Build RAG apps with embeddings

[@googlecloudtech]

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[@dheer211]

Beautifully explained thank you so much

[@gihankarunarathne585]

Really good explanation for beginners!

[@banzai316]

Very timely, good reference! Been trying to optimize size of the embeddings or use 16-bit integer math

[@mohamedkarim-p7j]

Thank for sharing👍

[@achen94]

Can you do a video on how to build a RAG app in Google cloud? I've seen a few of these videos and they are good but don't explain the process of building a rag app on Google cloud. For example, you have the embeddings API which can generate text embeddings of your query, then you have vector search which can find the closest related answer with semantic meaning. But what's next? How do you integrate these directly with LLMs to complete the experience?