courseraIT и технологии

Create Embeddings, Vector Search, and RAG with BigQuery

Курс от Google Cloud

Продвинутый≈ 2.5 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Retrieval-Augmented Generation Vector Databases Embeddings Google Gemini AI Integrations AI Workflows Generative AI LLM Application Google Cloud Platform

Программа курса

1 модулей · 9 учебных материалов

01Create Embeddings, Vector Search, and RAG with BigQuery9 материалов

Create Embeddings, Vector Search, and RAG with BigQuery

Why RAGВидео EmbeddingsВидео Vector searchВидео RAG in actionВидео SummaryВидео

Учитесь у экспертов

Google Cloud Training

Преподаватель курса