feat(memory): add episodic memory pagination and semantic memory list API
Split explicit memory overview into two independent endpoints: - GET /memory/explicit-memory/episodics: episodic memory paginated query with date range filter (millisecond timestamp) and episodic type filter using Neo4j datetime() for precise time comparison - GET /memory/explicit-memory/semantics: semantic memory full list query returns data as array directly Modified files: - api/app/controllers/memory_explicit_controller.py - api/app/services/memory_explicit_service.py
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miao
@@ -4,7 +4,8 @@
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处理显性记忆相关的业务逻辑，包括情景记忆和语义记忆的查询。
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"""
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from typing import Any, Dict
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from datetime import date
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from typing import Any, Dict, Optional
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from app.core.logging_config import get_logger
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from app.services.memory_base_service import MemoryBaseService
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@@ -104,7 +105,7 @@ class MemoryExplicitService(MemoryBaseService):
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e.description AS core_definition
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ORDER BY e.name ASC
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"""
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semantic_result = await self.neo4j_connector.execute_query(
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semantic_query,
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end_user_id=end_user_id
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@@ -146,6 +147,207 @@ class MemoryExplicitService(MemoryBaseService):
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logger.error(f"获取显性记忆总览时出错: {str(e)}", exc_info=True)
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raise
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async def get_episodic_memory_list(
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self,
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end_user_id: str,
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page: int,
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pagesize: int,
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start_date: Optional[int] = None,
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end_date: Optional[int] = None,
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episodic_type: str = "all",
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) -> Dict[str, Any]:
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"""
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获取情景记忆分页列表
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Args:
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end_user_id: 终端用户ID
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page: 页码
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pagesize: 每页数量
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start_date: 开始时间戳（毫秒），可选
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end_date: 结束时间戳（毫秒），可选
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episodic_type: 情景类型筛选
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Returns:
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{
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"total": int, # 该用户情景记忆总数（不受筛选影响）
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"items": [...], # 当前页数据
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"page": {
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"page": int,
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"pagesize": int,
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"total": int, # 筛选后总数
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"hasnext": bool
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}
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}
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"""
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try:
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logger.info(
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f"情景记忆分页查询: end_user_id={end_user_id}, "
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f"start_date={start_date}, end_date={end_date}, "
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f"episodic_type={episodic_type}, page={page}, pagesize={pagesize}"
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)
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# 1. 查询情景记忆总数（不受筛选条件限制）
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total_all_query = """
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MATCH (s:MemorySummary)
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WHERE s.end_user_id = $end_user_id
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RETURN count(s) AS total
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"""
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total_all_result = await self.neo4j_connector.execute_query(
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total_all_query, end_user_id=end_user_id
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)
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total_all = total_all_result[0]["total"] if total_all_result else 0
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# 2. 构建筛选条件
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where_clauses = ["s.end_user_id = $end_user_id"]
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params = {"end_user_id": end_user_id}
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# 时间戳筛选（毫秒时间戳转为 ISO 字符串，使用 Neo4j datetime() 精确比较）
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if start_date is not None and end_date is not None:
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from datetime import datetime
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start_dt = datetime.fromtimestamp(start_date / 1000)
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end_dt = datetime.fromtimestamp(end_date / 1000)
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# 开始时间取当天 00:00:00，结束时间取当天 23:59:59.999999
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start_iso = start_dt.strftime("%Y-%m-%dT") + "00:00:00.000000"
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end_iso = end_dt.strftime("%Y-%m-%dT") + "23:59:59.999999"
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where_clauses.append("datetime(s.created_at) >= datetime($start_iso) AND datetime(s.created_at) <= datetime($end_iso)")
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params["start_iso"] = start_iso
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params["end_iso"] = end_iso
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# 类型筛选下推到 Cypher（兼容中英文）
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if episodic_type != "all":
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type_mapping = {
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"conversation": "对话",
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"project_work": "项目/工作",
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"learning": "学习",
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"decision": "决策",
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"important_event": "重要事件"
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}
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chinese_type = type_mapping.get(episodic_type)
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if chinese_type:
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where_clauses.append(
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"(s.memory_type = $episodic_type OR s.memory_type = $chinese_type)"
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)
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params["episodic_type"] = episodic_type
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params["chinese_type"] = chinese_type
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else:
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where_clauses.append("s.memory_type = $episodic_type")
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params["episodic_type"] = episodic_type
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where_str = " AND ".join(where_clauses)
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# 3. 查询筛选后的总数
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count_query = f"""
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MATCH (s:MemorySummary)
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WHERE {where_str}
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RETURN count(s) AS total
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"""
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count_result = await self.neo4j_connector.execute_query(count_query, **params)
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filtered_total = count_result[0]["total"] if count_result else 0
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# 4. 查询分页数据
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skip = (page - 1) * pagesize
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data_query = f"""
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MATCH (s:MemorySummary)
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WHERE {where_str}
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RETURN elementId(s) AS id,
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s.name AS title,
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s.memory_type AS memory_type,
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s.content AS content,
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s.created_at AS created_at
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ORDER BY s.created_at DESC
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SKIP {skip} LIMIT {pagesize}
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"""
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result = await self.neo4j_connector.execute_query(data_query, **params)
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# 5. 处理结果
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items = []
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if result:
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for record in result:
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raw_created_at = record.get("created_at")
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created_at_timestamp = self.parse_timestamp(raw_created_at)
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items.append({
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"id": record["id"],
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"title": record.get("title") or "未命名",
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"memory_type": record.get("memory_type") or "其他",
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"content": record.get("content") or "",
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"created_at": created_at_timestamp
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})
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# 6. 构建返回结果
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return {
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"total": total_all,
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"items": items,
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"page": {
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"page": page,
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"pagesize": pagesize,
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"total": filtered_total,
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"hasnext": (page * pagesize) < filtered_total
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}
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}
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except Exception as e:
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logger.error(f"情景记忆分页查询出错: {str(e)}", exc_info=True)
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raise
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async def get_semantic_memory_list(
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self,
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end_user_id: str
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) -> list:
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"""
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获取语义记忆全量列表
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Args:
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end_user_id: 终端用户ID
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Returns:
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[
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{
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"id": str,
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"name": str,
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"entity_type": str,
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"core_definition": str
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}
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]
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"""
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try:
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logger.info(f"语义记忆列表查询: end_user_id={end_user_id}")
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semantic_query = """
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MATCH (e:ExtractedEntity)
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WHERE e.end_user_id = $end_user_id
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AND e.is_explicit_memory = true
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RETURN elementId(e) AS id,
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e.name AS name,
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e.entity_type AS entity_type,
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e.description AS core_definition
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ORDER BY e.name ASC
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"""
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result = await self.neo4j_connector.execute_query(
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semantic_query, end_user_id=end_user_id
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)
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items = []
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if result:
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for record in result:
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items.append({
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"id": record["id"],
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"name": record.get("name") or "未命名",
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"entity_type": record.get("entity_type") or "未分类",
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"core_definition": record.get("core_definition") or ""
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})
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logger.info(f"语义记忆列表查询成功: end_user_id={end_user_id}, total={len(items)}")
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return items
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except Exception as e:
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logger.error(f"语义记忆列表查询出错: {str(e)}", exc_info=True)
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raise
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async def get_explicit_memory_details(
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self,
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end_user_id: str,
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