"""
实现知识图谱节点name属性向量化
"""

from llm import Embedding
from neo4j import GraphDatabase
import time

# 初始化 Embedding 模型
embeddings = Embedding(url="http://172.20.0.145:9995/v1", api_key="xxx", model_name="bge-m3")

# Neo4j 连接信息
url = "bolt://172.20.0.145:7687"
username = "neo4j"
password = "password"

driver = GraphDatabase.driver(url, auth=(username, password))

# 定义需要处理的节点类型 - 使用正确的Neo4j标签格式
node_labels = [
    "ProjectDivisionSet",
    "ProjectDivisionTree",
    "ProjectDivisionItem",
    # 对于复合标签，在查询时使用多标签形式
    {"labels": ["ProjectQuantity", "Quota"], "display": "ProjectQuantity+Quota"},
    {"labels": ["ProjectQuantity", "MainMaterial"], "display": "ProjectQuantity+MainMaterial"},
    {"labels": ["ProjectQuantity", "Equipment"], "display": "ProjectQuantity+Equipment"},
    "MaterialOrEquipment",
    "FeeTableTemplateSet",
    "FeeTableTemplateItem",
    "FeeCollection",
    "FeeScheduleSet",
    "FeeScheduleItem",
    "Fee",
]


def create_vector_index():
    """为每个标签创建向量索引"""
    with driver.session() as session:
        dimension = 1024  # BGE-M3模型的向量维度

        for i, label_info in enumerate(node_labels):
            try:
                # 处理不同格式的标签
                if isinstance(label_info, dict):  # 复合标签
                    labels = label_info["labels"]
                    display_name = label_info["display"]
                    # 为每个单独的标签创建索引，而不是尝试创建复合标签的索引
                    for j, single_label in enumerate(labels):
                        index_name = f"entity_embedding_index_{i}_{j}"
                        create_single_index(
                            session, single_label, index_name, dimension, f"{display_name}的组成标签 {single_label}"
                        )
                else:  # 单一标签
                    index_name = f"entity_embedding_index_{i}"
                    create_single_index(session, label_info, index_name, dimension, label_info)
            except Exception as e:
                if isinstance(label_info, dict):
                    print(f"❌ 创建复合标签 {label_info['display']} 的索引失败: {e}")
                else:
                    print(f"❌ 创建标签 {label_info} 的索引失败: {e}")


def create_single_index(session, label, index_name, dimension, display_name):
    """为单个标签创建向量索引，使用最新的Neo4j语法"""
    try:
        # 检查索引是否存在
        check_index_query = """
        SHOW INDEXES
        YIELD name
        WHERE name = $index_name
        RETURN count(*) > 0 AS exists
        """

        result = session.run(check_index_query, index_name=index_name)
        index_exists = result.single()["exists"]

        if not index_exists:
            print(f"正在为标签 {display_name} 创建向量索引...")

            # 使用最新的Neo4j向量索引语法
            create_index_query = f"""
            CREATE VECTOR INDEX {index_name} 
            FOR (n:{label}) 
            ON (n.embedding)
            OPTIONS {{indexConfig: {{`vector.dimensions`: {dimension}}}}}
            """

            session.run(create_index_query)
            print(f"✅ 标签 {display_name} 的向量索引创建成功")
        else:
            print(f"✅ 标签 {display_name} 的向量索引已存在，跳过创建步骤")
    except Exception as e:
        print(f"❌ 创建标签 {display_name} 的向量索引失败: {e}")


def generate_and_store_embeddings():
    with driver.session() as session:
        for label_info in node_labels:
            # 处理不同格式的标签
            if isinstance(label_info, dict):  # 复合标签
                labels = label_info["labels"]
                display_name = label_info["display"]
                # 为Neo4j查询构建多标签模式
                label_pattern = ":".join(labels)
            else:  # 单一标签
                label_pattern = label_info
                display_name = label_info

            print(f"\n🔍 Processing nodes of type: {display_name}")
            start_time = time.time()

            # 查询该类型的所有节点 elementId 和 name
            query = f"""
                MATCH (n:{label_pattern})
                WHERE n.name IS NOT NULL
                RETURN elementId(n) AS id, n.name AS name
            """
            result = session.run(query)

            count = 0
            batch = []

            for record in result:
                node_id = record["id"]
                name = record["name"]

                if not name or not name.strip():
                    continue

                try:
                    vector = embeddings.embed(name)
                    batch.append({"id": node_id, "vector": vector})
                    count += 1

                    # 批量写入，减少数据库交互次数
                    if len(batch) >= 50:
                        write_batch(session, batch)
                        batch = []
                except Exception as e:
                    print(f"❌ Error processing node {node_id}: {e}")

            # 写入剩余批次
            if batch:
                write_batch(session, batch)

            duration = time.time() - start_time
            print(f"✅ Processed {count} nodes of type {display_name} in {duration:.2f} seconds.")


def write_batch(session, batch):
    """批量写入 embedding 到 Neo4j"""
    session.run(
        """
        UNWIND $batch AS item
        MATCH (n) WHERE elementId(n) = item.id
        SET n.embedding = item.vector
        """,
        batch=batch,
    )


def verify_embeddings():
    """验证embedding是否正确存储"""
    with driver.session() as session:
        verify_query = """
        MATCH (n) 
        WHERE n.embedding IS NOT NULL 
        RETURN n.name, size(n.embedding) as dimension 
        LIMIT 5
        """
        result = session.run(verify_query)
        print("\n验证embedding存储情况:")

        records = list(result)
        if not records:
            print("❌ 未找到任何带有embedding的节点")
        else:
            for record in records:
                print(f"节点: {record['n.name']}, 向量维度: {record['dimension']}")


if __name__ == "__main__":
    # 第一步：创建向量索引
    create_vector_index()

    # 第二步：生成并存储embeddings
    generate_and_store_embeddings()

    # 第三步：验证结果
    verify_embeddings()