refactor(memory): consolidate memory config extraction and remove unused validator
- Add workspace default LLM fallback for emotion model in extraction orchestrator - Consolidate memory config ID extraction logic into MemoryConfigService - Remove duplicate extraction methods from AppService (_extract_memory_config_id_from_agent, _extract_memory_config_id_from_workflow) - Remove unused validate_embedding_model function from validators - Simplify AppService by delegating memory config extraction to MemoryConfigService - Update validator exports to remove validate_embedding_model - Improve code maintainability by centralizing memory configuration logic
This commit is contained in:
Ke Sun
@@ -645,9 +645,25 @@ class ExtractionOrchestrator:
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logger.info(f"总陈述句: {total_statements}, 用户陈述句: {filtered_statements}, 开始全局并行提取情绪")
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# 初始化情绪提取服务
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# 如果 emotion_model_id 为空，回退到工作空间默认 LLM
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from app.services.emotion_extraction_service import EmotionExtractionService
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emotion_model_id = memory_config.emotion_model_id
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if not emotion_model_id and memory_config.workspace_id:
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from app.repositories.workspace_repository import get_workspace_models_configs
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from app.db import SessionLocal
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db = SessionLocal()
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try:
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workspace_models = get_workspace_models_configs(db, memory_config.workspace_id)
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if workspace_models and workspace_models.get("llm"):
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emotion_model_id = workspace_models["llm"]
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logger.info(f"emotion_model_id 为空，使用工作空间默认 LLM: {emotion_model_id}")
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finally:
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db.close()
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emotion_service = EmotionExtractionService(
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llm_id=memory_config.emotion_model_id if memory_config.emotion_model_id else None
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llm_id=emotion_model_id if emotion_model_id else None
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)
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# 全局并行处理所有陈述句
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@@ -4,7 +4,6 @@ Validators package for various validation utilities.
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from app.core.validators.file_validator import FileValidator, ValidationResult
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from app.core.validators.memory_config_validators import (
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validate_and_resolve_model_id,
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validate_embedding_model,
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validate_llm_model,
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validate_model_exists_and_active,
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)
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@@ -16,6 +15,5 @@ __all__ = [
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# Memory config validators
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"validate_model_exists_and_active",
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"validate_and_resolve_model_id",
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"validate_embedding_model",
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"validate_llm_model",
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]
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@@ -6,7 +6,6 @@ This module provides validation functions for memory configuration models.
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Functions:
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validate_model_exists_and_active: Validate model exists and is active
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validate_and_resolve_model_id: Validate and resolve model ID with DB lookup
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validate_embedding_model: Validate embedding model availability
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validate_llm_model: Validate LLM model availability
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"""
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@@ -203,58 +202,6 @@ def validate_and_resolve_model_id(
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return model_uuid, model_name
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def validate_embedding_model(
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config_id: UUID,
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embedding_id: Union[str, UUID, None],
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db: Session,
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tenant_id: Optional[UUID] = None,
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workspace_id: Optional[UUID] = None
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) -> tuple[UUID, str]:
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"""Validate that embedding model is available and return its UUID and name.
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Returns:
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Tuple of (embedding_uuid, embedding_name)
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Raises:
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InvalidConfigError: If embedding_id is not provided or invalid
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ModelNotFoundError: If embedding model does not exist
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ModelInactiveError: If embedding model is inactive
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"""
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if embedding_id is None or (isinstance(embedding_id, str) and not embedding_id.strip()):
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raise InvalidConfigError(
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f"Configuration {config_id} has no embedding model configured",
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field_name="embedding_model_id",
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invalid_value=embedding_id,
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config_id=config_id,
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workspace_id=workspace_id
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)
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embedding_uuid, embedding_name = validate_and_resolve_model_id(
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embedding_id, "embedding", db, tenant_id, required=True,
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config_id=config_id, workspace_id=workspace_id
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)
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logger.debug(
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"Embedding model validated",
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extra={
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"embedding_uuid": str(embedding_uuid),
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"embedding_name": embedding_name,
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"config_id": config_id
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}
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)
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if embedding_uuid is None:
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raise InvalidConfigError(
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f"Configuration {config_id} has no embedding model configured",
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field_name="embedding_model_id",
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invalid_value=embedding_id,
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config_id=config_id,
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workspace_id=workspace_id
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)
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return embedding_uuid, embedding_name
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def validate_llm_model(
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config_id: UUID,
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llm_id: Union[str, UUID, None],
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@@ -715,3 +715,95 @@ class MemoryConfigRepository:
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db_logger.error(f"删除记忆配置失败: config_id={config_id} - {str(e)}")
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raise
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@staticmethod
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def get_workspace_default(db: Session, workspace_id: uuid.UUID) -> Optional[MemoryConfig]:
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"""获取工作空间的默认记忆配置
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优先返回标记为默认的配置，如果没有则返回最早创建的活跃配置。
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Args:
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db: 数据库会话
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workspace_id: 工作空间ID
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Returns:
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Optional[MemoryConfig]: 默认配置对象，不存在则返回None
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"""
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db_logger.debug(f"查询工作空间默认配置: workspace_id={workspace_id}")
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try:
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# 优先查找显式标记为默认的配置
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stmt = (
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select(MemoryConfig)
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.where(
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MemoryConfig.workspace_id == workspace_id,
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MemoryConfig.is_default.is_(True),
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MemoryConfig.state.is_(True),
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)
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.limit(1)
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)
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config = db.scalars(stmt).first()
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if config:
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db_logger.debug(f"找到默认配置: config_id={config.config_id}")
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return config
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# 回退：获取最早创建的活跃配置
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stmt = (
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select(MemoryConfig)
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.where(
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MemoryConfig.workspace_id == workspace_id,
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MemoryConfig.state.is_(True),
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)
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.order_by(MemoryConfig.created_at.asc())
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.limit(1)
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)
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config = db.scalars(stmt).first()
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if config:
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db_logger.debug(f"使用最早创建的配置作为默认: config_id={config.config_id}")
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else:
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db_logger.warning(f"工作空间没有活跃的记忆配置: workspace_id={workspace_id}")
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return config
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except Exception as e:
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db_logger.error(f"查询工作空间默认配置失败: workspace_id={workspace_id} - {str(e)}")
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raise
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@staticmethod
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def get_with_fallback(
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db: Session,
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config_id: Optional[uuid.UUID],
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workspace_id: uuid.UUID
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) -> Optional[MemoryConfig]:
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"""获取记忆配置，支持回退到工作空间默认配置
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如果 config_id 为 None 或配置不存在，则回退到工作空间默认配置。
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Args:
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db: 数据库会话
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config_id: 配置ID（可为None）
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workspace_id: 工作空间ID，用于回退查询
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Returns:
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Optional[MemoryConfig]: 配置对象，如果都不存在则返回None
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"""
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db_logger.debug(f"查询配置（支持回退）: config_id={config_id}, workspace_id={workspace_id}")
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if not config_id:
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db_logger.debug("config_id 为空，使用工作空间默认配置")
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return MemoryConfigRepository.get_workspace_default(db, workspace_id)
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config = db.get(MemoryConfig, config_id)
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if config:
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return config
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db_logger.warning(
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f"配置不存在，回退到工作空间默认配置: missing_config_id={config_id}, workspace_id={workspace_id}"
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)
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return MemoryConfigRepository.get_workspace_default(db, workspace_id)
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@@ -1193,7 +1193,7 @@ class AppService:
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app_type: str,
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config: Dict[str, Any]
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) -> Tuple[Optional[uuid.UUID], bool]:
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"""从发布配置中提取 memory_config_id（根据应用类型分发）
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"""从发布配置中提取 memory_config_id（委托给 MemoryConfigService）
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Args:
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app_type: 应用类型 (agent, workflow, multi_agent)
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@@ -1204,128 +1204,10 @@ class AppService:
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- memory_config_id: 提取的配置ID，如果不存在或为旧格式则返回 None
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- is_legacy_int: 是否检测到旧格式 int 数据，需要回退到工作空间默认配置
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"""
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if app_type == AppType.AGENT:
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return self._extract_memory_config_id_from_agent(config)
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elif app_type == AppType.WORKFLOW:
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return self._extract_memory_config_id_from_workflow(config)
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elif app_type == AppType.MULTI_AGENT:
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# Multi-agent 暂不支持记忆配置提取
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logger.debug(f"多智能体应用暂不支持记忆配置提取: app_type={app_type}")
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return None, False
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else:
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logger.warning(f"不支持的应用类型，无法提取记忆配置: app_type={app_type}")
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return None, False
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def _extract_memory_config_id_from_agent(
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self,
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config: Dict[str, Any]
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) -> Tuple[Optional[uuid.UUID], bool]:
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"""从 Agent 应用配置中提取 memory_config_id
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from app.services.memory_config_service import MemoryConfigService
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路径: config.memory.memory_content
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Args:
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config: Agent 配置字典
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Returns:
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Tuple[Optional[uuid.UUID], bool]: (memory_config_id, is_legacy_int)
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- memory_config_id: 记忆配置ID，如果不存在或为旧格式则返回 None
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- is_legacy_int: 是否检测到旧格式 int 数据
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"""
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try:
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memory_dict = config.get("memory", {})
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# Support both field names: memory_config_id (new) and memory_content (legacy)
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memory_value = memory_dict.get("memory_config_id") or memory_dict.get("memory_content")
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logger.info(f"Extracting memory_config_id: memory_value={memory_value}, type={type(memory_value).__name__ if memory_value else 'None'}")
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if memory_value:
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# 处理字符串、UUID 和 int（旧数据兼容）三种情况
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if isinstance(memory_value, uuid.UUID):
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return memory_value, False
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elif isinstance(memory_value, str):
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# Check if it's a numeric string (legacy int format)
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if memory_value.isdigit():
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logger.warning(
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f"Agent 配置中 memory_config_id 为旧格式 int 字符串，将使用工作空间默认配置: "
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f"value={memory_value}"
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)
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return None, True
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try:
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return uuid.UUID(memory_value), False
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except ValueError:
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logger.warning(f"Invalid UUID string: {memory_value}")
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return None, False
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elif isinstance(memory_value, int):
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# 旧数据存储为 int，需要回退到工作空间默认配置
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logger.warning(
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f"Agent 配置中 memory_config_id 为旧格式 int，将使用工作空间默认配置: "
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f"value={memory_value}"
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)
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return None, True
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else:
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logger.warning(
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f"Agent 配置中 memory_config_id 格式无效: type={type(memory_value)}, "
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f"value={memory_value}"
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)
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return None, False
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except (ValueError, TypeError) as e:
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logger.warning(
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f"Agent 配置中 memory_config_id 格式无效: error={str(e)}"
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)
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return None, False
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def _extract_memory_config_id_from_workflow(
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self,
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config: Dict[str, Any]
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) -> Tuple[Optional[uuid.UUID], bool]:
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"""从 Workflow 应用配置中提取 memory_config_id
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扫描工作流节点，查找 MemoryRead 或 MemoryWrite 节点。
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返回第一个找到的记忆节点的 config_id。
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Args:
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config: Workflow 配置字典
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Returns:
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Tuple[Optional[uuid.UUID], bool]: (memory_config_id, is_legacy_int)
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- memory_config_id: 记忆配置ID，如果不存在或为旧格式则返回 None
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- is_legacy_int: 是否检测到旧格式 int 数据
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"""
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nodes = config.get("nodes", [])
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for node in nodes:
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node_type = node.get("type", "")
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# 检查是否为记忆节点 (support both formats: memory-read/memory-write and MemoryRead/MemoryWrite)
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if node_type.lower() in ["memoryread", "memorywrite", "memory-read", "memory-write"]:
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config_id = node.get("config", {}).get("config_id")
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if config_id:
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try:
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# 处理字符串、UUID 和 int（旧数据兼容）三种情况
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if isinstance(config_id, uuid.UUID):
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return config_id, False
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elif isinstance(config_id, str):
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return uuid.UUID(config_id), False
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elif isinstance(config_id, int):
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# 旧数据存储为 int，需要回退到工作空间默认配置
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logger.warning(
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f"工作流记忆节点 config_id 为旧格式 int，将使用工作空间默认配置: "
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f"node_id={node.get('id')}, node_type={node_type}, value={config_id}"
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)
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return None, True
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else:
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logger.warning(
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f"工作流记忆节点 config_id 格式无效: node_id={node.get('id')}, "
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f"node_type={node_type}, type={type(config_id)}"
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)
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except (ValueError, TypeError) as e:
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logger.warning(
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f"工作流记忆节点 config_id 格式无效: node_id={node.get('id')}, "
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f"node_type={node_type}, error={str(e)}"
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)
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logger.debug("工作流配置中未找到记忆节点")
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return None, False
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service = MemoryConfigService(self.db)
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return service.extract_memory_config_id(app_type, config)
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def _get_workspace_default_memory_config_id(
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self,
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@@ -1191,8 +1191,8 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
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"""
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获取终端用户关联的记忆配置
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使用 MemoryConfigService.get_config_with_fallback 获取配置，
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支持终端用户已分配配置和工作空间默认配置的回退机制。
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兼容旧数据：如果 end_user.memory_config_id 为空，则从 AppRelease.config 中获取
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并回填到 end_user.memory_config_id 字段（懒迁移）。
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Args:
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end_user_id: 终端用户ID
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@@ -1204,7 +1204,13 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
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Raises:
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ValueError: 当终端用户不存在或应用未发布时
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"""
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import json as json_module
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import uuid
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from sqlalchemy import select
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from app.models.app_model import App
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from app.models.app_release_model import AppRelease
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from app.models.end_user_model import EndUser
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from app.services.memory_config_service import MemoryConfigService
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@@ -1217,6 +1223,7 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
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raise ValueError(f"终端用户不存在: {end_user_id}")
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app_id = end_user.app_id
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logger.debug(f"Found end_user app_id: {app_id}")
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# 2. 获取应用以确定 workspace_id
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app = db.query(App).filter(App.id == app_id).first()
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@@ -1228,10 +1235,71 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
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logger.warning(f"No current release for app: {app_id}")
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raise ValueError(f"应用未发布: {app_id}")
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# 3. 使用 get_config_with_fallback 获取记忆配置
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# 3. 兼容旧数据：如果 memory_config_id 为空，从 AppRelease.config 获取并回填
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memory_config_id_to_use = end_user.memory_config_id
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# 如果已有 memory_config_id，直接使用
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# 如果新创建enduser，enduser.memory_config_id 必定为none
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# 那么使用从release中获取memory_config_id为预期行为，并且回填到
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# end_user.memory_config_id
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if not memory_config_id_to_use:
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logger.info(f"end_user.memory_config_id is None, migrating from AppRelease.config")
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|
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# 获取最新发布版本
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stmt = (
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select(AppRelease)
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.where(AppRelease.app_id == app_id, AppRelease.is_active.is_(True))
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||||
.order_by(AppRelease.version.desc())
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)
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# TODO: change to current_release_id
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latest_release = db.scalars(stmt).first()
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||||
|
||||
if latest_release:
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config = latest_release.config or {}
|
||||
|
||||
# 如果 config 是字符串，解析为字典
|
||||
if isinstance(config, str):
|
||||
try:
|
||||
config = json_module.loads(config)
|
||||
except json_module.JSONDecodeError:
|
||||
logger.warning(f"Failed to parse config JSON for release {latest_release.id}")
|
||||
config = {}
|
||||
|
||||
# 使用 MemoryConfigService 的提取方法
|
||||
memory_config_service = MemoryConfigService(db)
|
||||
legacy_config_id, is_legacy_int = memory_config_service.extract_memory_config_id(
|
||||
app_type=app.type,
|
||||
config=config
|
||||
)
|
||||
|
||||
if legacy_config_id:
|
||||
# 验证提取的 config_id 是否存在于数据库中
|
||||
from app.models.memory_config_model import MemoryConfig as MemoryConfigModel
|
||||
existing_config = db.get(MemoryConfigModel, legacy_config_id)
|
||||
|
||||
if existing_config:
|
||||
memory_config_id_to_use = legacy_config_id
|
||||
|
||||
# 回填到 end_user 表（lazy update）
|
||||
end_user.memory_config_id = memory_config_id_to_use
|
||||
db.commit()
|
||||
logger.info(
|
||||
f"Migrated memory_config_id for end_user {end_user_id}: {memory_config_id_to_use}"
|
||||
)
|
||||
else:
|
||||
logger.warning(
|
||||
f"Extracted memory_config_id does not exist, skipping backfill: "
|
||||
f"end_user_id={end_user_id}, config_id={legacy_config_id}"
|
||||
)
|
||||
elif is_legacy_int:
|
||||
logger.info(
|
||||
f"Legacy int config detected for end_user {end_user_id}, will use workspace default"
|
||||
)
|
||||
|
||||
# 4. 使用 get_config_with_fallback 获取记忆配置
|
||||
memory_config_service = MemoryConfigService(db)
|
||||
memory_config = memory_config_service.get_config_with_fallback(
|
||||
memory_config_id=end_user.memory_config_id,
|
||||
memory_config_id=memory_config_id_to_use,
|
||||
workspace_id=app.workspace_id
|
||||
)
|
||||
|
||||
@@ -1255,7 +1323,8 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
|
||||
|
||||
使用与 get_end_user_connected_config 相同的逻辑：
|
||||
1. 优先使用 end_user.memory_config_id
|
||||
2. 如果没有，回退到工作空间默认配置
|
||||
2. 如果没有，尝试从 AppRelease.config 提取并回填
|
||||
3. 如果仍然没有，回退到工作空间默认配置
|
||||
|
||||
Args:
|
||||
end_user_ids: 终端用户ID列表
|
||||
@@ -1269,7 +1338,12 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
|
||||
...
|
||||
}
|
||||
"""
|
||||
import json as json_module
|
||||
|
||||
from sqlalchemy import select
|
||||
|
||||
from app.models.app_model import App
|
||||
from app.models.app_release_model import AppRelease
|
||||
from app.models.end_user_model import EndUser
|
||||
from app.models.memory_config_model import MemoryConfig
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
@@ -1284,7 +1358,8 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
|
||||
# 1. 批量查询所有 end_user 及其 app_id 和 memory_config_id
|
||||
end_users = db.query(EndUser).filter(EndUser.id.in_(end_user_ids)).all()
|
||||
|
||||
# 创建映射
|
||||
# 创建映射 - 保留 EndUser 对象引用以便回填
|
||||
end_user_map = {str(eu.id): eu for eu in end_users}
|
||||
user_data = {str(eu.id): {"app_id": eu.app_id, "memory_config_id": eu.memory_config_id} for eu in end_users}
|
||||
|
||||
# 记录未找到的用户
|
||||
@@ -1295,15 +1370,116 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
|
||||
for user_id in missing_user_ids:
|
||||
result[user_id] = {"memory_config_id": None, "memory_config_name": None}
|
||||
|
||||
# 2. 批量获取所有相关应用以获取 workspace_id
|
||||
# 2. 批量获取所有相关应用以获取 workspace_id 和 type
|
||||
app_ids = list(set(data["app_id"] for data in user_data.values()))
|
||||
if not app_ids:
|
||||
return result
|
||||
|
||||
apps = db.query(App).filter(App.id.in_(app_ids)).all()
|
||||
app_map = {app.id: app for app in apps}
|
||||
app_to_workspace = {app.id: app.workspace_id for app in apps}
|
||||
|
||||
# 3. 收集需要查询的 memory_config_id 和需要回退的 workspace_id
|
||||
# 3. 对于没有 memory_config_id 的用户，尝试从 AppRelease.config 提取
|
||||
users_needing_migration = [
|
||||
(end_user_id, data["app_id"])
|
||||
for end_user_id, data in user_data.items()
|
||||
if not data["memory_config_id"]
|
||||
]
|
||||
|
||||
if users_needing_migration:
|
||||
# 批量获取相关应用的最新发布版本
|
||||
migration_app_ids = list(set(app_id for _, app_id in users_needing_migration))
|
||||
|
||||
# 查询每个应用的最新活跃发布版本
|
||||
app_latest_releases = {}
|
||||
for app_id in migration_app_ids:
|
||||
stmt = (
|
||||
select(AppRelease)
|
||||
.where(AppRelease.app_id == app_id, AppRelease.is_active.is_(True))
|
||||
.order_by(AppRelease.version.desc())
|
||||
.limit(1)
|
||||
)
|
||||
latest_release = db.scalars(stmt).first()
|
||||
if latest_release:
|
||||
app_latest_releases[app_id] = latest_release
|
||||
|
||||
# 为每个需要迁移的用户提取 memory_config_id
|
||||
config_service = MemoryConfigService(db)
|
||||
users_to_backfill = [] # [(end_user, memory_config_id), ...]
|
||||
|
||||
for end_user_id, app_id in users_needing_migration:
|
||||
latest_release = app_latest_releases.get(app_id)
|
||||
if not latest_release:
|
||||
continue
|
||||
|
||||
config = latest_release.config or {}
|
||||
|
||||
# 如果 config 是字符串，解析为字典
|
||||
if isinstance(config, str):
|
||||
try:
|
||||
config = json_module.loads(config)
|
||||
except json_module.JSONDecodeError:
|
||||
logger.warning(f"Failed to parse config JSON for release {latest_release.id}")
|
||||
continue
|
||||
|
||||
# 使用 MemoryConfigService 的提取方法
|
||||
app = app_map.get(app_id)
|
||||
if not app:
|
||||
continue
|
||||
|
||||
legacy_config_id, is_legacy_int = config_service.extract_memory_config_id(
|
||||
app_type=app.type,
|
||||
config=config
|
||||
)
|
||||
|
||||
if legacy_config_id:
|
||||
# 更新 user_data 中的 memory_config_id
|
||||
user_data[end_user_id]["memory_config_id"] = legacy_config_id
|
||||
|
||||
# 记录需要回填的用户（稍后验证配置存在后再回填）
|
||||
end_user = end_user_map.get(end_user_id)
|
||||
if end_user:
|
||||
users_to_backfill.append((end_user, legacy_config_id))
|
||||
elif is_legacy_int:
|
||||
logger.info(
|
||||
f"Legacy int config detected for end_user {end_user_id}, will use workspace default"
|
||||
)
|
||||
|
||||
# 验证提取的 config_id 是否存在于数据库中
|
||||
if users_to_backfill:
|
||||
config_ids_to_validate = list(set(cid for _, cid in users_to_backfill))
|
||||
existing_configs = db.query(MemoryConfig).filter(
|
||||
MemoryConfig.config_id.in_(config_ids_to_validate)
|
||||
).all()
|
||||
valid_config_ids = {mc.config_id for mc in existing_configs}
|
||||
|
||||
# 只回填存在的配置
|
||||
valid_backfills = [
|
||||
(eu, cid) for eu, cid in users_to_backfill
|
||||
if cid in valid_config_ids
|
||||
]
|
||||
invalid_backfills = [
|
||||
(eu, cid) for eu, cid in users_to_backfill
|
||||
if cid not in valid_config_ids
|
||||
]
|
||||
|
||||
if invalid_backfills:
|
||||
invalid_ids = [str(cid) for _, cid in invalid_backfills]
|
||||
logger.warning(
|
||||
f"Skipping backfill for non-existent memory_config_ids: {invalid_ids}"
|
||||
)
|
||||
# 清除 user_data 中无效的 config_id
|
||||
for eu, cid in invalid_backfills:
|
||||
user_data[str(eu.id)]["memory_config_id"] = None
|
||||
|
||||
# 批量回填 end_user.memory_config_id
|
||||
if valid_backfills:
|
||||
for end_user, memory_config_id in valid_backfills:
|
||||
end_user.memory_config_id = memory_config_id
|
||||
db.commit()
|
||||
logger.info(f"Migrated memory_config_id for {len(valid_backfills)} end_users")
|
||||
|
||||
# 4. 收集需要查询的 memory_config_id 和需要回退的 workspace_id
|
||||
direct_config_ids = []
|
||||
workspace_fallback_users = [] # [(end_user_id, workspace_id), ...]
|
||||
|
||||
@@ -1315,13 +1491,13 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
|
||||
if workspace_id:
|
||||
workspace_fallback_users.append((end_user_id, workspace_id))
|
||||
|
||||
# 4. 批量查询直接分配的配置
|
||||
# 5. 批量查询直接分配的配置
|
||||
config_id_to_config = {}
|
||||
if direct_config_ids:
|
||||
configs = db.query(MemoryConfig).filter(MemoryConfig.config_id.in_(direct_config_ids)).all()
|
||||
config_id_to_config = {mc.config_id: mc for mc in configs}
|
||||
|
||||
# 5. 获取工作空间默认配置（需要逐个查询，因为 get_workspace_default_config 有复杂逻辑）
|
||||
# 6. 获取工作空间默认配置（需要逐个查询，因为 get_workspace_default_config 有复杂逻辑）
|
||||
workspace_default_configs = {}
|
||||
unique_workspace_ids = list(set(ws_id for _, ws_id in workspace_fallback_users))
|
||||
|
||||
@@ -1332,7 +1508,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
|
||||
if default_config:
|
||||
workspace_default_configs[workspace_id] = default_config
|
||||
|
||||
# 6. 构建最终结果
|
||||
# 7. 构建最终结果
|
||||
for end_user_id, data in user_data.items():
|
||||
memory_config = None
|
||||
|
||||
|
||||
@@ -17,7 +17,6 @@ from sqlalchemy.orm import Session
|
||||
from app.core.logging_config import get_config_logger, get_logger
|
||||
from app.core.validators.memory_config_validators import (
|
||||
validate_and_resolve_model_id,
|
||||
validate_embedding_model,
|
||||
)
|
||||
from app.models.memory_config_model import MemoryConfig as MemoryConfigModel
|
||||
from app.repositories.memory_config_repository import MemoryConfigRepository
|
||||
@@ -217,53 +216,108 @@ class MemoryConfigService:
|
||||
|
||||
memory_config, workspace = result
|
||||
|
||||
# Step 2: Validate embedding model (returns both UUID and name)
|
||||
# Helper function to validate model with workspace fallback
|
||||
def _validate_model_with_fallback(
|
||||
model_id: str,
|
||||
model_type: str,
|
||||
workspace_default: str,
|
||||
required: bool = False
|
||||
) -> tuple:
|
||||
"""Validate model ID, falling back to workspace default if invalid.
|
||||
|
||||
Args:
|
||||
model_id: The model ID to validate
|
||||
model_type: Type of model (llm, embedding, rerank)
|
||||
workspace_default: Workspace default model ID to use as fallback
|
||||
required: Whether the model is required
|
||||
|
||||
Returns:
|
||||
Tuple of (model_uuid, model_name) or (None, None)
|
||||
"""
|
||||
# Try the configured model first
|
||||
if model_id:
|
||||
try:
|
||||
return validate_and_resolve_model_id(
|
||||
model_id,
|
||||
model_type,
|
||||
self.db,
|
||||
workspace.tenant_id,
|
||||
required=False,
|
||||
config_id=validated_config_id,
|
||||
workspace_id=workspace.id,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"{model_type} model validation failed, trying workspace default: {e}"
|
||||
)
|
||||
|
||||
# Fallback to workspace default
|
||||
if workspace_default:
|
||||
try:
|
||||
result = validate_and_resolve_model_id(
|
||||
workspace_default,
|
||||
model_type,
|
||||
self.db,
|
||||
workspace.tenant_id,
|
||||
required=required,
|
||||
config_id=validated_config_id,
|
||||
workspace_id=workspace.id,
|
||||
)
|
||||
if result[0]:
|
||||
logger.info(
|
||||
f"Using workspace default {model_type} model: {workspace_default}"
|
||||
)
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"Workspace default {model_type} model also invalid: {e}")
|
||||
if required:
|
||||
raise
|
||||
|
||||
if required:
|
||||
raise InvalidConfigError(
|
||||
f"{model_type.title()} model is required but not configured",
|
||||
field_name=f"{model_type}_model_id",
|
||||
invalid_value=model_id,
|
||||
config_id=validated_config_id,
|
||||
workspace_id=workspace.id
|
||||
)
|
||||
|
||||
return None, None
|
||||
|
||||
# Step 2: Validate embedding model with workspace fallback
|
||||
embed_start = time.time()
|
||||
embedding_uuid, embedding_name = validate_embedding_model(
|
||||
validated_config_id,
|
||||
embedding_uuid, embedding_name = _validate_model_with_fallback(
|
||||
memory_config.embedding_id,
|
||||
self.db,
|
||||
workspace.tenant_id,
|
||||
workspace.id,
|
||||
"embedding",
|
||||
workspace.embedding,
|
||||
required=True
|
||||
)
|
||||
embed_time = time.time() - embed_start
|
||||
logger.info(f"[PERF] Embedding validation: {embed_time:.4f}s")
|
||||
|
||||
# Step 3: Resolve LLM model
|
||||
# Step 3: Resolve LLM model with workspace fallback
|
||||
llm_start = time.time()
|
||||
llm_uuid, llm_name = validate_and_resolve_model_id(
|
||||
llm_uuid, llm_name = _validate_model_with_fallback(
|
||||
memory_config.llm_id,
|
||||
"llm",
|
||||
self.db,
|
||||
workspace.tenant_id,
|
||||
required=True,
|
||||
config_id=validated_config_id,
|
||||
workspace_id=workspace.id,
|
||||
workspace.llm,
|
||||
required=True
|
||||
)
|
||||
llm_time = time.time() - llm_start
|
||||
logger.info(f"[PERF] LLM validation: {llm_time:.4f}s")
|
||||
|
||||
# Step 4: Resolve optional rerank model
|
||||
# Step 4: Resolve optional rerank model with workspace fallback
|
||||
rerank_start = time.time()
|
||||
rerank_uuid = None
|
||||
rerank_name = None
|
||||
if memory_config.rerank_id:
|
||||
rerank_uuid, rerank_name = validate_and_resolve_model_id(
|
||||
memory_config.rerank_id,
|
||||
"rerank",
|
||||
self.db,
|
||||
workspace.tenant_id,
|
||||
required=False,
|
||||
config_id=validated_config_id,
|
||||
workspace_id=workspace.id,
|
||||
)
|
||||
rerank_uuid, rerank_name = _validate_model_with_fallback(
|
||||
memory_config.rerank_id,
|
||||
"rerank",
|
||||
workspace.rerank,
|
||||
required=False
|
||||
)
|
||||
rerank_time = time.time() - rerank_start
|
||||
if memory_config.rerank_id:
|
||||
if memory_config.rerank_id or workspace.rerank:
|
||||
logger.info(f"[PERF] Rerank validation: {rerank_time:.4f}s")
|
||||
|
||||
# Note: embedding_name is now returned from validate_embedding_model above
|
||||
# No need for redundant query!
|
||||
|
||||
# Create immutable MemoryConfig object
|
||||
config = MemoryConfig(
|
||||
config_id=memory_config.config_id,
|
||||
@@ -530,38 +584,7 @@ class MemoryConfigService:
|
||||
Returns:
|
||||
Optional[MemoryConfigModel]: Default config or None if no configs exist
|
||||
"""
|
||||
from sqlalchemy import select
|
||||
|
||||
from app.models.memory_config_model import MemoryConfig as MemoryConfigModel
|
||||
|
||||
# First, try to find the explicitly marked default config
|
||||
stmt = (
|
||||
select(MemoryConfigModel)
|
||||
.where(
|
||||
MemoryConfigModel.workspace_id == workspace_id,
|
||||
MemoryConfigModel.is_default.is_(True),
|
||||
MemoryConfigModel.state.is_(True),
|
||||
)
|
||||
.limit(1)
|
||||
)
|
||||
|
||||
config = self.db.scalars(stmt).first()
|
||||
|
||||
if config:
|
||||
return config
|
||||
|
||||
# Fallback: get the oldest active config if no explicit default
|
||||
stmt = (
|
||||
select(MemoryConfigModel)
|
||||
.where(
|
||||
MemoryConfigModel.workspace_id == workspace_id,
|
||||
MemoryConfigModel.state.is_(True),
|
||||
)
|
||||
.order_by(MemoryConfigModel.created_at.asc())
|
||||
.limit(1)
|
||||
)
|
||||
|
||||
config = self.db.scalars(stmt).first()
|
||||
config = MemoryConfigRepository.get_workspace_default(self.db, workspace_id)
|
||||
|
||||
if not config:
|
||||
logger.warning(
|
||||
@@ -588,29 +611,28 @@ class MemoryConfigService:
|
||||
Returns:
|
||||
Optional[MemoryConfigModel]: Memory config or None if no fallback available
|
||||
"""
|
||||
from app.models.memory_config_model import MemoryConfig as MemoryConfigModel
|
||||
|
||||
if not memory_config_id:
|
||||
logger.debug(
|
||||
"No memory config ID provided, using workspace default",
|
||||
extra={"workspace_id": str(workspace_id)}
|
||||
)
|
||||
return self.get_workspace_default_config(workspace_id)
|
||||
|
||||
config = self.db.get(MemoryConfigModel, memory_config_id)
|
||||
|
||||
if config:
|
||||
return config
|
||||
|
||||
logger.warning(
|
||||
"Memory config not found, falling back to workspace default",
|
||||
extra={
|
||||
"missing_config_id": str(memory_config_id),
|
||||
"workspace_id": str(workspace_id)
|
||||
}
|
||||
config = MemoryConfigRepository.get_with_fallback(
|
||||
self.db,
|
||||
memory_config_id,
|
||||
workspace_id
|
||||
)
|
||||
|
||||
return self.get_workspace_default_config(workspace_id)
|
||||
if not config and memory_config_id:
|
||||
logger.warning(
|
||||
"Memory config not found, falling back to workspace default",
|
||||
extra={
|
||||
"missing_config_id": str(memory_config_id),
|
||||
"workspace_id": str(workspace_id)
|
||||
}
|
||||
)
|
||||
|
||||
return config
|
||||
|
||||
def delete_config(
|
||||
self,
|
||||
@@ -624,7 +646,7 @@ class MemoryConfigService:
|
||||
|
||||
Args:
|
||||
config_id: Memory config ID to delete (UUID or legacy int)
|
||||
force: If True, delete even if end users are connected
|
||||
force: If True, clear end user references before deleting
|
||||
|
||||
Returns:
|
||||
Dict with status, message, and affected_users count
|
||||
@@ -632,8 +654,11 @@ class MemoryConfigService:
|
||||
Raises:
|
||||
ResourceNotFoundException: If config doesn't exist
|
||||
"""
|
||||
from sqlalchemy.exc import IntegrityError
|
||||
|
||||
from app.core.exceptions import ResourceNotFoundException
|
||||
from app.models.memory_config_model import MemoryConfig as MemoryConfigModel
|
||||
from app.repositories.end_user_repository import EndUserRepository
|
||||
|
||||
# 处理旧格式 int 类型的 config_id
|
||||
if isinstance(config_id, int):
|
||||
@@ -663,54 +688,227 @@ class MemoryConfigService:
|
||||
"is_default": True
|
||||
}
|
||||
|
||||
# TODO: add back delete warning
|
||||
# # Count connected end users
|
||||
# end_user_repo = EndUserRepository(self.db)
|
||||
# connected_count = end_user_repo.count_by_memory_config_id(config_id)
|
||||
# Use repository to count connected end users
|
||||
end_user_repo = EndUserRepository(self.db)
|
||||
connected_count = end_user_repo.count_by_memory_config_id(config_id)
|
||||
|
||||
# if connected_count > 0 and not force:
|
||||
# logger.warning(
|
||||
# "Attempted to delete memory config with connected end users",
|
||||
# extra={
|
||||
# "config_id": str(config_id),
|
||||
# "connected_count": connected_count
|
||||
# }
|
||||
# )
|
||||
if connected_count > 0 and not force:
|
||||
logger.warning(
|
||||
"Attempted to delete memory config with connected end users",
|
||||
extra={
|
||||
"config_id": str(config_id),
|
||||
"connected_count": connected_count
|
||||
}
|
||||
)
|
||||
|
||||
# return {
|
||||
# "status": "warning",
|
||||
# "message": f"Cannot delete memory config: {connected_count} end users are using it",
|
||||
# "connected_count": connected_count,
|
||||
# "force_required": True
|
||||
# }
|
||||
return {
|
||||
"status": "warning",
|
||||
"message": f"无法删除记忆配置：{connected_count} 个终端用户正在使用此配置",
|
||||
"connected_count": connected_count,
|
||||
"force_required": True
|
||||
}
|
||||
|
||||
# # Force delete: clear end user references first
|
||||
# if connected_count > 0 and force:
|
||||
# cleared_count = end_user_repo.clear_memory_config_id(config_id)
|
||||
# Force delete: use repository to clear end user references first
|
||||
if connected_count > 0 and force:
|
||||
cleared_count = end_user_repo.clear_memory_config_id(config_id)
|
||||
|
||||
# logger.warning(
|
||||
# "Force deleting memory config",
|
||||
# extra={
|
||||
# "config_id": str(config_id),
|
||||
# "cleared_end_users": cleared_count
|
||||
# }
|
||||
# )
|
||||
connected_count = 0
|
||||
logger.warning(
|
||||
"Force deleting memory config, clearing end user references",
|
||||
extra={
|
||||
"config_id": str(config_id),
|
||||
"cleared_end_users": cleared_count
|
||||
}
|
||||
)
|
||||
|
||||
self.db.delete(config)
|
||||
self.db.commit()
|
||||
|
||||
logger.info(
|
||||
"Memory config deleted",
|
||||
extra={
|
||||
"config_id": str(config_id),
|
||||
"force": force,
|
||||
try:
|
||||
self.db.delete(config)
|
||||
self.db.commit()
|
||||
|
||||
logger.info(
|
||||
"Memory config deleted",
|
||||
extra={
|
||||
"config_id": str(config_id),
|
||||
"force": force,
|
||||
"affected_users": connected_count
|
||||
}
|
||||
)
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"message": "记忆配置删除成功",
|
||||
"affected_users": connected_count
|
||||
}
|
||||
)
|
||||
|
||||
except IntegrityError as e:
|
||||
self.db.rollback()
|
||||
|
||||
# Handle foreign key violation gracefully
|
||||
error_str = str(e.orig) if e.orig else str(e)
|
||||
if "ForeignKeyViolation" in error_str or "foreign key constraint" in error_str.lower():
|
||||
logger.warning(
|
||||
"Delete failed due to foreign key constraint",
|
||||
extra={
|
||||
"config_id": str(config_id),
|
||||
"error": error_str
|
||||
}
|
||||
)
|
||||
return {
|
||||
"status": "error",
|
||||
"message": "无法删除记忆配置：仍有终端用户引用此配置，请使用 force=true 强制删除",
|
||||
"force_required": True
|
||||
}
|
||||
|
||||
# Re-raise other integrity errors
|
||||
logger.error(
|
||||
"Delete failed due to integrity error",
|
||||
extra={
|
||||
"config_id": str(config_id),
|
||||
"error": error_str
|
||||
},
|
||||
exc_info=True
|
||||
)
|
||||
raise
|
||||
|
||||
# ==================== 记忆配置提取方法 ====================
|
||||
|
||||
def extract_memory_config_id(
|
||||
self,
|
||||
app_type: str,
|
||||
config: dict
|
||||
) -> tuple[Optional[uuid.UUID], bool]:
|
||||
"""从发布配置中提取 memory_config_id（根据应用类型分发）
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"message": "Memory config deleted successfully",
|
||||
"affected_users": connected_count
|
||||
}
|
||||
Args:
|
||||
app_type: 应用类型 (agent, workflow, multi_agent)
|
||||
config: 发布配置字典
|
||||
|
||||
Returns:
|
||||
Tuple[Optional[uuid.UUID], bool]: (memory_config_id, is_legacy_int)
|
||||
- memory_config_id: 提取的配置ID，如果不存在或为旧格式则返回 None
|
||||
- is_legacy_int: 是否检测到旧格式 int 数据，需要回退到工作空间默认配置
|
||||
"""
|
||||
if app_type == "agent":
|
||||
return self._extract_memory_config_id_from_agent(config)
|
||||
elif app_type == "workflow":
|
||||
return self._extract_memory_config_id_from_workflow(config)
|
||||
elif app_type == "multi_agent":
|
||||
# Multi-agent 暂不支持记忆配置提取
|
||||
logger.debug(f"多智能体应用暂不支持记忆配置提取: app_type={app_type}")
|
||||
return None, False
|
||||
else:
|
||||
logger.warning(f"不支持的应用类型，无法提取记忆配置: app_type={app_type}")
|
||||
return None, False
|
||||
|
||||
def _extract_memory_config_id_from_agent(
|
||||
self,
|
||||
config: dict
|
||||
) -> tuple[Optional[uuid.UUID], bool]:
|
||||
"""从 Agent 应用配置中提取 memory_config_id
|
||||
|
||||
路径: config.memory.memory_content 或 config.memory.memory_config_id
|
||||
|
||||
Args:
|
||||
config: Agent 配置字典
|
||||
|
||||
Returns:
|
||||
Tuple[Optional[uuid.UUID], bool]: (memory_config_id, is_legacy_int)
|
||||
- memory_config_id: 记忆配置ID，如果不存在或为旧格式则返回 None
|
||||
- is_legacy_int: 是否检测到旧格式 int 数据
|
||||
"""
|
||||
try:
|
||||
memory_dict = config.get("memory", {})
|
||||
# Support both field names: memory_config_id (new) and memory_content (legacy)
|
||||
memory_value = memory_dict.get("memory_config_id") or memory_dict.get("memory_content")
|
||||
logger.info(
|
||||
f"Extracting memory_config_id: memory_value={memory_value}, "
|
||||
f"type={type(memory_value).__name__ if memory_value else 'None'}"
|
||||
)
|
||||
if memory_value:
|
||||
# 处理字符串、UUID 和 int（旧数据兼容）三种情况
|
||||
if isinstance(memory_value, uuid.UUID):
|
||||
return memory_value, False
|
||||
elif isinstance(memory_value, str):
|
||||
# Check if it's a numeric string (legacy int format)
|
||||
if memory_value.isdigit():
|
||||
logger.warning(
|
||||
f"Agent 配置中 memory_config_id 为旧格式 int 字符串，将使用工作空间默认配置: "
|
||||
f"value={memory_value}"
|
||||
)
|
||||
return None, True
|
||||
try:
|
||||
return uuid.UUID(memory_value), False
|
||||
except ValueError:
|
||||
logger.warning(f"Invalid UUID string: {memory_value}")
|
||||
return None, False
|
||||
elif isinstance(memory_value, int):
|
||||
# 旧数据存储为 int，需要回退到工作空间默认配置
|
||||
logger.warning(
|
||||
f"Agent 配置中 memory_config_id 为旧格式 int，将使用工作空间默认配置: "
|
||||
f"value={memory_value}"
|
||||
)
|
||||
return None, True
|
||||
else:
|
||||
logger.warning(
|
||||
f"Agent 配置中 memory_config_id 格式无效: type={type(memory_value)}, "
|
||||
f"value={memory_value}"
|
||||
)
|
||||
return None, False
|
||||
except (ValueError, TypeError) as e:
|
||||
logger.warning(
|
||||
f"Agent 配置中 memory_config_id 格式无效: error={str(e)}"
|
||||
)
|
||||
return None, False
|
||||
|
||||
def _extract_memory_config_id_from_workflow(
|
||||
self,
|
||||
config: dict
|
||||
) -> tuple[Optional[uuid.UUID], bool]:
|
||||
"""从 Workflow 应用配置中提取 memory_config_id
|
||||
|
||||
扫描工作流节点，查找 MemoryRead 或 MemoryWrite 节点。
|
||||
返回第一个找到的记忆节点的 config_id。
|
||||
|
||||
Args:
|
||||
config: Workflow 配置字典
|
||||
|
||||
Returns:
|
||||
Tuple[Optional[uuid.UUID], bool]: (memory_config_id, is_legacy_int)
|
||||
- memory_config_id: 记忆配置ID，如果不存在或为旧格式则返回 None
|
||||
- is_legacy_int: 是否检测到旧格式 int 数据
|
||||
"""
|
||||
nodes = config.get("nodes", [])
|
||||
|
||||
for node in nodes:
|
||||
node_type = node.get("type", "")
|
||||
|
||||
# 检查是否为记忆节点 (support both formats: memory-read/memory-write and MemoryRead/MemoryWrite)
|
||||
if node_type.lower() in ["memoryread", "memorywrite", "memory-read", "memory-write"]:
|
||||
config_id = node.get("config", {}).get("config_id")
|
||||
|
||||
if config_id:
|
||||
try:
|
||||
# 处理字符串、UUID 和 int（旧数据兼容）三种情况
|
||||
if isinstance(config_id, uuid.UUID):
|
||||
return config_id, False
|
||||
elif isinstance(config_id, str):
|
||||
return uuid.UUID(config_id), False
|
||||
elif isinstance(config_id, int):
|
||||
# 旧数据存储为 int，需要回退到工作空间默认配置
|
||||
logger.warning(
|
||||
f"工作流记忆节点 config_id 为旧格式 int，将使用工作空间默认配置: "
|
||||
f"node_id={node.get('id')}, node_type={node_type}, value={config_id}"
|
||||
)
|
||||
return None, True
|
||||
else:
|
||||
logger.warning(
|
||||
f"工作流记忆节点 config_id 格式无效: node_id={node.get('id')}, "
|
||||
f"node_type={node_type}, type={type(config_id)}"
|
||||
)
|
||||
except (ValueError, TypeError) as e:
|
||||
logger.warning(
|
||||
f"工作流记忆节点 config_id 格式无效: node_id={node.get('id')}, "
|
||||
f"node_type={node_type}, error={str(e)}"
|
||||
)
|
||||
|
||||
logger.debug("工作流配置中未找到记忆节点")
|
||||
return None, False
|
||||
|
||||
@@ -120,7 +120,14 @@ class WorkspaceAppService:
|
||||
return None
|
||||
|
||||
def _get_memory_config(self, memory_content: str) -> Dict[str, Any]:
|
||||
"""Retrieve memory_config information based on memory_content"""
|
||||
"""Retrieve memory_config information based on memory_content
|
||||
|
||||
Args:
|
||||
memory_content: Memory config ID string
|
||||
|
||||
Returns:
|
||||
Dict containing memory config info including workspace_id for model fallback
|
||||
"""
|
||||
try:
|
||||
memory_content = resolve_config_id(memory_content, self.db)
|
||||
memory_config_result = MemoryConfigRepository.query_reflection_config_by_id(self.db, (memory_content))
|
||||
@@ -128,6 +135,7 @@ class WorkspaceAppService:
|
||||
if memory_config_result:
|
||||
return {
|
||||
"config_id": memory_content,
|
||||
"workspace_id": memory_config_result.workspace_id,
|
||||
"enable_self_reflexion": memory_config_result.enable_self_reflexion,
|
||||
"iteration_period": memory_config_result.iteration_period,
|
||||
"reflexion_range": memory_config_result.reflexion_range,
|
||||
@@ -359,7 +367,17 @@ class MemoryReflectionService:
|
||||
}
|
||||
|
||||
def _create_reflection_config_from_data(self, config_data: Dict[str, Any]) -> ReflectionConfig:
|
||||
"""Create reflective configuration objects from configuration data"""
|
||||
"""Create reflective configuration objects from configuration data
|
||||
|
||||
If reflection_model_id is not set, falls back to workspace default LLM.
|
||||
|
||||
Args:
|
||||
config_data: Dict containing reflection config including workspace_id
|
||||
|
||||
Returns:
|
||||
ReflectionConfig object with model_id resolved
|
||||
"""
|
||||
from app.repositories.workspace_repository import get_workspace_models_configs
|
||||
|
||||
reflexion_range_value = config_data.get("reflexion_range")
|
||||
if reflexion_range_value is None or reflexion_range_value == "":
|
||||
@@ -392,6 +410,17 @@ class MemoryReflectionService:
|
||||
if reflection_model_id:
|
||||
reflection_model_id = str(reflection_model_id)
|
||||
|
||||
# 如果 reflection_model_id 为空，回退到工作空间默认 LLM
|
||||
if not reflection_model_id:
|
||||
workspace_id = config_data.get("workspace_id")
|
||||
if workspace_id:
|
||||
workspace_models = get_workspace_models_configs(self.db, workspace_id)
|
||||
if workspace_models and workspace_models.get("llm"):
|
||||
reflection_model_id = workspace_models["llm"]
|
||||
api_logger.info(
|
||||
f"reflection_model_id 为空，使用工作空间默认 LLM: {reflection_model_id}"
|
||||
)
|
||||
|
||||
return ReflectionConfig(
|
||||
enabled=config_data.get("enable_self_reflexion", False),
|
||||
iteration_period=str(iteration_period), # ReflectionConfig期望字符串
|
||||
|
||||
@@ -899,6 +899,8 @@ def update_workspace_models_configs(
|
||||
def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None:
|
||||
"""Ensure a workspace has a default memory config, creating one if missing.
|
||||
|
||||
Also fills empty model fields for all configs in this workspace.
|
||||
|
||||
Args:
|
||||
db: Database session
|
||||
workspace: The workspace to check
|
||||
@@ -911,28 +913,92 @@ def _ensure_default_memory_config(db: Session, workspace: Workspace) -> None:
|
||||
MemoryConfig.is_default == True
|
||||
).first()
|
||||
|
||||
if existing_default:
|
||||
if not existing_default:
|
||||
# No default config exists, create one
|
||||
business_logger.info(
|
||||
f"Workspace {workspace.id} missing default memory config, creating one"
|
||||
)
|
||||
|
||||
try:
|
||||
_create_default_memory_config(
|
||||
db=db,
|
||||
workspace_id=workspace.id,
|
||||
workspace_name=workspace.name,
|
||||
llm_id=uuid.UUID(workspace.llm) if workspace.llm else None,
|
||||
embedding_id=uuid.UUID(workspace.embedding) if workspace.embedding else None,
|
||||
rerank_id=uuid.UUID(workspace.rerank) if workspace.rerank else None,
|
||||
)
|
||||
except Exception as e:
|
||||
business_logger.error(
|
||||
f"Failed to create default memory config for workspace {workspace.id}: {str(e)}"
|
||||
)
|
||||
|
||||
# Fill empty model fields for ALL configs in this workspace
|
||||
_fill_workspace_configs_model_defaults(db, workspace)
|
||||
|
||||
|
||||
def _fill_workspace_configs_model_defaults(
|
||||
db: Session,
|
||||
workspace: Workspace
|
||||
) -> None:
|
||||
"""Fill empty model fields for all memory configs in a workspace.
|
||||
|
||||
Updates llm_id, embedding_id, rerank_id, reflection_model_id, and emotion_model_id
|
||||
if they are None, using the corresponding workspace default models.
|
||||
|
||||
Args:
|
||||
db: Database session
|
||||
workspace: The workspace containing default model settings
|
||||
"""
|
||||
from app.models.memory_config_model import MemoryConfig
|
||||
|
||||
# Get all configs for this workspace
|
||||
configs = db.query(MemoryConfig).filter(
|
||||
MemoryConfig.workspace_id == workspace.id
|
||||
).all()
|
||||
|
||||
if not configs:
|
||||
return
|
||||
|
||||
# No default config exists, create one
|
||||
business_logger.info(
|
||||
f"Workspace {workspace.id} missing default memory config, creating one"
|
||||
)
|
||||
# Map of memory_config field -> workspace field
|
||||
model_field_mappings = [
|
||||
("llm_id", "llm"),
|
||||
("embedding_id", "embedding"),
|
||||
("rerank_id", "rerank"),
|
||||
("reflection_model_id", "llm"), # reflection uses LLM
|
||||
("emotion_model_id", "llm"), # emotion uses LLM
|
||||
]
|
||||
|
||||
try:
|
||||
_create_default_memory_config(
|
||||
db=db,
|
||||
workspace_id=workspace.id,
|
||||
workspace_name=workspace.name,
|
||||
llm_id=uuid.UUID(workspace.llm) if workspace.llm else None,
|
||||
embedding_id=uuid.UUID(workspace.embedding) if workspace.embedding else None,
|
||||
rerank_id=uuid.UUID(workspace.rerank) if workspace.rerank else None,
|
||||
)
|
||||
except Exception as e:
|
||||
business_logger.error(
|
||||
f"Failed to create default memory config for workspace {workspace.id}: {str(e)}"
|
||||
)
|
||||
# Don't fail the workspace list operation if config creation fails
|
||||
configs_updated = 0
|
||||
|
||||
for memory_config in configs:
|
||||
updated_fields = []
|
||||
|
||||
for config_field, workspace_field in model_field_mappings:
|
||||
config_value = getattr(memory_config, config_field, None)
|
||||
workspace_value = getattr(workspace, workspace_field, None)
|
||||
|
||||
if not config_value and workspace_value:
|
||||
setattr(memory_config, config_field, workspace_value)
|
||||
updated_fields.append(config_field)
|
||||
|
||||
if updated_fields:
|
||||
configs_updated += 1
|
||||
business_logger.debug(
|
||||
f"Updated memory config {memory_config.config_id} fields: {updated_fields}"
|
||||
)
|
||||
|
||||
if configs_updated > 0:
|
||||
try:
|
||||
db.commit()
|
||||
business_logger.info(
|
||||
f"Updated {configs_updated} memory configs in workspace {workspace.id} with default models"
|
||||
)
|
||||
except Exception as e:
|
||||
db.rollback()
|
||||
business_logger.error(
|
||||
f"Failed to update memory configs in workspace {workspace.id}: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
def _create_default_memory_config(
|
||||
|
||||
Reference in New Issue
Block a user