diff --git a/backend/controller/conversation_api.py b/backend/controller/conversation_api.py index 41ec198..3a39b31 100644 --- a/backend/controller/conversation_api.py +++ b/backend/controller/conversation_api.py @@ -5,10 +5,7 @@ import json import asyncio import time from typing import Optional, TypedDict, List, Union -import threading -from collections import defaultdict -from sqlalchemy.orm import identity from ..utils.globals import set_language from ..utils.auth_utils import extract_and_store_api_key @@ -20,18 +17,15 @@ import base64 import os import shutil -from ..service.debug_agent import debug_workflow_errors +from ..service.debug_agent import debug_workflow_errors, debug_workflow_errors_single from ..dao.workflow_table import save_workflow_data, get_workflow_data_by_id, update_workflow_ui_by_id from ..service.mcp_client import comfyui_agent_invoke +from ..service.all_in_one_agent import all_in_one_invoke from ..utils.request_context import set_request_context, get_session_id from ..utils.logger import log from ..utils.modelscope_gateway import ModelScopeGateway import folder_paths -# 全局下载进度存储 -download_progress = {} -download_lock = threading.Lock() - # 不再使用内存存储会话消息，改为从前端传递历史消息 # 在文件开头添加 @@ -69,98 +63,6 @@ class ChatResponse(TypedDict): format: str # 返回的格式 ext: Optional[List[ExtItem]] # 扩展信息 -# 下载进度回调类 -class DownloadProgressCallback: - def __init__(self, id: str, filename: str, file_size: int, download_id: str): - self.id = id - self.filename = filename - self.file_size = file_size - self.download_id = download_id - self.progress = 0 - self.status = "downloading" # downloading, completed, failed - self.error_message = None - self.start_time = time.time() - - # 初始化进度记录 - with download_lock: - download_progress[download_id] = { - "id": id, - "filename": filename, - "file_size": file_size, - "progress": 0, - "percentage": 0.0, - "status": "downloading", - "start_time": self.start_time, - "estimated_time": None, - "speed": 0.0, - "error_message": None - } - - def update(self, size: int): - """更新下载进度""" - self.progress += size - current_time = time.time() - elapsed_time = current_time - self.start_time - - # 计算下载速度和预估时间 - if elapsed_time > 0: - speed = self.progress / elapsed_time # bytes per second - if speed > 0 and self.progress < self.file_size: - remaining_bytes = self.file_size - self.progress - estimated_time = remaining_bytes / speed - else: - estimated_time = None - else: - speed = 0.0 - estimated_time = None - - percentage = (self.progress / self.file_size) * 100 if self.file_size > 0 else 0 - - # 更新全局进度 - with download_lock: - if self.download_id in download_progress: - # 直接更新字典的值，而不是调用update方法 - progress_dict = download_progress[self.download_id] - progress_dict["progress"] = self.progress - progress_dict["percentage"] = round(percentage, 2) - progress_dict["speed"] = round(speed, 2) - progress_dict["estimated_time"] = round(estimated_time, 2) if estimated_time else None - - def end(self, success: bool = True, error_message: str = None): - """下载结束回调""" - current_time = time.time() - total_time = current_time - self.start_time - - if success: - self.status = "completed" - # 验证下载完整性 - assert self.progress == self.file_size, f"Download incomplete: {self.progress}/{self.file_size}" - else: - self.status = "failed" - self.error_message = error_message - - # 更新最终状态 - with download_lock: - if self.download_id in download_progress: - # 直接更新字典的值，而不是调用update方法 - progress_dict = download_progress[self.download_id] - progress_dict["status"] = self.status - progress_dict["progress"] = self.file_size if success else self.progress - if self.file_size > 0: - progress_dict["percentage"] = 100.0 if success else (self.progress / self.file_size) * 100 - else: - progress_dict["percentage"] = 0.0 - progress_dict["total_time"] = round(total_time, 2) - progress_dict["error_message"] = self.error_message - - def fail(self, error_message: str): - """下载失败回调""" - self.end(success=False, error_message=error_message) - -# 生成唯一下载ID -def generate_download_id() -> str: - import uuid - return str(uuid.uuid4()) async def upload_to_oss(file_data: bytes, filename: str) -> str: # TODO: Implement your OSS upload logic here @@ -225,9 +127,7 @@ async def invoke_chat(request): await response.prepare(request) session_id = req_json.get('session_id') - prompt = req_json.get('prompt') images = req_json.get('images', []) - intent = req_json.get('intent') ext = req_json.get('ext') historical_messages = req_json.get('messages', []) workflow_checkpoint_id = req_json.get('workflow_checkpoint_id') @@ -279,7 +179,6 @@ async def invoke_chat(request): accumulated_text = "" ext_data = None finished = True # Default to True - has_sent_response = False previous_text_length = 0 log.info(f"config: {config}") @@ -353,6 +252,306 @@ async def invoke_chat(request): return response +@server.PromptServer.instance.routes.post("/api/chat-single/invoke") +async def invoke_chat_single(request): + log.info("Received invoke_chat_single request") + + # Extract and store API key from Authorization header + extract_and_store_api_key(request) + + req_json = await request.json() + log.info("Request JSON:", req_json) + + response = web.StreamResponse( + status=200, + reason='OK', + headers={ + 'Content-Type': 'application/json', + 'X-Content-Type-Options': 'nosniff' + } + ) + await response.prepare(request) + + session_id = req_json.get('session_id') + images = req_json.get('images', []) + ext = req_json.get('ext') + historical_messages = req_json.get('messages', []) + workflow_checkpoint_id = req_json.get('workflow_checkpoint_id') + workflow_data = req_json.get('workflow_data') # Support debug-style payloads + + # Language + language = request.headers.get('Accept-Language', 'en') + set_language(language) + + # Config + config = { + "session_id": session_id, + "workflow_checkpoint_id": workflow_checkpoint_id, + "openai_api_key": request.headers.get('Openai-Api-Key'), + "openai_base_url": request.headers.get('Openai-Base-Url'), + # Optional Workflow LLM settings + "workflow_llm_api_key": request.headers.get('Workflow-LLM-Api-Key'), + "workflow_llm_base_url": request.headers.get('Workflow-LLM-Base-Url'), + "workflow_llm_model": request.headers.get('Workflow-LLM-Model'), + "model_select": next((x['data'][0] for x in ext if x['type'] == 'model_select' and x.get('data')), None) if ext else None + } + + # Set request context (isolation per session) + set_request_context(session_id, workflow_checkpoint_id, config) + + # If workflow_data provided (debug scenario), save it so tools like run_workflow/get_current_workflow can access + try: + if workflow_data: + version_id = save_workflow_data( + session_id=session_id, + workflow_data=workflow_data, + workflow_data_ui=None, + attributes={ + "checkpoint_type": "debug_start_single", + "description": "Initial workflow save for single-agent invoke", + "timestamp": time.time() + } + ) + log.info(f"[Single] Saved incoming workflow_data, version ID: {version_id}") + except Exception as save_err: + log.error(f"[Single] Failed to save workflow_data: {save_err}") + + processed_images = [] + if images and len(images) > 0: + log.info(f"Note: Received {len(images)} images in legacy format, but using OpenAI message format instead") + + # Ensure messages exist and start with a user message (required by agents streaming) + openai_messages = historical_messages if isinstance(historical_messages, list) else [] + needs_user_head = (len(openai_messages) == 0) or (openai_messages[0].get('role') != 'user') + if needs_user_head: + # If workflow_data present, default to a debug instruction; otherwise a generic chat starter + default_user_text = "Validate and debug this ComfyUI workflow." if workflow_data else "Start the conversation." + openai_messages = [{"role": "user", "content": default_user_text}] + openai_messages + + try: + accumulated_text = "" + ext_data = None + finished = True + previous_text_length = 0 + + log.info(f"[Single] config: {config}") + + async for result in all_in_one_invoke(openai_messages, None): + if isinstance(result, tuple) and len(result) == 2: + text, ext_with_finished = result + if text: + accumulated_text = text + if ext_with_finished: + ext_data = ext_with_finished.get("data") + finished = ext_with_finished.get("finished", True) + log.info(f"[Single] -- Received ext: {bool(ext_data)}, finished: {finished}") + else: + text_chunk = result + if text_chunk: + accumulated_text += text_chunk + log.info(f"[Single] -- Received text chunk: '{text_chunk}', total length: {len(accumulated_text)}") + + if accumulated_text and len(accumulated_text) > previous_text_length: + chat_response = ChatResponse( + session_id=session_id, + text=accumulated_text, + finished=False, + type="message", + format="markdown", + ext=None + ) + await response.write(json.dumps(chat_response).encode() + b"\n") + previous_text_length = len(accumulated_text) + await asyncio.sleep(0.01) + + final_response = ChatResponse( + session_id=session_id, + text=accumulated_text, + finished=finished, + type="message", + format="markdown", + ext=ext_data + ) + await response.write(json.dumps(final_response).encode() + b"\n") + + except Exception as e: + log.error(f"Error in invoke_chat_single: {str(e)}") + error_response = ChatResponse( + session_id=session_id, + text=f"I apologize, but an error occurred: {str(e)}", + finished=True, + type="message", + format="text", + ext=None + ) + await response.write(json.dumps(error_response).encode() + b"\n") + + await response.write_eof() + return response + + +@server.PromptServer.instance.routes.post("/api/debug-agent-single") +async def invoke_debug_single(request): + """ + Single-agent Debug endpoint for analyzing ComfyUI workflow errors + """ + log.info("Received debug-agent-single request") + + extract_and_store_api_key(request) + req_json = await request.json() + + response = web.StreamResponse( + status=200, + reason='OK', + headers={ + 'Content-Type': 'application/json', + 'X-Content-Type-Options': 'nosniff' + } + ) + await response.prepare(request) + + session_id = req_json.get('session_id') + workflow_data = req_json.get('workflow_data') + + config = { + "session_id": session_id, + "model": "gemini-2.5-flash", + "openai_api_key": request.headers.get('Openai-Api-Key'), + "openai_base_url": request.headers.get('Openai-Base-Url'), + "workflow_llm_api_key": request.headers.get('Workflow-LLM-Api-Key'), + "workflow_llm_base_url": request.headers.get('Workflow-LLM-Base-Url'), + "workflow_llm_model": request.headers.get('Workflow-LLM-Model'), + } + + language = request.headers.get('Accept-Language', 'en') + set_language(language) + + set_request_context(session_id, None, config) + + log.info(f"Single Debug agent config: {config}") + log.info(f"Session ID: {session_id}") + log.info(f"Workflow nodes: {list(workflow_data.keys()) if workflow_data else 'None'}") + + try: + accumulated_text = "" + final_ext_data = None + finished = False + + async for result in debug_workflow_errors_single(workflow_data): + if isinstance(result, tuple) and len(result) == 2: + text, ext = result + if text: + accumulated_text = text + if ext: + if isinstance(ext, dict) and "data" in ext and "finished" in ext: + final_ext_data = ext["data"] + finished = ext["finished"] + + has_realtime_ext = False + if final_ext_data: + for ext_item in final_ext_data: + if ext_item.get("type") in ["workflow_update", "param_update"]: + has_realtime_ext = True + break + if has_realtime_ext or finished: + chat_response = ChatResponse( + session_id=session_id, + text=accumulated_text, + finished=finished, + type="message", + format="markdown", + ext=final_ext_data + ) + await response.write(json.dumps(chat_response).encode() + b"\n") + elif not finished: + chat_response = ChatResponse( + session_id=session_id, + text=accumulated_text, + finished=False, + type="message", + format="markdown", + ext=None + ) + await response.write(json.dumps(chat_response).encode() + b"\n") + else: + final_ext_data = ext + finished = False + chat_response = ChatResponse( + session_id=session_id, + text=accumulated_text, + finished=False, + type="message", + format="markdown", + ext=ext + ) + await response.write(json.dumps(chat_response).encode() + b"\n") + else: + chat_response = ChatResponse( + session_id=session_id, + text=accumulated_text, + finished=False, + type="message", + format="markdown", + ext=None + ) + await response.write(json.dumps(chat_response).encode() + b"\n") + await asyncio.sleep(0.01) + + final_response = ChatResponse( + session_id=session_id, + text=accumulated_text, + finished=True, + type="message", + format="markdown", + ext=final_ext_data if final_ext_data else [{"type": "debug_complete", "data": {"status": "completed"}}] + ) + + if workflow_data and accumulated_text: + try: + current_session_id = get_session_id() + checkpoint_id = save_workflow_data( + session_id=current_session_id, + workflow_data=workflow_data, + workflow_data_ui=None, + attributes={ + "checkpoint_type": "debug_complete", + "description": "Workflow state after single-agent debug completion", + "timestamp": time.time() + } + ) + if final_response["ext"]: + final_response["ext"].append({ + "type": "debug_checkpoint", + "data": {"checkpoint_id": checkpoint_id, "checkpoint_type": "debug_complete"} + }) + else: + final_response["ext"] = [{ + "type": "debug_checkpoint", + "data": {"checkpoint_id": checkpoint_id, "checkpoint_type": "debug_complete"} + }] + log.info(f"Single Debug completion checkpoint saved with ID: {checkpoint_id}") + except Exception as checkpoint_error: + log.error(f"Failed to save single debug completion checkpoint: {checkpoint_error}") + + await response.write(json.dumps(final_response).encode() + b"\n") + log.info("Single Debug agent processing complete") + + except Exception as e: + log.error(f"Error in single debug agent: {str(e)}") + import traceback + traceback.print_exc() + error_response = ChatResponse( + session_id=session_id, + text=f"❌ Single Debug agent error: {str(e)}", + finished=True, + type="message", + format="text", + ext=[{"type": "error", "data": {"error": str(e)}}] + ) + await response.write(json.dumps(error_response).encode() + b"\n") + + await response.write_eof() + return response @server.PromptServer.instance.routes.post("/api/save-workflow-checkpoint") async def save_workflow_checkpoint(request): """ @@ -723,354 +922,4 @@ async def update_workflow_ui(request): return web.json_response({ "success": False, "message": f"Failed to update workflow UI data: {str(e)}" - }) - -@server.PromptServer.instance.routes.post("/api/download-model") -async def download_model(request): - """ - Download model from ModelScope using SDK - """ - log.info("Received download-model request") - req_json = await request.json() - - try: - id = req_json.get('id') - model_id = req_json.get('model_id') - model_type = req_json.get('model_type') - dest_dir = req_json.get('dest_dir') - - # 验证必需参数 - if not id: - return web.json_response({ - "success": False, - "message": "Missing required parameter: id" - }) - - if not model_id: - return web.json_response({ - "success": False, - "message": "Missing required parameter: model_id" - }) - - if not model_type: - return web.json_response({ - "success": False, - "message": "Missing required parameter: model_type" - }) - - log.info(f"Downloading model: {model_id} (type: {model_type})") - - # 生成下载ID - download_id = generate_download_id() - - # 计算目标目录：优先使用传入的dest_dir，否则使用ComfyUI的models目录下对应类型 - resolved_dest_dir = None - if dest_dir: - resolved_dest_dir = os.path.abspath(os.path.expanduser(f"models/{dest_dir}")) - else: - try: - model_type_paths = folder_paths.get_folder_paths(model_type) - resolved_dest_dir = model_type_paths[0] if model_type_paths else os.path.join(folder_paths.models_dir, model_type) - except Exception: - resolved_dest_dir = os.path.join(folder_paths.models_dir, model_type) - - # 创建进度回调 - progress_callback = DownloadProgressCallback( - id=id, - filename=f"{model_id}.{model_type}", - file_size=0, # 实际大小会在下载过程中获取 - download_id=download_id - ) - - # 启动下载任务（异步执行） - async def download_task(): - try: - # 调用下载方法 - 使用snapshot_download - from modelscope import snapshot_download - - # 创建进度回调包装器 - 实现ModelScope期望的接口 - class ProgressWrapper: - """Factory that returns a per-file progress object with update/end.""" - def __init__(self, download_id: str): - self.download_id = download_id - - def __call__(self, file_name: str, file_size: int): - # Create a per-file progress tracker expected by ModelScope - download_id = self.download_id - - class _PerFileProgress: - def __init__(self, fname: str, fsize: int): - self.file_name = fname - self.file_size = max(int(fsize or 0), 0) - self.progress = 0 - self.last_update_time = time.time() - self.last_downloaded = 0 - with download_lock: - if download_id in download_progress: - # If unknown size, keep 0 to avoid div-by-zero - download_progress[download_id]["file_size"] = self.file_size - - def update(self, size: int): - try: - self.progress += int(size or 0) - now = time.time() - # Update global progress - with download_lock: - if download_id in download_progress: - dp = download_progress[download_id] - dp["progress"] = self.progress - if self.file_size > 0: - dp["percentage"] = round(self.progress * 100.0 / self.file_size, 2) - # speed - elapsed = max(now - self.last_update_time, 1e-6) - speed = (self.progress - self.last_downloaded) / elapsed - dp["speed"] = round(speed, 2) - self.last_update_time = now - self.last_downloaded = self.progress - except Exception as e: - log.error(f"Error in progress update: {e}") - - def end(self): - # Called by modelscope when a file finishes - with download_lock: - if download_id in download_progress: - dp = download_progress[download_id] - if self.file_size > 0: - dp["progress"] = self.file_size - dp["percentage"] = 100.0 - - return _PerFileProgress(file_name, file_size) - - progress_wrapper = ProgressWrapper(download_id) - - # 添加调试日志 - log.info(f"Starting download with progress wrapper: {download_id}") - - # 在线程中执行阻塞的下载，避免阻塞事件循环 - from functools import partial - local_dir = await asyncio.to_thread( - partial( - snapshot_download, - model_id=model_id, - cache_dir=resolved_dest_dir, - progress_callbacks=[progress_wrapper] - ) - ) - - # 下载完成 - progress_callback.end(success=True) - log.info(f"Model downloaded successfully to: {local_dir}") - - # 下载后遍历目录，将所有重要权重/资源文件移动到最外层（与目录同级，即 resolved_dest_dir） - try: - moved_count = 0 - allowed_exts = { - ".safetensors", ".ckpt", ".pt", ".pth", ".bin" - # ".msgpack", ".json", ".yaml", ".yml", ".toml", ".png", ".onnx" - } - for root, dirs, files in os.walk(local_dir): - for name in files: - ext = os.path.splitext(name)[1].lower() - if ext in allowed_exts: - src_path = os.path.join(root, name) - target_dir = resolved_dest_dir - os.makedirs(target_dir, exist_ok=True) - target_path = os.path.join(target_dir, name) - # 如果已经在目标目录则跳过 - if os.path.abspath(os.path.dirname(src_path)) == os.path.abspath(target_dir): - continue - # 处理重名情况：自动追加 _1, _2 ... - if os.path.exists(target_path): - base, ext_real = os.path.splitext(name) - idx = 1 - while True: - candidate = f"{base}_{idx}{ext_real}" - candidate_path = os.path.join(target_dir, candidate) - if not os.path.exists(candidate_path): - target_path = candidate_path - break - idx += 1 - shutil.move(src_path, target_path) - moved_count += 1 - log.info(f"Moved {moved_count} files with extensions {sorted(list(allowed_exts))} to: {resolved_dest_dir}") - except Exception as move_err: - log.error(f"Post-download move failed: {move_err}") - - except Exception as e: - progress_callback.fail(str(e)) - log.error(f"Download failed: {str(e)}") - - # 启动异步下载任务 - asyncio.create_task(download_task()) - - return web.json_response({ - "success": True, - "data": { - "download_id": download_id, - "id": id, - "model_id": model_id, - "model_type": model_type, - "dest_dir": resolved_dest_dir, - "status": "started" - }, - "message": f"Download started for model '{model_id}'" - }) - - except ImportError as e: - log.error(f"ModelScope SDK not installed: {str(e)}") - return web.json_response({ - "success": False, - "message": "ModelScope SDK not installed. Please install with: pip install modelscope" - }) - - except Exception as e: - log.error(f"Error starting download: {str(e)}") - import traceback - traceback.print_exc() - return web.json_response({ - "success": False, - "message": f"Failed to start download: {str(e)}" - }) - -@server.PromptServer.instance.routes.get("/api/download-progress/{download_id}") -async def get_download_progress(request): - """ - Get download progress by download ID - """ - download_id = request.match_info.get('download_id') - - if not download_id: - return web.json_response({ - "success": False, - "message": "Missing download_id parameter" - }) - - with download_lock: - progress_info = download_progress.get(download_id) - - if not progress_info: - return web.json_response({ - "success": False, - "message": f"Download ID {download_id} not found" - }) - - return web.json_response({ - "success": True, - "data": progress_info, - "message": "Download progress retrieved successfully" - }) - -@server.PromptServer.instance.routes.get("/api/download-progress") -async def list_downloads(request): - """ - List all active downloads - """ - with download_lock: - downloads = list(download_progress.keys()) - - return web.json_response({ - "success": True, - "data": { - "downloads": downloads, - "count": len(downloads) - }, - "message": "Download list retrieved successfully" - }) - -@server.PromptServer.instance.routes.delete("/api/download-progress/{download_id}") -async def clear_download_progress(request): - """ - Clear download progress record (for cleanup) - """ - download_id = request.match_info.get('download_id') - - if not download_id: - return web.json_response({ - "success": False, - "message": "Missing download_id parameter" - }) - - with download_lock: - if download_id in download_progress: - del download_progress[download_id] - return web.json_response({ - "success": True, - "message": f"Download progress {download_id} cleared successfully" - }) - else: - return web.json_response({ - "success": False, - "message": f"Download ID {download_id} not found" - }) - -@server.PromptServer.instance.routes.get("/api/model-searchs") -async def model_suggests(request): - """ - Get model search list by keyword - """ - log.info("Received model-search request") - try: - keyword = request.query.get('keyword') - - if not keyword: - return web.json_response({ - "success": False, - "message": "Missing required parameter: keyword" - }) - - # 创建ModelScope网关实例 - gateway = ModelScopeGateway() - - suggests = gateway.search(name=keyword) - - list = suggests["data"] if suggests.get("data") else [] - - return web.json_response({ - "success": True, - "data": { - "searchs": list, - "total": len(list) - }, - "message": f"Get searchs successfully" - }) - - except ImportError as e: - log.error(f"ModelScope SDK not installed: {str(e)}") - return web.json_response({ - "success": False, - "message": "ModelScope SDK not installed. Please install with: pip install modelscope" - }) - - except Exception as e: - log.error(f"Error get model searchs: {str(e)}") - import traceback - traceback.print_exc() - return web.json_response({ - "success": False, - "message": f"Get model searchs failed: {str(e)}" - }) - -@server.PromptServer.instance.routes.get("/api/model-paths") -async def model_paths(request): - """ - Get model paths by type - """ - log.info("Received model-paths request") - try: - model_paths = list(folder_paths.folder_names_and_paths.keys()) - return web.json_response({ - "success": True, - "data": { - "paths": model_paths, - }, - "message": f"Get paths successfully" - }) - - except Exception as e: - log.error(f"Error get model path: {str(e)}") - import traceback - traceback.print_exc() - return web.json_response({ - "success": False, - "message": f"Get model failed: {str(e)}" }) \ No newline at end of file diff --git a/backend/controller/model_api.py b/backend/controller/model_api.py new file mode 100644 index 0000000..bfebc96 --- /dev/null +++ b/backend/controller/model_api.py @@ -0,0 +1,468 @@ +import threading +import asyncio +import time +import server +from aiohttp import web +import os +import shutil +from ..utils.logger import log +from ..utils.modelscope_gateway import ModelScopeGateway +import folder_paths + + +# 全局下载进度存储 +download_progress = {} +download_lock = threading.Lock() + +# 下载进度回调类 +class DownloadProgressCallback: + def __init__(self, id: str, filename: str, file_size: int, download_id: str): + self.id = id + self.filename = filename + self.file_size = file_size + self.download_id = download_id + self.progress = 0 + self.status = "downloading" # downloading, completed, failed + self.error_message = None + self.start_time = time.time() + + # 初始化进度记录 + with download_lock: + download_progress[download_id] = { + "id": id, + "filename": filename, + "file_size": file_size, + "progress": 0, + "percentage": 0.0, + "status": "downloading", + "start_time": self.start_time, + "estimated_time": None, + "speed": 0.0, + "error_message": None + } + + def update(self, size: int): + """更新下载进度""" + self.progress += size + current_time = time.time() + elapsed_time = current_time - self.start_time + + # 计算下载速度和预估时间 + if elapsed_time > 0: + speed = self.progress / elapsed_time # bytes per second + if speed > 0 and self.progress < self.file_size: + remaining_bytes = self.file_size - self.progress + estimated_time = remaining_bytes / speed + else: + estimated_time = None + else: + speed = 0.0 + estimated_time = None + + percentage = (self.progress / self.file_size) * 100 if self.file_size > 0 else 0 + + # 更新全局进度 + with download_lock: + if self.download_id in download_progress: + # 直接更新字典的值，而不是调用update方法 + progress_dict = download_progress[self.download_id] + progress_dict["progress"] = self.progress + progress_dict["percentage"] = round(percentage, 2) + progress_dict["speed"] = round(speed, 2) + progress_dict["estimated_time"] = round(estimated_time, 2) if estimated_time else None + + def end(self, success: bool = True, error_message: str = None): + """下载结束回调""" + current_time = time.time() + total_time = current_time - self.start_time + + if success: + self.status = "completed" + # 验证下载完整性 + assert self.progress == self.file_size, f"Download incomplete: {self.progress}/{self.file_size}" + else: + self.status = "failed" + self.error_message = error_message + + # 更新最终状态 + with download_lock: + if self.download_id in download_progress: + # 直接更新字典的值，而不是调用update方法 + progress_dict = download_progress[self.download_id] + progress_dict["status"] = self.status + progress_dict["progress"] = self.file_size if success else self.progress + if self.file_size > 0: + progress_dict["percentage"] = 100.0 if success else (self.progress / self.file_size) * 100 + else: + progress_dict["percentage"] = 0.0 + progress_dict["total_time"] = round(total_time, 2) + progress_dict["error_message"] = self.error_message + + def fail(self, error_message: str): + """下载失败回调""" + self.end(success=False, error_message=error_message) + + +# 生成唯一下载ID +def generate_download_id() -> str: + import uuid + return str(uuid.uuid4()) + + +@server.PromptServer.instance.routes.post("/api/download-model") +async def download_model(request): + """ + Download model from ModelScope using SDK + """ + log.info("Received download-model request") + req_json = await request.json() + + try: + id = req_json.get('id') + model_id = req_json.get('model_id') + model_type = req_json.get('model_type') + dest_dir = req_json.get('dest_dir') + + # 验证必需参数 + if not id: + return web.json_response({ + "success": False, + "message": "Missing required parameter: id" + }) + + if not model_id: + return web.json_response({ + "success": False, + "message": "Missing required parameter: model_id" + }) + + if not model_type: + return web.json_response({ + "success": False, + "message": "Missing required parameter: model_type" + }) + + log.info(f"Downloading model: {model_id} (type: {model_type})") + + # 生成下载ID + download_id = generate_download_id() + + # 计算目标目录：优先使用传入的dest_dir，否则使用ComfyUI的models目录下对应类型 + resolved_dest_dir = None + if dest_dir: + resolved_dest_dir = os.path.abspath(os.path.expanduser(f"models/{dest_dir}")) + else: + try: + model_type_paths = folder_paths.get_folder_paths(model_type) + resolved_dest_dir = model_type_paths[0] if model_type_paths else os.path.join(folder_paths.models_dir, + model_type) + except Exception: + resolved_dest_dir = os.path.join(folder_paths.models_dir, model_type) + + # 创建进度回调 + progress_callback = DownloadProgressCallback( + id=id, + filename=f"{model_id}.{model_type}", + file_size=0, # 实际大小会在下载过程中获取 + download_id=download_id + ) + + # 启动下载任务（异步执行） + async def download_task(): + try: + # 调用下载方法 - 使用snapshot_download + from modelscope import snapshot_download + + # 创建进度回调包装器 - 实现ModelScope期望的接口 + class ProgressWrapper: + """Factory that returns a per-file progress object with update/end.""" + + def __init__(self, download_id: str): + self.download_id = download_id + + def __call__(self, file_name: str, file_size: int): + # Create a per-file progress tracker expected by ModelScope + download_id = self.download_id + + class _PerFileProgress: + def __init__(self, fname: str, fsize: int): + self.file_name = fname + self.file_size = max(int(fsize or 0), 0) + self.progress = 0 + self.last_update_time = time.time() + self.last_downloaded = 0 + with download_lock: + if download_id in download_progress: + # If unknown size, keep 0 to avoid div-by-zero + download_progress[download_id]["file_size"] = self.file_size + + def update(self, size: int): + try: + self.progress += int(size or 0) + now = time.time() + # Update global progress + with download_lock: + if download_id in download_progress: + dp = download_progress[download_id] + dp["progress"] = self.progress + if self.file_size > 0: + dp["percentage"] = round(self.progress * 100.0 / self.file_size, 2) + # speed + elapsed = max(now - self.last_update_time, 1e-6) + speed = (self.progress - self.last_downloaded) / elapsed + dp["speed"] = round(speed, 2) + self.last_update_time = now + self.last_downloaded = self.progress + except Exception as e: + log.error(f"Error in progress update: {e}") + + def end(self): + # Called by modelscope when a file finishes + with download_lock: + if download_id in download_progress: + dp = download_progress[download_id] + if self.file_size > 0: + dp["progress"] = self.file_size + dp["percentage"] = 100.0 + + return _PerFileProgress(file_name, file_size) + + progress_wrapper = ProgressWrapper(download_id) + + # 添加调试日志 + log.info(f"Starting download with progress wrapper: {download_id}") + + # 在线程中执行阻塞的下载，避免阻塞事件循环 + from functools import partial + local_dir = await asyncio.to_thread( + partial( + snapshot_download, + model_id=model_id, + cache_dir=resolved_dest_dir, + progress_callbacks=[progress_wrapper] + ) + ) + + # 下载完成 + progress_callback.end(success=True) + log.info(f"Model downloaded successfully to: {local_dir}") + + # 下载后遍历目录，将所有重要权重/资源文件移动到最外层（与目录同级，即 resolved_dest_dir） + try: + moved_count = 0 + allowed_exts = { + ".safetensors", ".ckpt", ".pt", ".pth", ".bin" + # ".msgpack", ".json", ".yaml", ".yml", ".toml", ".png", ".onnx" + } + for root, dirs, files in os.walk(local_dir): + for name in files: + ext = os.path.splitext(name)[1].lower() + if ext in allowed_exts: + src_path = os.path.join(root, name) + target_dir = resolved_dest_dir + os.makedirs(target_dir, exist_ok=True) + target_path = os.path.join(target_dir, name) + # 如果已经在目标目录则跳过 + if os.path.abspath(os.path.dirname(src_path)) == os.path.abspath(target_dir): + continue + # 处理重名情况：自动追加 _1, _2 ... + if os.path.exists(target_path): + base, ext_real = os.path.splitext(name) + idx = 1 + while True: + candidate = f"{base}_{idx}{ext_real}" + candidate_path = os.path.join(target_dir, candidate) + if not os.path.exists(candidate_path): + target_path = candidate_path + break + idx += 1 + shutil.move(src_path, target_path) + moved_count += 1 + log.info( + f"Moved {moved_count} files with extensions {sorted(list(allowed_exts))} to: {resolved_dest_dir}") + except Exception as move_err: + log.error(f"Post-download move failed: {move_err}") + + except Exception as e: + progress_callback.fail(str(e)) + log.error(f"Download failed: {str(e)}") + + # 启动异步下载任务 + asyncio.create_task(download_task()) + + return web.json_response({ + "success": True, + "data": { + "download_id": download_id, + "id": id, + "model_id": model_id, + "model_type": model_type, + "dest_dir": resolved_dest_dir, + "status": "started" + }, + "message": f"Download started for model '{model_id}'" + }) + + except ImportError as e: + log.error(f"ModelScope SDK not installed: {str(e)}") + return web.json_response({ + "success": False, + "message": "ModelScope SDK not installed. Please install with: pip install modelscope" + }) + + except Exception as e: + log.error(f"Error starting download: {str(e)}") + import traceback + traceback.print_exc() + return web.json_response({ + "success": False, + "message": f"Failed to start download: {str(e)}" + }) + + +@server.PromptServer.instance.routes.get("/api/download-progress/{download_id}") +async def get_download_progress(request): + """ + Get download progress by download ID + """ + download_id = request.match_info.get('download_id') + + if not download_id: + return web.json_response({ + "success": False, + "message": "Missing download_id parameter" + }) + + with download_lock: + progress_info = download_progress.get(download_id) + + if not progress_info: + return web.json_response({ + "success": False, + "message": f"Download ID {download_id} not found" + }) + + return web.json_response({ + "success": True, + "data": progress_info, + "message": "Download progress retrieved successfully" + }) + + +@server.PromptServer.instance.routes.get("/api/download-progress") +async def list_downloads(request): + """ + List all active downloads + """ + with download_lock: + downloads = list(download_progress.keys()) + + return web.json_response({ + "success": True, + "data": { + "downloads": downloads, + "count": len(downloads) + }, + "message": "Download list retrieved successfully" + }) + + +@server.PromptServer.instance.routes.delete("/api/download-progress/{download_id}") +async def clear_download_progress(request): + """ + Clear download progress record (for cleanup) + """ + download_id = request.match_info.get('download_id') + + if not download_id: + return web.json_response({ + "success": False, + "message": "Missing download_id parameter" + }) + + with download_lock: + if download_id in download_progress: + del download_progress[download_id] + return web.json_response({ + "success": True, + "message": f"Download progress {download_id} cleared successfully" + }) + else: + return web.json_response({ + "success": False, + "message": f"Download ID {download_id} not found" + }) + + +@server.PromptServer.instance.routes.get("/api/model-searchs") +async def model_suggests(request): + """ + Get model search list by keyword + """ + log.info("Received model-search request") + try: + keyword = request.query.get('keyword') + + if not keyword: + return web.json_response({ + "success": False, + "message": "Missing required parameter: keyword" + }) + + # 创建ModelScope网关实例 + gateway = ModelScopeGateway() + + suggests = gateway.search(name=keyword) + + list = suggests["data"] if suggests.get("data") else [] + + return web.json_response({ + "success": True, + "data": { + "searchs": list, + "total": len(list) + }, + "message": f"Get searchs successfully" + }) + + except ImportError as e: + log.error(f"ModelScope SDK not installed: {str(e)}") + return web.json_response({ + "success": False, + "message": "ModelScope SDK not installed. Please install with: pip install modelscope" + }) + + except Exception as e: + log.error(f"Error get model searchs: {str(e)}") + import traceback + traceback.print_exc() + return web.json_response({ + "success": False, + "message": f"Get model searchs failed: {str(e)}" + }) + + +@server.PromptServer.instance.routes.get("/api/model-paths") +async def model_paths(request): + """ + Get model paths by type + """ + log.info("Received model-paths request") + try: + model_paths = list(folder_paths.folder_names_and_paths.keys()) + return web.json_response({ + "success": True, + "data": { + "paths": model_paths, + }, + "message": f"Get paths successfully" + }) + + except Exception as e: + log.error(f"Error get model path: {str(e)}") + import traceback + traceback.print_exc() + return web.json_response({ + "success": False, + "message": f"Get model failed: {str(e)}" + }) \ No newline at end of file diff --git a/backend/service/all_in_one_agent.py b/backend/service/all_in_one_agent.py new file mode 100644 index 0000000..f26031a --- /dev/null +++ b/backend/service/all_in_one_agent.py @@ -0,0 +1,254 @@ +''' +All-in-one Single Agent for Chat + Debug + Rewrite (streaming) +''' +from typing import List, Dict, Any, Optional +import asyncio +import traceback + +from ..utils.globals import BACKEND_BASE_URL, get_comfyui_copilot_api_key, WORKFLOW_MODEL_NAME +from ..utils.logger import log +from ..utils.request_context import get_session_id, get_config +from ..utils.key_utils import workflow_config_adapt + +# OpenAI Agents SDK imports +try: + from agents._config import set_default_openai_api + from agents.items import ItemHelpers + from agents.mcp import MCPServerSse + from agents.run import Runner + from agents.tracing import set_tracing_disabled +except Exception: + raise ImportError( + "Detected incorrect or missing 'agents' package while loading All-in-one agent. " + "Please install 'openai-agents'. Commands:\n" + " python -m pip uninstall -y agents gym tensorflow\n" + " python -m pip install -U openai-agents" + ) + +from openai.types.responses import ResponseTextDeltaEvent + +# Import tools from existing modules +from .debug_agent import run_workflow, analyze_error_type, save_current_workflow +from .link_agent_tools import analyze_missing_connections, apply_connection_fixes +from .workflow_rewrite_tools import get_current_workflow, get_node_info, update_workflow, remove_node +from .parameter_tools import ( + find_matching_parameter_value, + get_model_files, + suggest_model_download, + update_workflow_parameter, +) + +from ..agent_factory import create_agent + + +async def all_in_one_invoke(messages: List[Dict[str, Any]], images: Optional[List[Any]] = None): + """ + Invoke a single agent that can handle chat, debug, and rewrite in one place. + + Yields tuples of (accumulated_text, {"data": ext_or_None, "finished": bool}). + """ + try: + session_id = get_session_id() + config = get_config() + if not session_id: + raise ValueError("No session_id found in request context") + if not config: + raise ValueError("No config found in request context") + + # Adapt config if needed (keys, endpoints, etc.) + config = workflow_config_adapt(config) + + # Prepare MCP servers to expose external tools (recall_workflow, gen_workflow, searches, etc.) + mcp_server = MCPServerSse( + params={ + "url": BACKEND_BASE_URL + "/mcp-server/mcp", + "timeout": 300.0, + "headers": {"X-Session-Id": session_id, "Authorization": f"Bearer {get_comfyui_copilot_api_key()}"} + }, + cache_tools_list=True, + client_session_timeout_seconds=300.0 + ) + + bing_server = MCPServerSse( + params={ + "url": "https://mcp.api-inference.modelscope.net/8c9fe550938e4f/sse", + "timeout": 300.0, + "headers": {"X-Session-Id": session_id, "Authorization": f"Bearer {get_comfyui_copilot_api_key()}"} + }, + cache_tools_list=True, + client_session_timeout_seconds=300.0 + ) + + async with mcp_server, bing_server: + agent = create_agent( + name="ComfyUI-AllInOne", + model=WORKFLOW_MODEL_NAME, + instructions=( + "You are a single powerful assistant for ComfyUI.\n\n" + "- Respond in the user's language.\n" + "- For workflow generation requests, ALWAYS call recall_workflow first, then gen_workflow, and merge results.\n" + "- For debug intents (workflow failed, node error): validate with run_workflow(), analyze with analyze_error_type(), then use link/parameter/rewrite tools accordingly and re-validate.\n" + "- For rewrite intents (modify current canvas): use get_current_workflow(), get_node_info(), update_workflow(), remove_node(), and ensure connections/types are valid.\n" + "- Prefer concise streaming updates.\n" + ), + # Bind ALL local tools + tools=[ + # Debug/validation + run_workflow, analyze_error_type, save_current_workflow, + # Link/connectivity + analyze_missing_connections, apply_connection_fixes, + # Rewrite/structure + get_current_workflow, get_node_info, update_workflow, remove_node, + # Parameters/models + find_matching_parameter_value, get_model_files, suggest_model_download, update_workflow_parameter, + ], + # Expose MCP tool servers for chat/search/workflow tools + mcp_servers=[mcp_server, bing_server], + config={ + "max_tokens": 8192, + **config + } + ) + + # Stream the session + agent_input = messages + result = Runner.run_streamed( + agent, + input=agent_input, + max_turns=30, + ) + + current_text = '' + last_yield_length = 0 + tool_results: Dict[str, Dict[str, Any]] = {} + tool_call_queue: List[str] = [] + workflow_update_ext = None # collect workflow_update/param_update ext from tools + + async for event in result.stream_events(): + if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent): + delta_text = event.data.delta + if delta_text: + current_text += delta_text + if len(current_text) > last_yield_length: + last_yield_length = len(current_text) + yield (current_text, None) + continue + + if event.type == "run_item_stream_event": + if event.item.type == "tool_call_item": + tool_name = getattr(event.item.raw_item, 'name', 'unknown_tool') + tool_call_queue.append(tool_name) + log.info(f"[AllInOne] Tool called: {tool_name}") + continue + + if event.item.type == "tool_call_output_item": + tool_output_data_str = str(event.item.output) + # Associate with FIFO queue + if tool_call_queue: + tool_name = tool_call_queue.pop(0) + else: + tool_name = 'unknown_tool' + + # Try to parse output as JSON and capture ext + try: + import json + tool_output_data = json.loads(tool_output_data_str) + if "ext" in tool_output_data and tool_output_data["ext"]: + tool_ext_items = tool_output_data["ext"] + # capture any workflow_update/param_update; keep entire list + for ext_item in tool_ext_items: + if ext_item.get("type") in ["workflow_update", "param_update"]: + workflow_update_ext = tool_ext_items + log.info(f"[AllInOne] Captured workflow-related ext from tool '{tool_name}'") + break + + if "text" in tool_output_data and tool_output_data.get('text'): + parsed_output = json.loads(tool_output_data['text']) + if isinstance(parsed_output, dict): + answer = parsed_output.get("answer") + data = parsed_output.get("data") + tool_ext = parsed_output.get("ext") + else: + answer = None + data = parsed_output if isinstance(parsed_output, list) else None + tool_ext = None + + tool_results[tool_name] = { + "answer": answer, + "data": data, + "ext": tool_ext, + "content_dict": parsed_output + } + except Exception: + # Non-JSON output; store raw preview + tool_results[tool_name] = { + "answer": tool_output_data_str, + "data": None, + "ext": None, + "content_dict": None + } + continue + + # Post-stream processing: merge workflow tool results if present + ext = None + finished = True + + workflow_tools_found = [t for t in ["recall_workflow", "gen_workflow"] if t in tool_results] + if workflow_tools_found: + if "recall_workflow" in tool_results and "gen_workflow" in tool_results: + successful_workflows: List[Dict[str, Any]] = [] + recall_result = tool_results["recall_workflow"] + gen_result = tool_results["gen_workflow"] + if recall_result.get("data"): + successful_workflows.extend(recall_result["data"]) + if gen_result.get("data"): + # prioritize generated ones first + successful_workflows = list(gen_result["data"]) + successful_workflows + + # de-duplicate by id + seen_ids = set() + unique_workflows = [] + for wf in successful_workflows: + wf_id = wf.get('id') + if wf_id and wf_id not in seen_ids: + seen_ids.add(wf_id) + unique_workflows.append(wf) + elif not wf_id: + unique_workflows.append(wf) + + if unique_workflows: + ext = [{"type": "workflow", "data": unique_workflows}] + elif "gen_workflow" in tool_results: + gen_result = tool_results["gen_workflow"] + if gen_result.get("data"): + ext = [{"type": "workflow", "data": gen_result["data"]}] + else: + # only recall_workflow was called; keep unfinished to encourage gen_workflow next time + ext = None + finished = False + + # Include any workflow_update/param_update ext captured during tool calls + final_ext = ext + if workflow_update_ext: + if isinstance(workflow_update_ext, list): + final_ext = workflow_update_ext + (ext if ext else []) + else: + final_ext = [workflow_update_ext] + (ext if ext else []) + + ext_with_finished = {"data": final_ext, "finished": finished} + yield (current_text, ext_with_finished) + + except Exception as e: + log.error(f"Error in all_in_one_invoke: {str(e)}") + log.error(f"Traceback: {traceback.format_exc()}") + error_message = f"I apologize, but an error occurred while processing your request: {str(e)}" + yield (error_message, {"data": None, "finished": True}) + +''' +Author: ai-business-hql ai.bussiness.hql@gmail.com +Date: 2025-10-29 16:51:18 +LastEditors: ai-business-hql ai.bussiness.hql@gmail.com +LastEditTime: 2025-10-29 17:01:17 +FilePath: /ComfyUI-Copilot/backend/service/all_in_one_agent.py +Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE +''' diff --git a/backend/service/debug_agent.py b/backend/service/debug_agent.py index d9c5f09..ab36cec 100644 --- a/backend/service/debug_agent.py +++ b/backend/service/debug_agent.py @@ -1,6 +1,8 @@ ''' Debug Agent for ComfyUI Workflow Error Analysis ''' +import json +from typing import Dict, Any from ..utils.key_utils import workflow_config_adapt from ..agent_factory import create_agent from agents.items import ItemHelpers @@ -706,6 +708,200 @@ Start by validating the workflow to see its current state.""", yield (error_message, ext_with_finished) +async def debug_workflow_errors_single(workflow_data: Dict[str, Any]): + """ + Analyze and debug workflow errors using a single-agent architecture. + The single agent has access to all tools used across the multi-agent system + and iteratively validates, analyzes, fixes, and re-validates until success or limits. + + Args: + workflow_data: Current workflow data from app.graphToPrompt() + + Yields: + tuple: (text, ext) where text is accumulated text and ext is structured data + """ + try: + session_id = get_session_id() + config = get_config() + config = workflow_config_adapt(config) + + if not session_id: + session_id = str(uuid.uuid4()) + + # Save initial workflow snapshot + log.info(f"[SingleAgent] Saving workflow data for session {session_id}") + save_result = save_workflow_data( + session_id, + workflow_data, + attributes={"action": "debug_start_single", "description": "Initial workflow save for single-agent debugging"} + ) + log.info(f"[SingleAgent] Workflow saved with version ID: {save_result}") + + single_agent = create_agent( + name="ComfyUI-Debug-SingleAgent", + instructions=f""" +You are a single ComfyUI workflow debugging expert with access to all specialized tools. + +Your iterative loop (up to 10 cycles): +1) Validate workflow using run_workflow() +2) If errors: analyze with analyze_error_type() and decide fix strategy + - Connection errors: use analyze_missing_connections() then apply_connection_fixes() + - Parameter errors: use find_matching_parameter_value(), get_model_files(), update_workflow_parameter() + • For model downloads required: use suggest_model_download() and STOP (final response) + - Structural issues: use get_current_workflow(), get_node_info(), update_workflow() +3) After any fix, immediately re-run run_workflow() to verify +4) Repeat until validation succeeds or loop limit reached + +Guidelines: +- Be concise and stream updates during the process +- Prefer high-confidence fixes first; add nodes only when necessary +- Do not modify unrelated parameters +- Language: {get_language()} +""", + model=WORKFLOW_MODEL_NAME, + tools=[ + # Coordinator tools + run_workflow, analyze_error_type, save_current_workflow, + # Link/structure tools + analyze_missing_connections, apply_connection_fixes, get_current_workflow, get_node_info, update_workflow, + # Parameter tools + find_matching_parameter_value, get_model_files, suggest_model_download, update_workflow_parameter, + ], + config={ + "max_tokens": 8192, + **config + } + ) + + messages = [{"role": "user", "content": "Validate and debug this ComfyUI workflow using the iterative loop until success."}] + + log.info(f"[SingleAgent] Starting validation process for session {session_id}") + result = Runner.run_streamed( + single_agent, + input=messages, + max_turns=30, + ) + + current_text = '' + current_agent = "ComfyUI-Debug-SingleAgent" + last_yielded_length = 0 + debug_events = [] + workflow_update_ext = None + + async for event in result.stream_events(): + if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent): + delta_text = event.data.delta + if delta_text: + current_text += delta_text + if len(current_text) > last_yielded_length: + last_yielded_length = len(current_text) + yield (current_text, None) + elif event.type == "run_item_stream_event": + item_updated = False + if event.item.type == "tool_call_item": + tool_name = getattr(event.item.raw_item, 'name', 'unknown_tool') + log.info(f"[SingleAgent] -- Tool called: {tool_name}") + current_text += f"\n\n⚙ *{current_agent} is using {tool_name}...*\n\n" + item_updated = True + debug_events.append({ + "type": "tool_call", + "tool": tool_name, + "agent": current_agent, + "timestamp": len(current_text) + }) + elif event.item.type == "tool_call_output_item": + output = str(event.item.output) + output_preview = output[:200] + "..." if len(output) > 200 else output + current_text += f"\n\n● *Tool execution completed*\n\n```\n{output_preview}\n```\n\n" + item_updated = True + try: + tool_output_json = json.loads(output) + if "ext" in tool_output_json and tool_output_json["ext"]: + for ext_item in tool_output_json["ext"]: + if ext_item.get("type") == "workflow_update" or ext_item.get("type") == "param_update": + workflow_update_ext = ext_item + log.info(f"[SingleAgent] -- Captured {ext_item.get('type')} ext, yielding immediately") + ext_with_finished = {"data": [ext_item], "finished": False} + yield (current_text, ext_with_finished) + break + except (json.JSONDecodeError, TypeError): + pass + debug_events.append({ + "type": "tool_result", + "output_preview": output_preview, + "agent": current_agent, + "timestamp": len(current_text) + }) + elif event.item.type == "message_output_item": + try: + message_content = ItemHelpers.text_message_output(event.item) + if message_content and message_content.strip(): + if message_content not in current_text: + current_text += f"\n\n{message_content}\n\n" + item_updated = True + debug_events.append({ + "type": "message_complete", + "content_length": len(message_content), + "agent": current_agent, + "timestamp": len(current_text) + }) + except Exception as e: + log.error(f"[SingleAgent] Error processing message output: {str(e)}") + if item_updated: + last_yielded_length = len(current_text) + yield (current_text, None) + + log.info("[SingleAgent] === Debug process complete ===") + + debug_completion_checkpoint_id = None + try: + current_workflow = get_workflow_data(session_id) + if current_workflow: + debug_completion_checkpoint_id = save_workflow_data( + session_id, + current_workflow, + workflow_data_ui=None, + attributes={ + "checkpoint_type": "debug_complete", + "description": "Workflow state after single-agent debug completion", + "action": "debug_complete_single", + "final_agent": current_agent + } + ) + log.info(f"[SingleAgent] Debug completion checkpoint saved with ID: {debug_completion_checkpoint_id}") + except Exception as checkpoint_error: + log.error(f"[SingleAgent] Failed to save debug completion checkpoint: {checkpoint_error}") + + debug_ext = [{ + "type": "debug_complete", + "data": { + "status": "completed", + "final_agent": current_agent, + "events": debug_events, + "total_events": len(debug_events) + } + }] + if debug_completion_checkpoint_id: + debug_ext.append({ + "type": "debug_checkpoint", + "data": { + "checkpoint_id": debug_completion_checkpoint_id, + "checkpoint_type": "debug_complete" + } + }) + final_ext = debug_ext + if workflow_update_ext: + final_ext = [workflow_update_ext] + debug_ext + log.info("[SingleAgent] -- Including workflow_update ext in final response") + ext_with_finished = {"data": final_ext, "finished": True} + yield (current_text, ext_with_finished) + except Exception as e: + log.error(f"[SingleAgent] Error in debug_workflow_errors_single: {str(e)}") + error_message = f"\n\n× Error occurred during single-agent debugging: {str(e)}\n\n" + ext_with_finished = {"finished": True} + yield (error_message, ext_with_finished) + + # Test function async def test_debug(): """Test the debug agent with a sample workflow""" diff --git a/dist/copilot_web/App-fluNqnYs.js b/dist/copilot_web/App-TDLVaCDl.js similarity index 87% rename from dist/copilot_web/App-fluNqnYs.js rename to dist/copilot_web/App-TDLVaCDl.js index 64cb8cc..e821a48 100644 --- a/dist/copilot_web/App-fluNqnYs.js +++ b/dist/copilot_web/App-TDLVaCDl.js @@ -1,7 +1,7 @@ function getImportPath(filename) { return `./${filename}`; } - const __vite__mapDeps=(i,m=__vite__mapDeps,d=(m.f||(m.f=["copilot_web/workflowChat-CTj3qusy.js","copilot_web/vendor-markdown-B4Av9P7l.js","copilot_web/vendor-react-ByKzdfMq.js","copilot_web/message-components-CzGdQ9TQ.js","copilot_web/input.js","copilot_web/assets/input-UcJyCCDl.css","copilot_web/fonts.css"].map(path => { + const __vite__mapDeps=(i,m=__vite__mapDeps,d=(m.f||(m.f=["copilot_web/workflowChat-DCaYfVyA.js","copilot_web/vendor-markdown-B4Av9P7l.js","copilot_web/vendor-react-ByKzdfMq.js","copilot_web/message-components-CDlGtWLl.js","copilot_web/input.js","copilot_web/assets/input-UcJyCCDl.css","copilot_web/fonts.css"].map(path => { const apiBase = window.comfyAPI?.api?.api?.api_base; if (apiBase) { // 有 API base 时，使用完整路径 @@ -11,4 +11,4 @@ function getImportPath(filename) { return `./${path}`; } }))))=>i.map(i=>d[i]); -import{_ as a}from"./input.js";import{j as o}from"./vendor-markdown-B4Av9P7l.js";import{r as t,R as i}from"./vendor-react-ByKzdfMq.js";import{C as n}from"./message-components-CzGdQ9TQ.js";const s={EXPLAIN_NODE:"copilot:explain-node",TOOLBOX_USAGE:"copilot:toolbox-usage",TOOLBOX_PARAMETERS:"copilot:toolbox-parameters",TOOLBOX_DOWNSTREAMNODES:"copilot:toolbox-downstreamnodes"},d=i.lazy(()=>a(()=>import(getImportPath("workflowChat-CTj3qusy.js")).then(e=>e.w),__vite__mapDeps([0,1,2,3,4,5,6])).then(e=>({default:e.default})));function c(){const[e,r]=t.useState(!1);return t.useEffect(()=>{const l=()=>{r(!0)};return window.addEventListener(s.EXPLAIN_NODE,l),()=>window.removeEventListener(s.EXPLAIN_NODE,l)},[]),o.jsx(n,{children:o.jsx("div",{className:"h-full w-full flex flex-col",children:o.jsx(t.Suspense,{fallback:o.jsx("div",{className:"h-full w-full flex items-center justify-center",children:"Loading..."}),children:o.jsx(d,{visible:!0,triggerUsage:e,onUsageTriggered:()=>r(!1)})})})})}const O=Object.freeze(Object.defineProperty({__proto__:null,default:c},Symbol.toStringTag,{value:"Module"}));export{O as A,s as C}; +import{_ as a}from"./input.js";import{j as o}from"./vendor-markdown-B4Av9P7l.js";import{r as t,R as i}from"./vendor-react-ByKzdfMq.js";import{C as n}from"./message-components-CDlGtWLl.js";const s={EXPLAIN_NODE:"copilot:explain-node",TOOLBOX_USAGE:"copilot:toolbox-usage",TOOLBOX_PARAMETERS:"copilot:toolbox-parameters",TOOLBOX_DOWNSTREAMNODES:"copilot:toolbox-downstreamnodes"},d=i.lazy(()=>a(()=>import(getImportPath("workflowChat-DCaYfVyA.js")).then(e=>e.w),__vite__mapDeps([0,1,2,3,4,5,6])).then(e=>({default:e.default})));function c(){const[e,r]=t.useState(!1);return t.useEffect(()=>{const l=()=>{r(!0)};return window.addEventListener(s.EXPLAIN_NODE,l),()=>window.removeEventListener(s.EXPLAIN_NODE,l)},[]),o.jsx(n,{children:o.jsx("div",{className:"h-full w-full flex flex-col",children:o.jsx(t.Suspense,{fallback:o.jsx("div",{className:"h-full w-full flex items-center justify-center",children:"Loading..."}),children:o.jsx(d,{visible:!0,triggerUsage:e,onUsageTriggered:()=>r(!1)})})})})}const O=Object.freeze(Object.defineProperty({__proto__:null,default:c},Symbol.toStringTag,{value:"Module"}));export{O as A,s as C}; diff --git a/dist/copilot_web/DebugGuide-BpkLL6XH.js b/dist/copilot_web/DebugGuide-RaSXIzC5.js similarity index 98% rename from dist/copilot_web/DebugGuide-BpkLL6XH.js rename to dist/copilot_web/DebugGuide-RaSXIzC5.js index 0498837..8605f8b 100644 --- a/dist/copilot_web/DebugGuide-BpkLL6XH.js +++ b/dist/copilot_web/DebugGuide-RaSXIzC5.js @@ -1,5 +1,5 @@ function getImportPath(filename) { return `./${filename}`; } - import{j as s}from"./vendor-markdown-B4Av9P7l.js";import{r as i}from"./vendor-react-ByKzdfMq.js";import{u as A,p as E,R as D,o as m,W as f,a as b}from"./message-components-CzGdQ9TQ.js";function P({content:h,name:k="Assistant",avatar:O,onAddMessage:y,onUpdateMessage:c,messageId:_}){const{dispatch:u,abortControllerRef:l}=A(),[d,w]=i.useState(!1),[T,G]=i.useState(""),[J,S]=i.useState(!1),[j,v]=i.useState(null),r=i.useMemo(()=>{try{return JSON.parse(h)}catch(e){return console.error("Failed to parse message content:",e),null}},[h]),x=i.useMemo(()=>{if(!r||!r.ext)return null;const e=r.ext.find(t=>t.type==="debug_start_checkpoint"||t.type==="debug_checkpoint"&&t.data?.checkpoint_type==="debug_start");return e&&e.data&&e.data.checkpoint_id?e.data.checkpoint_id:null},[r])||j,N=async()=>{if(d)return;const e=m(),t={id:e,role:"ai",content:JSON.stringify({text:`🔍 Starting workflow analysis... + import{j as s}from"./vendor-markdown-B4Av9P7l.js";import{r as i}from"./vendor-react-ByKzdfMq.js";import{u as A,p as E,R as D,o as m,W as f,a as b}from"./message-components-CDlGtWLl.js";function P({content:h,name:k="Assistant",avatar:O,onAddMessage:y,onUpdateMessage:c,messageId:_}){const{dispatch:u,abortControllerRef:l}=A(),[d,w]=i.useState(!1),[T,G]=i.useState(""),[J,S]=i.useState(!1),[j,v]=i.useState(null),r=i.useMemo(()=>{try{return JSON.parse(h)}catch(e){return console.error("Failed to parse message content:",e),null}},[h]),x=i.useMemo(()=>{if(!r||!r.ext)return null;const e=r.ext.find(t=>t.type==="debug_start_checkpoint"||t.type==="debug_checkpoint"&&t.data?.checkpoint_type==="debug_start");return e&&e.data&&e.data.checkpoint_id?e.data.checkpoint_id:null},[r])||j,N=async()=>{if(d)return;const e=m(),t={id:e,role:"ai",content:JSON.stringify({text:`🔍 Starting workflow analysis... `,ext:[]}),format:"markdown",name:"Assistant",finished:!1,debugGuide:!0};y?.(t);try{f.trackEvent({event_type:"debug_trigger",message_type:"debug",message_id:e,data:{}}),await C(),u({type:"SET_LOADING",payload:!0}),await I(e)}finally{w(!1)}},C=async()=>{try{const e=await b.graphToPrompt(),t=localStorage.getItem("sessionId")||"";if(t&&e){const n=await f.saveWorkflowCheckpoint(t,e.output,e.workflow,"debug_start");if(v(n.version_id),console.log(`Saved debug start checkpoint: ${n.version_id}`),c&&r){const o=[...r.ext||[]].filter(g=>g.type!=="debug_start_checkpoint"&&!(g.type==="debug_checkpoint"&&g.data?.checkpoint_type==="debug_start"));o.push({type:"debug_start_checkpoint",data:{checkpoint_id:n.version_id,checkpoint_type:"debug_start"}});const p={id:_||m(),role:"ai",content:JSON.stringify({text:r.text,ext:o}),format:"debug_guide",name:"Assistant"};c(p)}}}catch(e){console.error("Failed to save checkpoint before debug:",e)}},I=async e=>{try{const t=await b.graphToPrompt();let n="",a=null;l&&(l.current=new AbortController);for await(const o of f.streamDebugAgent(t,l?.current?.signal||void 0))if(o.text){n=o.text,o.ext&&(a=o.ext);const p={id:e,role:"ai",content:JSON.stringify({text:n,ext:a||[]}),format:"markdown",name:"Assistant",finished:!1,debugGuide:!0};c?.(p)}c?.({id:e,role:"ai",content:JSON.stringify({text:n,ext:a||[]}),format:"markdown",name:"Assistant",finished:!0,debugGuide:!0}),u({type:"SET_LOADING",payload:!1}),l.current=null}catch(t){console.error("Error calling debug agent:",t),S(!1);const a={id:e,role:"ai",content:JSON.stringify({text:`I encountered an error while analyzing your workflow: ${t.message||"Unknown error"}`,ext:[]}),format:"markdown",name:"Assistant",finished:!0};c?.(a),u({type:"SET_LOADING",payload:!1}),l.current=null}};return r?s.jsx(E,{name:k,children:s.jsxs("div",{className:"bg-gray-100 pt-4 px-4 pb-3 rounded-lg",children:[s.jsx("div",{className:"flex justify-between items-start",children:s.jsx("p",{className:"text-gray-700 text-sm flex-1",children:r.text})}),s.jsx("div",{className:"flex justify-end mt-4",children:s.jsx("button",{onClick:N,disabled:d,className:`debug-btn bg-debug-btn 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-1,7 +1,7 @@ function getImportPath(filename) { return `./${filename}`; } - const __vite__mapDeps=(i,m=__vite__mapDeps,d=(m.f||(m.f=["copilot_web/App-fluNqnYs.js","copilot_web/vendor-markdown-B4Av9P7l.js","copilot_web/vendor-react-ByKzdfMq.js","copilot_web/message-components-CzGdQ9TQ.js"].map(path => { + const __vite__mapDeps=(i,m=__vite__mapDeps,d=(m.f||(m.f=["copilot_web/App-TDLVaCDl.js","copilot_web/vendor-markdown-B4Av9P7l.js","copilot_web/vendor-react-ByKzdfMq.js","copilot_web/message-components-CDlGtWLl.js"].map(path => { const apiBase = window.comfyAPI?.api?.api?.api_base; if (apiBase) { // 有 API base 时，使用完整路径 @@ -11,7 +11,7 @@ function getImportPath(filename) { return `./${path}`; } }))))=>i.map(i=>d[i]); -import{j as Fu}from"./vendor-markdown-B4Av9P7l.js";import{r as d0,e as Pv,g as ly,R as s0}from"./vendor-react-ByKzdfMq.js";import{w as ay,a as ty}from"./message-components-CzGdQ9TQ.js";/* empty css */const 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m0={exports:{}},an={},S0={exports:{}},b0={};/** +import{j as Fu}from"./vendor-markdown-B4Av9P7l.js";import{r as d0,e as Pv,g as ly,R as s0}from"./vendor-react-ByKzdfMq.js";import{w as ay,a as ty}from"./message-components-CDlGtWLl.js";/* empty css */const uy="modulepreload",ey=function(l){return"/"+l},Jc={},ny=function(a,t,u){let e=Promise.resolve();if(t&&t.length>0){document.getElementsByTagName("link");const f=document.querySelector("meta[property=csp-nonce]"),c=f?.nonce||f?.getAttribute("nonce");e=Promise.allSettled(t.map(i=>{if(i=ey(i),i in Jc)return;Jc[i]=!0;const h=i.endsWith(".css"),b=h?'[rel="stylesheet"]':"";if(document.querySelector(`link[href="${i}"]${b}`))return;const S=document.createElement("link");if(S.rel=h?"stylesheet":uy,h||(S.as="script"),S.crossOrigin="",S.href=i,c&&S.setAttribute("nonce",c),document.head.appendChild(S),h)return new Promise((d,m)=>{S.addEventListener("load",d),S.addEventListener("error",()=>m(new Error(`Unable to preload CSS for 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