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Indeed this tutorial on Haystack is a good one as an example: https://haystack.deepset.ai/tutorials/22_pipeline_with_promp... It combines a retrieval step followed by a prompt layer which inserts the relevant context into the prompt. You can however change the 'retrieval step' with something that uses a proper embedding model and OpenAI also provides those if you want to. I tend to use lighter (cheaper) OSS models for this step though. PS: There's some functionality in the PromptNode to make sure you don't exceed prompt limit.

That's great - thanks!

Yes, you can use a local embeddings model like gtr-t5-xl alongside retriever augmentation. This can point you in the right direction: https://haystack.deepset.ai/tutorials/22_pipeline_with_promp...

Thanks!

open ai has an embeddings api that ppl use for that https://platform.openai.com/docs/guides/embeddings, though whether it's the best model to do that is congested.

Contriever is an example of a strong model to do that yourself. see their paper too to learn about the domain. https://github.com/facebookresearch/contriever

Thanks!