Create Embeddings, Vector Search, and RAG with BigQuery
Create Embeddings, Vector Search, and RAG with BigQuery
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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.
Course Info
Objectives
- 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.
Prerequisites
Prior experience with programming languages including SQL or Python
Basic knowledge of ML and generative AI
Audience
Data scientists, data analysts, AI developers
Available languages
English, Deutsch, español (Latinoamérica), français, bahasa Indonesia, 日本語, 한국어, português (Brasil), 简体中文, and 繁體中文
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