Knowledgebase embeddings #2158
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Knowledgebase embeddings #2158
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I still need to write the tests, but I am sharing the PR already to get feedbacks on the implementation. Little note: You will see that in the load methods you can select another llm or embedding client than the one that was used to generate the KnowledgeBase. I added this purely to add flexibility and improve developer experience, but it was not necessary nor asked in the issue. |
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TODO
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davidberenstein1957
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davidberenstein1957
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Hi @GTimothee, this already looks nice. I left some comments to improve some things..
| Hugging Face token for authentication. If None, will use local token. | ||
| private : bool | ||
| Whether to make the repo private or public. | ||
| """ |
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should we also pass kwargs for the push to hub/upload file etc?
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| kb = cls( | ||
| data=data, | ||
| columns=config.get("columns"), | ||
| llm_client=llm_client, | ||
| embedding_model=embedding_model, | ||
| chunk_size=config.get("chunk_size", 2048), | ||
| seed=config.get("seed"), | ||
| min_topic_size=config.get("min_topic_size"), | ||
| ) |
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Doesn't this already create the embeddings during initialisation?
Co-authored-by: David Berenstein <[email protected]>
Co-authored-by: David Berenstein <[email protected]>
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Description
Related Issue
#2145
Type of Change
It is an improvement as it avoids embeddings to be recomputed.
It is a new feature because we can now save/load to/from disk and hugginface hub.
Checklist
CODE_OF_CONDUCT.mddocument.CONTRIBUTING.mdguide.pdm.lockrunningpdm update-lock(only applicable whenpyproject.tomlhas beenmodified)