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Créer des embeddings et utiliser la recherche vectorielle et le RAG avec BigQuery

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Créer des embeddings et utiliser la recherche vectorielle et le RAG avec BigQuery

Instructor: Google Cloud Training

Included with

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1 module

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

1 module

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

Générer des embeddings à l'aide de modèles d'embeddings avec BigQuery
Effectuer une recherche vectorielle dans BigQuery et comprendre son fonctionnement
Créer un pipeline de RAG (génération augmentée par récupération) avec BigQuery

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in French

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There is 1 module in this course

Ce cours présente une solution de génération augmentée par récupération (RAG) dans BigQuery permettant de réduire les hallucinations de l'IA. Il décrit un workflow RAG qui couvre la création d'embeddings, la recherche dans un espace vectoriel et la génération de réponses améliorées. Il explique aussi les raisons conceptuelles derrière ces étapes et leur implémentation pratique avec BigQuery. À la fin du cours, les participants seront à même de créer un pipeline de RAG à l'aide de BigQuery et de modèles d'IA générative tels que Gemini, ainsi que des modèles d'embeddings pour traiter leurs propres cas d'hallucinations de l'IA.

Ce cours présente une solution de génération augmentée par récupération (RAG) dans BigQuery permettant de réduire les hallucinations de l'IA. Il décrit un workflow RAG qui couvre la création d'embeddings, la recherche dans un espace vectoriel et la génération de réponses améliorées. Il explique aussi les raisons conceptuelles derrière ces étapes et leur implémentation pratique avec BigQuery. À la fin du cours, les participants seront à même de créer un pipeline de RAG à l'aide BigQuery et de modèles d'IA générative tels que Gemini, ainsi que des modèles d'embeddings pour traiter leurs propres cas d'hallucinations de l'IA.

What's included

6 videos1 reading1 assignment1 app item

6 videos Total 30 minutes

Pourquoi le RAG ? 4 minutes
Embeddings 8 minutes
Recherche vectorielle 7 minutes
Le RAG en action 4 minutes
Résumé 3 minutes
Coursera: Premiers pas avec Google Cloud Platform et Qwiklabs 5 minutes

1 reading Total 10 minutes

Lecture 10 minutes

1 assignment Total 10 minutes

Questionnaire du cours 10 minutes

1 app item Total 60 minutes

Atelier : Créer une application RAG avec BigQuery 60 minutes

Instructor

Google Cloud Training

Google Cloud

2,113 Courses 4,017,584 learners

Offered by

Google Cloud

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.