"""
数据模型核心业务逻辑模块

本模块包含了数据模型相关的所有核心业务逻辑函数，包括：
- 数据模型的创建、更新、删除
- 数据模型与数据资源、元数据之间的关系处理
- 数据模型血缘关系管理
- 数据模型图谱生成
- 数据模型层级计算等功能
"""

import math
import threading
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
from py2neo import Relationship
import logging
import json

# Configure logger
logger = logging.getLogger(__name__)

from app.core.graph.graph_operations import relationship_exists
from app.core.graph.graph_operations import connect_graph,create_or_get_node,get_node
from app.services.neo4j_driver import neo4j_driver
from app.core.meta_data import get_formatted_time, handle_id_unstructured
from app.core.common import delete_relationships, update_or_create_node, get_node_by_id_no_label
from app.core.data_resource.resource import get_node_by_id, serialize_node_properties


# 根据child关系计算数据模型当前的level自动保存
def calculate_model_level(id):
    """
    根据child关系计算数据模型当前的level并自动保存
    
    Args:
        id: 数据模型的节点ID(整数)
        
    Returns:
        None
    """
    # 确保id是整数类型
    node_id = int(id) if id is not None else None
    
    cql = """
    MATCH (start_node:DataModel)
    WHERE id(start_node) = $nodeId
    CALL {
        WITH start_node
        OPTIONAL MATCH path = (start_node)-[:child*]->(end_node)
        RETURN length(path) AS level
    }
    WITH coalesce(max(level), 0) AS max_level
    RETURN max_level
    """
    
    with connect_graph().session() as session:
        result = session.run(cql, nodeId=node_id)
        record = result.single()
        data = record["max_level"] if record and "max_level" in record else 0
        
    # 更新level属性
    update_query = """
    MATCH (n:DataModel)
    WHERE id(n) = $nodeId
    SET n.level = $level
    RETURN n
    """
    
    with connect_graph().session() as session:
        session.run(update_query, nodeId=node_id, level=data)


# 处理数据模型血缘关系
def handle_model_relation(resource_ids):
    """
    处理数据模型血缘关系
    
    Args:
        resource_ids: 数据资源ID
        
    Returns:
        血缘关系数据
    """
    query = """
            MATCH (search:DataResource)-[:connection]->(common_node:meta_node)<-[:connection]-(connect:DataResource)
            WHERE id(search) = $resource_Ids
            WITH search, connect, common_node
            MATCH (search)-[:connection]->(search_node:meta_node)
            WITH search, connect, common_node, collect(DISTINCT id(search_node)) AS search_nodes
            MATCH (connect)-[:connection]->(connect_node:meta_node)
            WITH search, connect, common_node, search_nodes, collect(DISTINCT id(connect_node)) AS connect_nodes
            WITH search, connect, search_nodes, connect_nodes, collect(DISTINCT id(common_node)) AS common_nodes

            // 剔除 search_nodes 和 connect_nodes 中包含在 common_nodes 中的内容
            WITH search, connect, common_nodes,
                 [node IN search_nodes WHERE NOT node IN common_nodes] AS filtered_search_nodes,
                 [node IN connect_nodes WHERE NOT node IN common_nodes] AS filtered_connect_nodes

            RETURN  id(connect) as blood_resources, common_nodes, 
            filtered_search_nodes as origin_nodes, filtered_connect_nodes as blood_nodes
            """

    with connect_graph().session() as session:
        result = session.run(query, resource_Ids=resource_ids)
    return result.data()


# 创建一个数据模型节点
def handle_data_model(data_model, result_list, result, receiver):
    """
    创建一个数据模型节点
    
    Args:
        data_model: 数据模型名称
        result_list: 数据模型英文名列表
        result: 序列化的ID列表
        receiver: 接收到的请求参数
        
    Returns:
        tuple: (id, data_model_node)
    """
    try:
        # 添加数据资源 血缘关系的字段 blood_resource
        data_model_en = result_list[0] if result_list and len(result_list) > 0 else ""
        receiver['id_list'] = result
        add_attribute = {
            'time': get_formatted_time(),
            'en_name': data_model_en
        }
        receiver.update(add_attribute)
        data_model_node = get_node('DataModel', name=data_model) or create_or_get_node('DataModel', **receiver)

        logger.info(f"通过查询或创建节点获得节点ID111，data_model_node: {data_model_node}")
        # 获取节点ID，确保我们能安全地访问节点ID
        node_id = data_model_node
        if hasattr(data_model_node, 'id'):
            logger.info(f"通过节点ID获取节点ID222，data_model_node: {data_model_node}")
            node_id = data_model_node.id
        else:
            logger.info(f"通过查询节点名称获取节点ID333，data_model_node: {data_model_node}")
            # 如果节点没有id属性，尝试通过查询获取
            query = """
            MATCH (n:DataModel {name: $name})
            RETURN id(n) as node_id
            """
            with connect_graph().session() as session:
                result = session.run(query, name=data_model)
                record = result.single()
                logger.info(f"通过查询节点名称获取节点ID444，record: {record}")
                if record and "node_id" in record:
                    logger.info(f"通过查询节点名称获取节点ID555，record: {record}")
                    node_id = record["node_id"]
        
        # 安全地处理子节点关系
        child_list = receiver.get('childrenId', [])
        for child_id in child_list:
            child_node = get_node_by_id_no_label(child_id)
            if child_node:
                # 直接使用Cypher查询检查关系是否存在
                with connect_graph().session() as session:
                    rel_query = """
                    MATCH (a)-[r:child]->(b)
                    WHERE id(a) = $start_id AND id(b) = $end_id
                    RETURN count(r) > 0 as exists
                    """
                    rel_result = session.run(rel_query, 
                                            start_id=int(node_id), 
                                            end_id=int(child_node.id)).single()
                    
                    # 如果关系不存在，则创建关系
                    if not (rel_result and rel_result["exists"]):
                        session.execute_write(
                            lambda tx: tx.run(
                                "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:child]->(b)",
                                a_id=int(node_id), b_id=int(child_node.id)
                            )
                        )

        # 根据传入参数id，和数据标签建立关系
        if receiver.get('tag'):
            tag = get_node_by_id('DataLabel', receiver['tag'])
            if tag:
                # 直接使用Cypher查询检查关系是否存在
                with connect_graph().session() as session:
                    rel_query = """
                    MATCH (a)-[r:LABEL]->(b)
                    WHERE id(a) = $start_id AND id(b) = $end_id
                    RETURN count(r) > 0 as exists
                    """
                    rel_result = session.run(rel_query, 
                                            start_id=int(node_id), 
                                            end_id=int(tag.id)).single()
                    
                    # 如果关系不存在，则创建关系
                    if not (rel_result and rel_result["exists"]):
                        session.execute_write(
                            lambda tx: tx.run(
                                "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:LABEL]->(b)",
                                a_id=int(node_id), b_id=int(tag.id)
                            )
                        )

        return node_id, data_model_node
    except Exception as e:
        logging.error(f"Error in handle_data_model: {str(e)}")
        raise


# (从数据资源中选取）
def resource_handle_meta_data_model(id_lists, data_model_node_id):
    """
    处理从数据资源中选取的数据模型与元数据的关系
    
    Args:
        id_lists: ID列表
        data_model_node_id: 数据模型节点ID
        
    Returns:
        None
    """
    try:
        logger.info(f"开始处理数据模型与元数据的关系，数据模型ID: {data_model_node_id}")
        
        # 构建meta_id和resouce_id的列表
        resouce_ids = [record['resource_id'] for record in id_lists]
        meta_ids = [record['id'] for id_list in id_lists for record in id_list['metaData']]
        
        logger.info(f"资源ID列表: {resouce_ids}")
        logger.info(f"元数据ID列表: {meta_ids}")
        
        # 创建与meta_node的关系 组成关系
        if meta_ids:
            logger.info("开始创建数据模型与元数据的关系")
            query = """
            MATCH (source:DataModel), (target:DataMeta)
            WHERE id(source)=$source_id AND id(target) IN $target_ids
            MERGE (source)-[:INCLUDES]->(target)
            RETURN count(*) as count
            """
            with connect_graph().session() as session:
                result = session.run(query, source_id=data_model_node_id, target_ids=meta_ids)
                count = result.single()["count"]
                logger.info(f"成功创建 {count} 个数据模型与元数据的关系")

        # 创建与DataResource的关系 资源关系
        # 不在创建Modle时创建资源关系，将资源关系创建放在数据流程创建时处理
        # 关系名称为DERIVED_FROM
        # commented by mxl 2025-06-27
        # 
        # if resouce_ids:
        #     logger.info("开始创建数据模型与数据资源的关系")
        #     query = """
        #     MATCH (source:DataModel), (target:DataResource)
        #     WHERE id(source)=$source_id AND id(target) IN $target_ids
        #     MERGE (source)-[:DERIVED_FROM]->(target)
        #     RETURN count(*) as count
        #     """
        #     with connect_graph().session() as session:
        #         result = session.run(query, source_id=data_model_node_id, target_ids=resouce_ids)
        #         count = result.single()["count"]
        #         logger.info(f"成功创建 {count} 个数据模型与数据资源的关系")
                
    except Exception as e:
        logger.error(f"处理数据模型与元数据的关系时发生错误: {str(e)}")
        raise


# (从数据模型中选取）
def model_handle_meta_data_model(id_lists, data_model_node_id):
    """
    处理从数据模型中选取的数据模型与元数据的关系
    
    Args:
        id_lists: ID列表
        data_model_node_id: 数据模型节点ID
        
    Returns:
        None
    """
    # 构建meta_id和model_id的列表
    model_ids = [record['model_id'] for record in id_lists]
    meta_ids = [record['id'] for id_list in id_lists for record in id_list['metaData']]
    
    # 创建与meta_node的关系 组成关系
    if meta_ids:
        query = """
        MATCH (source:DataModel), (target:DataMeta)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:component]->(target)
        """
        with neo4j_driver.get_session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=meta_ids)

    # 创建与data_model的关系 模型关系
    if model_ids:
        query = """
        MATCH (source:DataModel), (target:DataModel)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:use]->(target)
        """
        with neo4j_driver.get_session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=model_ids)


# (从DDL中选取）
def handle_no_meta_data_model(id_lists, receiver, data_model_node):
    """
    处理从DDL中选取的没有元数据的数据模型
    
    Args:
        id_lists: ID列表
        receiver: 接收到的请求参数
        data_model_node: 数据模型节点
        
    Returns:
        None
    """
    # 构建meta_id和resouce_id的列表
    resouce_ids = [record['resource_id'] for record in id_lists]
    meta_ids = [record['id'] for id_list in id_lists for record in id_list['metaData']]
    
    # 获取数据模型节点ID
    data_model_node_id = None
    if hasattr(data_model_node, 'id'):
        data_model_node_id = data_model_node.id
    else:
        # 如果节点没有id属性，尝试通过查询获取
        query = """
        MATCH (n:DataModel {name: $name})
        RETURN id(n) as node_id
        """
        with connect_graph().session() as session:
            result = session.run(query, name=data_model_node.get('name'))
            record = result.single()
            if record:
                data_model_node_id = record["node_id"]
    
    if not data_model_node_id:
        return
    
    # 创建与DataResource的关系 资源关系
    if resouce_ids:
        query = """
        MATCH (source:DataModel), (target:DataResource)
        WHERE id(source)=$source_id AND id(target) IN $target_ids
        MERGE (source)-[:resource]->(target)
        """
        with connect_graph().session() as session:
            session.run(query, source_id=data_model_node_id, target_ids=resouce_ids)

    if meta_ids:
        meta_node_list = []
        for id in meta_ids:
            query = """
            MATCH (n)
            WHERE id(n) = $node_id
            RETURN n
            """
            with connect_graph().session() as session:
                result = session.run(query, node_id=id)
            if result:
                record = result.data()
                if record:
                    meta_node_list.append(record[0]['n'])
        
        # 提取接收到的数据并创建meta_node节点
        meta_node = None
        resource_ids = []
        
        for item in id_lists:
            resource_id = item['resource_id']
            resource_ids.append(resource_id)
            
            for meta_item in item['metaData']:
                meta_id = meta_item['id']
                data_standard = meta_item.get('data_standard', '')
                en_name_zh = meta_item.get('en_name_zh', '')
                data_name = meta_item.get('data_name', '')
                
                # 使用传递的参数创建meta_node节点
                meta_params = {
                    'name': data_name,
                    'cn_name': en_name_zh,
                    'standard': data_standard,
                    'time': get_formatted_time()
                }
                
                # 创建meta_node节点
                meta_node = create_or_get_node('DataMeta', **meta_params)
                
                # 获取数据模型节点ID
                dm_id = data_model_node_id if data_model_node_id is not None else data_model_node
                
                if meta_node:
                    # 直接使用Cypher查询检查关系是否存在
                    with connect_graph().session() as session:
                        rel_query = """
                        MATCH (a)-[r:component]->(b)
                        WHERE id(a) = $start_id AND id(b) = $end_id
                        RETURN count(r) > 0 as exists
                        """
                        rel_result = session.run(rel_query, 
                                               start_id=int(dm_id), 
                                               end_id=int(meta_node)).single()
                        
                        # 如果关系不存在，则创建关系
                        if not (rel_result and rel_result["exists"]):
                            session.execute_write(
                                lambda tx: tx.run(
                                    "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:component]->(b)",
                                    a_id=int(dm_id), b_id=int(meta_node)
                                )
                            )


# 数据模型-详情接口
def handle_id_model(id):
    """
    获取数据模型详情
    
    Args:
        id: 数据模型的节点ID
        
    Returns:
        dict: 包含数据模型详情的字典，格式为：
        {"data_model": {
            "resource_selected": [...],
            "leader": ...,
            "origin": ...,
            "frequency": ...,
            "childrenId": [...],
            "organization": ...,
            "name": ...,
            "en_name": ...,
            "data_sensitivity": ...,
            "describe": ...,
            "tag": ...,
            "time": ...,
            "category": ...,
            "status": ...
        }}
    """
    node_id = id
    cql = """
        MATCH (n:DataModel) WHERE id(n) = $nodeId
        OPTIONAL MATCH (n)-[:INCLUDES]->(meta:DataMeta)  
        OPTIONAL MATCH (n)-[:DERIVED_FROM]->(resource:DataResource)
        OPTIONAL MATCH (n)-[:LABEL]->(tag:DataLabel)
        OPTIONAL MATCH (uses:model_use)-[:use]->(n)
        OPTIONAL MATCH (n)-[:has_component]->(component)
        WITH n, 
            collect(DISTINCT meta) as meta_nodes,
            collect(DISTINCT resource) as resources,
            collect(DISTINCT component) as components,
            collect(DISTINCT uses) as uses,
            collect(DISTINCT tag) as tags,
            CASE WHEN n.childrenId IS NOT NULL THEN n.childrenId ELSE [] END as children
        RETURN {
            // 基本信息
            id: id(n),
            name: n.name,
            en_name: n.en_name,
            time: n.time,
            describe: n.describe,  
            category: n.category,
            level: n.level,
            tag: CASE WHEN size(tags) > 0 AND tags[0] IS NOT NULL THEN {id: id(tags[0]), name: tags[0].name} ELSE null END,
            // 添加其他必需字段
            leader: n.leader,
            origin: n.origin,
            blood_resource: n.blood_resource,
            frequency: n.frequency,
            organization: n.organization,
            data_sensitivity: n.data_sensitivity,
            status: n.status,
            // 子节点列表
            childrenId: children
        } AS result,
        // 资源列表
        [{
            data_resource: [resource IN resources WHERE resource IS NOT NULL | {
                id: id(resource),
                name: resource.name,
                en_name: resource.en_name,
                description: resource.description
            }],
            resource_id: [resource IN resources WHERE resource IS NOT NULL | id(resource)],
            meta_ids: [meta IN meta_nodes WHERE meta IS NOT NULL | {
                id: id(meta),
                name: meta.name,
                en_name: meta.en_name,
                data_type: meta.data_type
            }]
        }] AS resource_selected
        """
        
    with connect_graph().session() as session:
        result = session.run(cql, nodeId=node_id)
        
        # 处理查询结果
        record = result.single()
        logging.info(f"获得查询结果---------->>>{record}")
        
        if record:
            # 获取基本属性和资源选择列表
            properties = record["result"]
            resource_selected = record["resource_selected"]
            
            # 确保所有必需字段都有默认值，避免空值
            required_fields = ['tag', 'leader', 'origin', 'blood_resource', 
                              'frequency', 'describe', 'organization', 'name', 'en_name', 
                              'data_sensitivity', 'time', 'category', 'status', 'childrenId']
            
            for field in required_fields:
                if field not in properties or properties[field] is None:
                    if field == 'tag':
                        properties[field] = {}
                    elif field == 'childrenId':
                        properties[field] = []
                    else:
                        properties[field] = ""
            
            # 构建最终返回格式
            final_data = {
                "resource_selected": resource_selected,
                **properties
            }
            
            return {"data_model": final_data}
        else:
            # 如果没有查询到结果，返回空的结构
            return {"data_model": {
                "resource_selected": [{"meta_ids": [], "data_resource": None, "resource_id": None}],
                "leader": None, "origin": None, "frequency": None, "childrenId": [],
                "organization": None, "name": None, "en_name": None, "data_sensitivity": None,
                "describe": None, "tag": {}, "time": None, "category": None, "status": None
            }}


# 数据模型列表
def model_list(skip_count, page_size, en_name_filter=None, name_filter=None, 
               category=None, tag=None, level=None):
    """
    获取数据模型列表
    
    Args:
        skip_count: 跳过的数量
        page_size: 页面大小
        en_name_filter: 英文名称过滤条件
        name_filter: 名称过滤条件
        category: 类别过滤条件
        tag: 标签过滤条件
        level: 层级过滤条件
        
    Returns:
        tuple: (数据模型列表, 总数量)
    """
    try:
        # 构建where子句 - 只针对DataModel节点的过滤条件
        datamodel_where_clause = []
        params = {}

        if name_filter is not None:
            datamodel_where_clause.append("n.name =~ $name")
            params['name'] = f".*{name_filter}.*"
        
        if en_name_filter is not None:
            datamodel_where_clause.append("n.en_name =~ $en_name")
            params['en_name'] = f".*{en_name_filter}.*"

        if category is not None:
            datamodel_where_clause.append("n.category = $category")
            params['category'] = category
    
        if level is not None:
            datamodel_where_clause.append("n.level = $level")
            params['level'] = level

        # 处理标签查询
        if tag is not None:
            # 确保tag参数是整数类型
            try:
                tag_id = int(tag)
                params['tag'] = tag_id
            except (ValueError, TypeError):
                logger.warning(f"Invalid tag parameter: {tag}, expected integer")
                return [], 0
                
            # 有标签查询条件时，需要确保标签关系存在
            match_clause = "MATCH (n:DataModel)-[:LABEL]->(t)"
            datamodel_where_clause.append("id(t) = $tag")
        else:
            # 没有标签查询条件时，先匹配DataModel，然后可选连接标签
            match_clause = "MATCH (n:DataModel)"

        # 构建DataModel节点的WHERE子句
        datamodel_where_str = " AND ".join(datamodel_where_clause)
        if datamodel_where_str:
            datamodel_where_str = f"WHERE {datamodel_where_str}"
    
        # 构建查询
        with connect_graph().session() as session:
            # 计算总数量
            if tag is not None:
                # 有标签查询时，直接使用标签连接
                count_query = f"""
                {match_clause}
                {datamodel_where_str}
                RETURN COUNT(DISTINCT n) AS count
                """
            else:
                # 无标签查询时，只计算DataModel节点
                count_query = f"""
                MATCH (n:DataModel)
                {datamodel_where_str}
                RETURN COUNT(n) AS count
                """
            
            logger.debug(f"Count query: {count_query}")
            logger.debug(f"Query parameters: {params}")
            
            count_result = session.run(count_query, **params)
            count_record = count_result.single()
            total = count_record['count'] if count_record else 0

            # 查询数据 - 修复OPTIONAL MATCH的笛卡尔积问题
            if tag is not None:
                # 有标签查询时，直接使用标签连接
                query = f"""
                {match_clause}
                {datamodel_where_str}
                RETURN DISTINCT 
                id(n) as id, 
                n.name as name, 
                n.en_name as en_name, 
                n.time as time,
                n.describe as describe,
                n.level as level,
                n.category as category,
                n.status as status,
                n.leader as leader,
                n.origin as origin,
                n.blood_resource as blood_resource,
                n.organization as organization,
                id(t) as tag_id,
                t.name as tag_name
                ORDER BY time DESC
                SKIP $skip
                LIMIT $limit
                """
            else:
                # 无标签查询时，先过滤DataModel节点，然后可选连接标签
                query = f"""
                MATCH (n:DataModel)
                {datamodel_where_str}
                WITH n
                OPTIONAL MATCH (n)-[:LABEL]->(t)
                RETURN 
                id(n) as id, 
                n.name as name, 
                n.en_name as en_name, 
                n.time as time,
                n.describe as describe,
                n.level as level,
                n.category as category,
                n.status as status,
                n.leader as leader,
                n.origin as origin,
                n.blood_resource as blood_resource,
                n.organization as organization,
                id(t) as tag_id,
                t.name as tag_name
                ORDER BY n.time DESC
                SKIP $skip
                LIMIT $limit
                """
            
            logger.debug(f"Main query: {query}")
        
            result = session.run(query, skip=skip_count, limit=page_size, **params)
            
            # 处理结果
            data = []
            for record in result:
                item = {
                    "id": record['id'],
                    "name": record['name'],
                    "en_name": record['en_name'],
                    "time": record['time'],
                    "describe": record['describe'],
                    "category": record['category'],
                    "status": record['status'],
                    "leader": record['leader'],
                    "origin": record['origin'],
                    "blood_resource": record['blood_resource'],
                    "organization": record['organization'],
                    "level": record['level'],
                    "tag": {"id": record['tag_id'], "name": record['tag_name']} if record['tag_id'] is not None else None
                }
                data.append(item)
            
            logger.info(f"Query returned {len(data)} items out of {total} total")
            return data, total
            
    except Exception as e:
        logger.error(f"Error in model_list: {str(e)}")
        import traceback
        traceback.print_exc()
        return [], 0


# 有血缘关系的数据资源列表
def model_resource_list(skip_count, page_size, name_filter=None, id=None, 
                        category=None, time=None):
    """
    获取数据模型相关的数据资源列表
    
    Args:
        skip_count: 跳过的数量
        page_size: 页面大小
        name_filter: 名称过滤条件
        id: 数据模型ID
        category: 类别过滤条件
        time: 时间过滤条件
        
    Returns:
        tuple: (数据资源列表, 总数量)
    """
    try:
        # 构建基础查询
        base_query = """
        MATCH (n:DataModel)
        WHERE id(n) = $nodeId
        MATCH (n)-[:children]->(m:DataResource)
        """
        
        # 计算总数量
        count_query = base_query + """
        RETURN COUNT(m) as count
        """
        
        with connect_graph().session() as session:
            # 执行计数查询
            count_result = session.run(count_query, nodeId=id)
            count_record = count_result.single()
            total = count_record['count'] if count_record else 0
            
            # 使用分页和筛选条件构建主查询
            main_query = base_query + """
            MATCH (m)-[:LABEL]->(l)
            WHERE id(n) = $nodeId and labels(m) <> ['DataMeta']
            RETURN m.name as name, 
                   m.en_name as en_name,
                   id(m) as id,
                   l.name as label,
                   m.time as time,
                   m.description as description,
                   m.category as category 
            ORDER BY m.time DESC
            SKIP $skip LIMIT $limit
            """
            
            # 执行主查询
            result = session.run(main_query, nodeId=id, skip=skip_count, limit=page_size)
            
            # 处理结果
            data = []
            for record in result:
                item = {
                    "name": record['name'],
                    "en_name": record['en_name'],
                    "id": record['id'],
                    "label": record['label'],
                    "time": record['time'],
                    "description": record['description'],
                    "category": record['category']
                }
                data.append(item)
            
            return data, total
    except Exception as e:
        print(f"Error in model_resource_list: {str(e)}")
        import traceback
        traceback.print_exc()
        return [], 0


# 数据模型血缘图谱
def model_kinship_graph(nodeid, meta=False):
    """
    生成数据模型的血缘关系图谱
    按照DERIVED_FROM关系进行递归查找，从当前节点作为起点查找所有DERIVED_FROM关系指向的节点
    
    Args:
        nodeid: 节点ID
        meta: 是否包含元数据
        
    Returns:
        dict: 包含节点和连线信息的图谱数据
    """
    try:
        with connect_graph().session() as session:
            # 确保nodeid为整数
            try:
                nodeid_int = int(nodeid)
            except (ValueError, TypeError):
                logger.error(f"节点ID不是有效的整数: {nodeid}")
                return {"nodes": [], "lines": []}
            
            # 查询起始模型节点是否存在
            start_node_query = """
            MATCH (n:DataModel)
            WHERE id(n) = $nodeId
            RETURN n
            """
            
            start_result = session.run(start_node_query, nodeId=nodeid_int)
            start_record = start_result.single()
            
            if not start_record:
                logger.error(f"未找到ID为{nodeid_int}的DataModel节点")
                return {"nodes": [], "lines": []}
            
            # 递归查找DERIVED_FROM关系
            cypher = """
            MATCH (start:DataModel)
            WHERE id(start) = $nodeId
            MATCH path = (start)-[:DERIVED_FROM*0..]->(target)
            WHERE target:DataResource OR target:DataModel
            RETURN path
            """
                
            result = session.run(cypher, nodeId=nodeid_int)
            
            # 收集节点和关系
            nodes = {}
            lines = {}
            
            for record in result:
                # 处理路径
                path = record['path']
                logger.debug(f"处理路径，长度: {len(path)}, 节点数: {len(path.nodes)}, 关系数: {len(path.relationships)}")
                
                # 处理路径中的所有节点
                for node in path.nodes:
                    node_id = int(node.id)  # 直接转换为整数
                    if node_id not in nodes:
                        node_dict = serialize_node_properties(node)
                        node_dict["id"] = str(node_id)
                        node_dict["node_type"] = list(node.labels)[0] if node.labels else ""
                        nodes[node_id] = node_dict
                        logger.debug(f"添加节点: ID={node_id}, 标签={list(node.labels)}")
                
                # 处理路径中的所有关系
                for rel in path.relationships:
                    rel_id = int(rel.id)  # 直接转换为整数
                    if rel_id not in lines:
                        rel_dict = {
                            "id": str(rel_id),
                            "from": str(int(rel.start_node.id)),
                            "to": str(int(rel.end_node.id)),
                            "text": rel.type
                        }
                        lines[rel_id] = rel_dict
                        logger.debug(f"添加关系: ID={rel_id}, 类型={rel.type}, 从{int(rel.start_node.id)}到{int(rel.end_node.id)}")
            
            # 如果需要元数据，查询INCLUDES关系
            if meta:
                meta_cypher = """
                MATCH (start:DataModel)-[r:INCLUDES]->(meta:DataMeta)
                WHERE id(start) = $nodeId
                RETURN start, r, meta
                """
                meta_result = session.run(meta_cypher, nodeId=nodeid_int)
                
                for meta_record in meta_result:
                    start_node = meta_record['start']
                    rel = meta_record['r']
                    meta_node = meta_record['meta']
                    
                    # 添加元数据节点
                    meta_node_id = int(meta_node.id)
                    if meta_node_id not in nodes:
                        node_dict = serialize_node_properties(meta_node)
                        node_dict["id"] = str(meta_node_id)
                        node_dict["node_type"] = list(meta_node.labels)[0] if meta_node.labels else ""
                        nodes[meta_node_id] = node_dict
                    
                    # 添加INCLUDES关系
                    rel_id = int(rel.id)
                    if rel_id not in lines:
                        rel_dict = {
                            "id": str(rel_id),
                            "from": str(nodeid_int),
                            "to": str(meta_node_id),
                            "text": rel.type
                        }
                        lines[rel_id] = rel_dict
            
            logger.info(f"成功获取血缘关系图谱，ID: {nodeid_int}, 节点数: {len(nodes)}, 关系数: {len(lines)}")
            return {
                "nodes": list(nodes.values()),
                "lines": list(lines.values())
            }
            
    except Exception as e:
        logger.error(f"获取数据模型血缘关系图谱失败: {str(e)}")
        import traceback
        logger.error(f"错误详情: {traceback.format_exc()}")
        return {"nodes": [], "lines": []}


# 数据模型影响图谱
def model_impact_graph(nodeid, meta=False):
    """
    生成数据模型的影响关系图谱
    按照DERIVED_FROM关系进行递归查找，从当前节点作为终点查找所有指向这个终点的节点
    
    Args:
        nodeid: 节点ID
        meta: 是否包含元数据
        
    Returns:
        dict: 包含节点和连线信息的图谱数据
    """
    try:
        with connect_graph().session() as session:
            # 确保nodeid为整数
            try:
                nodeid_int = int(nodeid)
            except (ValueError, TypeError):
                logger.error(f"节点ID不是有效的整数: {nodeid}")
                return {"nodes": [], "lines": []}
            
            # 查询起始模型节点是否存在
            start_node_query = """
            MATCH (n:DataModel)
            WHERE id(n) = $nodeId
            RETURN n
            """
            
            start_result = session.run(start_node_query, nodeId=nodeid_int)
            start_record = start_result.single()
            
            if not start_record:
                logger.error(f"未找到ID为{nodeid_int}的DataModel节点")
                return {"nodes": [], "lines": []}
            
            # 递归查找指向当前节点的DERIVED_FROM关系
            cypher = """
            MATCH (target:DataModel)
            WHERE id(target) = $nodeId
            MATCH path = (source)-[:DERIVED_FROM*0..]->(target)
            WHERE source:DataResource OR source:DataModel
            RETURN path
            """
                
            result = session.run(cypher, nodeId=nodeid_int)
            
            # 收集节点和关系
            nodes = {}
            lines = {}
            
            for record in result:
                # 处理路径
                path = record['path']
                logger.debug(f"处理影响路径，长度: {len(path)}, 节点数: {len(path.nodes)}, 关系数: {len(path.relationships)}")
                
                # 处理路径中的所有节点
                for node in path.nodes:
                    node_id = int(node.id)  # 直接转换为整数
                    if node_id not in nodes:
                        node_dict = serialize_node_properties(node)
                        node_dict["id"] = str(node_id)
                        node_dict["node_type"] = list(node.labels)[0] if node.labels else ""
                        nodes[node_id] = node_dict
                        logger.debug(f"添加影响节点: ID={node_id}, 标签={list(node.labels)}")
                
                # 处理路径中的所有关系
                for rel in path.relationships:
                    rel_id = int(rel.id)  # 直接转换为整数
                    if rel_id not in lines:
                        rel_dict = {
                            "id": str(rel_id),
                            "from": str(int(rel.start_node.id)),
                            "to": str(int(rel.end_node.id)),
                            "text": rel.type
                        }
                        lines[rel_id] = rel_dict
                        logger.debug(f"添加影响关系: ID={rel_id}, 类型={rel.type}, 从{int(rel.start_node.id)}到{int(rel.end_node.id)}")
            
            # 如果需要元数据，查询INCLUDES关系
            if meta:
                meta_cypher = """
                MATCH (target:DataModel)-[r:INCLUDES]->(meta:DataMeta)
                WHERE id(target) = $nodeId
                RETURN target, r, meta
                """
                meta_result = session.run(meta_cypher, nodeId=nodeid_int)
                
                for meta_record in meta_result:
                    target_node = meta_record['target']
                    rel = meta_record['r']
                    meta_node = meta_record['meta']
                    
                    # 添加元数据节点
                    meta_node_id = int(meta_node.id)
                    if meta_node_id not in nodes:
                        node_dict = serialize_node_properties(meta_node)
                        node_dict["id"] = str(meta_node_id)
                        node_dict["node_type"] = list(meta_node.labels)[0] if meta_node.labels else ""
                        nodes[meta_node_id] = node_dict
                    
                    # 添加INCLUDES关系
                    rel_id = int(rel.id)
                    if rel_id not in lines:
                        rel_dict = {
                            "id": str(rel_id),
                            "from": str(nodeid_int),
                            "to": str(meta_node_id),
                            "text": rel.type
                        }
                        lines[rel_id] = rel_dict
            
            logger.info(f"成功获取影响关系图谱，ID: {nodeid_int}, 节点数: {len(nodes)}, 关系数: {len(lines)}")
            return {
                "nodes": list(nodes.values()),
                "lines": list(lines.values())
            }
            
    except Exception as e:
        logger.error(f"获取数据模型影响关系图谱失败: {str(e)}")
        import traceback
        logger.error(f"错误详情: {traceback.format_exc()}")
        return {"nodes": [], "lines": []}


# 数据模型全部图谱
def model_all_graph(nodeid, meta=False):
    """
    生成数据模型的所有关系图谱
    分别调用model_impact_graph查找影响关系，调用model_kinship_graph查找血缘关系，
    然后合并两部分数据返回
    
    Args:
        nodeid: 节点ID
        meta: 是否包含元数据
        
    Returns:
        dict: 包含节点和连线信息的图谱数据
    """
    try:
        # 获取血缘关系图谱
        kinship_data = model_kinship_graph(nodeid, meta)
        
        # 获取影响关系图谱
        impact_data = model_impact_graph(nodeid, meta)
        
        # 合并节点数据，使用字典去重
        merged_nodes = {}
        merged_lines = {}
        
        # 添加血缘关系的节点和连线
        if kinship_data and 'nodes' in kinship_data:
            for node in kinship_data['nodes']:
                node_id = node.get('id')
                if node_id:
                    merged_nodes[node_id] = node
        
        if kinship_data and 'lines' in kinship_data:
            for line in kinship_data['lines']:
                line_id = line.get('id')
                if line_id:
                    merged_lines[line_id] = line
        
        # 添加影响关系的节点和连线
        if impact_data and 'nodes' in impact_data:
            for node in impact_data['nodes']:
                node_id = node.get('id')
                if node_id:
                    merged_nodes[node_id] = node
        
        if impact_data and 'lines' in impact_data:
            for line in impact_data['lines']:
                line_id = line.get('id')
                if line_id:
                    merged_lines[line_id] = line
        
        # 构建最终结果
        result = {
            "nodes": list(merged_nodes.values()),
            "lines": list(merged_lines.values())
        }
        
        logger.info(f"成功获取完整关系图谱，ID: {nodeid}, 节点数: {len(merged_nodes)}, 关系数: {len(merged_lines)}")
        return result
        
    except Exception as e:
        logger.error(f"获取数据模型完整关系图谱失败: {str(e)}")
        return {"nodes": [], "lines": []}


# 更新数据模型
def data_model_edit(receiver):
    """
    更新数据模型
    
    Args:
        receiver: 接收到的请求参数
        
    Returns:
        更新结果
    """
    id = receiver.get('id')
    name = receiver.get('name')
    en_name = receiver.get('en_name')
    category = receiver.get('category')
    describe = receiver.get('describe')
    tag = receiver.get('tag')
    frequency = receiver.get('frequency')
    leader = receiver.get('leader')
    organization = receiver.get('organization')
    status = bool(receiver.get('status')) if receiver.get('status') is not None else None
    meta_data = receiver.get('metaData', [])
    
    # 更新数据模型节点 - 添加新的字段
    query = """
    MATCH (n:DataModel) WHERE id(n) = $id
    SET n.name = $name, 
        n.en_name = $en_name, 
        n.category = $category, 
        n.describe = $describe,
        n.frequency = $frequency,
        n.leader = $leader,
        n.organization = $organization,
        n.status = $status,
        n.updateTime = $update_time
    RETURN n
    """
    
    update_time = get_formatted_time()
    with connect_graph().session() as session:
        result = session.run(query, 
                             id=id, 
                             name=name, 
                             en_name=en_name, 
                             category=category, 
                             describe=describe,
                             frequency=frequency,
                             leader=leader,
                             organization=organization,
                             status=status,
                             update_time=update_time).data()
    
    # 处理标签关系
    if tag:
        # 先删除所有标签关系
        delete_query = """
        MATCH (n:DataModel)-[r:LABEL]->() WHERE id(n) = $id
        DELETE r
        """
        with connect_graph().session() as session:
            session.run(delete_query, id=id)
        
        # 再创建新的标签关系
        tag_node = get_node_by_id('DataLabel', tag)
        if tag_node:
            model_node = get_node_by_id_no_label(id)
            if model_node:
                # 获取节点ID
                model_id = model_node.id if hasattr(model_node, 'id') else model_node
                tag_id = tag_node.id if hasattr(tag_node, 'id') else tag_node
                
                # 直接使用Cypher查询检查关系是否存在
                with connect_graph().session() as session:
                    rel_query = """
                    MATCH (a)-[r:LABEL]->(b)
                    WHERE id(a) = $start_id AND id(b) = $end_id
                    RETURN count(r) > 0 as exists
                    """
                    rel_result = session.run(rel_query, 
                                          start_id=int(model_id), 
                                          end_id=int(tag_id)).single()
                    
                    # 如果关系不存在，则创建关系
                    if not (rel_result and rel_result["exists"]):
                        session.execute_write(
                            lambda tx: tx.run(
                                "MATCH (a), (b) WHERE id(a) = $a_id AND id(b) = $b_id CREATE (a)-[:LABEL]->(b)",
                                a_id=int(model_id), b_id=int(tag_id)
                            )
                        )
    
    # 处理DataMeta节点关系更新
    with connect_graph().session() as session:
        # 先删除DataModel关联的所有DataMeta关系
        delete_meta_query = """
        MATCH (n:DataModel)-[r:INCLUDES]->(m:DataMeta)
        WHERE id(n) = $id
        DELETE r
        """
        session.run(delete_meta_query, id=id)
        logger.info(f"已删除DataModel({id})的所有DataMeta关系")
        
        # 根据上传的metaData数据是否有值来决定是否重新构建INCLUDES关系
        if meta_data:
            # 根据上传的metaData数据重新构建INCLUDES关系
            for meta_item in meta_data:
                meta_id = meta_item.get('id')
                if meta_id:
                    try:
                        meta_id = int(meta_id)
                        # 验证DataMeta节点是否存在
                        check_meta_query = """
                        MATCH (m:DataMeta)
                        WHERE id(m) = $meta_id
                        RETURN m
                        """
                        meta_result = session.run(check_meta_query, meta_id=meta_id)
                        
                        if meta_result.single():
                            # 创建INCLUDES关系
                            create_includes_query = """
                            MATCH (n:DataModel), (m:DataMeta)
                            WHERE id(n) = $model_id AND id(m) = $meta_id
                            CREATE (n)-[:INCLUDES]->(m)
                            RETURN n, m
                            """
                            session.run(create_includes_query, model_id=id, meta_id=meta_id)
                            logger.info(f"成功创建INCLUDES关系: DataModel({id}) -> DataMeta({meta_id})")
                        else:
                            logger.warning(f"DataMeta节点不存在，ID: {meta_id}")
                    except (ValueError, TypeError) as e:
                        logger.error(f"无效的meta_id: {meta_id}, 错误: {str(e)}")
        else:
            logger.info(f"meta_data为空，不需要重新创建INCLUDES关系，DataModel({id})将不关联任何DataMeta节点")
    
    return {"message": "数据模型更新成功"}


def model_community(tag=None):
    """
    查询DataModel的所有节点及DERIVED_FROM关系
    
    Args:
        tag: 可选的标签ID，如果指定则只查找有该标签的DataModel节点
        
    Returns:
        dict: 包含节点和连线信息的图谱数据，格式与model_kinship_graph相同
    """
    try:
        with connect_graph().session() as session:
            # 构建查询条件
            if tag is not None:
                # 确保tag参数是整数类型
                try:
                    tag_id = int(tag)
                except (ValueError, TypeError):
                    logger.warning(f"Invalid tag parameter: {tag}, expected integer")
                    return {"nodes": [], "lines": []}
                
                # 有标签查询条件时，查询有指定标签的DataModel节点及其DERIVED_FROM关系
                cypher = """
                MATCH (dm:DataModel)-[:LABEL]->(t)
                WHERE id(t) = $tag_id
                WITH dm
                MATCH path = (dm)-[:DERIVED_FROM*0..]->(target:DataModel)
                RETURN path
                UNION
                MATCH (dm:DataModel)-[:LABEL]->(t)
                WHERE id(t) = $tag_id
                WITH dm
                MATCH path = (source:DataModel)-[:DERIVED_FROM*0..]->(dm)
                RETURN path
                """
                
                result = session.run(cypher, tag_id=tag_id)
            else:
                # 没有标签查询条件时，查询所有DataModel节点及其DERIVED_FROM关系
                cypher = """
                MATCH (dm:DataModel)
                WITH dm
                MATCH path = (dm)-[:DERIVED_FROM*0..]->(target:DataModel)
                RETURN path
                UNION
                MATCH (dm:DataModel)
                WITH dm
                MATCH path = (source:DataModel)-[:DERIVED_FROM*0..]->(dm)
                RETURN path
                """
                
                result = session.run(cypher)
            
            # 收集节点和关系
            nodes = {}
            lines = {}
            
            for record in result:
                # 处理路径
                path = record['path']
                logger.debug(f"处理社区路径，长度: {len(path)}, 节点数: {len(path.nodes)}, 关系数: {len(path.relationships)}")
                
                # 处理路径中的所有节点
                for node in path.nodes:
                    node_id = int(node.id)  # 直接转换为整数
                    if node_id not in nodes:
                        node_dict = serialize_node_properties(node)
                        node_dict["id"] = str(node_id)
                        node_dict["node_type"] = list(node.labels)[0] if node.labels else ""
                        nodes[node_id] = node_dict
                        logger.debug(f"添加社区节点: ID={node_id}, 标签={list(node.labels)}")
                
                # 处理路径中的所有关系
                for rel in path.relationships:
                    rel_id = int(rel.id)  # 直接转换为整数
                    if rel_id not in lines:
                        rel_dict = {
                            "id": str(rel_id),
                            "from": str(int(rel.start_node.id)),
                            "to": str(int(rel.end_node.id)),
                            "text": rel.type
                        }
                        lines[rel_id] = rel_dict
                        logger.debug(f"添加社区关系: ID={rel_id}, 类型={rel.type}, 从{int(rel.start_node.id)}到{int(rel.end_node.id)}")
            
            logger.info(f"成功获取数据模型社区图谱，标签ID: {tag}, 节点数: {len(nodes)}, 关系数: {len(lines)}")
            return {
                "nodes": list(nodes.values()),
                "lines": list(lines.values())
            }
            
    except Exception as e:
        logger.error(f"获取数据模型社区图谱失败: {str(e)}")
        import traceback
        logger.error(f"错误详情: {traceback.format_exc()}")
        return {"nodes": [], "lines": []}


def model_search_list(model_id, page, page_size, en_name_filter=None,
                     name_filter=None, category_filter=None, tag_filter=None):
    """获取特定数据模型关联的元数据列表"""
    try:
        with connect_graph().session() as session:
            # 确保model_id为整数
            try:
                model_id_int = int(model_id)
            except (ValueError, TypeError):
                logger.error(f"模型ID不是有效的整数: {model_id}")
                return [], 0
            
            # 基本匹配语句 - 支持DataMeta和Metadata标签
            match_clause = """
            MATCH (n:DataModel)-[:INCLUDES]->(m)
            WHERE id(n) = $model_id
            AND (m:DataMeta OR m:Metadata)
            """
            
            where_conditions = []
            
            if en_name_filter:
                where_conditions.append(f"m.en_name CONTAINS '{en_name_filter}'")
                
            if name_filter:
                where_conditions.append(f"m.name CONTAINS '{name_filter}'")
                
            if category_filter:
                where_conditions.append(f"m.category = '{category_filter}'")
                
            # 标签过滤需要额外的匹配
            tag_match = ""
            if tag_filter:
                tag_match = "MATCH (m)-[:HAS_TAG]->(t:Tag) WHERE t.name = $tag_filter"
            
            where_clause = " AND " + " AND ".join(where_conditions) if where_conditions else ""
            
            # 计算总数
            count_cypher = f"""
            {match_clause}{where_clause}
            {tag_match}
            RETURN count(m) as count
            """
            count_params = {"model_id": model_id_int}
            if tag_filter:
                count_params["tag_filter"] = tag_filter
                
            count_result = session.run(count_cypher, **count_params)
            total_count = count_result.single()["count"]
            
            # 分页查询
            skip = (page - 1) * page_size
            cypher = f"""
            {match_clause}{where_clause}
            {tag_match}
            RETURN m
            ORDER BY m.name
            SKIP {skip} LIMIT {page_size}
            """
            
            result = session.run(cypher, **count_params)
            
            # 格式化结果
            metadata_list = []
            for record in result:
                meta = serialize_node_properties(record["m"])
                meta["id"] = record["m"].id
                metadata_list.append(meta)
            
            logger.info(f"成功获取数据模型关联元数据，ID: {model_id_int}, 元数据数量: {total_count}")
            return metadata_list, total_count
    except Exception as e:
        logger.error(f"获取数据模型关联的元数据列表失败: {str(e)}")
        return [], 0