Merge #31 into develop from memory-summary
[feature]开发用户记忆详情的接口 * memory-summary: (69 commits squashed) - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - Merge branch 'memory-summary' of codeup.aliyun.com:redbearai/python/redbear-mem-open into memory-summary - [feature]Develop the relationship graph interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Develop the relationship graph interface - Merge branch 'memory-summary' of codeup.aliyun.com:redbearai/python/redbear-mem-open into memory-summary - [feature]Develop the end_user/profile interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Develop the relationship graph interface - [feature]Develop the end_user/profile interface - Merge branch 'memory-summary' of codeup.aliyun.com:redbearai/python/redbear-mem-open into memory-summary - [updated]Base change operation - [refactor]1.Convert timestamp;2.Remove unnecessary code - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Develop the relationship graph interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Develop the relationship graph interface - [feature]Develop the end_user/profile interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [feature]Develop the relationship graph interface - [feature]Develop the end_user/profile interface - [updated]Base change operation - [refactor]1.Convert timestamp;2.Remove unnecessary code - Merge branch 'memory-summary' of codeup.aliyun.com:redbearai/python/redbear-mem-open into memory-summary - [check]check_code.py checks the quality of the code - [fix]Fix insecure database connections - [refactor]refactor memory_storage_controller and memory_storage_service - [add]The /total_memory_count interface returns the "name" field. - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Develop the relationship graph interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Develop the relationship graph interface - [feature]Develop the end_user/profile interface - [feature]Memory Insights and User Summary Cache Storage Ingestion - [feature]Develop the relationship graph interface - [feature]Develop the end_user/profile interface - [updated]Base change operation - [refactor]1.Convert timestamp;2.Remove unnecessary code - [feature]Memory Insights and User Summary Cache Storage Ingestion - [featrue]Develop a memory classification interface - [feature]Develop the relationship graph interface - [feature]Develop the end_user/profile interface - [updated]Base change operation - [refactor]1.Convert timestamp;2.Remove unnecessary code - [check]check_code.py checks the quality of the code - [fix]Fix insecure database connections - [refactor]refactor memory_storage_controller and memory_storage_service - [add]The /total_memory_count interface returns the "name" field. - Merge branch 'memory-summary' of codeup.aliyun.com:redbearai/python/redbear-mem-open into memory-summary - [refactor]Reconstruct the user's memory location - add uv.lock Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com> Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/31
This commit is contained in:
乐力齐
@@ -268,10 +268,20 @@ async def get_workspace_total_memory_count(
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# 如果提供了 end_user_id，只查询该用户
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if end_user_id:
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search_result = await memory_storage_service.search_all(end_user_id=end_user_id)
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# 查询用户名称
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from app.repositories.end_user_repository import EndUserRepository
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(uuid.UUID(end_user_id))
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user_name = end_user.name if end_user else None
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return {
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"total_memory_count": search_result.get("total", 0),
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"host_count": 1,
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"details": [{"end_user_id": end_user_id, "count": search_result.get("total", 0)}]
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"details": [{
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"end_user_id": end_user_id,
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"count": search_result.get("total", 0),
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"name": user_name
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}]
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}
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for host in hosts:
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@@ -287,17 +297,19 @@ async def get_workspace_total_memory_count(
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details.append({
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"end_user_id": end_user_id_str,
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"count": host_total
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"count": host_total,
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"name": host.name # 添加 name 字段
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})
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business_logger.debug(f"EndUser {end_user_id_str} 记忆数: {host_total}")
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business_logger.debug(f"EndUser {end_user_id_str} ({host.name}) 记忆数: {host_total}")
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except Exception as e:
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business_logger.warning(f"获取 end_user {host.id} 记忆数失败: {str(e)}")
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# 失败的 host 记为 0
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details.append({
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"end_user_id": str(host.id),
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"count": 0
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"count": 0,
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"name": host.name # 添加 name 字段
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})
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result = {
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@@ -15,11 +15,9 @@ from sqlalchemy.orm import Session
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from dotenv import load_dotenv
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from app.models.user_model import User
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from app.models.end_user_model import EndUser
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from app.core.logging_config import get_logger
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from app.utils.sse_utils import format_sse_message
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from app.schemas.memory_storage_schema import (
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ConfigFilter,
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ConfigPilotRun,
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ConfigParamsCreate,
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ConfigParamsDelete,
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@@ -34,7 +32,8 @@ from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
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from app.core.memory.analytics.memory_insight import MemoryInsight
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from app.core.memory.analytics.recent_activity_stats import get_recent_activity_stats
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from app.core.memory.analytics.user_summary import generate_user_summary
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from app.repositories.data_config_repository import DataConfigRepository
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from app.repositories.end_user_repository import EndUserRepository
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import uuid
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logger = get_logger(__name__)
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@@ -67,6 +66,7 @@ class MemoryStorageService:
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}
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return result
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class DataConfigService: # 数据配置服务类（PostgreSQL）
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"""Service layer for config params CRUD.
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@@ -85,7 +85,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
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@staticmethod
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def _convert_timestamps_to_format(data_list: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""将 created_at 和 updated_at 字段从 datetime 对象转换为 YYYYMMDDHHmmss 格式"""
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from datetime import datetime
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for item in data_list:
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for field in ['created_at', 'updated_at']:
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@@ -576,14 +575,6 @@ async def analytics_hot_memory_tags(
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return [{"name": t, "frequency": f} for t, f in top_tags]
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async def analytics_memory_insight_report(end_user_id: Optional[str] = None) -> Dict[str, Any]:
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insight = MemoryInsight(end_user_id)
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report = await insight.generate_insight_report()
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await insight.close()
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data = {"report": report}
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return data
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async def analytics_recent_activity_stats() -> Dict[str, Any]:
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stats, _msg = get_recent_activity_stats()
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total = (
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@@ -617,8 +608,3 @@ async def analytics_recent_activity_stats() -> Dict[str, Any]:
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data = {"total": total, "stats": stats, "latest_relative": latest_relative}
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return data
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async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, Any]:
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summary = await generate_user_summary(end_user_id)
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data = {"summary": summary}
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return data
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831
api/app/services/user_memory_service.py
Normal file
831
api/app/services/user_memory_service.py
Normal file
@@ -0,0 +1,831 @@
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"""
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User Memory Service
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处理用户记忆相关的业务逻辑，包括记忆洞察、用户摘要、节点统计和图数据等。
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"""
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from typing import Dict, List, Optional, Any
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import uuid
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from sqlalchemy.orm import Session
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from app.core.logging_config import get_logger
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from app.repositories.end_user_repository import EndUserRepository
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.core.memory.analytics.memory_insight import MemoryInsight
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from app.core.memory.analytics.user_summary import generate_user_summary
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logger = get_logger(__name__)
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# Neo4j connector instance
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_neo4j_connector = Neo4jConnector()
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class UserMemoryService:
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"""用户记忆服务类"""
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def __init__(self):
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logger.info("UserMemoryService initialized")
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async def get_cached_memory_insight(
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self,
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db: Session,
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end_user_id: str
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) -> Dict[str, Any]:
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"""
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从数据库获取缓存的记忆洞察
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Args:
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db: 数据库会话
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end_user_id: 终端用户ID (UUID)
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Returns:
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{
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"report": str,
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"updated_at": datetime,
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"is_cached": bool
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}
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"""
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try:
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# 转换为UUID并查询用户
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user_uuid = uuid.UUID(end_user_id)
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(user_uuid)
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if not end_user:
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logger.warning(f"未找到 end_user_id 为 {end_user_id} 的用户")
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return {
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"report": None,
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"updated_at": None,
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"is_cached": False,
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"message": "用户不存在"
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}
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# 检查是否有缓存数据
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if end_user.memory_insight:
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logger.info(f"成功获取 end_user_id {end_user_id} 的缓存记忆洞察")
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return {
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"report": end_user.memory_insight,
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"updated_at": end_user.memory_insight_updated_at,
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"is_cached": True
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}
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else:
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logger.info(f"end_user_id {end_user_id} 的记忆洞察缓存为空")
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return {
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"report": None,
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"updated_at": None,
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"is_cached": False,
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"message": "数据尚未生成，请稍后重试或联系管理员"
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}
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except ValueError:
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logger.error(f"无效的 end_user_id 格式: {end_user_id}")
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return {
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"report": None,
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"updated_at": None,
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"is_cached": False,
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"message": "无效的用户ID格式"
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}
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except Exception as e:
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logger.error(f"获取缓存记忆洞察时出错: {str(e)}")
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raise
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async def get_cached_user_summary(
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self,
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db: Session,
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end_user_id: str
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) -> Dict[str, Any]:
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"""
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从数据库获取缓存的用户摘要
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Args:
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db: 数据库会话
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end_user_id: 终端用户ID (UUID)
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Returns:
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{
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"summary": str,
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"updated_at": datetime,
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"is_cached": bool
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}
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"""
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try:
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# 转换为UUID并查询用户
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user_uuid = uuid.UUID(end_user_id)
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(user_uuid)
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if not end_user:
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logger.warning(f"未找到 end_user_id 为 {end_user_id} 的用户")
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return {
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"summary": None,
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"updated_at": None,
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"is_cached": False,
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"message": "用户不存在"
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}
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# 检查是否有缓存数据
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if end_user.user_summary:
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logger.info(f"成功获取 end_user_id {end_user_id} 的缓存用户摘要")
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return {
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"summary": end_user.user_summary,
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"updated_at": end_user.user_summary_updated_at,
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"is_cached": True
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}
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else:
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logger.info(f"end_user_id {end_user_id} 的用户摘要缓存为空")
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return {
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"summary": None,
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"updated_at": None,
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"is_cached": False,
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"message": "数据尚未生成，请稍后重试或联系管理员"
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}
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except ValueError:
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logger.error(f"无效的 end_user_id 格式: {end_user_id}")
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return {
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"summary": None,
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"updated_at": None,
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"is_cached": False,
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"message": "无效的用户ID格式"
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}
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except Exception as e:
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logger.error(f"获取缓存用户摘要时出错: {str(e)}")
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raise
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async def generate_and_cache_insight(
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self,
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db: Session,
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end_user_id: str,
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workspace_id: Optional[uuid.UUID] = None
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) -> Dict[str, Any]:
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"""
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生成并缓存记忆洞察
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Args:
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db: 数据库会话
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end_user_id: 终端用户ID (UUID)
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workspace_id: 工作空间ID (可选)
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Returns:
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{
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"success": bool,
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"report": str,
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"error": Optional[str]
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}
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"""
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try:
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logger.info(f"开始为 end_user_id {end_user_id} 生成记忆洞察")
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# 转换为UUID并查询用户
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user_uuid = uuid.UUID(end_user_id)
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(user_uuid)
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if not end_user:
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logger.error(f"end_user_id {end_user_id} 不存在")
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return {
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"success": False,
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"report": None,
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"error": "用户不存在"
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}
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# 使用 end_user_id 调用分析函数
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try:
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logger.info(f"使用 end_user_id={end_user_id} 生成记忆洞察")
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result = await analytics_memory_insight_report(end_user_id)
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report = result.get("report", "")
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if not report:
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logger.warning(f"end_user_id {end_user_id} 的记忆洞察生成结果为空")
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return {
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"success": False,
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"report": None,
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"error": "生成的洞察报告为空,可能Neo4j中没有该用户的数据"
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}
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# 更新数据库缓存
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success = repo.update_memory_insight(user_uuid, report)
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if success:
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logger.info(f"成功为 end_user_id {end_user_id} 生成并缓存记忆洞察")
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return {
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"success": True,
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"report": report,
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"error": None
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}
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else:
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logger.error(f"更新 end_user_id {end_user_id} 的记忆洞察缓存失败")
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return {
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"success": False,
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"report": report,
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"error": "数据库更新失败"
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}
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except Exception as e:
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logger.error(f"调用分析函数生成记忆洞察时出错: {str(e)}")
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return {
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"success": False,
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"report": None,
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"error": f"Neo4j或LLM服务不可用: {str(e)}"
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}
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except ValueError:
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logger.error(f"无效的 end_user_id 格式: {end_user_id}")
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return {
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"success": False,
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"report": None,
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"error": "无效的用户ID格式"
|
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}
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except Exception as e:
|
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logger.error(f"生成并缓存记忆洞察时出错: {str(e)}")
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return {
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"success": False,
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"report": None,
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"error": str(e)
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}
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async def generate_and_cache_summary(
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self,
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db: Session,
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end_user_id: str,
|
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workspace_id: Optional[uuid.UUID] = None
|
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) -> Dict[str, Any]:
|
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"""
|
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生成并缓存用户摘要
|
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|
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Args:
|
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db: 数据库会话
|
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end_user_id: 终端用户ID (UUID)
|
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workspace_id: 工作空间ID (可选)
|
||||
|
||||
Returns:
|
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{
|
||||
"success": bool,
|
||||
"summary": str,
|
||||
"error": Optional[str]
|
||||
}
|
||||
"""
|
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try:
|
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logger.info(f"开始为 end_user_id {end_user_id} 生成用户摘要")
|
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|
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# 转换为UUID并查询用户
|
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user_uuid = uuid.UUID(end_user_id)
|
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repo = EndUserRepository(db)
|
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end_user = repo.get_by_id(user_uuid)
|
||||
|
||||
if not end_user:
|
||||
logger.error(f"end_user_id {end_user_id} 不存在")
|
||||
return {
|
||||
"success": False,
|
||||
"summary": None,
|
||||
"error": "用户不存在"
|
||||
}
|
||||
|
||||
# 使用 end_user_id 调用分析函数
|
||||
try:
|
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logger.info(f"使用 end_user_id={end_user_id} 生成用户摘要")
|
||||
result = await analytics_user_summary(end_user_id)
|
||||
summary = result.get("summary", "")
|
||||
|
||||
if not summary:
|
||||
logger.warning(f"end_user_id {end_user_id} 的用户摘要生成结果为空")
|
||||
return {
|
||||
"success": False,
|
||||
"summary": None,
|
||||
"error": "生成的用户摘要为空,可能Neo4j中没有该用户的数据"
|
||||
}
|
||||
|
||||
# 更新数据库缓存
|
||||
success = repo.update_user_summary(user_uuid, summary)
|
||||
|
||||
if success:
|
||||
logger.info(f"成功为 end_user_id {end_user_id} 生成并缓存用户摘要")
|
||||
return {
|
||||
"success": True,
|
||||
"summary": summary,
|
||||
"error": None
|
||||
}
|
||||
else:
|
||||
logger.error(f"更新 end_user_id {end_user_id} 的用户摘要缓存失败")
|
||||
return {
|
||||
"success": False,
|
||||
"summary": summary,
|
||||
"error": "数据库更新失败"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"调用分析函数生成用户摘要时出错: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"summary": None,
|
||||
"error": f"Neo4j或LLM服务不可用: {str(e)}"
|
||||
}
|
||||
|
||||
except ValueError:
|
||||
logger.error(f"无效的 end_user_id 格式: {end_user_id}")
|
||||
return {
|
||||
"success": False,
|
||||
"summary": None,
|
||||
"error": "无效的用户ID格式"
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"生成并缓存用户摘要时出错: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"summary": None,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
async def generate_cache_for_workspace(
|
||||
self,
|
||||
db: Session,
|
||||
workspace_id: uuid.UUID
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
为整个工作空间生成缓存
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
workspace_id: 工作空间ID
|
||||
|
||||
Returns:
|
||||
{
|
||||
"total_users": int,
|
||||
"successful": int,
|
||||
"failed": int,
|
||||
"errors": List[Dict]
|
||||
}
|
||||
"""
|
||||
logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存")
|
||||
|
||||
total_users = 0
|
||||
successful = 0
|
||||
failed = 0
|
||||
errors = []
|
||||
|
||||
try:
|
||||
# 获取工作空间的所有终端用户
|
||||
repo = EndUserRepository(db)
|
||||
end_users = repo.get_all_by_workspace(workspace_id)
|
||||
total_users = len(end_users)
|
||||
|
||||
logger.info(f"工作空间 {workspace_id} 共有 {total_users} 个终端用户")
|
||||
|
||||
# 遍历每个用户并生成缓存
|
||||
for end_user in end_users:
|
||||
end_user_id = str(end_user.id)
|
||||
|
||||
try:
|
||||
# 生成记忆洞察
|
||||
insight_result = await self.generate_and_cache_insight(db, end_user_id)
|
||||
|
||||
# 生成用户摘要
|
||||
summary_result = await self.generate_and_cache_summary(db, end_user_id)
|
||||
|
||||
# 检查是否都成功
|
||||
if insight_result["success"] and summary_result["success"]:
|
||||
successful += 1
|
||||
logger.info(f"成功为终端用户 {end_user_id} 生成缓存")
|
||||
else:
|
||||
failed += 1
|
||||
error_info = {
|
||||
"end_user_id": end_user_id,
|
||||
"insight_error": insight_result.get("error"),
|
||||
"summary_error": summary_result.get("error")
|
||||
}
|
||||
errors.append(error_info)
|
||||
logger.warning(f"终端用户 {end_user_id} 的缓存生成部分失败: {error_info}")
|
||||
|
||||
except Exception as e:
|
||||
# 单个用户失败不影响其他用户
|
||||
failed += 1
|
||||
error_info = {
|
||||
"end_user_id": end_user_id,
|
||||
"error": str(e)
|
||||
}
|
||||
errors.append(error_info)
|
||||
logger.error(f"为终端用户 {end_user_id} 生成缓存时出错: {str(e)}")
|
||||
|
||||
# 记录统计信息
|
||||
logger.info(
|
||||
f"工作空间 {workspace_id} 批量生成完成: "
|
||||
f"总数={total_users}, 成功={successful}, 失败={failed}"
|
||||
)
|
||||
|
||||
return {
|
||||
"total_users": total_users,
|
||||
"successful": successful,
|
||||
"failed": failed,
|
||||
"errors": errors
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"为工作空间 {workspace_id} 批量生成缓存时出错: {str(e)}")
|
||||
return {
|
||||
"total_users": total_users,
|
||||
"successful": successful,
|
||||
"failed": failed,
|
||||
"errors": errors + [{"error": f"批量处理失败: {str(e)}"}]
|
||||
}
|
||||
|
||||
|
||||
# 独立的分析函数
|
||||
|
||||
async def analytics_memory_insight_report(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
生成记忆洞察报告
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID
|
||||
|
||||
Returns:
|
||||
包含报告的字典
|
||||
"""
|
||||
insight = MemoryInsight(end_user_id)
|
||||
report = await insight.generate_insight_report()
|
||||
await insight.close()
|
||||
data = {"report": report}
|
||||
return data
|
||||
|
||||
|
||||
async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
生成用户摘要
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID
|
||||
|
||||
Returns:
|
||||
包含摘要的字典
|
||||
"""
|
||||
summary = await generate_user_summary(end_user_id)
|
||||
data = {"summary": summary}
|
||||
return data
|
||||
|
||||
|
||||
async def analytics_node_statistics(
|
||||
db: Session,
|
||||
end_user_id: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
统计 Neo4j 中四种节点类型的数量和百分比
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 可选的终端用户ID (UUID)，用于过滤特定用户的节点
|
||||
|
||||
Returns:
|
||||
{
|
||||
"total": int, # 总节点数
|
||||
"nodes": [
|
||||
{
|
||||
"type": str, # 节点类型
|
||||
"count": int, # 节点数量
|
||||
"percentage": float # 百分比
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
# 定义四种节点类型的查询
|
||||
node_types = ["Chunk", "MemorySummary", "Statement", "ExtractedEntity"]
|
||||
|
||||
# 存储每种节点类型的计数
|
||||
node_counts = {}
|
||||
|
||||
# 查询每种节点类型的数量
|
||||
for node_type in node_types:
|
||||
# 构建查询语句
|
||||
if end_user_id:
|
||||
query = f"""
|
||||
MATCH (n:{node_type})
|
||||
WHERE n.group_id = $group_id
|
||||
RETURN count(n) as count
|
||||
"""
|
||||
result = await _neo4j_connector.execute_query(query, group_id=end_user_id)
|
||||
else:
|
||||
query = f"""
|
||||
MATCH (n:{node_type})
|
||||
RETURN count(n) as count
|
||||
"""
|
||||
result = await _neo4j_connector.execute_query(query)
|
||||
|
||||
# 提取计数结果
|
||||
count = result[0]["count"] if result and len(result) > 0 else 0
|
||||
node_counts[node_type] = count
|
||||
|
||||
# 计算总数
|
||||
total = sum(node_counts.values())
|
||||
|
||||
# 构建返回数据，包含百分比
|
||||
nodes = []
|
||||
for node_type in node_types:
|
||||
count = node_counts[node_type]
|
||||
percentage = round((count / total * 100), 2) if total > 0 else 0.0
|
||||
nodes.append({
|
||||
"type": node_type,
|
||||
"count": count,
|
||||
"percentage": percentage
|
||||
})
|
||||
|
||||
data = {
|
||||
"total": total,
|
||||
"nodes": nodes
|
||||
}
|
||||
|
||||
return data
|
||||
|
||||
|
||||
async def analytics_graph_data(
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
node_types: Optional[List[str]] = None,
|
||||
limit: int = 100,
|
||||
depth: int = 1,
|
||||
center_node_id: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取 Neo4j 图数据，用于前端可视化
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID
|
||||
node_types: 可选的节点类型列表
|
||||
limit: 返回节点数量限制
|
||||
depth: 图遍历深度
|
||||
center_node_id: 可选的中心节点ID
|
||||
|
||||
Returns:
|
||||
包含节点、边和统计信息的字典
|
||||
"""
|
||||
try:
|
||||
# 1. 获取 group_id
|
||||
user_uuid = uuid.UUID(end_user_id)
|
||||
repo = EndUserRepository(db)
|
||||
end_user = repo.get_by_id(user_uuid)
|
||||
|
||||
if not end_user:
|
||||
logger.warning(f"未找到 end_user_id 为 {end_user_id} 的用户")
|
||||
return {
|
||||
"nodes": [],
|
||||
"edges": [],
|
||||
"statistics": {
|
||||
"total_nodes": 0,
|
||||
"total_edges": 0,
|
||||
"node_types": {},
|
||||
"edge_types": {}
|
||||
},
|
||||
"message": "用户不存在"
|
||||
}
|
||||
|
||||
# 2. 构建节点查询
|
||||
if center_node_id:
|
||||
# 基于中心节点的扩展查询
|
||||
node_query = f"""
|
||||
MATCH path = (center)-[*1..{depth}]-(connected)
|
||||
WHERE center.group_id = $group_id
|
||||
AND elementId(center) = $center_node_id
|
||||
WITH collect(DISTINCT center) + collect(DISTINCT connected) as all_nodes
|
||||
UNWIND all_nodes as n
|
||||
RETURN DISTINCT
|
||||
elementId(n) as id,
|
||||
labels(n)[0] as label,
|
||||
properties(n) as properties
|
||||
LIMIT $limit
|
||||
"""
|
||||
node_params = {
|
||||
"group_id": end_user_id,
|
||||
"center_node_id": center_node_id,
|
||||
"limit": limit
|
||||
}
|
||||
elif node_types:
|
||||
# 按节点类型过滤查询
|
||||
node_query = """
|
||||
MATCH (n)
|
||||
WHERE n.group_id = $group_id
|
||||
AND labels(n)[0] IN $node_types
|
||||
RETURN
|
||||
elementId(n) as id,
|
||||
labels(n)[0] as label,
|
||||
properties(n) as properties
|
||||
LIMIT $limit
|
||||
"""
|
||||
node_params = {
|
||||
"group_id": end_user_id,
|
||||
"node_types": node_types,
|
||||
"limit": limit
|
||||
}
|
||||
else:
|
||||
# 查询所有节点
|
||||
node_query = """
|
||||
MATCH (n)
|
||||
WHERE n.group_id = $group_id
|
||||
RETURN
|
||||
elementId(n) as id,
|
||||
labels(n)[0] as label,
|
||||
properties(n) as properties
|
||||
LIMIT $limit
|
||||
"""
|
||||
node_params = {
|
||||
"group_id": end_user_id,
|
||||
"limit": limit
|
||||
}
|
||||
|
||||
# 执行节点查询
|
||||
node_results = await _neo4j_connector.execute_query(node_query, **node_params)
|
||||
|
||||
# 3. 格式化节点数据
|
||||
nodes = []
|
||||
node_ids = []
|
||||
node_type_counts = {}
|
||||
|
||||
for record in node_results:
|
||||
node_id = record["id"]
|
||||
node_label = record["label"]
|
||||
node_props = record["properties"]
|
||||
|
||||
# 根据节点类型提取需要的属性字段
|
||||
filtered_props = _extract_node_properties(node_label, node_props)
|
||||
|
||||
# 直接使用数据库中的 caption，如果没有则使用节点类型作为默认值
|
||||
caption = filtered_props.get("caption", node_label)
|
||||
|
||||
nodes.append({
|
||||
"id": node_id,
|
||||
"label": node_label,
|
||||
"properties": filtered_props,
|
||||
"caption": caption
|
||||
})
|
||||
|
||||
node_ids.append(node_id)
|
||||
node_type_counts[node_label] = node_type_counts.get(node_label, 0) + 1
|
||||
|
||||
# 4. 查询节点之间的关系
|
||||
if len(node_ids) > 0:
|
||||
edge_query = """
|
||||
MATCH (n)-[r]->(m)
|
||||
WHERE elementId(n) IN $node_ids
|
||||
AND elementId(m) IN $node_ids
|
||||
RETURN
|
||||
elementId(r) as id,
|
||||
elementId(n) as source,
|
||||
elementId(m) as target,
|
||||
type(r) as rel_type,
|
||||
properties(r) as properties
|
||||
"""
|
||||
edge_results = await _neo4j_connector.execute_query(
|
||||
edge_query,
|
||||
node_ids=node_ids
|
||||
)
|
||||
else:
|
||||
edge_results = []
|
||||
|
||||
# 5. 格式化边数据
|
||||
edges = []
|
||||
edge_type_counts = {}
|
||||
|
||||
for record in edge_results:
|
||||
edge_id = record["id"]
|
||||
source = record["source"]
|
||||
target = record["target"]
|
||||
rel_type = record["rel_type"]
|
||||
edge_props = record["properties"]
|
||||
|
||||
# 清理边属性中的 Neo4j 特殊类型
|
||||
# 对于边，我们保留所有属性，但清理特殊类型
|
||||
cleaned_edge_props = {}
|
||||
if edge_props:
|
||||
for key, value in edge_props.items():
|
||||
cleaned_edge_props[key] = _clean_neo4j_value(value)
|
||||
|
||||
# 直接使用关系类型作为 caption，如果 properties 中有 caption 则使用它
|
||||
caption = cleaned_edge_props.get("caption", rel_type)
|
||||
|
||||
edges.append({
|
||||
"id": edge_id,
|
||||
"source": source,
|
||||
"target": target,
|
||||
"type": rel_type,
|
||||
"properties": cleaned_edge_props,
|
||||
"caption": caption
|
||||
})
|
||||
|
||||
edge_type_counts[rel_type] = edge_type_counts.get(rel_type, 0) + 1
|
||||
|
||||
# 6. 构建统计信息
|
||||
statistics = {
|
||||
"total_nodes": len(nodes),
|
||||
"total_edges": len(edges),
|
||||
"node_types": node_type_counts,
|
||||
"edge_types": edge_type_counts
|
||||
}
|
||||
|
||||
logger.info(
|
||||
f"成功获取图数据: end_user_id={end_user_id}, "
|
||||
f"nodes={len(nodes)}, edges={len(edges)}"
|
||||
)
|
||||
|
||||
return {
|
||||
"nodes": nodes,
|
||||
"edges": edges,
|
||||
"statistics": statistics
|
||||
}
|
||||
|
||||
except ValueError:
|
||||
logger.error(f"无效的 end_user_id 格式: {end_user_id}")
|
||||
return {
|
||||
"nodes": [],
|
||||
"edges": [],
|
||||
"statistics": {
|
||||
"total_nodes": 0,
|
||||
"total_edges": 0,
|
||||
"node_types": {},
|
||||
"edge_types": {}
|
||||
},
|
||||
"message": "无效的用户ID格式"
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"获取图数据失败: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
# 辅助函数
|
||||
|
||||
def _extract_node_properties(label: str, properties: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
根据节点类型提取需要的属性字段
|
||||
|
||||
Args:
|
||||
label: 节点类型标签
|
||||
properties: 节点的所有属性
|
||||
|
||||
Returns:
|
||||
过滤后的属性字典
|
||||
"""
|
||||
# 定义每种节点类型需要的字段（白名单）
|
||||
field_whitelist = {
|
||||
"Dialogue": ["content", "created_at"],
|
||||
"Chunk": ["content", "created_at"],
|
||||
"Statement": ["temporal_info", "stmt_type", "statement", "valid_at", "created_at", "caption"],
|
||||
"ExtractedEntity": ["description", "name", "entity_type", "created_at", "caption"],
|
||||
"MemorySummary": ["summary", "content", "created_at", "caption"] # 添加 content 字段
|
||||
}
|
||||
|
||||
# 获取该节点类型的白名单字段
|
||||
allowed_fields = field_whitelist.get(label, [])
|
||||
|
||||
# 如果没有定义白名单，返回空字典（或者可以返回所有字段）
|
||||
if not allowed_fields:
|
||||
# 对于未定义的节点类型，只返回基本字段
|
||||
allowed_fields = ["name", "created_at", "caption"]
|
||||
|
||||
# 提取白名单中的字段
|
||||
filtered_props = {}
|
||||
for field in allowed_fields:
|
||||
if field in properties:
|
||||
value = properties[field]
|
||||
# 清理 Neo4j 特殊类型
|
||||
filtered_props[field] = _clean_neo4j_value(value)
|
||||
|
||||
return filtered_props
|
||||
|
||||
|
||||
def _clean_neo4j_value(value: Any) -> Any:
|
||||
"""
|
||||
清理单个值的 Neo4j 特殊类型
|
||||
|
||||
Args:
|
||||
value: 需要清理的值
|
||||
|
||||
Returns:
|
||||
清理后的值
|
||||
"""
|
||||
if value is None:
|
||||
return None
|
||||
|
||||
# 处理列表
|
||||
if isinstance(value, list):
|
||||
return [_clean_neo4j_value(item) for item in value]
|
||||
|
||||
# 处理字典
|
||||
if isinstance(value, dict):
|
||||
return {k: _clean_neo4j_value(v) for k, v in value.items()}
|
||||
|
||||
# 处理 Neo4j DateTime 类型
|
||||
if hasattr(value, '__class__') and 'neo4j.time' in str(type(value)):
|
||||
try:
|
||||
if hasattr(value, 'to_native'):
|
||||
native_dt = value.to_native()
|
||||
return native_dt.isoformat()
|
||||
return str(value)
|
||||
except Exception:
|
||||
return str(value)
|
||||
|
||||
# 处理其他 Neo4j 特殊类型
|
||||
if hasattr(value, '__class__') and 'neo4j' in str(type(value)):
|
||||
try:
|
||||
return str(value)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
# 返回原始值
|
||||
return value
|
||||
Reference in New Issue
Block a user