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end_user_id清理干净

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This commit is contained in:
lixinyue
2026-01-23 17:20:07 +08:00
parent 4ad51c1b24
commit af6e1e2b99
10 changed files with 23 additions and 35 deletions
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10
api/app/controllers/memory_forget_controller.py
View File

@@ -107,7 +107,7 @@ async def trigger_forgetting_cycle(
# 调用服务层执行遗忘周期
report = await forget_service.trigger_forgetting_cycle(
db=db,
group_id=end_user_id, # 服务层方法的参数名是 group_id
end_user_id=end_user_id, # 服务层方法的参数名是 end_user_id
max_merge_batch_size=payload.max_merge_batch_size,
min_days_since_access=payload.min_days_since_access,
config_id=config_id
@@ -247,7 +247,7 @@ async def get_forgetting_stats(
返回知识层节点统计、激活值分布等信息。
Args:
group_id: 组ID（即 end_user_id，可选）
end_user_id: 组ID（即 end_user_id，可选）
current_user: 当前用户
db: 数据库会话
@@ -261,7 +261,7 @@ async def get_forgetting_stats(
api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间")
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
# 如果提供了 group_id，通过它获取 config_id
# 如果提供了 end_user_id，通过它获取 config_id
config_id = None
if end_user_id:
try:
@@ -274,7 +274,7 @@ async def get_forgetting_stats(
api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None")
api_logger.debug(f"通过 group_id={end_user_id} 获取到 config_id={config_id}")
api_logger.debug(f"通过 end_user_id={end_user_id} 获取到 config_id={config_id}")
except ValueError as e:
api_logger.warning(f"获取终端用户配置失败: {str(e)}")
return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError")
@@ -284,7 +284,7 @@ async def get_forgetting_stats(
api_logger.info(
f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: "
f"group_id={end_user_id}, config_id={config_id}"
f"end_user_id={end_user_id}, config_id={config_id}"
)
try:

18
api/app/controllers/user_memory_controllers.py
View File

@@ -135,27 +135,27 @@ async def generate_cache_api(
api_logger.warning(f"用户 {current_user.username} 尝试生成缓存但未选择工作空间")
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
group_id = request.end_user_id
end_user_id = request.end_user_id
api_logger.info(
f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, "
f"end_user_id={group_id if group_id else '全部用户'}"
f"end_user_id={end_user_id if end_user_id else '全部用户'}"
)
try:
if group_id:
if end_user_id:
# 为单个用户生成
api_logger.info(f"开始为单个用户生成缓存: end_user_id={group_id}")
api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}")
# 生成记忆洞察
insight_result = await user_memory_service.generate_and_cache_insight(db, group_id, workspace_id)
insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id)
# 生成用户摘要
summary_result = await user_memory_service.generate_and_cache_summary(db, group_id, workspace_id)
summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id)
# 构建响应
result = {
"end_user_id": group_id,
"end_user_id": end_user_id,
"insight_success": insight_result["success"],
"summary_success": summary_result["success"],
"errors": []
@@ -175,9 +175,9 @@ async def generate_cache_api(
# 记录结果
if result["insight_success"] and result["summary_success"]:
api_logger.info(f"成功为用户 {group_id} 生成缓存")
api_logger.info(f"成功为用户 {end_user_id} 生成缓存")
else:
api_logger.warning(f"用户 {group_id} 的缓存生成部分失败: {result['errors']}")
api_logger.warning(f"用户 {end_user_id} 的缓存生成部分失败: {result['errors']}")
return success(data=result, msg="生成完成")

4
api/app/core/agent/langchain_agent.py
View File

@@ -155,7 +155,7 @@ class LangChainAgent:
# userid=end_user_end,
# messages=messages,
# apply_id=end_user_end,
# group_id=end_user_end,
# end_user_id=end_user_end,
# aimessages=aimessages
# )
# store.delete_duplicate_sessions()
@@ -228,7 +228,7 @@ class LangChainAgent:
# 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段
logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
write_id = write_message_task.delay(
actual_end_user_id, # group_id: 用户ID
actual_end_user_id, # end_user_id: 用户ID
structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
actual_config_id, # config_id: 配置ID
storage_type, # storage_type: "neo4j"

2
api/app/core/memory/agent/utils/get_dialogs.py
View File

@@ -18,7 +18,7 @@ async def get_chunked_dialogs(
Args:
chunker_strategy: The chunking strategy to use (default: RecursiveChunker)
group_id: Group identifier
end_user_id: Group identifier
messages: Structured message list [{"role": "user", "content": "..."}, ...]
ref_id: Reference identifier
config_id: Configuration ID for processing

2
api/app/core/memory/agent/utils/write_tools.py
View File

@@ -40,7 +40,7 @@ async def write(
Args:
user_id: User identifier
apply_id: Application identifier
group_id: Group identifier
end_user_id: Group identifier
memory_config: MemoryConfig object containing all configuration
messages: Structured message list [{"role": "user", "content": "..."}, ...]
ref_id: Reference ID, defaults to "wyl20251027"

2
api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py
View File

@@ -1307,7 +1307,7 @@ def main():
result = asyncio.run(
run_longmemeval_test(
sample_size=sample_size,
group_id=args.group_id,
end_user_id=args.end_user_id,
search_limit=args.search_limit,
context_char_budget=args.context_char_budget,
llm_temperature=args.llm_temperature,

4
api/app/core/memory/models/message_models.py
View File

@@ -246,9 +246,9 @@ class DialogData(BaseModel):
return []
def assign_group_id_to_statements(self) -> None:
"""Assign this dialog's group_id to all statements in all chunks.
"""Assign this dialog's end_user_id to all statements in all chunks.
This method updates statements that don't have a group_id set.
This method updates statements that don't have a end_user_id set.
"""
for chunk in self.chunks:
for statement in chunk.statements:

12
api/app/schemas/memory_storage_schema.py
View File

@@ -8,20 +8,8 @@ import uuid
from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator
# ============================================================================
# 原 UserInput 相关 Schema (保留原有功能)
# ============================================================================
class UserInput(BaseModel):
message: str
history: list[dict]
search_switch: str
group_id: str
class Write_UserInput(BaseModel):
message: str
group_id: str
# ============================================================================
# 从 json_schema.py 迁移的 Schema

2
api/app/services/memory_agent_service.py
View File

@@ -716,7 +716,7 @@ class MemoryAgentService:
if "No memory configuration found" in str(e):
raise # Re-raise our specific error
logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}")
raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}")
raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}")
logger.info(f"Generating summary from retrieve info for query: {query[:50]}...")
try:

2
api/app/services/memory_storage_service.py
View File

@@ -572,7 +572,7 @@ async def analytics_hot_memory_tags(
# 步骤4: 只调用一次LLM进行筛选
tag_names = [tag for tag, _ in sorted_tags]
# 使用第一个用户的group_id来获取LLM配置
# 使用第一个用户的end_user_id来获取LLM配置
# 因为同一工作空间下的用户应该使用相同的配置
first_end_user_id = str(end_users[0].id)
filtered_tag_names = await filter_tags_with_llm(tag_names, first_end_user_id)