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Treat it just like any other std ML workflow , your client code call goes thru RAG model(embedding model) and then goes to the main model. While you can use sagemaker for RAG setup , however please note you will have to setup whole pipeline of your own , including RAG model call , DB setup etc, data sources , packaging deployment etc. I would highly recommend to use Bedrock service where-in you can easily plugin externals RAG platforms/frameworks thru cli/sdk or use inbuild managed RAG experience called Knowledge bases.
Just with few clicks you will have your app ready. Few ref links if it picks your interest: https://aws.amazon.com/bedrock/knowledge-bases/
One nice blog to rea thru for RAG - https://aws.amazon.com/blogs/aws/knowledge-bases-now-delivers-fully-managed-rag-experience-in-amazon-bedrock/
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- demandé il y a un an
- demandé il y a un an
- AWS OFFICIELA mis à jour il y a 3 ans
- AWS OFFICIELA mis à jour il y a 4 mois
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- AWS OFFICIELA mis à jour il y a 2 ans
sorry but my question is can I make an call to an function present inside my sagemaker notebook and get the response for that ?