Fix/develop memory deail (#69)
* 新增记忆空间详情 * 新增记忆空间详情 * 新增记忆关联的数量 * 修改记忆时间线 * 修改记忆时间线 * 修改记忆时间线 * Parameterize elementId in Cypher query * 关系演化，互动频率优化 * 关系演化，互动频率优化 * 关系演化，互动频率优化 * 关系演化，互动频率优化 * 关系演化，互动频率优化 * 关系演化，互动频率优化 --------- Co-authored-by: Ke Sun <33739460+keeees@users.noreply.github.com>
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lixinyue11
@@ -727,7 +727,6 @@ SET m += {
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dialog_id: summary.dialog_id,
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chunk_ids: summary.chunk_ids,
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content: summary.content,
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memory_type: summary.memory_type,
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summary_embedding: summary.summary_embedding,
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config_id: summary.config_id,
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importance_score: CASE WHEN summary.importance_score IS NOT NULL THEN summary.importance_score ELSE coalesce(m.importance_score, 0.5) END,
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@@ -942,7 +941,7 @@ RETURN
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"""
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Memory_Timeline_Statement="""
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MATCH (n)
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WHERE elementId(n) = "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77003"
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WHERE elementId(n) = $id
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CALL {
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WITH n
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@@ -995,7 +994,7 @@ RETURN
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"""
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Memory_Space_Emotion_MemorySummary="""
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MATCH (n)-[]-(e)
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WHERE elementId(n) = "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77019"
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WHERE elementId(n) = $id
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AND EXISTS {
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MATCH (e)-[]-(ms)
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WHERE ms:MemorySummary OR ms:ExtractedEntity
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@@ -1020,36 +1019,23 @@ RETURN DISTINCT
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"""
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'''获取实体'''
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Memory_Space_Interaction_Statement="""
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Memory_Space_User="""
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MATCH (n)-[r]->(m)
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WHERE n.group_id = $group_id AND m.name="用户"
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return DISTINCT elementId(m) as id
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"""
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Memory_Space_Entity="""
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MATCH (n)-[]-(m)
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WHERE elementId(n) = $id
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AND m.entity_type = "Person"
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WHERE elementId(m) = $id AND m.entity_type = "Person"
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RETURN
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m.name AS name,
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m.importance_score AS importance_score;
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DISTINCT m.name as name,m.group_id as group_id
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"""
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Memory_Space_Interaction_ExtractedEntity="""
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MATCH (n)-[]-(e)
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WHERE elementId(n) = $id
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AND EXISTS {
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MATCH (e)-[]-(ms:ExtractedEntity)
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}
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Memory_Space_Associative="""
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MATCH (u)-[]-(x)-[]-(h)
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WHERE elementId(u) = $user_id
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AND elementId(h) = $id
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RETURN DISTINCT
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e.name AS name,
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e.importance_score AS importance_score;
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x.statement as statement,x.created_at as created_at
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"""
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Memory_Space_Interaction_Summary="""
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MATCH (n)-[]-(e)
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WHERE elementId(n) = $id
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AND EXISTS {
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MATCH (e)-[]-(ms:ExtractedEntity)
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}
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RETURN DISTINCT
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e.name AS name,
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e.importance_score AS importance_score;
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"""
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@@ -6,9 +6,7 @@ Memory_Timeline_Statement,
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Memory_Space_Emotion_Statement,
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Memory_Space_Emotion_MemorySummary,
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Memory_Space_Emotion_ExtractedEntity,
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Memory_Space_Interaction_Statement,
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Memory_Space_Interaction_ExtractedEntity,
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Memory_Space_Interaction_Summary
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Memory_Space_Associative,Memory_Space_User,Memory_Space_Entity
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)
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from typing import Dict, List, Any, Optional
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@@ -578,6 +576,7 @@ class MemoryInteraction:
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# 如果解析失败，返回原始字符串
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return iso_string
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async def get_interaction_frequency(self) -> Dict[str, Any]:
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"""
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获取交互频率数据
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@@ -588,26 +587,36 @@ class MemoryInteraction:
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try:
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logger.info(f"获取交互数据 - ID: {self.id}, Table: {self.table}")
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if self.table == 'Statement':
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results = await self.connector.execute_query(Memory_Space_Interaction_Statement, id=self.id)
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elif self.table == 'ExtractedEntity':
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results = await self.connector.execute_query(Memory_Space_Interaction_ExtractedEntity, id=self.id)
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else:
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# MemorySummary/Chunk类型查询
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results = await self.connector.execute_query(Memory_Space_Interaction_Summary, id=self.id)
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ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id)
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if ori_data!=[]:
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# name = ori_data[0]['name']
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group_id = ori_data[0]['group_id']
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Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id)
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if not Space_User:
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return "不存在用户"
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user_id=Space_User[0]['id']
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# 处理查询结果
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interaction_data = self._process_interaction_results(results)
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# 转换Neo4j类型
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final_data = self._convert_neo4j_types(interaction_data)
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logger.info(f"成功获取 {len(final_data)} 条交互数据")
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results = await self.connector.execute_query(Memory_Space_Associative, id=self.id,user_id=user_id)
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# 处理查询结果
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interaction_data = self._process_interaction_results(results)
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# 转换Neo4j类型
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final_data = self._convert_neo4j_types(interaction_data)
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logger.info(f"成功获取 {len(final_data)} 条交互数据")
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return {
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'success': True,
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'data': final_data,
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'total': len(final_data)
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}
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return {
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'success': True,
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'data': final_data,
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'total': len(final_data)
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'success': False,
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'data': [],
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'total': 0
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}
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except Exception as e:
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@@ -621,36 +630,57 @@ class MemoryInteraction:
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def _process_interaction_results(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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处理交互查询结果
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处理交互查询结果，按季度统计交互频率
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Args:
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results: Neo4j查询结果
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Returns:
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处理后的交互数据列表
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按季度统计的交互数据列表，格式: [{"created_at": "2026Q1", "count": 3}]
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"""
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interaction_data = []
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from collections import defaultdict
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from datetime import datetime
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# 检查results是否为空或不是列表
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if not results or not isinstance(results, list):
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logger.warning(f"交互查询结果为空或格式不正确: {type(results)}")
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return interaction_data
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# 用于按季度分组计数
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quarterly_counts = defaultdict(int)
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for record in results:
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# 检查record是否为字典类型
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if not isinstance(record, dict):
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logger.warning(f"跳过非字典类型的记录: {type(record)} - {record}")
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# 过滤掉statement为None的记录
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if not isinstance(record, dict) or record.get('statement') is None:
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continue
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# 只保留交互相关的字段
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name = record.get('name')
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if name is not None:
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interaction_record = {
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'name': name,
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'importance_score': record.get('importance_score', 0.0),
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'interaction_count': record.get('interaction_count', 1) # 默认交互次数为1
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}
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interaction_data.append(interaction_record)
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created_at = record.get('created_at')
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if not created_at:
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continue
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try:
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# 处理不同类型的时间格式
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if isinstance(created_at, str):
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# 解析ISO格式时间字符串
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dt = datetime.fromisoformat(created_at.replace('Z', '+00:00'))
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elif hasattr(created_at, 'year') and hasattr(created_at, 'month'):
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# 处理Neo4j DateTime对象
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dt = datetime(created_at.year, created_at.month, created_at.day)
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else:
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continue
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# 计算季度
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quarter = (dt.month - 1) // 3 + 1
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quarter_key = f"{dt.year}.Q{quarter}"
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# 增加该季度的计数
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quarterly_counts[quarter_key] += 1
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except (ValueError, AttributeError) as e:
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logger.warning(f"解析时间失败: {e}, 原始值: {created_at}")
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continue
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# 转换为所需格式并按时间排序
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interaction_data = [
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{"created_at": quarter, "count": count}
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for quarter, count in quarterly_counts.items()
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]
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# 按季度排序（最新的在前）
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interaction_data.sort(key=lambda x: x["created_at"], reverse=True)
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return interaction_data
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