feat(document-search): LiteLLM Reranker #109
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Code Coverage SummaryDiff against mainResults for commit: 5efc234 Minimum allowed coverage is ♻️ This comment has been updated with latest results |
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Trivy scanning results. .venv/lib/python3.10/site-packages/PyJWT-2.9.0.dist-info/METADATA (secrets)Total: 1 (MEDIUM: 1, HIGH: 0, CRITICAL: 0) MEDIUM: JWT (jwt-token) .venv/lib/python3.10/site-packages/litellm/llms/huggingface_llms_metadata/hf_text_generation_models.txt (secrets)Total: 1 (MEDIUM: 0, HIGH: 0, CRITICAL: 1) CRITICAL: HuggingFace (hugging-face-access-token) .venv/lib/python3.10/site-packages/litellm/proxy/_types.py (secrets)Total: 1 (MEDIUM: 1, HIGH: 0, CRITICAL: 0) MEDIUM: Slack (slack-web-hook) |
akotyla
reviewed
Oct 21, 2024
| from ragbits.document_search.documents.element import Element | ||
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| class Reranker(abc.ABC): | ||
| class Reranker(BaseModel, abc.ABC): |
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maybe a stupid question, but why is pydantic needed here?
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| documents = [chunk.content if isinstance(chunk, TextElement) else None for chunk in chunks] | ||
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| response = litellm.rerank( |
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maybe it would be better to use arerank here: https://docs.litellm.ai/docs/rerank#async-usage, what do you think?
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| class LiteLLMReranker(Reranker): | ||
| """ | ||
| A LiteLLM reranker for providers such as Cohere, Together AI, Azure AI. |
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I think it might be helpful to describe a bit these attributes below or link the documentation, I couldn't find litellm, but cohere is probably the same https://docs.cohere.com/v2/reference/rerank#request
akotyla
reviewed
Oct 21, 2024
| from ragbits.document_search.retrieval.rerankers.base import Reranker | ||
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| class LiteLLMReranker(Reranker): |
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i think you should add it here: https://github.com/deepsense-ai/ragbits/blob/main/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/__init__.py#L9 for it to work with config like this:
"reranker": {
"type": "LiteLLMReranker",
"config": {"model": "cohere/rerank-english-v3.0"},
}
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