DataOps-platform/app/core/data_interface/interface.py

"""
数据接口核心业务逻辑模块

本模块包含了数据接口相关的所有核心业务逻辑函数，包括：
- 数据标准(data_standard)相关功能
- 数据标签(DataLabel)相关功能
- 图谱生成
- 动态标签识别等功能
"""

import logging
import re

from app.core.graph.graph_operations import connect_graph
from app.services.neo4j_driver import neo4j_driver

# 配置logger
logger = logging.getLogger(__name__)


def _build_category_filter_conditions(category_filter, params):
    """
    将 category_filter 转换为 Cypher 查询条件列表。
    支持:
    - 字典: {field: value, ...}
    - 列表: [{"field": "...", "value": "..."}, {"category": "xxx"}]
    - 字符串: 兼容旧用法，等同于按 category 字段过滤
    """
    conditions = []
    param_index = 0

    def add_condition(field, value):
        nonlocal param_index
        if value is None:
            return
        if not isinstance(field, str):
            return
        if not re.match(r"^[A-Za-z_][A-Za-z0-9_]*$", field):
            logger.warning(f"忽略非法属性字段: {field}")
            return
        param_key = f"category_filter_{param_index}"
        param_index += 1
        conditions.append(f"n.{field} CONTAINS ${param_key}")
        params[param_key] = value

    if isinstance(category_filter, dict):
        for field, value in category_filter.items():
            add_condition(field, value)
    elif isinstance(category_filter, list):
        for item in category_filter:
            if not isinstance(item, dict):
                continue
            if "field" in item and "value" in item:
                add_condition(item.get("field"), item.get("value"))
            elif len(item) == 1:
                field, value = next(iter(item.items()))
                add_condition(field, value)
    elif category_filter:
        add_condition("category", category_filter)

    return conditions


# 数据标准列表展示
def standard_list(
    skip_count,
    page_size,
    name_en_filter=None,
    name_zh_filter=None,
    category_filter=None,
    create_time_filter=None,
):
    """
    获取数据标准列表

    Args:
        skip_count: 跳过的记录数量
        page_size: 每页记录数量
        name_en_filter: 英文名称过滤条件
        name_zh_filter: 名称过滤条件
        category_filter: 分类过滤条件
        create_time_filter: 时间过滤条件

    Returns:
        tuple: (数据列表, 总记录数)
    """
    data = []

    # 构建查询条件
    where_clause = []
    params = {}
    if name_zh_filter:
        where_clause.append("n.name_zh CONTAINS $name_zh_filter")
        params["name_zh_filter"] = name_zh_filter
    if name_en_filter:
        where_clause.append("n.name_en CONTAINS $name_en_filter")
        params["name_en_filter"] = name_en_filter
    if category_filter:
        where_clause.append("n.category CONTAINS $category_filter")
        params["category_filter"] = category_filter
    if create_time_filter:
        where_clause.append("n.create_time CONTAINS $create_time_filter")
        params["create_time_filter"] = create_time_filter
    else:
        where_clause.append("TRUE")

    where_str = " AND ".join(where_clause)

    # 构建完整的查询语句
    cql = f"""
    MATCH (n:data_standard)
    WHERE {where_str}
    RETURN
        properties(n) as properties,
        n.create_time as create_time,
        id(n) as nodeid,
        size([(n)<-[]-() | 1]) + size([(n)-[]->() | 1]) as relationship_count
    ORDER BY create_time desc
    SKIP $skip_count
    LIMIT $page_size
    """
    params["skip_count"] = skip_count
    params["page_size"] = page_size

    # 修复：使用正确的session方式执行查询
    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, **params)
            for record in result:
                properties = {
                    key: value
                    for key, value in record["properties"].items()
                    if key not in ["input", "code", "output"]
                }
                properties.setdefault("describe", None)

                new_attr = {
                    "id": record["nodeid"],
                    "number": record["relationship_count"],
                }
                properties.update(new_attr)
                data.append(properties)

            # 获取总量
            total_query = (
                f"MATCH (n:data_standard) WHERE {where_str} RETURN COUNT(n) AS total"
            )
            total_record = session.run(total_query, **params).single()
            total = total_record["total"] if total_record else 0

            return data, total
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return [], 0
    finally:
        if driver:
            driver.close()


# 数据标准图谱展示（血缘关系）父节点
def standard_kinship_graph(nodeid):
    """
    生成数据标准的血缘关系图谱

    Args:
        nodeid: 节点ID

    Returns:
        图谱数据
    """
    # 查询语句
    cql = """
    MATCH(da:data_standard)
    WHERE id(da)=$nodeId
    OPTIONAL MATCH(a:DataResource)-[:clean_resource]-(da)
    OPTIONAL MATCH(b:DataModel)-[:clean_model]-(da)
    WITH
        collect({
            id:toString(id(a)),
            text:a.name,
            type:split(labels(a)[0],'_')[1]
        })+
        collect({
            id:toString(id(b)),
            text:b.name,
            type:split(labels(b)[0],'_')[1]
        })+
        collect({
            id:toString(id(da)),
            text:da.name,
            type:split(labels(da)[0],'_')[1]
        }) as nodes,
        da,
        collect({from:toString(id(a)),to:toString(id(da)),text:'标准'})+
        collect({from:toString(id(b)),to:toString(id(da)),text:'标准'}) as lines
    WITH
        toString(id(da)) as rootId,
        apoc.coll.toSet(lines) as lines,
        apoc.coll.toSet(nodes) as nodes
    RETURN nodes,lines,rootId
    """

    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, nodeId=nodeid)
            res = {}
            for item in result:
                res = {
                    "nodes": [record for record in item["nodes"] if record["id"]],
                    "lines": [
                        record
                        for record in item["lines"]
                        if record["from"] and record["to"]
                    ],
                    "rootId": item["rootId"],
                }
            return res
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return {}
    finally:
        if driver:
            driver.close()


# 数据标准图谱展示（影响关系）下游
def standard_impact_graph(nodeid):
    """
    生成数据标准的影响关系图谱

    Args:
        nodeid: 节点ID

    Returns:
        图谱数据
    """
    # 查询语句
    cql = """
        MATCH(da:data_standard)
        WHERE id(da)=$nodeId
        OPTIONAL MATCH(da)-[:clean_model]-(m1:DataMeta)-[:clean_model]-(da)
        OPTIONAL MATCH(da)-[:clean_model]-(m2:DataMeta)-[:clean_model]-(da)
        WITH
            collect({
                id:toString(id(da)),
                text:da.name,
                type:split(labels(da)[0],'_')[1]
            })+
            collect({id:toString(id(m1)),text:m1.name})+
            collect({id:toString(id(m2)),text:m2.name}) as nodes,
            da,
            collect({
                from:toString(id(da)),
                to:toString(id(m1)),
                text:'标准清洗'
            })+
            collect({
                from:toString(id(da)),
                to:toString(id(m2)),
                text:'标准清洗'
            }) as lines
        WITH
            toString(id(da)) as rootId,
            apoc.coll.toSet(lines) as lines,
            apoc.coll.toSet(nodes) as nodes
        RETURN nodes,lines,rootId
        """

    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, nodeId=nodeid)
            res = {}
            for item in result:
                res = {
                    "nodes": [record for record in item["nodes"] if record["id"]],
                    "lines": [
                        record
                        for record in item["lines"]
                        if record["from"] and record["to"]
                    ],
                    "rootId": item["rootId"],
                }
            return res
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return {}
    finally:
        if driver:
            driver.close()


# 数据标准图谱展示（所有关系）
def standard_all_graph(nodeid):
    """
    生成数据标准的所有关系图谱

    Args:
        nodeid: 节点ID

    Returns:
        图谱数据
    """
    # 查询语句
    cql = """
    MATCH(da:data_standard)
    WHERE id(da)=$nodeId
    OPTIONAL MATCH(a:DataResource)-[:clean_resource]-(da)
    OPTIONAL MATCH(b:DataModel)-[:clean_model]-(da)
    OPTIONAL MATCH(da)-[:clean_model]-(m1:DataMeta)-[:clean_model]-(da)
    OPTIONAL MATCH(da)-[:clean_model]-(m2:DataMeta)-[:clean_model]-(da)
    WITH
        collect({
            id:toString(id(a)),
            text:a.name,
            type:split(labels(a)[0],'_')[1]
        })+
        collect({
            id:toString(id(b)),
            text:b.name,
            type:split(labels(b)[0],'_')[1]
        })+
        collect({
            id:toString(id(da)),
            text:da.name,
            type:split(labels(da)[0],'_')[1]
        })+
        collect({id:toString(id(m1)),text:m1.name})+
        collect({id:toString(id(m2)),text:m2.name}) as nodes,
        da,
        collect({from:toString(id(a)),to:toString(id(da)),text:'标准'})+
        collect({from:toString(id(b)),to:toString(id(da)),text:'标准'})+
        collect({
            from:toString(id(da)),
            to:toString(id(m1)),
            text:'标准清洗'
        })+
        collect({
            from:toString(id(da)),
            to:toString(id(m2)),
            text:'标准清洗'
        }) as lines
    WITH
        toString(id(da)) as rootId,
        apoc.coll.toSet(lines) as lines,
        apoc.coll.toSet(nodes) as nodes
    RETURN nodes,lines,rootId
    """
    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, nodeId=nodeid)
            res = {}
            for item in result:
                res = {
                    "nodes": [record for record in item["nodes"] if record["id"]],
                    "lines": [
                        record
                        for record in item["lines"]
                        if record["from"] and record["to"]
                    ],
                    "rootId": item["rootId"],
                }
            return res
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return {}
    finally:
        if driver:
            driver.close()


# 数据标签列表展示
def label_list(
    skip_count,
    page_size,
    name_en_filter=None,
    name_zh_filter=None,
    category_filter=None,
    group_filter=None,
):
    """
    获取数据标签列表

    Args:
        skip_count: 跳过的记录数量
        page_size: 每页记录数量
        name_en_filter: 英文名称过滤条件
        name_zh_filter: 名称过滤条件
        category_filter: 分类过滤条件
        group_filter: 分组过滤条件

    Returns:
        tuple: (数据列表, 总记录数)
    """
    data = []

    # 构建查询条件
    where_clause = []
    params = {}
    if name_zh_filter:
        where_clause.append("n.name_zh CONTAINS $name_zh_filter")
        params["name_zh_filter"] = name_zh_filter
    if name_en_filter:
        where_clause.append("n.name_en CONTAINS $name_en_filter")
        params["name_en_filter"] = name_en_filter
    where_clause.extend(_build_category_filter_conditions(category_filter, params))
    if group_filter:
        where_clause.append("n.group CONTAINS $group_filter")
        params["group_filter"] = group_filter

    if not where_clause:
        where_clause.append("TRUE")

    where_str = " AND ".join(where_clause)

    # 构建完整的查询语句
    cql = f"""
    MATCH (n:DataLabel)
    WHERE {where_str}
    WITH
        n,
        properties(n) as properties,
        n.create_time as create_time,
        id(n) as nodeid
    OPTIONAL MATCH (n)<-[r]-()
    WITH
        n,
        properties,
        create_time,
        nodeid,
        count(r) as incoming
    OPTIONAL MATCH (n)-[r]->()
    WITH
        n,
        properties,
        create_time,
        nodeid,
        incoming,
        count(r) as outgoing
    RETURN
        properties,
        create_time,
        nodeid,
        incoming + outgoing as relationship_count
    ORDER BY create_time desc
    SKIP $skip_count
    LIMIT $page_size
    """
    params["skip_count"] = skip_count
    params["page_size"] = page_size

    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, **params)
            for record in result:
                properties = record["properties"]
                new_attr = {
                    "id": record["nodeid"],
                    "number": record["relationship_count"],
                }
                if "describe" not in properties:
                    properties["describe"] = None
                if "scope" not in properties:
                    properties["scope"] = None
                properties.update(new_attr)
                data.append(properties)

            # 获取总量
            total_query = (
                f"MATCH (n:DataLabel) WHERE {where_str} RETURN COUNT(n) AS total"
            )
            total_record = session.run(total_query, **params).single()
            total = total_record["total"] if total_record else 0

            return data, total
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return [], 0
    finally:
        if driver:
            driver.close()


# 数据标签图谱展示
def id_label_graph(id):
    """
    根据ID生成数据标签图谱

    Args:
        id: 节点ID

    Returns:
        图谱数据
    """
    query = """
    MATCH (n:DataLabel)
    WHERE id(n) = $nodeId
    OPTIONAL MATCH (a)-[:LABEL]-(n)
    WITH
       collect({
           from: toString(id(a)),
           to: toString(id(n)),
           text: "标签"
       }) AS line1,
       collect({
           id: toString(id(n)),
           text: n.name_zh,
           type:"label"
       }) AS node1,
       collect({
           id: toString(id(a)),
           text: a.name_zh,
           type: split(labels(a)[0], '_')[1]
       }) AS node2,
       n
    WITH
        apoc.coll.toSet(line1) AS lines,
        apoc.coll.toSet(node1 + node2) AS nodes,
        toString(id(n)) AS res
    RETURN lines, nodes, res
    """
    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(query, nodeId=id)
            res = {}
            for item in result:
                res = {
                    "nodes": [record for record in item["nodes"] if record["id"]],
                    "lines": [
                        record
                        for record in item["lines"]
                        if record["from"] and record["to"]
                    ],
                    "rootId": item["res"],
                }
            return res
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return {}
    finally:
        if driver:
            driver.close()


# 数据标签图谱展示（血缘关系）父节点/（所有关系）
def label_kinship_graph(nodeid):
    """
    生成数据标签的血缘关系图谱

    Args:
        nodeid: 节点ID

    Returns:
        图谱数据
    """
    # 查询语句
    cql = """
    MATCH(la:DataLabel)
    WHERE id(la)=$nodeId
    OPTIONAL MATCH(a:DataResource)-[:LABEL]-(la)
    OPTIONAL MATCH(b:DataModel)-[:LABEL]-(la)
    OPTIONAL MATCH(meta:DataMeta)-[:LABEL]-(la)
    OPTIONAL MATCH(d:data_standard)-[:LABEL]-(la)
    WITH
        collect({
            id:toString(id(a)),
            text:a.name_zh,
            type:split(labels(a)[0],'_')[1]
        })+
        collect({
            id:toString(id(b)),
            text:b.name_zh,
            type:split(labels(b)[0],'_')[1]
        })+
        collect({
            id:toString(id(d)),
            text:d.name_zh,
            type:split(labels(e)[0],'_')[1]
        })+
        collect({
            id:toString(id(la)),
            text:la.name_zh,
            type:split(labels(la)[0],'_')[1]
        })+
        collect({id:toString(id(meta)),text:meta.name_zh}) as nodes,
        la,
        collect({from:toString(id(a)),to:toString(id(la)),text:'标签'})+
        collect({from:toString(id(b)),to:toString(id(la)),text:'标签'})+
        collect({from:toString(id(meta)),to:toString(id(la)),text:'标签'})+
        collect({from:toString(id(d)),to:toString(id(la)),text:'标签'})+
        collect({from:toString(id(e)),to:toString(id(la)),text:'标签'}) as lines
    WITH
        toString(id(la)) as rootId,
        apoc.coll.toSet(lines) as lines,
        apoc.coll.toSet(nodes) as nodes
    RETURN nodes,lines,rootId
    """
    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, nodeId=nodeid)
            res = {}
            for item in result:
                res = {
                    "nodes": [record for record in item["nodes"] if record["id"]],
                    "lines": [
                        record
                        for record in item["lines"]
                        if record["from"] and record["to"]
                    ],
                    "rootId": item["rootId"],
                }
            return res
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return {}
    finally:
        if driver:
            driver.close()


# 数据标签图谱展示（影响关系）下游
def label_impact_graph(nodeid):
    """
    生成数据标签的影响关系图谱

    Args:
        nodeid: 节点ID

    Returns:
        图谱数据
    """
    # 查询语句
    cql = """
        MATCH(n:DataLabel)
        WHERE id(n)=$nodeId
        RETURN {
            id:toString(id(n)),
            text:(n.name_zh),
            type:"label"
        } AS nodes,
        toString(id(n)) as rootId
        """
    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, nodeId=nodeid)
            res = {}
            for item in result:
                res = {"nodes": item["nodes"], "rootId": item["rootId"], "lines": []}
            return res
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return {}
    finally:
        if driver:
            driver.close()


# 数据标签按照提交内容查询相似分组，并且返回
def dynamic_label_list(name_filter=None):
    """
    根据内容查询相似的数据标签分组

    Args:
        name_filter: 内容过滤条件

    Returns:
        标签分组列表
    """
    # 构建完整的查询语句
    cql = """
    MATCH (n:DataLabel)
    WITH
        n,
        apoc.text.levenshteinSimilarity(n.group, $name_filter) AS similarity
    WHERE similarity > 0.1 // 设置相似度阈值
    RETURN DISTINCT n.group as name_zh, id(n) as nodeid
    """

    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(cql, name_filter=name_filter or "")
            data = []
            for record in result:
                data.append(
                    {
                        "name_zh": record["name_zh"],
                        "id": record["nodeid"],
                    }
                )

            return data
    except (ConnectionError, ValueError) as e:
        logger.error(f"Neo4j数据库连接失败: {str(e)}")
        return []
    finally:
        if driver:
            driver.close()


def search_info(key, value):
    """
    搜索指定属性的节点信息

    Args:
        key: 搜索属性键
        value: 搜索属性值

    Returns:
        搜索结果列表
    """
    field_pattern = r"^[A-Za-z_][A-Za-z0-9_]*$"
    if not re.match(field_pattern, str(key)):
        logger.warning("非法属性键: %s", key)
        return []

    query = """
    MATCH (n)
    WHERE n[$field] =~ $pattern
    WITH
        n,
        properties(n) as properties,
        n.create_time as create_time,
        id(n) as nodeid
    RETURN properties, nodeid, create_time, labels(n) as labels
    LIMIT 30
    """

    driver = None
    try:
        driver = connect_graph()
    except (ConnectionError, ValueError) as e:
        logger.error(f"无法连接到Neo4j数据库: {str(e)}")
        return []

    try:
        with driver.session() as session:
            result = session.run(
                query,
                field=key,
                pattern=f"(?i).*{value}.*",
            )

            results = []
            for record in result:
                results.append(
                    {
                        "properties": record["properties"],
                        "id": record["nodeid"],
                        "create_time": record["create_time"],
                        "labels": record["labels"],
                    }
                )

            return results
    except Exception as e:
        logger.error(f"搜索节点信息失败: {str(e)}")
        return []
    finally:
        if driver:
            driver.close()


def label_info(id):
    """
    获取标签节点的信息

    Args:
        id: 节点ID

    Returns:
        标签节点信息
    """
    query = """
    MATCH (n)
    WHERE id(n) = $nodeId
    RETURN {
        id:toString(id(n)),
        text:(n.name_zh),
        type:"label"
    } AS nodes,
    toString(id(n)) as rootId
    """

    driver = None
    try:
        driver = connect_graph()
        with driver.session() as session:
            result = session.run(query, nodeId=id).data()
            return result[0] if result else {}
    except (ConnectionError, ValueError) as e:
        logger.error(f"无法连接到Neo4j数据库: {str(e)}")
        return {}
    finally:
        if driver:
            driver.close()


def graph_all(domain_id, include_meta=True):
    """
    获取完整关系图谱

    从指定的 domain_id 节点开始，通过 INPUT 和 OUTPUT 关系遍历找出所有的
    DataFlow 节点和 BusinessDomain 节点。

    Args:
        domain_id: 起始节点ID（通常是 BusinessDomain 节点）
        include_meta: 是否包含元数据节点。如果为 True，会包含：
            - domain_id 指定的节点本身
            - 通过 INCLUDES 关系连接到 domain_id 节点的 DataMeta 节点

    Returns:
        dict: 包含 nodes 与 lines 的图谱数据
    """
    try:
        domain_id_int = int(domain_id)
    except (ValueError, TypeError):
        logger.error(f"节点ID不是有效的整数: {domain_id}")
        return {"nodes": [], "lines": []}

    try:
        with neo4j_driver.get_session() as session:
            nodes = {}  # 节点字典: {node_id: node_props}
            lines = {}  # 关系字典: {rel_id: rel_props}

            # 1. 验证起始节点是否存在
            check_node_query = """
            MATCH (n)
            WHERE id(n) = $domain_id
            RETURN n, labels(n) as labels
            """
            result = session.run(check_node_query, domain_id=domain_id_int)
            record = result.single()

            if not record:
                logger.warning(f"未找到节点: {domain_id_int}")
                return {"nodes": [], "lines": []}

            start_node = record["n"]
            start_labels = record["labels"]
            start_node_type = start_labels[0] if start_labels else ""

            # 2. 如果 include_meta=True，添加起始节点及其 INCLUDES 关系的 DataMeta 节点
            if include_meta:
                # 添加起始节点
                start_props = dict(start_node)
                start_props["id"] = domain_id_int
                start_props["node_type"] = start_node_type
                nodes[domain_id_int] = start_props

                # 查找通过 INCLUDES 关系连接的 DataMeta 节点
                meta_query = """
                MATCH (n)-[r:INCLUDES]->(m:DataMeta)
                WHERE id(n) = $domain_id
                RETURN m, id(m) as meta_id, id(r) as rel_id
                """
                meta_results = session.run(meta_query, domain_id=domain_id_int)

                for meta_record in meta_results:
                    meta_node = meta_record["m"]
                    meta_id = meta_record["meta_id"]
                    rel_id = meta_record["rel_id"]

                    # 添加 DataMeta 节点
                    meta_props = dict(meta_node)
                    meta_props["id"] = meta_id
                    meta_props["node_type"] = "DataMeta"
                    nodes[meta_id] = meta_props

                    # 添加 INCLUDES 关系
                    lines[str(rel_id)] = {
                        "id": str(rel_id),
                        "from": str(domain_id_int),
                        "to": str(meta_id),
                        "text": "INCLUDES",
                    }

            # 3. 通过 INPUT 和 OUTPUT 关系遍历，找出所有相关的 DataFlow 和 BusinessDomain 节点
            # 使用广度优先遍历，确保 BusinessDomain 和 DataFlow 两种节点都加入队列进行二次遍历
            queue = [(domain_id_int, start_node_type)]  # (node_id, node_type)
            processed_bd = set()  # 已处理的 BusinessDomain 节点 ID
            processed_df = set()  # 已处理的 DataFlow 节点 ID

            while queue:
                current_id, current_type = queue.pop(0)

                # 如果是 BusinessDomain，查找所有相关的 DataFlow（INPUT 和 OUTPUT 两个方向）
                if current_type == "BusinessDomain" and current_id not in processed_bd:
                    processed_bd.add(current_id)

                    # 添加当前 BusinessDomain 节点（如果还未添加）
                    if current_id not in nodes:
                        bd_query = """
                        MATCH (bd:BusinessDomain)
                        WHERE id(bd) = $bd_id
                        RETURN bd
                        """
                        bd_result = session.run(bd_query, bd_id=current_id).single()
                        if bd_result:
                            bd_node = bd_result["bd"]
                            bd_props = dict(bd_node)
                            bd_props["id"] = current_id
                            bd_props["node_type"] = "BusinessDomain"
                            nodes[current_id] = bd_props

                    # 查找通过 INPUT 关系连接的 DataFlow（BD-[INPUT]->DF）
                    input_query = """
                    MATCH (bd:BusinessDomain)-[r:INPUT]->(df:DataFlow)
                    WHERE id(bd) = $bd_id
                    RETURN df, id(df) as df_id, id(r) as rel_id
                    """
                    input_results = session.run(input_query, bd_id=current_id)

                    for input_record in input_results:
                        df_node = input_record["df"]
                        df_id = input_record["df_id"]
                        rel_id = input_record["rel_id"]

                        # 添加 DataFlow 节点
                        if df_id not in nodes:
                            df_props = dict(df_node)
                            df_props["id"] = df_id
                            df_props["node_type"] = "DataFlow"
                            nodes[df_id] = df_props

                        # 添加 INPUT 关系
                        lines[str(rel_id)] = {
                            "id": str(rel_id),
                            "from": str(current_id),
                            "to": str(df_id),
                            "text": "INPUT",
                        }

                        # 将 DataFlow 加入队列继续遍历
                        if df_id not in processed_df:
                            queue.append((df_id, "DataFlow"))

                    # 查找通过 OUTPUT 关系连接的 DataFlow（DF-[OUTPUT]->BD，反向查找）
                    reverse_output_query = """
                    MATCH (df:DataFlow)-[r:OUTPUT]->(bd:BusinessDomain)
                    WHERE id(bd) = $bd_id
                    RETURN df, id(df) as df_id, id(r) as rel_id
                    """
                    reverse_output_results = session.run(
                        reverse_output_query, bd_id=current_id
                    )

                    for reverse_record in reverse_output_results:
                        df_node = reverse_record["df"]
                        df_id = reverse_record["df_id"]
                        rel_id = reverse_record["rel_id"]

                        # 添加 DataFlow 节点
                        if df_id not in nodes:
                            df_props = dict(df_node)
                            df_props["id"] = df_id
                            df_props["node_type"] = "DataFlow"
                            nodes[df_id] = df_props

                        # 添加 OUTPUT 关系
                        lines[str(rel_id)] = {
                            "id": str(rel_id),
                            "from": str(df_id),
                            "to": str(current_id),
                            "text": "OUTPUT",
                        }

                        # 将 DataFlow 加入队列继续遍历
                        if df_id not in processed_df:
                            queue.append((df_id, "DataFlow"))

                # 如果是 DataFlow，查找所有相关的 BusinessDomain（INPUT 和 OUTPUT 两个方向）
                elif current_type == "DataFlow" and current_id not in processed_df:
                    processed_df.add(current_id)

                    # 添加当前 DataFlow 节点（如果还未添加）
                    if current_id not in nodes:
                        df_query = """
                        MATCH (df:DataFlow)
                        WHERE id(df) = $df_id
                        RETURN df
                        """
                        df_result = session.run(df_query, df_id=current_id).single()
                        if df_result:
                            df_node = df_result["df"]
                            df_props = dict(df_node)
                            df_props["id"] = current_id
                            df_props["node_type"] = "DataFlow"
                            nodes[current_id] = df_props

                    # 查找通过 OUTPUT 关系连接的目标 BusinessDomain（DF-[OUTPUT]->BD）
                    output_query = """
                    MATCH (df:DataFlow)-[r:OUTPUT]->(bd:BusinessDomain)
                    WHERE id(df) = $df_id
                    RETURN bd, id(bd) as bd_id, id(r) as rel_id
                    """
                    output_results = session.run(output_query, df_id=current_id)

                    for output_record in output_results:
                        bd_node = output_record["bd"]
                        bd_id = output_record["bd_id"]
                        rel_id = output_record["rel_id"]

                        # 添加 BusinessDomain 节点
                        if bd_id not in nodes:
                            bd_props = dict(bd_node)
                            bd_props["id"] = bd_id
                            bd_props["node_type"] = "BusinessDomain"
                            nodes[bd_id] = bd_props

                        # 添加 OUTPUT 关系
                        lines[str(rel_id)] = {
                            "id": str(rel_id),
                            "from": str(current_id),
                            "to": str(bd_id),
                            "text": "OUTPUT",
                        }

                        # 将 BusinessDomain 加入队列继续遍历
                        if bd_id not in processed_bd:
                            queue.append((bd_id, "BusinessDomain"))

                    # 查找通过 INPUT 关系连接的源 BusinessDomain（BD-[INPUT]->DF，反向查找）
                    reverse_input_query = """
                    MATCH (bd:BusinessDomain)-[r:INPUT]->(df:DataFlow)
                    WHERE id(df) = $df_id
                    RETURN bd, id(bd) as bd_id, id(r) as rel_id
                    """
                    reverse_input_results = session.run(
                        reverse_input_query, df_id=current_id
                    )

                    for reverse_record in reverse_input_results:
                        bd_node = reverse_record["bd"]
                        bd_id = reverse_record["bd_id"]
                        rel_id = reverse_record["rel_id"]

                        # 添加 BusinessDomain 节点
                        if bd_id not in nodes:
                            bd_props = dict(bd_node)
                            bd_props["id"] = bd_id
                            bd_props["node_type"] = "BusinessDomain"
                            nodes[bd_id] = bd_props

                        # 添加 INPUT 关系
                        lines[str(rel_id)] = {
                            "id": str(rel_id),
                            "from": str(bd_id),
                            "to": str(current_id),
                            "text": "INPUT",
                        }

                        # 将 BusinessDomain 加入队列继续遍历
                        if bd_id not in processed_bd:
                            queue.append((bd_id, "BusinessDomain"))

            logger.info(
                f"graph_all 结果: node_id={domain_id_int}, "
                f"nodes={len(nodes)}, lines={len(lines)}, "
                f"include_meta={include_meta}"
            )

            return {
                "nodes": list(nodes.values()),
                "lines": list(lines.values()),
            }
    except Exception as e:
        logger.error(f"获取图谱失败: {str(e)}")
        import traceback

        logger.error(traceback.format_exc())
        return {"nodes": [], "lines": []}


def node_delete(node_id):
    """
    删除 DataLabel 节点及其所有关联关系

    Args:
        node_id: 节点ID（整数）

    Returns:
        dict: 删除结果，包含 success 状态和 message 信息
    """
    driver = None
    try:
        driver = connect_graph()
    except (ConnectionError, ValueError) as e:
        logger.error(f"无法连接到Neo4j数据库: {str(e)}")
        return {"success": False, "message": "无法连接到数据库"}

    try:
        with driver.session() as session:
            # 首先检查节点是否存在且为 DataLabel 类型
            check_query = """
            MATCH (n:DataLabel)
            WHERE id(n) = $nodeId
            RETURN n
            """
            check_result = session.run(
                check_query,
                nodeId=node_id,
            ).single()

            if not check_result:
                logger.warning(f"DataLabel 节点不存在: ID={node_id}")
                return {
                    "success": False,
                    "message": f"DataLabel 节点不存在 (ID: {node_id})",
                }

            # 删除节点及其所有关系
            delete_query = """
            MATCH (n:DataLabel)
            WHERE id(n) = $nodeId
            DETACH DELETE n
            RETURN count(n) as deleted_count
            """
            delete_result = session.run(
                delete_query,
                nodeId=node_id,
            ).single()
            if not delete_result:
                logger.warning(f"删除结果为空: ID={node_id}")
                return {
                    "success": False,
                    "message": "删除失败，未获取到删除结果",
                }
            deleted_count = delete_result["deleted_count"]

            if deleted_count > 0:
                logger.info(f"成功删除 DataLabel 节点: ID={node_id}")
                return {
                    "success": True,
                    "message": (f"成功删除 DataLabel 节点 (ID: {node_id})"),
                }
            else:
                logger.warning(f"删除失败，节点可能已被删除: ID={node_id}")
                return {"success": False, "message": "删除失败，节点可能已被删除"}
    except Exception as e:
        logger.error(f"删除 DataLabel 节点失败: {str(e)}")
        return {"success": False, "message": f"删除失败: {str(e)}"}
    finally:
        if driver:
            driver.close()