feat(inference): add tutorial how to implement RAG with managed infer… #3744
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tgenaitay
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Thanks for the first version!
Thoughts
- I couldn't follow as it was unclear where each piece of code should go. I mean, past the .env file, I'm left with python snippets all around. Perhaps we can split into what's setup, and what goes into a python file.
- I believe some of the .env variables are not used.
- I believe we are not using the scaleway sdk and wonder why invoking it
- The tutorial is around langchain, so I think its key components should be introduced (watch out I believe one is deprecated already)
- Overall to be very practical it would be ideal in the end to have a functioning RAG. The critical thing I'm missing is perhaps a fileset I can really import so that I get a "wow" effect in last chapter. Without this element, I wouldn't reach the goal you have set as "What are the commands to set up XX with the CLI of Scaleway?"
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Co-authored-by: ldecarvalho-doc <[email protected]>
Co-authored-by: ldecarvalho-doc <[email protected]>
Co-authored-by: ldecarvalho-doc <[email protected]>
Co-authored-by: ldecarvalho-doc <[email protected]>
Co-authored-by: ldecarvalho-doc <[email protected]>
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remyleone
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bene2k1
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Co-authored-by: Benedikt Rollik <[email protected]>
Co-authored-by: Benedikt Rollik <[email protected]>
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Co-authored-by: Rowena Jones <[email protected]>
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Description
A tutorial for inference to.known: how to implement Retrieval-Augmented Generation (RAG) using Scaleway's managed inference, PostgreSQL, pgvector, and object storage.