector Search, and RAG with BigQuery
Google Cloud

Create Embeddings, Vector Search, and RAG with BigQuery

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Gain insight into a topic and learn the fundamentals.
Advanced level
Designed for those already in the industry
2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Advanced level
Designed for those already in the industry
2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Generate embeddings using the embedding models with BigQuery.

  • Perform vector search in BigQuery and understand its process.

  • Create a RAG (Retrieval Augmented Generation) pipeline with BigQuery.

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Recently updated!

February 2025

Assessments

1 assignment

Taught in English

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There is 1 module in this course

This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.

What's included

6 videos1 reading1 assignment1 app item

Instructor

Google Cloud Training
Google Cloud
1,734 Courses2,981,115 learners

Offered by

Google Cloud

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