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