Comments (1)
Hi, I'm not very familiar with the Azure setup, but it should be easy to set up RAPTOR with any inference setup. You can define your own inference code to use Azure by defining your custom SummarizationModel, QAModel, and/or Embedding model. We have a tutorial for how to extend to custon models here and you can do something similar for Azure.
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Related Issues (20)
- Question: How to get the context used when calling `RA.answer_question`? HOT 2
- Want to use RAPTOR for legal research. How to add legislation citation? HOT 2
- Clarification about the experiment setting in the paper HOT 2
- NarrativeQA metrics
- TypeError: Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k. HOT 4
- Question about multiple texts HOT 3
- Question about experiment HOT 3
- When the text gets longer, the embedding API does not seem to work. HOT 5
- Missing implied functionality `add to existing` HOT 3
- Support for Azure OpenAi HOT 2
- Return Citation HOT 2
- Constructing Layer Issue HOT 3
- Adding new Document to the existing RAPTOR setup
- num_layers acts like max_num_layers HOT 2
- UMAP n_neighbors must be greater than 1 HOT 11
- Inquiring about Vector DB implementation HOT 2
- RAG seems lazy and don't understand the sample HOT 1
- Sorry! We've encountered an issue with repetitive patterns in your prompt. Please try again with a different prompt HOT 10
- `RA.retrieve`: AttributeError: 'NoneType' object has no attribute 'encode'" HOT 7
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