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feat(memory, model): update multi-modal memory write and model list API

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- Adjust multi-modal memory write behavior for text and visual data
- Mask API keys in model list response to prevent exposure
- Add capability-based filtering to the model list API
This commit is contained in:
Eternity
2026-03-24 13:54:15 +08:00
parent 2ff81ba101
commit 6bba574ca6
21 changed files with 389 additions and 401 deletions
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108
api/app/controllers/user_memory_controllers.py
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@@ -5,7 +5,7 @@
from typing import Optional
import datetime
from sqlalchemy.orm import Session
from fastapi import APIRouter, Depends,Header
from fastapi import APIRouter, Depends, Header
from app.db import get_db
from app.core.language_utils import get_language_from_header
@@ -19,7 +19,7 @@ from app.services.user_memory_service import (
analytics_graph_data,
analytics_community_graph_data,
)
from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction
from app.services.memory_entity_relationship_service import MemoryEntityService, MemoryEmotion, MemoryInteraction
from app.schemas.response_schema import ApiResponse
from app.schemas.memory_storage_schema import GenerateCacheRequest
from app.repositories.workspace_repository import WorkspaceRepository
@@ -45,9 +45,9 @@ router = APIRouter(
@router.get("/analytics/memory_insight/report", response_model=ApiResponse)
async def get_memory_insight_report_api(
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""
获取缓存的记忆洞察报告
@@ -73,10 +73,10 @@ async def get_memory_insight_report_api(
@router.get("/analytics/user_summary", response_model=ApiResponse)
async def get_user_summary_api(
end_user_id: str,
language_type: str = Header(default=None, alias="X-Language-Type"),
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
end_user_id: str,
language_type: str = Header(default=None, alias="X-Language-Type"),
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""
获取缓存的用户摘要
@@ -90,7 +90,7 @@ async def get_user_summary_api(
"""
# 使用集中化的语言校验
language = get_language_from_header(language_type)
workspace_id = current_user.current_workspace_id
workspace_repo = WorkspaceRepository(db)
workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
@@ -102,7 +102,7 @@ async def get_user_summary_api(
api_logger.info(f"用户摘要查询请求: end_user_id={end_user_id}, user={current_user.username}")
try:
# 调用服务层获取缓存数据
result = await user_memory_service.get_cached_user_summary(db, end_user_id,model_id,language)
result = await user_memory_service.get_cached_user_summary(db, end_user_id, model_id, language)
if result["is_cached"]:
api_logger.info(f"成功返回缓存的用户摘要: end_user_id={end_user_id}")
@@ -117,10 +117,10 @@ async def get_user_summary_api(
@router.post("/analytics/generate_cache", response_model=ApiResponse)
async def generate_cache_api(
request: GenerateCacheRequest,
language_type: str = Header(default=None, alias="X-Language-Type"),
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
request: GenerateCacheRequest,
language_type: str = Header(default=None, alias="X-Language-Type"),
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""
手动触发缓存生成
@@ -134,7 +134,7 @@ async def generate_cache_api(
"""
# 使用集中化的语言校验
language = get_language_from_header(language_type)
workspace_id = current_user.current_workspace_id
# 检查用户是否已选择工作空间
@@ -155,10 +155,12 @@ async def generate_cache_api(
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, end_user_id, workspace_id, language=language)
insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id,
language=language)
# 生成用户摘要
summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id, language=language)
summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id,
language=language)
# 构建响应
result = {
@@ -209,9 +211,9 @@ async def generate_cache_api(
@router.get("/analytics/node_statistics", response_model=ApiResponse)
async def get_node_statistics_api(
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
workspace_id = current_user.current_workspace_id
@@ -220,7 +222,8 @@ async def get_node_statistics_api(
api_logger.warning(f"用户 {current_user.username} 尝试查询节点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")
api_logger.info(f"记忆类型统计请求: end_user_id={end_user_id}, user={current_user.username}, workspace={workspace_id}")
api_logger.info(
f"记忆类型统计请求: end_user_id={end_user_id}, user={current_user.username}, workspace={workspace_id}")
try:
# 调用新的记忆类型统计函数
@@ -228,21 +231,23 @@ async def get_node_statistics_api(
# 计算总数用于日志
total_count = sum(item["count"] for item in result)
api_logger.info(f"成功获取记忆类型统计: end_user_id={end_user_id}, 总记忆数={total_count}, 类型数={len(result)}")
api_logger.info(
f"成功获取记忆类型统计: end_user_id={end_user_id}, 总记忆数={total_count}, 类型数={len(result)}")
return success(data=result, msg="查询成功")
except Exception as e:
api_logger.error(f"记忆类型查询失败: end_user_id={end_user_id}, error={str(e)}")
return fail(BizCode.INTERNAL_ERROR, "记忆类型查询失败", str(e))
@router.get("/analytics/graph_data", response_model=ApiResponse)
async def get_graph_data_api(
end_user_id: str,
node_types: Optional[str] = None,
limit: int = 100,
depth: int = 1,
center_node_id: Optional[str] = None,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
end_user_id: str,
node_types: Optional[str] = None,
limit: int = 100,
depth: int = 1,
center_node_id: Optional[str] = None,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
workspace_id = current_user.current_workspace_id
@@ -298,9 +303,9 @@ async def get_graph_data_api(
@router.get("/analytics/community_graph", response_model=ApiResponse)
async def get_community_graph_data_api(
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
workspace_id = current_user.current_workspace_id
@@ -334,9 +339,9 @@ async def get_community_graph_data_api(
@router.get("/read_end_user/profile", response_model=ApiResponse)
async def get_end_user_profile(
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
end_user_id: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
workspace_id = current_user.current_workspace_id
workspace_repo = WorkspaceRepository(db)
@@ -385,9 +390,9 @@ async def get_end_user_profile(
@router.post("/updated_end_user/profile", response_model=ApiResponse)
async def update_end_user_profile(
profile_update: EndUserProfileUpdate,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
profile_update: EndUserProfileUpdate,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
) -> dict:
"""
更新终端用户的基本信息
@@ -417,7 +422,7 @@ async def update_end_user_profile(
else:
error_msg = result["error"]
api_logger.error(f"用户信息更新失败: end_user_id={end_user_id}, error={error_msg}")
# 根据错误类型映射到合适的业务错误码
if error_msg == "终端用户不存在":
return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", error_msg)
@@ -427,15 +432,18 @@ async def update_end_user_profile(
# 只有未预期的错误才使用 INTERNAL_ERROR
return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", error_msg)
@router.get("/memory_space/timeline_memories", response_model=ApiResponse)
async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str = Header(default=None, alias="X-Language-Type"),
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
):
async def memory_space_timeline_of_shared_memories(
id: str, label: str,
language_type: str = Header(default=None, alias="X-Language-Type"),
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
):
# 使用集中化的语言校验
language = get_language_from_header(language_type)
workspace_id=current_user.current_workspace_id
workspace_id = current_user.current_workspace_id
workspace_repo = WorkspaceRepository(db)
workspace_models = workspace_repo.get_workspace_models_configs(workspace_id)
@@ -447,11 +455,13 @@ async def memory_space_timeline_of_shared_memories(id: str, label: str,language_
timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language)
return success(data=timeline_memories_result, msg="共同记忆时间线")
@router.get("/memory_space/relationship_evolution", response_model=ApiResponse)
async def memory_space_relationship_evolution(id: str, label: str,
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
):
current_user: User = Depends(get_current_user),
db: Session = Depends(get_db),
):
try:
api_logger.info(f"关系演变查询请求: id={id}, table={label}, user={current_user.username}")