"""检查 Qdrant 中存储的向量质量。"""

import os
import sys
import numpy as np
from dotenv import load_dotenv
from qdrant_client import QdrantClient

# 添加项目根目录和 backend 目录到 Python 路径
project_root = os.path.join(os.path.dirname(__file__), "..")
backend_dir = os.path.join(project_root, "backend")
sys.path.insert(0, project_root)
sys.path.insert(0, backend_dir)
load_dotenv()

from rag_core import LlamaCppEmbedder

QDRANT_URL = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
COLLECTION_NAME = "rag_documents"

client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
embedder = LlamaCppEmbedder()

# 获取样本
points, _ = client.scroll(
    collection_name=COLLECTION_NAME,
    limit=1,
    with_vectors=True,
    with_payload=True,
)

if not points:
    print(f"集合 '{COLLECTION_NAME}' 为空")
    exit()

sample = points[0]
raw_vec = sample.vector
if isinstance(raw_vec, dict):
    stored_vec = list(raw_vec.values())[0]
elif isinstance(raw_vec, list):
    stored_vec = raw_vec
else:
    stored_vec = []

stored_payload = sample.payload or {}
stored_text = str(stored_payload.get("page_content", ""))[:200]

print(f"内容预览:\n{stored_text}...\n")
print(f"向量维度: {len(stored_vec)}")  # type: ignore
print(f"前5个值: {stored_vec[:5]}")  # type: ignore
print(f"是否全零: {all(v == 0.0 for v in stored_vec)}")  # type: ignore

# 重新编码对比
if stored_text:
    new_vec = embedder.embed_query(stored_text)
    similarity = np.dot(stored_vec, new_vec) / (np.linalg.norm(stored_vec) * np.linalg.norm(new_vec))  # type: ignore
    print(f"\n重新编码前5个值: {new_vec[:5]}")
    print(f"余弦相似度: {similarity:.4f}")

    if similarity < 0.8:
        print("\n⚠️  相似度过低，建议删除集合并重建索引")
    else:
        print("\n✅ 向量一致")
else:
    print("\n⚠️  样本无文本内容")