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your own data with a pretrained LLM. Another way to do this is to finetune the LLM on your data. Finetuning has some pros and cons. On
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LLM. Another way to do this is to finetune the LLM on your data. Finetuning has some pros and cons. On the plus side, it reduces the
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remains keeping your data in an external database, rather than finetuning on it, there are other ways to combine it with LLMs rather
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document elements into coherent chunks optimized for embedding.
An
Embeder
node, which uses a model like OpenAI’s
to turn those chunks
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models. The Donut [3] model first processes an input image with an an image transformer and then feeds it to a decoder to generate a
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accurately. Or creating internal support tools that helps new team mates get up to speed faster by providing relevant past solutions
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a table called in your catalog under the schema. You should see it in in your catalog now: Now, we just need to make sure we have access