feat: DeepSeek v4 模型对齐、作业助手脚本与 Agent 对比测试
- 前端 WorkflowEditor/ModelConfigs/NodeTemplates：deepseek-v4-flash、v4-pro，弃用提示 - llm_service 默认 deepseek-v4-flash；workflow_engine 等与模型配置注入 - 作业管理脚本支持 AGENT_NAME 与 v4-pro；新增 compare_homework_agents 脚本 - 文档重命名为 (红头)项目核心文档汇总.md 并更新 DeepSeek 说明 Made-with: Cursor
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renjianbo
@@ -92,6 +92,7 @@ class WorkflowEngine:
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logger=None,
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db=None,
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budget_limits: Optional[Dict[str, Any]] = None,
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trusted_model_config_user_id: Optional[str] = None,
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):
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"""
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初始化工作流引擎
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@@ -101,6 +102,7 @@ class WorkflowEngine:
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workflow_data: 工作流数据（包含nodes和edges）
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logger: 执行日志记录器（可选）
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db: 数据库会话（可选，用于Agent节点加载Agent配置）
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trusted_model_config_user_id: 允许加载「模型配置」解密密钥的用户 ID（通常为当前执行所属 Workflow/Agent 的 owner）
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"""
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self.workflow_id = workflow_id
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self.nodes = {node['id']: node for node in workflow_data.get('nodes', [])}
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@@ -115,6 +117,7 @@ class WorkflowEngine:
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self._llm_invocations: int = 0
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self._tool_calls_used: int = 0
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self.budget_limits: Dict[str, Any] = dict(budget_limits or {})
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self.trusted_model_config_user_id: Optional[str] = trusted_model_config_user_id
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self._cap_steps: int = max(
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1, int(getattr(settings, "WORKFLOW_MAX_STEPS_PER_RUN", 2000) or 2000)
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)
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@@ -178,6 +181,71 @@ class WorkflowEngine:
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detail=f"已超过工具调用预算（{self._cap_tool} 次）",
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)
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def _resolve_llm_credentials_from_model_config(
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self, node_data: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""
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若节点 data 含 model_config_id，则从数据库加载加密密钥并校验归属；
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返回 {"api_key","base_url","provider","model"}，否则返回 None。
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"""
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raw = node_data.get("model_config_id") or node_data.get("modelConfigId")
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if raw is None or raw == "":
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return None
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cfg_id = str(raw).strip()
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if not cfg_id:
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return None
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if not self.trusted_model_config_user_id:
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logger.warning(
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"LLM 节点配置了 model_config_id=%s，但未绑定 trusted_model_config_user_id，"
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"将跳过模型配置密钥注入（仍使用节点与环境变量）。",
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cfg_id,
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)
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return None
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from app.models.model_config import ModelConfig
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from app.services.encryption_service import EncryptionService
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db = self.db or SessionLocal()
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own_db = self.db is None
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try:
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cfg = db.query(ModelConfig).filter(ModelConfig.id == cfg_id).first()
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if not cfg:
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raise ValueError(f"模型配置不存在: {cfg_id}")
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if cfg.user_id != self.trusted_model_config_user_id:
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raise ValueError("无权使用该模型配置（仅创建者可调用）")
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api_key_plain = EncryptionService.decrypt(cfg.api_key)
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if not (api_key_plain or "").strip():
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raise ValueError("模型配置中的 API Key 无效")
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base_url = (cfg.base_url or "").strip() or None
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raw_prov = (cfg.provider or "").strip().lower()
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if raw_prov == "local":
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llm_prov = "openai"
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elif raw_prov in ("openai", "deepseek"):
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llm_prov = raw_prov
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elif raw_prov == "anthropic":
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raise ValueError(
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"当前 LLM 节点暂不支持从模型配置加载 Anthropic；请改用 OpenAI 或 DeepSeek 兼容配置"
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)
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else:
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raise ValueError(f"不支持的模型配置提供商: {cfg.provider}")
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model_name = (cfg.model_name or "").strip()
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if not model_name:
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raise ValueError("模型配置中模型名称为空")
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return {
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"api_key": api_key_plain.strip(),
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"base_url": base_url,
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"provider": llm_prov,
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"model": model_name,
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}
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finally:
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if own_db:
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db.close()
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def _get_persist_scope(self) -> Tuple[Optional[str], Optional[str]]:
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"""(scope_kind, scope_id) 或 (None, None)，用于持久化 user_memory_*。"""
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if self._persist_scope_cache is None:
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@@ -1625,9 +1693,33 @@ class WorkflowEngine:
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max_tokens = int(max_tokens_raw) if max_tokens_raw is not None else None
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else:
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max_tokens = None
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# 不传递 api_key 和 base_url，让 LLM 服务使用系统默认配置（与节点测试保持一致）
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api_key = None
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base_url = None
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# 默认使用环境变量中的 Key；若节点绑定 model_config_id 且执行上下文可信，则注入用户保存的密钥与 endpoint
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api_key: Optional[str] = None
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base_url: Optional[str] = None
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mc_cred: Optional[Dict[str, Any]] = None
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try:
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mc_cred = self._resolve_llm_credentials_from_model_config(node_data)
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if mc_cred:
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api_key = mc_cred.get("api_key")
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base_url = mc_cred.get("base_url")
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provider = mc_cred.get("provider", provider)
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model = mc_cred.get("model", model)
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except Exception as mc_err:
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if self.logger:
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duration = int((time.time() - start_time) * 1000)
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self.logger.log_node_error(node_id, node_type, mc_err, duration)
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logger.error(f"[rjb] LLM 模型配置解析失败: {mc_err}", exc_info=True)
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return {
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'output': None,
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'status': 'failed',
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'error': str(mc_err),
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}
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llm_extra_kw: Dict[str, Any] = {}
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if api_key is not None:
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llm_extra_kw["api_key"] = api_key
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if base_url is not None:
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llm_extra_kw["base_url"] = base_url
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# 记录实际发送给LLM的prompt
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logger.info(f"[rjb] 准备调用LLM: node_id={node_id}, provider={provider}, model={model}, prompt前200字符='{prompt[:200] if len(prompt) > 200 else prompt}'")
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@@ -1660,7 +1752,10 @@ class WorkflowEngine:
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# 调用LLM服务
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try:
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if self.logger:
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logger.debug(f"[rjb] LLM节点配置: provider={provider}, model={model}, 使用系统默认API Key配置, 工具调用: {'启用' if tools else '禁用'}")
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key_src = "模型配置(model_config_id)" if mc_cred else "环境变量默认"
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logger.debug(
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f"[rjb] LLM节点配置: provider={provider}, model={model}, API密钥来源={key_src}, 工具调用: {'启用' if tools else '禁用'}"
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)
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self.logger.info(f"调用LLM服务: {provider}/{model}", node_id=node_id, node_type=node_type)
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# 根据是否启用工具选择不同的调用方式
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@@ -1684,6 +1779,8 @@ class WorkflowEngine:
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_tool_extra["request_timeout"] = max(10.0, float(_rt))
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except (TypeError, ValueError):
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pass
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_merged_tool_kw = dict(llm_extra_kw)
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_merged_tool_kw.update(_tool_extra)
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result = await llm_service.call_llm_with_tools(
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prompt=prompt,
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tools=tools,
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@@ -1694,7 +1791,7 @@ class WorkflowEngine:
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execution_logger=self.logger,
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tool_choice=_tool_choice,
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on_tool_executed=self._on_tool_executed_budget,
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**_tool_extra,
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**_merged_tool_kw,
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)
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result = self._enrich_llm_json_user_profile(result, input_data)
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else:
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@@ -1703,8 +1800,8 @@ class WorkflowEngine:
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provider=provider,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens
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# 不传递 api_key 和 base_url，使用系统默认配置
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max_tokens=max_tokens,
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**llm_extra_kw,
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)
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result = self._enrich_llm_json_user_profile(result, input_data)
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@@ -4786,6 +4883,7 @@ class WorkflowEngine:
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logger=child_logger,
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db=self.db,
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budget_limits=child_budget,
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trusted_model_config_user_id=self.trusted_model_config_user_id,
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)
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try:
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child_result = await child_engine.execute(sub_input)
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