from typing import List
from langchain_core.documents import Document
from flashrank import Ranker, RerankRequest
from app.config import config
from app.utils.logger import logger


class DocumentReranker:
    def __init__(self):
        self.ranker = None
        self.enabled = config["models"]["reranker"]["enabled"]
        self.top_k = config["models"]["reranker"]["top_n"]
        
        if self.enabled:
            model_name = config["models"]["reranker"]["model"]
            self.ranker = Ranker(model_name=model_name)
            logger.info(f"FlashRank reranker initialized: {model_name}")
    
    def rerank(self, query: str, documents: List[Document], top_k: int = None) -> List[Document]:
        if not self.enabled or not documents:
            return documents
        
        if top_k is None:
            top_k = self.top_k
        
        passages = [
            {"id": i, "text": doc.page_content}
            for i, doc in enumerate(documents)
        ]
        
        rerank_request = RerankRequest(query=query, passages=passages)
        results = self.ranker.rerank(rerank_request)
        
        reranked_docs = []
        for result in results[:top_k]:
            doc_idx = result["id"]
            doc = documents[doc_idx]
            doc.metadata["rerank_score"] = result["score"]
            reranked_docs.append(doc)
        
        logger.info(f"Reranked {len(documents)} → {len(reranked_docs)} documents")
        return reranked_docs


document_reranker = DocumentReranker()