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61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
from typing import Optional
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from core.model_manager import ModelInstance
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from core.rag.models.document import Document
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from core.rag.rerank.rerank_base import BaseRerankRunner
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class RerankModelRunner(BaseRerankRunner):
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def __init__(self, rerank_model_instance: ModelInstance) -> None:
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self.rerank_model_instance = rerank_model_instance
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def run(
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self,
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query: str,
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documents: list[Document],
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score_threshold: Optional[float] = None,
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top_n: Optional[int] = None,
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user: Optional[str] = None,
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) -> list[Document]:
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"""
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Run rerank model
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:param query: search query
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:param documents: documents for reranking
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:param score_threshold: score threshold
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:param top_n: top n
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:param user: unique user id if needed
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:return:
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"""
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docs = []
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doc_id = set()
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unique_documents = []
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for document in documents:
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if document.provider == "dify" and document.metadata["doc_id"] not in doc_id:
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doc_id.add(document.metadata["doc_id"])
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docs.append(document.page_content)
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unique_documents.append(document)
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elif document.provider == "external":
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if document not in unique_documents:
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docs.append(document.page_content)
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unique_documents.append(document)
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documents = unique_documents
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rerank_result = self.rerank_model_instance.invoke_rerank(
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query=query, docs=docs, score_threshold=score_threshold, top_n=top_n, user=user
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)
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rerank_documents = []
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for result in rerank_result.docs:
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# format document
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rerank_document = Document(
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page_content=result.text,
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metadata=documents[result.index].metadata,
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provider=documents[result.index].provider,
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)
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rerank_document.metadata["score"] = result.score
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rerank_documents.append(rerank_document)
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return rerank_documents
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