from fastapi import APIRouter, HTTPException, Query
from pydantic import BaseModel
from app.core.embedding import embedder
from app.core.config import settings
from llama_index.vector_stores.faiss import FaissVectorStore
from llama_index import VectorStoreIndex, ServiceContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
import os

router = APIRouter()

class QueryRequest(BaseModel):
    query: str

@router.post("/search")
def search_docs(request: QueryRequest, user_id: str = Query(..., description="用户ID")):
    try:
        index_path = os.path.join("index_data", f"{user_id}.index")
        if not os.path.exists(index_path):
            raise HTTPException(status_code=404, detail="用户索引不存在")

        # 构建 LlamaIndex 检索器
        faiss_store = FaissVectorStore.from_persist_path(index_path)
        service_context = ServiceContext.from_defaults(embed_model=embedder)
        index = VectorStoreIndex.from_vector_store(faiss_store, service_context=service_context)

        # 检索结果（真实文本）
        retriever = index.as_retriever(similarity_top_k=settings.TOP_K)
        nodes = retriever.retrieve(request.query)

        return {
            "user_id": user_id,
            "query": request.query,
            "results": [
                {"score": float(node.score or 0), "text": node.get_content()}
                for node in nodes
            ]
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))