Create Embeddings, Vector Search, and RAG with BigQuery
Google Cloud
Learn how to build Retrieval Augmented Generation (RAG) pipelines in BigQuery to reduce AI hallucinations. Create embeddings, search vector spaces, and generate more accurate answers using Gemini and embedding models.
About 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, as well as embedding models to address their own AI hallucination use cases.
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