dify/api/core/rag/rerank/rerank_model.py
Jyong 9d221a5e19
external knowledge api (#8913)
Co-authored-by: Yi <yxiaoisme@gmail.com>
2024-09-30 15:38:43 +08:00

61 lines
2.0 KiB
Python

from typing import Optional
from core.model_manager import ModelInstance
from core.rag.models.document import Document
class RerankModelRunner:
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 = []
unique_documents = []
dify_documents = [item for item in documents if item.provider == "dify"]
external_documents = [item for item in documents if item.provider == "external"]
for document in dify_documents:
if document.metadata["doc_id"] not in doc_id:
doc_id.append(document.metadata["doc_id"])
docs.append(document.page_content)
unique_documents.append(document)
for document in external_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