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"""
Rerank with cross encoder.
Ref:
https://medium.aiplanet.com/advanced-rag-cohere-re-ranker-99acc941601c
https://github.com/langchain-ai/langchain/issues/13076
"""
from __future__ import annotations
from typing import Optional, Sequence
from langchain.schema import Document
from langchain.pydantic_v1 import Extra
from langchain.callbacks.manager import Callbacks
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from sentence_transformers import CrossEncoder
class BgeRerank(BaseDocumentCompressor):
"""
Re-rank with CrossEncoder.
Ref:
https://medium.aiplanet.com/advanced-rag-cohere-re-ranker-99acc941601c
https://github.com/langchain-ai/langchain/issues/13076
good to read:
https://zhuanlan.zhihu.com/p/676008717 or its source https://teemukanstren.com/2023/12/25/llmrag-based-question-answering/
"""
# Note: switch to jina-turbo due to speed consideration
# original was "BAAI/bge-reranker-large"
model_name: str = "jinaai/jina-reranker-v1-turbo-en"
"""Model name to use for reranking."""
top_n: int = 6
"""Number of documents to return."""
model: CrossEncoder = CrossEncoder(model_name, trust_remote_code=True)
"""CrossEncoder instance to use for reranking."""
def bge_rerank(self, query, docs):
model_inputs = [[query, doc] for doc in docs]
scores = self.model.predict(model_inputs)
results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)
return results[: self.top_n]
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callbacks] = None,
) -> Sequence[Document]:
"""
Compress documents using BAAI/bge-reranker models.
Args:
documents: A sequence of documents to compress.
query: The query to use for compressing the documents.
callbacks: Callbacks to run during the compression process.
Returns:
A sequence of compressed documents.
"""
if len(documents) == 0: # to avoid empty api call
return []
doc_list = list(documents)
_docs = [d.page_content for d in doc_list]
results = self.bge_rerank(query, _docs)
final_results = []
for r in results:
doc = doc_list[r[0]]
doc.metadata["relevance_score"] = r[1]
final_results.append(doc)
return final_results
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