Add agent workflow engine foundation and theme-style page switcher.
Introduce WorkflowEngine with state machine, tool registry, and builtin chat template; migrate stream chat to engine callbacks. Move page mode switching to TopBar actions cluster as a ThemeToggle-style dropdown (聊天/工作室/爽文/房间). Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
@@ -17,6 +17,7 @@ try:
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from backend.utils.llm_client import LLMClient
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from backend.services.task_queue_manager import task_queue_manager, TaskType
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from backend.core.config import settings
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from backend.services.workflow_engine import workflow_engine
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except ImportError:
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from services.character_service import CharacterService
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from services.worldbook_service import WorldBookService
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@@ -24,6 +25,7 @@ except ImportError:
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from utils.llm_client import LLMClient
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from services.task_queue_manager import task_queue_manager, TaskType
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from core.config import settings
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from services.workflow_engine import workflow_engine
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class ChatWorkflowService:
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@@ -128,201 +130,17 @@ class ChatWorkflowService:
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self,
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request_data: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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处理聊天请求的核心工作流
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Args:
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request_data: 前端发送的完整数据
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Returns:
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{
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"success": bool,
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"content": str, # 生成的回复内容
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"error": str | None
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}
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"""
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try:
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# === 第1步：解析请求数据 ===
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current_role = request_data.get("currentRole")
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current_chat = request_data.get("currentChat")
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user_message = request_data.get("mes", "")
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if not current_role or not user_message:
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return {
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"success": False,
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"content": "",
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"error": "缺少必要的参数：currentRole 或 mes"
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}
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print(f"[ChatWorkflow] 开始处理请求: role={current_role}, chat={current_chat}")
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# ✅ 获取预设名称（用于加载预设绑定的正则规则）
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preset_config = request_data.get("presetConfig", {})
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preset_name = preset_config.get("selectedPreset")
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# ✅ 第1.5步：应用用户输入的正则规则
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from services.regex_service import regex_service
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from models.regex_rules import RegexPlacement
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processed_user_message = regex_service.apply_rules_by_placement(
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text=user_message,
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placement=RegexPlacement.USER_INPUT.value,
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character_name=current_role,
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preset_name=preset_name,
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message_depth=0,
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is_for_llm=True, # ✅ 用户输入会发送给LLM
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is_markdown_rendered=False
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)
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if processed_user_message != user_message:
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print(f"[Regex] ✅ 已应用用户输入正则规则")
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user_message = processed_user_message
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# === 第2步：加载角色卡 ===
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character_data = request_data.get("characterData")
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if not character_data:
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# 如果没有提供角色卡数据，从后端加载
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character = self.character_service.get_character_by_name(current_role)
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if not character:
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return {
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"success": False,
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"content": "",
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"error": f"角色 '{current_role}' 不存在"
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}
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else:
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# 使用前端提供的角色卡数据（可能包含用户修改）
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try:
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from backend.models.internal import CharacterCard
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except ImportError:
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from models.internal import CharacterCard
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character = CharacterCard(**character_data)
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print(f"[ChatWorkflow] 已加载角色卡: {character.name}")
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# === 第3步：收集并激活世界书条目 ===
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active_entries = await self._collect_and_activate_worldbooks(
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request_data,
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character
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)
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print(f"[ChatWorkflow] 激活了 {len(active_entries)} 个世界书条目")
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# === 第4步：加载聊天历史 ===
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chat_history = await self._load_chat_history(current_role, current_chat)
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print(f"[ChatWorkflow] 加载了 {len(chat_history)} 条历史消息")
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# === 第5步：组装提示词 ===
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prompt_messages = self._assemble_prompt(
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character,
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chat_history,
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user_message,
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active_entries,
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request_data
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)
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print(f"[ChatWorkflow] 组装了 {len(prompt_messages)} 条提示消息")
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# === 第6步：调用LLM生成回复 ===
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api_config = request_data.get("apiConfig", {})
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preset_config = request_data.get("presetConfig", {})
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stream_output = request_data.get("stream", False)
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result = await self._generate_response(
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prompt_messages,
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api_config,
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preset_config,
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stream_output
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)
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generated_content = result["content"]
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token_usage = result.get("usage", {})
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duration = result.get("duration")
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print(f"[ChatWorkflow] 生成完成，内容长度: {len(generated_content)}")
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print(f"[ChatWorkflow] Token 使用: {token_usage}")
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# ✅ 第6.5步：应用 AI 输出的正则规则
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processed_ai_output = regex_service.apply_rules_by_placement(
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text=generated_content,
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placement=RegexPlacement.AI_OUTPUT.value,
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character_name=current_role,
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preset_name=preset_name,
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message_depth=0,
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is_for_llm=False, # ✅ AI输出是显示给用户的
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is_markdown_rendered=False
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)
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if processed_ai_output != generated_content:
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print(f"[Regex] ✅ 已应用 AI 输出正则规则")
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generated_content = processed_ai_output
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# === 第7步：记录 Token 使用 ===
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chat_id = f"{current_role}/{request_data.get('currentChat', '')}"
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floor = request_data.get("floor", 0)
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try:
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try:
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from backend.services.token_usage_service import token_usage_service
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from backend.models.internal import TokenUsageStatus
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except ImportError:
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from services.token_usage_service import token_usage_service
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from models.internal import TokenUsageStatus
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await token_usage_service.record_usage(
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chat_id=chat_id,
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role_name=current_role,
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chat_name=request_data.get('currentChat', ''),
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prompt_tokens=token_usage.get("prompt_tokens", 0),
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completion_tokens=token_usage.get("completion_tokens", 0),
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total_tokens=token_usage.get("total_tokens", 0),
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status=TokenUsageStatus.COMPLETED,
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floor=floor + 1, # AI 回复的楼层
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duration=duration,
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model=api_config.get("model"),
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api_provider="openai", # TODO: 从 API URL 检测提供商
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api_url=api_config.get("api_url") # ✅ 记录 API URL
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)
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except Exception as e:
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print(f"[ChatWorkflow] 记录 Token 使用失败: {e}")
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# === 第8步：启动异步并行任务 ===
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# 创建任务ID
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image_task_id = None
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table_task_id = None
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options = request_data.get("options", {})
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if options.get("imageWorkflow", False):
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import uuid
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image_task_id = f"img_{uuid.uuid4().hex[:8]}"
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await task_queue_manager.add_task(image_task_id, TaskType.IMAGE_WORKFLOW, chat_id)
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if options.get("dynamicTable", False):
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import uuid
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table_task_id = f"tbl_{uuid.uuid4().hex[:8]}"
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await task_queue_manager.add_task(table_task_id, TaskType.DYNAMIC_TABLE, chat_id)
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await self._start_parallel_tasks(request_data, generated_content, image_task_id, table_task_id)
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return {
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"success": True,
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"content": generated_content,
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"error": None,
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"activeEntries": active_entries,
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"taskIds": {
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"imageWorkflow": image_task_id,
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"dynamicTable": table_task_id
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}
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}
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except Exception as e:
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print(f"[ChatWorkflow] 错误: {str(e)}")
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import traceback
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traceback.print_exc()
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return {
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"success": False,
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"content": "",
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"error": f"工作流执行失败: {str(e)}"
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}
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"""处理聊天请求 – delegates to WorkflowEngine.run_turn."""
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result = await workflow_engine.run_turn(request_data, stream=False)
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return {
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"success": result.success,
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"content": result.content,
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"error": result.error,
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"activeEntries": result.active_entries,
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"taskIds": result.task_ids,
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"engineRunId": result.run_id,
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"workflowTemplateId": result.workflow_template_id,
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}
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def _normalize_worldbook_entry(self, entry_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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@@ -1211,280 +1029,24 @@ class ChatWorkflowService:
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async def process_chat_request_stream(
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self,
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request_data: Dict[str, Any],
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on_chunk
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on_chunk,
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on_worldbook_active=None,
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on_tasks_created=None,
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) -> Dict[str, Any]:
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"""
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处理流式聊天请求
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Args:
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request_data: 前端发送的完整数据
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on_chunk: 回调函数，每次收到 chunk 时调用
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Returns:
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{
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"success": bool,
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"content": str, # 完整的生成内容
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"error": str | None,
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"activeEntries": List,
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"taskIds": Dict
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}
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"""
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try:
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# === 第1步：解析请求数据 ===
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current_role = request_data.get("currentRole")
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current_chat = request_data.get("currentChat")
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user_message = request_data.get("mes", "")
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if not current_role or not user_message:
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return {
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"success": False,
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"content": "",
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"error": "缺少必要的参数：currentRole 或 mes"
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}
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print(f"\n{'#'*80}")
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print(f"[ChatWorkflow-Stream] 🚀 开始处理请求")
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print(f" - Role: {current_role}")
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print(f" - Chat: {current_chat}")
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print(f" - Message Length: {len(user_message)}")
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print(f"{'#'*80}\n")
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# ✅ 获取预设名称（用于加载预设绑定的正则规则）
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preset_config = request_data.get("presetConfig", {})
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preset_name = preset_config.get("selectedPreset")
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# ✅ 第1.5步：应用用户输入的正则规则
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from services.regex_service import regex_service
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from models.regex_rules import RegexPlacement
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processed_user_message = regex_service.apply_rules_by_placement(
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text=user_message,
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placement=RegexPlacement.USER_INPUT.value,
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character_name=current_role,
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preset_name=preset_name,
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message_depth=0,
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is_for_llm=True, # ✅ 用户输入会发送给LLM
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is_markdown_rendered=False
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)
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if processed_user_message != user_message:
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print(f"[Regex] ✅ 已应用用户输入正则规则")
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user_message = processed_user_message
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# === 第2步：加载角色卡 ===
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character_data = request_data.get("characterData")
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if not character_data:
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character = self.character_service.get_character_by_name(current_role)
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if not character:
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return {
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"success": False,
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"content": "",
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"error": f"角色 '{current_role}' 不存在"
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}
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else:
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try:
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from backend.models.internal import CharacterCard
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except ImportError:
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from models.internal import CharacterCard
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character = CharacterCard(**character_data)
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print(f"[ChatWorkflow-Stream] ✅ 已加载角色卡: {character.name}")
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# === 第3步：收集并激活世界书条目 ===
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active_entries = await self._collect_and_activate_worldbooks(
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request_data,
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character
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)
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print(f"\n[ChatWorkflow-Stream] 📚 世界书激活结果: {len(active_entries)} 个条目")
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for i, entry in enumerate(active_entries, 1):
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print(f" {i}. {getattr(entry, 'name', 'N/A')} (UID: {getattr(entry, 'uid', 'N/A')})")
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# === 第4步：加载聊天历史 ===
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chat_history = await self._load_chat_history(current_role, current_chat)
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print(f"[ChatWorkflow-Stream] 💬 聊天历史加载结果: {len(chat_history)} 条消息")
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# === 第5步：组装提示词 ===
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prompt_messages = self._assemble_prompt(
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character,
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chat_history,
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user_message,
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active_entries,
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request_data
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)
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print(f"[ChatWorkflow-Stream] 📝 提示词组装结果: {len(prompt_messages)} 条消息")
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for i, msg in enumerate(prompt_messages, 1):
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role = getattr(msg, 'role', 'unknown')
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content_preview = str(getattr(msg, 'content', ''))[:50]
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print(f" {i}. [{role}] {content_preview}...")
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# === 第6步：流式调用LLM生成回复 ===
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api_config = request_data.get("apiConfig", {})
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preset_config = request_data.get("presetConfig", {})
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print(f"\n[ChatWorkflow-Stream] 🤖 开始流式调用 LLM")
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print(f" - Model: {api_config.get('model', 'N/A')}")
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print(f" - API URL: {api_config.get('api_url', 'N/A')[:50]}...")
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print(f" - API Key: {'已设置' if api_config.get('api_key') else '⚠️ 未设置'}")
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print(f" - Temperature: {preset_config.get('parameters', {}).get('temperature', 1.0)}")
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print(f" - Max Tokens: {preset_config.get('parameters', {}).get('max_tokens', 30000)}")
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print(f" - Request Timeout: {preset_config.get('parameters', {}).get('request_timeout', 60)}s")
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print(f"{'~'*80}")
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# ✅ 验证 API Key
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if not api_config.get('api_key'):
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print(f"[ChatWorkflow-Stream] ❌ 错误: API Key 为空！")
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print(f" - 请检查配置文件中是否保存了 API Key")
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print(f" - Profile ID: {request_data.get('currentProfile', {}).get('id', 'N/A')}")
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raise Exception("API Key 未配置，请先在 API 配置页面保存密钥")
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generated_content = ""
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token_usage = {}
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duration = 0
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chunk_count = 0
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start_time = time.time()
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print(f"[ChatWorkflow-Stream] 📡 正在调用 llm_client.stream_chat()...")
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try:
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async for chunk_dict in self.llm_client.stream_chat(
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messages=prompt_messages,
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api_url=api_config.get("api_url", ""),
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api_key=api_config.get("api_key", ""),
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model=api_config.get("model", ""),
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temperature=preset_config.get("parameters", {}).get("temperature", 1.0),
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max_tokens=preset_config.get("parameters", {}).get("max_tokens", 30000),
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request_timeout=preset_config.get("parameters", {}).get("request_timeout", 60) # ✅ 传递超时时间
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):
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# ✅ 处理 LLM 返回的字典格式数据
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if isinstance(chunk_dict, dict):
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if chunk_dict.get("type") == "chunk":
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chunk_content = chunk_dict.get("content", "")
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elif chunk_dict.get("type") == "usage":
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# 跳过 usage 信息，不拼接到内容中
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continue
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else:
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# 兼容旧格式或未知类型，尝试直接获取 content
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chunk_content = chunk_dict.get("content", str(chunk_dict))
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else:
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# 如果直接返回字符串（兼容情况）
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chunk_content = str(chunk_dict)
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# ✅ 拼接纯文本内容
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generated_content += chunk_content
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chunk_count += 1
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# 第一个 chunk 到达时记录
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if chunk_count == 1:
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first_chunk_time = time.time()
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print(f"[ChatWorkflow-Stream] ✨ 收到第一个 chunk (耗时: {first_chunk_time - start_time:.2f}s)")
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# 每20个chunk记录一次进度
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if chunk_count % 20 == 0:
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elapsed = time.time() - start_time
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print(f"[ChatWorkflow-Stream] 📊 已接收 {chunk_count} 个 chunks, 当前长度: {len(generated_content)}, 耗时: {elapsed:.2f}s")
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# ✅ 调用回调函数发送纯文本 chunk
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await on_chunk(chunk_content)
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except Exception as stream_error:
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elapsed = time.time() - start_time
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print(f"[ChatWorkflow-Stream] ❌ 流式调用失败 (耗时: {elapsed:.2f}s)")
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print(f" - 错误类型: {type(stream_error).__name__}")
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print(f" - 错误信息: {str(stream_error)}")
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raise
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elapsed = time.time() - start_time
|
||||
print(f"{'~'*80}")
|
||||
print(f"[ChatWorkflow-Stream] ✅ LLM 流式调用完成")
|
||||
print(f" - 总 Chunks: {chunk_count}")
|
||||
print(f" - 内容长度: {len(generated_content)}")
|
||||
print(f" - 总耗时: {elapsed:.2f}s")
|
||||
print(f" - 平均速度: {len(generated_content)/elapsed if elapsed > 0 else 0:.0f} chars/s")
|
||||
print(f"{'#'*80}\n")
|
||||
|
||||
# ✅ 第6.5步：应用 AI 输出的正则规则
|
||||
processed_ai_output = regex_service.apply_rules_by_placement(
|
||||
text=generated_content,
|
||||
placement=RegexPlacement.AI_OUTPUT.value,
|
||||
character_name=current_role,
|
||||
preset_name=preset_name,
|
||||
message_depth=0,
|
||||
is_for_llm=False, # ✅ AI输出是显示给用户的
|
||||
is_markdown_rendered=False
|
||||
)
|
||||
|
||||
if processed_ai_output != generated_content:
|
||||
print(f"[Regex] ✅ 已应用 AI 输出正则规则")
|
||||
generated_content = processed_ai_output
|
||||
|
||||
# === 第7步：记录 Token 使用（估算） ===
|
||||
chat_id = f"{current_role}/{request_data.get('currentChat', '')}"
|
||||
floor = request_data.get("floor", 0)
|
||||
|
||||
try:
|
||||
try:
|
||||
from backend.services.token_usage_service import token_usage_service
|
||||
from backend.models.internal import TokenUsageStatus
|
||||
except ImportError:
|
||||
from services.token_usage_service import token_usage_service
|
||||
from models.internal import TokenUsageStatus
|
||||
|
||||
# 估算 token 数量
|
||||
prompt_tokens = len(str(prompt_messages)) // 4
|
||||
completion_tokens = len(generated_content) // 4
|
||||
|
||||
await token_usage_service.record_usage(
|
||||
chat_id=chat_id,
|
||||
role_name=current_role,
|
||||
chat_name=request_data.get('currentChat', ''),
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=prompt_tokens + completion_tokens,
|
||||
status=TokenUsageStatus.COMPLETED,
|
||||
floor=floor + 1,
|
||||
duration=duration,
|
||||
model=api_config.get("model"),
|
||||
api_provider="openai",
|
||||
api_url=api_config.get("api_url") # ✅ 记录 API URL
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"[ChatWorkflow-Stream] 记录 Token 使用失败: {e}")
|
||||
|
||||
# === 第8步：启动异步并行任务 ===
|
||||
image_task_id = None
|
||||
table_task_id = None
|
||||
|
||||
options = request_data.get("options", {})
|
||||
if options.get("imageWorkflow", False):
|
||||
import uuid
|
||||
image_task_id = f"img_{uuid.uuid4().hex[:8]}"
|
||||
await task_queue_manager.add_task(image_task_id, TaskType.IMAGE_WORKFLOW, chat_id)
|
||||
|
||||
if options.get("dynamicTable", False):
|
||||
import uuid
|
||||
table_task_id = f"tbl_{uuid.uuid4().hex[:8]}"
|
||||
await task_queue_manager.add_task(table_task_id, TaskType.DYNAMIC_TABLE, chat_id)
|
||||
|
||||
await self._start_parallel_tasks(request_data, generated_content, image_task_id, table_task_id)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"content": generated_content,
|
||||
"error": None,
|
||||
"activeEntries": active_entries,
|
||||
"taskIds": {
|
||||
"imageWorkflow": image_task_id,
|
||||
"dynamicTable": table_task_id
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"[ChatWorkflow-Stream] 错误: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return {
|
||||
"success": False,
|
||||
"content": "",
|
||||
"error": f"工作流执行失败: {str(e)}"
|
||||
}
|
||||
"""处理流式聊天请求 – delegates to WorkflowEngine.run_turn with callbacks."""
|
||||
result = await workflow_engine.run_turn(
|
||||
request_data,
|
||||
stream=True,
|
||||
on_chunk=on_chunk,
|
||||
on_worldbook_active=on_worldbook_active,
|
||||
on_tasks_created=on_tasks_created,
|
||||
)
|
||||
return {
|
||||
"success": result.success,
|
||||
"content": result.content,
|
||||
"error": result.error,
|
||||
"activeEntries": result.active_entries,
|
||||
"taskIds": result.task_ids,
|
||||
"engineRunId": result.run_id,
|
||||
"workflowTemplateId": result.workflow_template_id,
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user