Comments (2)
Hey! You can define your own inference code by defining your custom SummarizationModel, QAModel, and Embedding model. We have a tutorial for this available here.
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Thanks, that is true. The library is very flexible. Maybe should be good idea make simple examples of this.
from raptor.
Related Issues (20)
- 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
- Is it not possible to use in AZURE? HOT 1
- 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 10
- Inquiring about Vector DB implementation HOT 2
- multi doc HOT 2
- There seems to be a bug in the demo code.
- Evaluation code HOT 1
- Support for seeing Created Tree ? HOT 4
- **Outdated Dependencies in requirements.txt Causing Conflicts** HOT 1
- Bug in chunk splitting HOT 1
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from raptor.