"""
验证 RAG 索引完整性。

检查 Qdrant 向量库、PostgreSQL 文档存储及检索功能。
"""

import asyncio
import os
import sys

sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../.."))

from dotenv import load_dotenv
load_dotenv()

QDRANT_URL = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
DB_URI = os.getenv("DB_URI", "postgresql://postgres:huang1998@115.190.121.151:5432/langgraph_db?sslmode=disable")
COLLECTION_NAME = "rag_documents"
TABLE_NAME = "parent_documents"


def check_qdrant():
    """检查 Qdrant 向量库。"""
    from qdrant_client import QdrantClient

    print("=" * 60)
    print("Qdrant 向量库")
    print("=" * 60)

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

    # 集合列表
    collections = client.get_collections().collections
    print(f"\n集合数: {len(collections)}")
    for c in collections:
        print(f"  - {c.name}")

    # 目标集合信息
    if not any(c.name == COLLECTION_NAME for c in collections):
        print(f"\n集合 '{COLLECTION_NAME}' 不存在")
        return

    info = client.get_collection(COLLECTION_NAME)
    print(f"\n集合 '{COLLECTION_NAME}':")
    print(f"  状态: {info.status}")
    print(f"  向量数: {info.points_count}")

    vectors_config = info.config.params.vectors
    if isinstance(vectors_config, dict):
        for name, vc in vectors_config.items():
            print(f"  向量 '{name}': 维度={vc.size}, 距离={vc.distance}")
    else:
        print(f"  向量维度: {vectors_config.size}")

    # 抽样查看
    print(f"\n前 3 个向量:")
    points = client.scroll(
        collection_name=COLLECTION_NAME,
        limit=3,
        with_payload=True,
        with_vectors=False
    )
    for i, point in enumerate(points[0]):
        print(f"\n  {i+1}. ID: {point.id}")
        payload = point.payload or {}
        print(f"     内容: {payload.get('page_content', '')[:100]}...")


async def check_postgres():
    """检查 PostgreSQL 文档存储。"""
    import asyncpg

    print("\n" + "=" * 60)
    print("PostgreSQL 文档存储")
    print("=" * 60)

    conn = await asyncpg.connect(dsn=DB_URI)

    try:
        # 表是否存在
        tables = await conn.fetch(
            "SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'"
        )
        table_names = [t['table_name'] for t in tables]

        if TABLE_NAME not in table_names:
            print(f"\n表 '{TABLE_NAME}' 不存在")
            return

        # 统计
        count = await conn.fetchval(f"SELECT COUNT(*) FROM {TABLE_NAME}")
        print(f"\n表 '{TABLE_NAME}': {count} 条记录")

        # 抽样
        print(f"\n前 3 个文档:")
        rows = await conn.fetch(
            f"SELECT key, value FROM {TABLE_NAME} ORDER BY key LIMIT 3"
        )
        for i, row in enumerate(rows):
            print(f"\n  {i+1}. Key: {row['key']}")
            val = row['value']
            if isinstance(val, dict) and 'page_content' in val:
                print(f"     内容: {val['page_content'][:100]}...")

        # Key 前缀分布
        key_prefixes = await conn.fetch(
            f"""
            SELECT 
                CASE 
                    WHEN key LIKE '%:%' THEN split_part(key, ':', 1)
                    ELSE 'no_prefix'
                END AS prefix,
                COUNT(*) AS cnt
            FROM {TABLE_NAME}
            GROUP BY prefix
            ORDER BY cnt DESC
            LIMIT 10
            """
        )
        print(f"\nKey 前缀分布:")
        for row in key_prefixes:
            print(f"  {row['prefix']}: {row['cnt']}")

    finally:
        await conn.close()


async def test_search():
    """测试检索功能。"""
    from rag_indexer.index_builder import IndexBuilder, IndexBuilderConfig
    from rag_indexer.splitters import SplitterType

    print("\n" + "=" * 60)
    print("检索测试")
    print("=" * 60)

    # 使用配置对象初始化（与默认构建方式一致）
    config = IndexBuilderConfig(
        collection_name=COLLECTION_NAME,
        splitter_type=SplitterType.PARENT_CHILD,
    )
    builder = IndexBuilder(config)

    # 确保检索器已初始化
    if builder.retriever is None:
        print("错误: 检索器未初始化，请检查切分策略")
        return

    query = input("\n查询 (回车使用默认): ").strip() or "你好"
    print(f"\n查询: {query}")

    # 标准检索（返回父块，因为 ParentDocumentRetriever 默认返回父块）
    print("\n--- 标准检索 (返回父块) ---")
    results = await builder.retriever.ainvoke(query)
    for i, doc in enumerate(results):
        content = doc.page_content[:200] if hasattr(doc, 'page_content') else str(doc)[:200]
        print(f"\n  {i+1}. {content}...")
        if hasattr(doc, 'metadata'):
            source = doc.metadata.get('source', '')
            if source:
                print(f"     来源: {source}")

    # 若需要仅返回子块，可以临时修改检索器的 search_type
    # （注意：ParentDocumentRetriever 的 search_type 默认为 "similarity"）
    print("\n--- 检索子块 (通过修改检索器参数) ---")
    # 创建一个新的检索器副本，设置为返回子块
    # 简单起见，直接调用 vectorstore 进行相似度搜索获取子块
    vectorstore = builder.vector_store.get_langchain_vectorstore()
    sub_results = await vectorstore.asimilarity_search(query, k=3)
    for i, doc in enumerate(sub_results):
        content = doc.page_content[:200] if hasattr(doc, 'page_content') else str(doc)[:200]
        print(f"\n  {i+1}. {content}...")
        if hasattr(doc, 'metadata'):
            parent_id = doc.metadata.get('parent_id', '')
            if parent_id:
                print(f"     父块 ID: {parent_id}")


async def main():
    check_qdrant()
    await check_postgres()
    await test_search()


if __name__ == "__main__":
    asyncio.run(main())