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
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Ke Sun
@@ -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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