#!/usr/bin/env python3
"""
简单测试脚本：检查 Qdrant 内容，测试各种检索方式
"""

import asyncio
import os
import sys

from qdrant_client import models
from backend.rag_core import QdrantVectorStore, get_sparse_embedder
from backend.app.model_services import get_embedding_service


def check_qdrant_content():
    """检查 Qdrant 里的内容"""
    print("="*70)
    print("检查 Qdrant 内容...")
    print("="*70)
    
    embeddings = get_embedding_service()
    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
    client = vs.get_qdrant_client()
    
    # 滚动获取前 5 个点
    points, _ = client.scroll(
        collection_name="rag_documents",
        limit=5,
        with_payload=True,
        with_vectors=False
    )
    
    print(f"\n找到 {len(points)} 个文档\n")
    for i, point in enumerate(points):
        print(f"--- 文档 {i+1} ---")
        print(f"ID: {point.id}")
        print(f"Payload 键: {list(point.payload.keys())}")
        
        # 打印完整 payload
        for k, v in point.payload.items():
            if isinstance(v, str) and len(v) > 150:
                v = v[:150] + "..."
            print(f"  {k}: {v}")
        print()


def test_dense_retrieval():
    """测试稠密检索"""
    print("="*70)
    print("测试稠密检索...")
    print("="*70)
    
    embeddings = get_embedding_service()
    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
    
    query = "蚂蚁"  # 用中文查询
    print(f"\n查询: {query}")
    
    results = vs.similarity_search(query, k=3)
    
    print(f"\n找到 {len(results)} 个结果\n")
    for i, doc in enumerate(results):
        print(f"--- 结果 {i+1} ---")
        print(doc.page_content[:200])
        print()


def test_sparse_retrieval():
    """测试稀疏检索"""
    print("="*70)
    print("测试稀疏检索（BM25）...")
    print("="*70)
    
    embeddings = get_embedding_service()
    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
    client = vs.get_qdrant_client()
    sparse_embedder = get_sparse_embedder()
    
    query = "冬天"
    print(f"\n查询: {query}")
    
    sparse_query = sparse_embedder.embed_query(query)
    sparse_vec = models.SparseVector(
        indices=sparse_query["indices"],
        values=sparse_query["values"]
    )
    
    response = client.query_points(
        collection_name="rag_documents",
        query=sparse_vec,
        using="sparse",
        limit=3,
        with_payload=True
    )
    
    print(f"\n找到 {len(response.points)} 个结果\n")
    for i, point in enumerate(response.points):
        print(f"--- 结果 {i+1} ---")
        print(f"分数: {point.score:.4f}")
        text = point.payload.get("page_content", point.payload.get("text", ""))
        print(text[:200])
        print()


def test_hybrid_retrieval():
    """测试混合检索"""
    print("="*70)
    print("测试混合检索（稠密+稀疏 RRF 融合）...")
    print("="*70)
    
    embeddings = get_embedding_service()
    vs = QdrantVectorStore(collection_name="rag_documents", embeddings=embeddings)
    client = vs.get_qdrant_client()
    sparse_embedder = get_sparse_embedder()
    
    query = "蚂蚁和蚱蜢"
    print(f"\n查询: {query}")
    
    dense_query = embeddings.embed_query(query)
    sparse_query = sparse_embedder.embed_query(query)
    sparse_vec = models.SparseVector(
        indices=sparse_query["indices"],
        values=sparse_query["values"]
    )
    
    response = client.query_points(
        collection_name="rag_documents",
        prefetch=[
            models.Prefetch(query=dense_query, using="dense", limit=3),
            models.Prefetch(query=sparse_vec, using="sparse", limit=3)
        ],
        query=models.FusionQuery(fusion=models.Fusion.RRF),
        limit=3,
        with_payload=True
    )
    
    print(f"\n找到 {len(response.points)} 个结果\n")
    for i, point in enumerate(response.points):
        print(f"--- 结果 {i+1} ---")
        print(f"分数: {point.score:.4f}")
        text = point.payload.get("page_content", point.payload.get("text", ""))
        print(text[:200])
        print()


if __name__ == "__main__":
    check_qdrant_content()
    test_dense_retrieval()
    test_sparse_retrieval()
    test_hybrid_retrieval()