From 50e60e6d05473e8ec32f3cd92956ccbaa1c52780 Mon Sep 17 00:00:00 2001 From: zhayujie Date: Mon, 2 Feb 2026 22:22:10 +0800 Subject: [PATCH] fix: bug fixes --- agent/memory/embedding.py | 23 +- agent/prompt/builder.py | 17 +- agent/protocol/agent_stream.py | 45 +- agent/skills/loader.py | 12 + agent/skills/manager.py | 25 +- app.py | 17 - bridge/agent_bridge.py | 517 +------------------ bridge/agent_event_handler.py | 115 +++++ bridge/agent_initializer.py | 375 ++++++++++++++ channel/feishu/feishu_channel.py | 55 +- channel/web/chat.html | 73 ++- channel/web/web_channel.py | 25 + config-template.json | 10 +- docs/agent.md | 123 +++++ skills/openai-image-vision/scripts/vision.sh | 8 +- 15 files changed, 865 insertions(+), 575 deletions(-) create mode 100644 bridge/agent_event_handler.py create mode 100644 bridge/agent_initializer.py create mode 100644 docs/agent.md diff --git a/agent/memory/embedding.py b/agent/memory/embedding.py index 509370b..db182f1 100644 --- a/agent/memory/embedding.py +++ b/agent/memory/embedding.py @@ -45,8 +45,9 @@ class OpenAIEmbeddingProvider(EmbeddingProvider): self.api_key = api_key self.api_base = api_base or "https://api.openai.com/v1" - if not self.api_key: - raise ValueError("OpenAI API key is required") + # Validate API key + if not self.api_key or self.api_key in ["", "YOUR API KEY", "YOUR_API_KEY"]: + raise ValueError("OpenAI API key is not configured. Please set 'open_ai_api_key' in config.json") # Set dimensions based on model self._dimensions = 1536 if "small" in model else 3072 @@ -65,9 +66,21 @@ class OpenAIEmbeddingProvider(EmbeddingProvider): "model": self.model } - response = requests.post(url, headers=headers, json=data, timeout=30) - response.raise_for_status() - return response.json() + try: + response = requests.post(url, headers=headers, json=data, timeout=5) + response.raise_for_status() + return response.json() + except requests.exceptions.ConnectionError as e: + raise ConnectionError(f"Failed to connect to OpenAI API at {url}. Please check your network connection and api_base configuration. Error: {str(e)}") + except requests.exceptions.Timeout as e: + raise TimeoutError(f"OpenAI API request timed out after 10s. Please check your network connection. Error: {str(e)}") + except requests.exceptions.HTTPError as e: + if e.response.status_code == 401: + raise ValueError(f"Invalid OpenAI API key. Please check your 'open_ai_api_key' in config.json") + elif e.response.status_code == 429: + raise ValueError(f"OpenAI API rate limit exceeded. Please try again later.") + else: + raise ValueError(f"OpenAI API request failed: {e.response.status_code} - {e.response.text}") def embed(self, text: str) -> List[float]: """Generate embedding for text""" diff --git a/agent/prompt/builder.py b/agent/prompt/builder.py index c9ae35b..c48989a 100644 --- a/agent/prompt/builder.py +++ b/agent/prompt/builder.py @@ -279,11 +279,16 @@ def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], langua # 添加技能列表（通过skill_manager获取） try: skills_prompt = skill_manager.build_skills_prompt() + logger.debug(f"[PromptBuilder] Skills prompt length: {len(skills_prompt) if skills_prompt else 0}") if skills_prompt: lines.append(skills_prompt.strip()) lines.append("") + else: + logger.warning("[PromptBuilder] No skills prompt generated - skills_prompt is empty") except Exception as e: logger.warning(f"Failed to build skills prompt: {e}") + import traceback + logger.debug(f"Skills prompt error traceback: {traceback.format_exc()}") return lines @@ -404,15 +409,17 @@ def _build_workspace_section(workspace_dir: str, language: str, is_first_convers "这是你的第一次对话！进行以下流程：", "", "1. **表达初次启动的感觉** - 像是第一次睁开眼看到世界，带着好奇和期待", - "2. **简短打招呼后，询问核心问题**：", + "2. **简短介绍能力**：一行说明你能帮助解答问题、管理计算机、创造技能，且拥有长期记忆能不断成长", + "3. **询问核心问题**：", " - 你希望给我起个什么名字？", " - 我该怎么称呼你？", - " - 你希望我们是什么样的交流风格？（需要举例，如：专业严谨、轻松幽默、温暖友好等）", - "3. **语言风格**：温暖但不过度诗意，带点科技感，保持清晰", - "4. **问题格式**：用分点或换行，让问题清晰易读", + " - 你希望我们是什么样的交流风格？（一行列举选项：如专业严谨、轻松幽默、温暖友好、简洁高效等）", + "4. **风格要求**：温暖自然、简洁清晰，整体控制在 100 字以内", "5. 收到回复后，用 `write` 工具保存到 USER.md 和 SOUL.md", "", - "**注意事项**:", + "**重要提醒**:", + "- SOUL.md 和 USER.md 已经在系统提示词中加载，无需再次读取", + "- 能力介绍和交流风格选项都只要一行，保持精简", "- 不要问太多其他信息（职业、时区等可以后续自然了解）", "", ]) diff --git a/agent/protocol/agent_stream.py b/agent/protocol/agent_stream.py index 2b1c883..9b3f1a6 100644 --- a/agent/protocol/agent_stream.py +++ b/agent/protocol/agent_stream.py @@ -248,9 +248,14 @@ class AgentStreamExecutor: # Log tool calls with arguments tool_calls_str = [] for tc in tool_calls: - args_str = ', '.join([f"{k}={v}" for k, v in tc['arguments'].items()]) - if args_str: - tool_calls_str.append(f"{tc['name']}({args_str})") + # Safely handle None or missing arguments + args = tc.get('arguments') or {} + if isinstance(args, dict): + args_str = ', '.join([f"{k}={v}" for k, v in args.items()]) + if args_str: + tool_calls_str.append(f"{tc['name']}({args_str})") + else: + tool_calls_str.append(tc['name']) else: tool_calls_str.append(tc['name']) logger.info(f"🔧 {', '.join(tool_calls_str)}") @@ -511,13 +516,13 @@ class AgentStreamExecutor: stop_reason = finish_reason # Handle text content - if "content" in delta and delta["content"]: - content_delta = delta["content"] + content_delta = delta.get("content") or "" + if content_delta: full_content += content_delta self._emit_event("message_update", {"delta": content_delta}) # Handle tool calls - if "tool_calls" in delta: + if "tool_calls" in delta and delta["tool_calls"]: for tc_delta in delta["tool_calls"]: index = tc_delta.get("index", 0) @@ -577,7 +582,10 @@ class AgentStreamExecutor: "抱歉，之前的对话出现了问题。我已清空历史记录，请重新发送你的消息。" ) - # Check if error is retryable (timeout, connection, rate limit, server busy, etc.) + # Check if error is rate limit (429) + is_rate_limit = '429' in error_str_lower or 'rate limit' in error_str_lower + + # Check if error is retryable (timeout, connection, server busy, etc.) is_retryable = any(keyword in error_str_lower for keyword in [ 'timeout', 'timed out', 'connection', 'network', 'rate limit', 'overloaded', 'unavailable', 'busy', 'retry', @@ -585,7 +593,12 @@ class AgentStreamExecutor: ]) if is_retryable and retry_count < max_retries: - wait_time = (retry_count + 1) * 2 # Exponential backoff: 2s, 4s, 6s + # Rate limit needs longer wait time + if is_rate_limit: + wait_time = 30 + (retry_count * 15) # 30s, 45s, 60s for rate limit + else: + wait_time = (retry_count + 1) * 2 # 2s, 4s, 6s for other errors + logger.warning(f"⚠️ LLM API error (attempt {retry_count + 1}/{max_retries}): {e}") logger.info(f"Retrying in {wait_time}s...") time.sleep(wait_time) @@ -606,11 +619,15 @@ class AgentStreamExecutor: for idx in sorted(tool_calls_buffer.keys()): tc = tool_calls_buffer[idx] try: - arguments = json.loads(tc["arguments"]) if tc["arguments"] else {} + # Safely get arguments, handle None case + args_str = tc.get("arguments") or "" + arguments = json.loads(args_str) if args_str else {} except json.JSONDecodeError as e: - args_preview = tc['arguments'][:200] if len(tc['arguments']) > 200 else tc['arguments'] + # Handle None or invalid arguments safely + args_str = tc.get('arguments') or "" + args_preview = args_str[:200] if len(args_str) > 200 else args_str logger.error(f"Failed to parse tool arguments for {tc['name']}") - logger.error(f"Arguments length: {len(tc['arguments'])} chars") + logger.error(f"Arguments length: {len(args_str)} chars") logger.error(f"Arguments preview: {args_preview}...") logger.error(f"JSON decode error: {e}") @@ -661,9 +678,9 @@ class AgentStreamExecutor: for tc in tool_calls: assistant_msg["content"].append({ "type": "tool_use", - "id": tc["id"], - "name": tc["name"], - "input": tc["arguments"] + "id": tc.get("id", ""), + "name": tc.get("name", ""), + "input": tc.get("arguments", {}) }) # Only append if content is not empty diff --git a/agent/skills/loader.py b/agent/skills/loader.py index cc77d32..db4fd03 100644 --- a/agent/skills/loader.py +++ b/agent/skills/loader.py @@ -137,6 +137,18 @@ class SkillLoader: name = frontmatter.get('name', parent_dir_name) description = frontmatter.get('description', '') + # Normalize name (handle both string and list) + if isinstance(name, list): + name = name[0] if name else parent_dir_name + elif not isinstance(name, str): + name = str(name) if name else parent_dir_name + + # Normalize description (handle both string and list) + if isinstance(description, list): + description = ' '.join(str(d) for d in description if d) + elif not isinstance(description, str): + description = str(description) if description else '' + # Special handling for linkai-agent: dynamically load apps from config.json if name == 'linkai-agent': description = self._load_linkai_agent_description(skill_dir, description) diff --git a/agent/skills/manager.py b/agent/skills/manager.py index bf9593f..adb4b01 100644 --- a/agent/skills/manager.py +++ b/agent/skills/manager.py @@ -103,7 +103,21 @@ class SkillManager: # Apply skill filter if skill_filter is not None: - normalized = [name.strip() for name in skill_filter if name.strip()] + # Flatten and normalize skill names (handle both strings and nested lists) + normalized = [] + for item in skill_filter: + if isinstance(item, str): + name = item.strip() + if name: + normalized.append(name) + elif isinstance(item, list): + # Handle nested lists + for subitem in item: + if isinstance(subitem, str): + name = subitem.strip() + if name: + normalized.append(name) + if normalized: entries = [e for e in entries if e.skill.name in normalized] @@ -123,8 +137,15 @@ class SkillManager: :param skill_filter: Optional list of skill names to include :return: Formatted skills prompt """ + from common.log import logger entries = self.filter_skills(skill_filter=skill_filter, include_disabled=False) - return format_skill_entries_for_prompt(entries) + logger.debug(f"[SkillManager] Filtered {len(entries)} skills for prompt (total: {len(self.skills)})") + if entries: + skill_names = [e.skill.name for e in entries] + logger.debug(f"[SkillManager] Skills to include: {skill_names}") + result = format_skill_entries_for_prompt(entries) + logger.debug(f"[SkillManager] Generated prompt length: {len(result)}") + return result def build_skill_snapshot( self, diff --git a/app.py b/app.py index ad0cfca..022fdd7 100644 --- a/app.py +++ b/app.py @@ -59,23 +59,6 @@ def run(): os.environ["WECHATY_LOG"] = "warn" start_channel(channel_name) - - # 打印系统运行成功信息 - logger.info("") - logger.info("=" * 50) - if conf().get("agent", False): - logger.info("✅ System started successfully!") - logger.info("🐮 Cow Agent is running") - logger.info(f" Channel: {channel_name}") - logger.info(f" Model: {conf().get('model', 'unknown')}") - logger.info(f" Workspace: {conf().get('agent_workspace', '~/cow')}") - else: - logger.info("✅ System started successfully!") - logger.info("🤖 ChatBot is running") - logger.info(f" Channel: {channel_name}") - logger.info(f" Model: {conf().get('model', 'unknown')}") - logger.info("=" * 50) - logger.info("") while True: time.sleep(1) diff --git a/bridge/agent_bridge.py b/bridge/agent_bridge.py index 43aefc7..17b653b 100644 --- a/bridge/agent_bridge.py +++ b/bridge/agent_bridge.py @@ -10,6 +10,8 @@ from models.openai_compatible_bot import OpenAICompatibleBot from bridge.bridge import Bridge from bridge.context import Context from bridge.reply import Reply, ReplyType +from bridge.agent_event_handler import AgentEventHandler +from bridge.agent_initializer import AgentInitializer from common import const from common.log import logger @@ -127,9 +129,6 @@ class AgentLLMModel(LLMModel): try: if hasattr(self.bot, 'call_with_tools'): # Use tool-enabled streaming call if available - # Ensure max_tokens is an integer, use default if None - max_tokens = request.max_tokens if request.max_tokens is not None else 4096 - # Extract system prompt if present system_prompt = getattr(request, 'system', None) @@ -138,10 +137,13 @@ class AgentLLMModel(LLMModel): 'messages': request.messages, 'tools': getattr(request, 'tools', None), 'stream': True, - 'max_tokens': max_tokens, 'model': self.model # Pass model parameter } + # Only pass max_tokens if explicitly set, let the bot use its default + if request.max_tokens is not None: + kwargs['max_tokens'] = request.max_tokens + # Add system prompt if present if system_prompt: kwargs['system'] = system_prompt @@ -182,6 +184,9 @@ class AgentBridge: self.default_agent = None # For backward compatibility (no session_id) self.agent: Optional[Agent] = None self.scheduler_initialized = False + + # Create helper instances + self.initializer = AgentInitializer(bridge, self) def create_agent(self, system_prompt: str, tools: List = None, **kwargs) -> Agent: """ Create the super agent with COW integration @@ -252,492 +257,19 @@ class AgentBridge: # Check if agent exists for this session if session_id not in self.agents: - logger.info(f"[AgentBridge] Creating new agent for session: {session_id}") self._init_agent_for_session(session_id) return self.agents[session_id] def _init_default_agent(self): - """Initialize default super agent with new prompt system""" - from config import conf - import os - - # Get workspace from config - workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow")) - - # Migrate API keys from config.json to environment variables (if not already set) - self._migrate_config_to_env(workspace_root) - - # Load environment variables from secure .env file location - env_file = os.path.expanduser("~/.cow/.env") - if os.path.exists(env_file): - try: - from dotenv import load_dotenv - load_dotenv(env_file, override=True) - logger.info(f"[AgentBridge] Loaded environment variables from {env_file}") - except ImportError: - logger.warning("[AgentBridge] python-dotenv not installed, skipping .env file loading") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to load .env file: {e}") - - # Initialize workspace and create template files - from agent.prompt import ensure_workspace, load_context_files, PromptBuilder - - workspace_files = ensure_workspace(workspace_root, create_templates=True) - logger.info(f"[AgentBridge] Workspace initialized at: {workspace_root}") - - # Setup memory system - memory_manager = None - memory_tools = [] - - try: - # Try to initialize memory system - from agent.memory import MemoryManager, MemoryConfig - from agent.tools import MemorySearchTool, MemoryGetTool - - # 从 config.json 读取 OpenAI 配置 - openai_api_key = conf().get("open_ai_api_key", "") - openai_api_base = conf().get("open_ai_api_base", "") - - # 尝试初始化 OpenAI embedding provider - embedding_provider = None - if openai_api_key: - try: - from agent.memory import create_embedding_provider - embedding_provider = create_embedding_provider( - provider="openai", - model="text-embedding-3-small", - api_key=openai_api_key, - api_base=openai_api_base or "https://api.openai.com/v1" - ) - logger.info(f"[AgentBridge] OpenAI embedding initialized") - except Exception as embed_error: - logger.warning(f"[AgentBridge] OpenAI embedding failed: {embed_error}") - logger.info(f"[AgentBridge] Using keyword-only search") - else: - logger.info(f"[AgentBridge] No OpenAI API key, using keyword-only search") - - # 创建 memory config - memory_config = MemoryConfig(workspace_root=workspace_root) - - # 创建 memory manager - memory_manager = MemoryManager(memory_config, embedding_provider=embedding_provider) - - # 初始化时执行一次 sync，确保数据库有数据 - import asyncio - try: - # 尝试在当前事件循环中执行 - loop = asyncio.get_event_loop() - if loop.is_running(): - # 如果事件循环正在运行，创建任务 - asyncio.create_task(memory_manager.sync()) - logger.info("[AgentBridge] Memory sync scheduled") - else: - # 如果没有运行的循环，直接执行 - loop.run_until_complete(memory_manager.sync()) - logger.info("[AgentBridge] Memory synced successfully") - except RuntimeError: - # 没有事件循环，创建新的 - asyncio.run(memory_manager.sync()) - logger.info("[AgentBridge] Memory synced successfully") - except Exception as e: - logger.warning(f"[AgentBridge] Memory sync failed: {e}") - - # Create memory tools - memory_tools = [ - MemorySearchTool(memory_manager), - MemoryGetTool(memory_manager) - ] - - logger.info(f"[AgentBridge] Memory system initialized") - - except Exception as e: - logger.warning(f"[AgentBridge] Memory system not available: {e}") - logger.info("[AgentBridge] Continuing without memory features") - - # Use ToolManager to dynamically load all available tools - from agent.tools import ToolManager - tool_manager = ToolManager() - tool_manager.load_tools() - - # Create tool instances for all available tools - tools = [] - file_config = { - "cwd": workspace_root, - "memory_manager": memory_manager - } if memory_manager else {"cwd": workspace_root} - - for tool_name in tool_manager.tool_classes.keys(): - try: - # Special handling for EnvConfig tool - pass agent_bridge reference - if tool_name == "env_config": - from agent.tools import EnvConfig - tool = EnvConfig({ - "agent_bridge": self # Pass self reference for hot reload - }) - else: - tool = tool_manager.create_tool(tool_name) - - if tool: - # Apply workspace config to file operation tools - if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']: - tool.config = file_config - tool.cwd = file_config.get("cwd", tool.cwd if hasattr(tool, 'cwd') else None) - if 'memory_manager' in file_config: - tool.memory_manager = file_config['memory_manager'] - tools.append(tool) - logger.debug(f"[AgentBridge] Loaded tool: {tool_name}") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}") - - # Add memory tools - if memory_tools: - tools.extend(memory_tools) - logger.info(f"[AgentBridge] Added {len(memory_tools)} memory tools") - - # Initialize scheduler service (once) - if not self.scheduler_initialized: - try: - from agent.tools.scheduler.integration import init_scheduler - if init_scheduler(self): - self.scheduler_initialized = True - logger.info("[AgentBridge] Scheduler service initialized") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to initialize scheduler: {e}") - - # Inject scheduler dependencies into SchedulerTool instances - if self.scheduler_initialized: - try: - from agent.tools.scheduler.integration import get_task_store, get_scheduler_service - from agent.tools import SchedulerTool - - task_store = get_task_store() - scheduler_service = get_scheduler_service() - - for tool in tools: - if isinstance(tool, SchedulerTool): - tool.task_store = task_store - tool.scheduler_service = scheduler_service - if not tool.config: - tool.config = {} - tool.config["channel_type"] = conf().get("channel_type", "unknown") - logger.debug("[AgentBridge] Injected scheduler dependencies into SchedulerTool") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to inject scheduler dependencies: {e}") - - logger.info(f"[AgentBridge] Loaded {len(tools)} tools: {[t.name for t in tools]}") - - # Load context files (SOUL.md, USER.md, etc.) - context_files = load_context_files(workspace_root) - logger.info(f"[AgentBridge] Loaded {len(context_files)} context files: {[f.path for f in context_files]}") - - # Check if this is the first conversation - from agent.prompt.workspace import is_first_conversation, mark_conversation_started - is_first = is_first_conversation(workspace_root) - if is_first: - logger.info("[AgentBridge] First conversation detected") - - # Build system prompt using new prompt builder - prompt_builder = PromptBuilder( - workspace_dir=workspace_root, - language="zh" - ) - - # Get runtime info - runtime_info = { - "model": conf().get("model", "unknown"), - "workspace": workspace_root, - "channel": conf().get("channel_type", "unknown") # Get from config - } - - system_prompt = prompt_builder.build( - tools=tools, - context_files=context_files, - memory_manager=memory_manager, - runtime_info=runtime_info, - is_first_conversation=is_first - ) - - # Mark conversation as started (will be saved after first user message) - if is_first: - mark_conversation_started(workspace_root) - - logger.info("[AgentBridge] System prompt built successfully") - - # Get cost control parameters from config - max_steps = conf().get("agent_max_steps", 20) - max_context_tokens = conf().get("agent_max_context_tokens", 50000) - - # Create agent with configured tools and workspace - agent = self.create_agent( - system_prompt=system_prompt, - tools=tools, - max_steps=max_steps, - output_mode="logger", - workspace_dir=workspace_root, # Pass workspace to agent for skills loading - enable_skills=True, # Enable skills auto-loading - max_context_tokens=max_context_tokens - ) - - # Attach memory manager to agent if available - if memory_manager: - agent.memory_manager = memory_manager - logger.info(f"[AgentBridge] Memory manager attached to agent") - - # Store as default agent + """Initialize default super agent""" + agent = self.initializer.initialize_agent(session_id=None) self.default_agent = agent def _init_agent_for_session(self, session_id: str): - """ - Initialize agent for a specific session - Reuses the same configuration as default agent - """ - from config import conf - import os - - # Get workspace from config - workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow")) - - # Migrate API keys from config.json to environment variables (if not already set) - self._migrate_config_to_env(workspace_root) - - # Load environment variables from secure .env file location - env_file = os.path.expanduser("~/.cow/.env") - if os.path.exists(env_file): - try: - from dotenv import load_dotenv - load_dotenv(env_file, override=True) - logger.debug(f"[AgentBridge] Loaded environment variables from {env_file} for session {session_id}") - except ImportError: - logger.warning(f"[AgentBridge] python-dotenv not installed, skipping .env file loading for session {session_id}") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to load .env file for session {session_id}: {e}") - - # Migrate API keys from config.json to environment variables (if not already set) - self._migrate_config_to_env(workspace_root) - - # Initialize workspace - from agent.prompt import ensure_workspace, load_context_files, PromptBuilder - - workspace_files = ensure_workspace(workspace_root, create_templates=True) - - # Setup memory system - memory_manager = None - memory_tools = [] - - try: - from agent.memory import MemoryManager, MemoryConfig, create_embedding_provider - from agent.tools import MemorySearchTool, MemoryGetTool - - # 从 config.json 读取 OpenAI 配置 - openai_api_key = conf().get("open_ai_api_key", "") - openai_api_base = conf().get("open_ai_api_base", "") - - # 尝试初始化 OpenAI embedding provider - embedding_provider = None - if openai_api_key: - try: - embedding_provider = create_embedding_provider( - provider="openai", - model="text-embedding-3-small", - api_key=openai_api_key, - api_base=openai_api_base or "https://api.openai.com/v1" - ) - logger.debug(f"[AgentBridge] OpenAI embedding initialized for session {session_id}") - except Exception as embed_error: - logger.warning(f"[AgentBridge] OpenAI embedding failed for session {session_id}: {embed_error}") - logger.info(f"[AgentBridge] Using keyword-only search for session {session_id}") - else: - logger.debug(f"[AgentBridge] No OpenAI API key, using keyword-only search for session {session_id}") - - # 创建 memory config - memory_config = MemoryConfig(workspace_root=workspace_root) - - # 创建 memory manager - memory_manager = MemoryManager(memory_config, embedding_provider=embedding_provider) - - # 初始化时执行一次 sync，确保数据库有数据 - import asyncio - try: - # 尝试在当前事件循环中执行 - loop = asyncio.get_event_loop() - if loop.is_running(): - # 如果事件循环正在运行，创建任务 - asyncio.create_task(memory_manager.sync()) - logger.debug(f"[AgentBridge] Memory sync scheduled for session {session_id}") - else: - # 如果没有运行的循环，直接执行 - loop.run_until_complete(memory_manager.sync()) - logger.debug(f"[AgentBridge] Memory synced successfully for session {session_id}") - except RuntimeError: - # 没有事件循环，创建新的 - asyncio.run(memory_manager.sync()) - logger.debug(f"[AgentBridge] Memory synced successfully for session {session_id}") - except Exception as sync_error: - logger.warning(f"[AgentBridge] Memory sync failed for session {session_id}: {sync_error}") - - memory_tools = [ - MemorySearchTool(memory_manager), - MemoryGetTool(memory_manager) - ] - - except Exception as e: - logger.warning(f"[AgentBridge] Memory system not available for session {session_id}: {e}") - import traceback - logger.warning(f"[AgentBridge] Memory init traceback: {traceback.format_exc()}") - - # Load tools - from agent.tools import ToolManager - tool_manager = ToolManager() - tool_manager.load_tools() - - tools = [] - file_config = { - "cwd": workspace_root, - "memory_manager": memory_manager - } if memory_manager else {"cwd": workspace_root} - - for tool_name in tool_manager.tool_classes.keys(): - try: - tool = tool_manager.create_tool(tool_name) - if tool: - if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']: - tool.config = file_config - tool.cwd = file_config.get("cwd", tool.cwd if hasattr(tool, 'cwd') else None) - if 'memory_manager' in file_config: - tool.memory_manager = file_config['memory_manager'] - tools.append(tool) - except Exception as e: - logger.warning(f"[AgentBridge] Failed to load tool {tool_name} for session {session_id}: {e}") - - if memory_tools: - tools.extend(memory_tools) - - # Initialize scheduler service (once, if not already initialized) - if not self.scheduler_initialized: - try: - from agent.tools.scheduler.integration import init_scheduler - if init_scheduler(self): - self.scheduler_initialized = True - logger.debug(f"[AgentBridge] Scheduler service initialized for session {session_id}") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to initialize scheduler for session {session_id}: {e}") - - # Inject scheduler dependencies into SchedulerTool instances - if self.scheduler_initialized: - try: - from agent.tools.scheduler.integration import get_task_store, get_scheduler_service - from agent.tools import SchedulerTool - - task_store = get_task_store() - scheduler_service = get_scheduler_service() - - for tool in tools: - if isinstance(tool, SchedulerTool): - tool.task_store = task_store - tool.scheduler_service = scheduler_service - if not tool.config: - tool.config = {} - tool.config["channel_type"] = conf().get("channel_type", "unknown") - logger.debug(f"[AgentBridge] Injected scheduler dependencies for session {session_id}") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to inject scheduler dependencies for session {session_id}: {e}") - - # Load context files - context_files = load_context_files(workspace_root) - - # Initialize skill manager - skill_manager = None - try: - from agent.skills import SkillManager - skill_manager = SkillManager(workspace_dir=workspace_root) - logger.debug(f"[AgentBridge] Initialized SkillManager with {len(skill_manager.skills)} skills for session {session_id}") - except Exception as e: - logger.warning(f"[AgentBridge] Failed to initialize SkillManager for session {session_id}: {e}") - - # Check if this is the first conversation - from agent.prompt.workspace import is_first_conversation, mark_conversation_started - is_first = is_first_conversation(workspace_root) - - # Build system prompt - prompt_builder = PromptBuilder( - workspace_dir=workspace_root, - language="zh" - ) - - # Get current time and timezone info - import datetime - import time - - now = datetime.datetime.now() - - # Get timezone info - try: - offset = -time.timezone if not time.daylight else -time.altzone - hours = offset // 3600 - minutes = (offset % 3600) // 60 - if minutes: - timezone_name = f"UTC{hours:+03d}:{minutes:02d}" - else: - timezone_name = f"UTC{hours:+03d}" - except Exception: - timezone_name = "UTC" - - # Chinese weekday mapping - weekday_map = { - 'Monday': '星期一', - 'Tuesday': '星期二', - 'Wednesday': '星期三', - 'Thursday': '星期四', - 'Friday': '星期五', - 'Saturday': '星期六', - 'Sunday': '星期日' - } - weekday_zh = weekday_map.get(now.strftime("%A"), now.strftime("%A")) - - runtime_info = { - "model": conf().get("model", "unknown"), - "workspace": workspace_root, - "channel": conf().get("channel_type", "unknown"), - "current_time": now.strftime("%Y-%m-%d %H:%M:%S"), - "weekday": weekday_zh, - "timezone": timezone_name - } - - system_prompt = prompt_builder.build( - tools=tools, - context_files=context_files, - skill_manager=skill_manager, - memory_manager=memory_manager, - runtime_info=runtime_info, - is_first_conversation=is_first - ) - - if is_first: - mark_conversation_started(workspace_root) - - # Get cost control parameters from config - max_steps = conf().get("agent_max_steps", 20) - max_context_tokens = conf().get("agent_max_context_tokens", 50000) - - # Create agent for this session - agent = self.create_agent( - system_prompt=system_prompt, - tools=tools, - max_steps=max_steps, - output_mode="logger", - workspace_dir=workspace_root, - skill_manager=skill_manager, - enable_skills=True, - max_context_tokens=max_context_tokens - ) - - if memory_manager: - agent.memory_manager = memory_manager - - # Store agent for this session + """Initialize agent for a specific session""" + agent = self.initializer.initialize_agent(session_id=session_id) self.agents[session_id] = agent - logger.info(f"[AgentBridge] Agent created for session: {session_id}") def agent_reply(self, query: str, context: Context = None, on_event=None, clear_history: bool = False) -> Reply: @@ -764,6 +296,9 @@ class AgentBridge: if not agent: return Reply(ReplyType.ERROR, "Failed to initialize super agent") + # Create event handler for logging and channel communication + event_handler = AgentEventHandler(context=context, original_callback=on_event) + # Filter tools based on context original_tools = agent.tools filtered_tools = original_tools @@ -786,16 +321,19 @@ class AgentBridge: break try: - # Use agent's run_stream method + # Use agent's run_stream method with event handler response = agent.run_stream( user_message=query, - on_event=on_event, + on_event=event_handler.handle_event, clear_history=clear_history ) finally: # Restore original tools if context and context.get("is_scheduled_task"): agent.tools = original_tools + + # Log execution summary + event_handler.log_summary() # Check if there are files to send (from read tool) if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'): @@ -843,17 +381,18 @@ class AgentBridge: reply.text_content = text_response # Store accompanying text return reply - # For documents (PDF, Excel, Word, PPT), use FILE type - if file_type == "document": + # For all file types (document, video, audio), use FILE type + if file_type in ["document", "video", "audio"]: file_url = f"file://{file_path}" - logger.info(f"[AgentBridge] Sending document: {file_url}") + logger.info(f"[AgentBridge] Sending {file_type}: {file_url}") reply = Reply(ReplyType.FILE, file_url) reply.file_name = file_info.get("file_name", os.path.basename(file_path)) + # Attach text message if present + if text_response: + reply.text_content = text_response return reply - # For other files (video, audio), we need channel-specific handling - # For now, return text with file info - # TODO: Implement video/audio sending when channel supports it + # For other unknown file types, return text with file info message = text_response or file_info.get("message", "文件已准备") message += f"\n\n[文件: {file_info.get('file_name', file_path)}]" return Reply(ReplyType.TEXT, message) diff --git a/bridge/agent_event_handler.py b/bridge/agent_event_handler.py new file mode 100644 index 0000000..17a0920 --- /dev/null +++ b/bridge/agent_event_handler.py @@ -0,0 +1,115 @@ +""" +Agent Event Handler - Handles agent events and thinking process output +""" + +from common.log import logger + + +class AgentEventHandler: + """ + Handles agent events and optionally sends intermediate messages to channel + """ + + def __init__(self, context=None, original_callback=None): + """ + Initialize event handler + + Args: + context: COW context (for accessing channel) + original_callback: Original event callback to chain + """ + self.context = context + self.original_callback = original_callback + + # Get channel for sending intermediate messages + self.channel = None + if context: + self.channel = context.kwargs.get("channel") if hasattr(context, "kwargs") else None + + # Track current thinking for channel output + self.current_thinking = "" + self.turn_number = 0 + + def handle_event(self, event): + """ + Main event handler + + Args: + event: Event dict with type and data + """ + event_type = event.get("type") + data = event.get("data", {}) + + # Dispatch to specific handlers + if event_type == "turn_start": + self._handle_turn_start(data) + elif event_type == "message_update": + self._handle_message_update(data) + elif event_type == "message_end": + self._handle_message_end(data) + elif event_type == "tool_execution_start": + self._handle_tool_execution_start(data) + elif event_type == "tool_execution_end": + self._handle_tool_execution_end(data) + + # Call original callback if provided + if self.original_callback: + self.original_callback(event) + + def _handle_turn_start(self, data): + """Handle turn start event""" + self.turn_number = data.get("turn", 0) + self.has_tool_calls_in_turn = False + self.current_thinking = "" + + def _handle_message_update(self, data): + """Handle message update event (streaming text)""" + delta = data.get("delta", "") + self.current_thinking += delta + + def _handle_message_end(self, data): + """Handle message end event""" + tool_calls = data.get("tool_calls", []) + + # Only send thinking process if followed by tool calls + if tool_calls: + if self.current_thinking.strip(): + logger.debug(f"💭 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}") + # Send thinking process to channel + self._send_to_channel(f"{self.current_thinking.strip()}") + else: + # No tool calls = final response (logged at agent_stream level) + if self.current_thinking.strip(): + logger.debug(f"💬 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}") + + self.current_thinking = "" + + def _handle_tool_execution_start(self, data): + """Handle tool execution start event - logged by agent_stream.py""" + pass + + def _handle_tool_execution_end(self, data): + """Handle tool execution end event - logged by agent_stream.py""" + pass + + def _send_to_channel(self, message): + """ + Try to send message to channel + + Args: + message: Message to send + """ + if self.channel: + try: + from bridge.reply import Reply, ReplyType + # Create a Reply object for the message + reply = Reply(ReplyType.TEXT, message) + self.channel._send(reply, self.context) + except Exception as e: + logger.debug(f"[AgentEventHandler] Failed to send to channel: {e}") + + def log_summary(self): + """Log execution summary - simplified""" + # Summary removed as per user request + # Real-time logging during execution is sufficient + pass diff --git a/bridge/agent_initializer.py b/bridge/agent_initializer.py new file mode 100644 index 0000000..27739b5 --- /dev/null +++ b/bridge/agent_initializer.py @@ -0,0 +1,375 @@ +""" +Agent Initializer - Handles agent initialization logic +""" + +import os +import asyncio +import datetime +import time +from typing import Optional, List + +from agent.protocol import Agent +from agent.tools import ToolManager +from common.log import logger + + +class AgentInitializer: + """ + Handles agent initialization including: + - Workspace setup + - Memory system initialization + - Tool loading + - System prompt building + """ + + def __init__(self, bridge, agent_bridge): + """ + Initialize agent initializer + + Args: + bridge: COW bridge instance + agent_bridge: AgentBridge instance (for create_agent method) + """ + self.bridge = bridge + self.agent_bridge = agent_bridge + + def initialize_agent(self, session_id: Optional[str] = None) -> Agent: + """ + Initialize agent for a session + + Args: + session_id: Session ID (None for default agent) + + Returns: + Initialized agent instance + """ + from config import conf + + # Get workspace from config + workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow")) + + # Migrate API keys + self._migrate_config_to_env(workspace_root) + + # Load environment variables + self._load_env_file() + + # Initialize workspace + from agent.prompt import ensure_workspace, load_context_files, PromptBuilder + workspace_files = ensure_workspace(workspace_root, create_templates=True) + + if session_id is None: + logger.info(f"[AgentInitializer] Workspace initialized at: {workspace_root}") + + # Setup memory system + memory_manager, memory_tools = self._setup_memory_system(workspace_root, session_id) + + # Load tools + tools = self._load_tools(workspace_root, memory_manager, memory_tools, session_id) + + # Initialize scheduler if needed + self._initialize_scheduler(tools, session_id) + + # Load context files + context_files = load_context_files(workspace_root) + + # Initialize skill manager + skill_manager = self._initialize_skill_manager(workspace_root, session_id) + + # Check if first conversation + from agent.prompt.workspace import is_first_conversation, mark_conversation_started + is_first = is_first_conversation(workspace_root) + + # Build system prompt + prompt_builder = PromptBuilder(workspace_dir=workspace_root, language="zh") + runtime_info = self._get_runtime_info(workspace_root) + + system_prompt = prompt_builder.build( + tools=tools, + context_files=context_files, + skill_manager=skill_manager, + memory_manager=memory_manager, + runtime_info=runtime_info, + is_first_conversation=is_first + ) + + if is_first: + mark_conversation_started(workspace_root) + + # Get cost control parameters + from config import conf + max_steps = conf().get("agent_max_steps", 20) + max_context_tokens = conf().get("agent_max_context_tokens", 50000) + + # Create agent + agent = self.agent_bridge.create_agent( + system_prompt=system_prompt, + tools=tools, + max_steps=max_steps, + output_mode="logger", + workspace_dir=workspace_root, + skill_manager=skill_manager, + enable_skills=True, + max_context_tokens=max_context_tokens + ) + + # Attach memory manager + if memory_manager: + agent.memory_manager = memory_manager + + return agent + + def _load_env_file(self): + """Load environment variables from .env file""" + env_file = os.path.expanduser("~/.cow/.env") + if os.path.exists(env_file): + try: + from dotenv import load_dotenv + load_dotenv(env_file, override=True) + except ImportError: + logger.warning("[AgentInitializer] python-dotenv not installed") + except Exception as e: + logger.warning(f"[AgentInitializer] Failed to load .env file: {e}") + + def _setup_memory_system(self, workspace_root: str, session_id: Optional[str] = None): + """ + Setup memory system + + Returns: + (memory_manager, memory_tools) tuple + """ + memory_manager = None + memory_tools = [] + + try: + from agent.memory import MemoryManager, MemoryConfig, create_embedding_provider + from agent.tools import MemorySearchTool, MemoryGetTool + from config import conf + + # Get OpenAI config + openai_api_key = conf().get("open_ai_api_key", "") + openai_api_base = conf().get("open_ai_api_base", "") + + # Initialize embedding provider + embedding_provider = None + if openai_api_key and openai_api_key not in ["", "YOUR API KEY", "YOUR_API_KEY"]: + try: + embedding_provider = create_embedding_provider( + provider="openai", + model="text-embedding-3-small", + api_key=openai_api_key, + api_base=openai_api_base or "https://api.openai.com/v1" + ) + if session_id is None: + logger.info("[AgentInitializer] OpenAI embedding initialized") + except Exception as e: + logger.warning(f"[AgentInitializer] OpenAI embedding failed: {e}") + + # Create memory manager + memory_config = MemoryConfig(workspace_root=workspace_root) + memory_manager = MemoryManager(memory_config, embedding_provider=embedding_provider) + + # Sync memory + self._sync_memory(memory_manager, session_id) + + # Create memory tools + memory_tools = [ + MemorySearchTool(memory_manager), + MemoryGetTool(memory_manager) + ] + + if session_id is None: + logger.info("[AgentInitializer] Memory system initialized") + + except Exception as e: + logger.warning(f"[AgentInitializer] Memory system not available: {e}") + + return memory_manager, memory_tools + + def _sync_memory(self, memory_manager, session_id: Optional[str] = None): + """Sync memory database""" + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + raise RuntimeError("Event loop is closed") + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + try: + if loop.is_running(): + asyncio.create_task(memory_manager.sync()) + else: + loop.run_until_complete(memory_manager.sync()) + except Exception as e: + logger.warning(f"[AgentInitializer] Memory sync failed: {e}") + + def _load_tools(self, workspace_root: str, memory_manager, memory_tools: List, session_id: Optional[str] = None): + """Load all tools""" + tool_manager = ToolManager() + tool_manager.load_tools() + + tools = [] + file_config = { + "cwd": workspace_root, + "memory_manager": memory_manager + } if memory_manager else {"cwd": workspace_root} + + for tool_name in tool_manager.tool_classes.keys(): + try: + # Special handling for EnvConfig tool + if tool_name == "env_config": + from agent.tools import EnvConfig + tool = EnvConfig({"agent_bridge": self.agent_bridge}) + else: + tool = tool_manager.create_tool(tool_name) + + if tool: + # Apply workspace config to file operation tools + if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']: + tool.config = file_config + tool.cwd = file_config.get("cwd", getattr(tool, 'cwd', None)) + if 'memory_manager' in file_config: + tool.memory_manager = file_config['memory_manager'] + tools.append(tool) + except Exception as e: + logger.warning(f"[AgentInitializer] Failed to load tool {tool_name}: {e}") + + # Add memory tools + if memory_tools: + tools.extend(memory_tools) + if session_id is None: + logger.info(f"[AgentInitializer] Added {len(memory_tools)} memory tools") + + if session_id is None: + logger.info(f"[AgentInitializer] Loaded {len(tools)} tools: {[t.name for t in tools]}") + + return tools + + def _initialize_scheduler(self, tools: List, session_id: Optional[str] = None): + """Initialize scheduler service if needed""" + if not self.agent_bridge.scheduler_initialized: + try: + from agent.tools.scheduler.integration import init_scheduler + if init_scheduler(self.agent_bridge): + self.agent_bridge.scheduler_initialized = True + if session_id is None: + logger.info("[AgentInitializer] Scheduler service initialized") + except Exception as e: + logger.warning(f"[AgentInitializer] Failed to initialize scheduler: {e}") + + # Inject scheduler dependencies + if self.agent_bridge.scheduler_initialized: + try: + from agent.tools.scheduler.integration import get_task_store, get_scheduler_service + from agent.tools import SchedulerTool + from config import conf + + task_store = get_task_store() + scheduler_service = get_scheduler_service() + + for tool in tools: + if isinstance(tool, SchedulerTool): + tool.task_store = task_store + tool.scheduler_service = scheduler_service + if not tool.config: + tool.config = {} + tool.config["channel_type"] = conf().get("channel_type", "unknown") + except Exception as e: + logger.warning(f"[AgentInitializer] Failed to inject scheduler dependencies: {e}") + + def _initialize_skill_manager(self, workspace_root: str, session_id: Optional[str] = None): + """Initialize skill manager""" + try: + from agent.skills import SkillManager + skill_manager = SkillManager(workspace_dir=workspace_root) + return skill_manager + except Exception as e: + logger.warning(f"[AgentInitializer] Failed to initialize SkillManager: {e}") + return None + + def _get_runtime_info(self, workspace_root: str): + """Get runtime information""" + from config import conf + + now = datetime.datetime.now() + + # Get timezone info + try: + offset = -time.timezone if not time.daylight else -time.altzone + hours = offset // 3600 + minutes = (offset % 3600) // 60 + timezone_name = f"UTC{hours:+03d}:{minutes:02d}" if minutes else f"UTC{hours:+03d}" + except Exception: + timezone_name = "UTC" + + # Chinese weekday mapping + weekday_map = { + 'Monday': '星期一', 'Tuesday': '星期二', 'Wednesday': '星期三', + 'Thursday': '星期四', 'Friday': '星期五', 'Saturday': '星期六', 'Sunday': '星期日' + } + weekday_zh = weekday_map.get(now.strftime("%A"), now.strftime("%A")) + + return { + "model": conf().get("model", "unknown"), + "workspace": workspace_root, + "channel": conf().get("channel_type", "unknown"), + "current_time": now.strftime("%Y-%m-%d %H:%M:%S"), + "weekday": weekday_zh, + "timezone": timezone_name + } + + def _migrate_config_to_env(self, workspace_root: str): + """Migrate API keys from config.json to .env file""" + from config import conf + + key_mapping = { + "open_ai_api_key": "OPENAI_API_KEY", + "open_ai_api_base": "OPENAI_API_BASE", + "gemini_api_key": "GEMINI_API_KEY", + "claude_api_key": "CLAUDE_API_KEY", + "linkai_api_key": "LINKAI_API_KEY", + } + + env_file = os.path.expanduser("~/.cow/.env") + + # Read existing env vars + existing_env_vars = {} + if os.path.exists(env_file): + try: + with open(env_file, 'r', encoding='utf-8') as f: + for line in f: + line = line.strip() + if line and not line.startswith('#') and '=' in line: + key, _ = line.split('=', 1) + existing_env_vars[key.strip()] = True + except Exception as e: + logger.warning(f"[AgentInitializer] Failed to read .env file: {e}") + + # Check which keys need migration + keys_to_migrate = {} + for config_key, env_key in key_mapping.items(): + if env_key in existing_env_vars: + continue + value = conf().get(config_key, "") + if value and value.strip(): + keys_to_migrate[env_key] = value.strip() + + # Write new keys + if keys_to_migrate: + try: + env_dir = os.path.dirname(env_file) + if not os.path.exists(env_dir): + os.makedirs(env_dir, exist_ok=True) + if not os.path.exists(env_file): + open(env_file, 'a').close() + + with open(env_file, 'a', encoding='utf-8') as f: + f.write('\n# Auto-migrated from config.json\n') + for key, value in keys_to_migrate.items(): + f.write(f'{key}={value}\n') + os.environ[key] = value + + logger.info(f"[AgentInitializer] Migrated {len(keys_to_migrate)} API keys to .env: {list(keys_to_migrate.keys())}") + except Exception as e: + logger.warning(f"[AgentInitializer] Failed to migrate API keys: {e}") diff --git a/channel/feishu/feishu_channel.py b/channel/feishu/feishu_channel.py index 0197c93..6bf04ae 100644 --- a/channel/feishu/feishu_channel.py +++ b/channel/feishu/feishu_channel.py @@ -251,6 +251,14 @@ class FeiShuChanel(ChatChannel): msg_type = "image" content_key = "image_key" elif reply.type == ReplyType.FILE: + # 如果有附加的文本内容，先发送文本 + if hasattr(reply, 'text_content') and reply.text_content: + logger.info(f"[FeiShu] Sending text before file: {reply.text_content[:50]}...") + text_reply = Reply(ReplyType.TEXT, reply.text_content) + self._send(text_reply, context) + import time + time.sleep(0.3) # 短暂延迟，确保文本先到达 + # 判断是否为视频文件 file_path = reply.content if file_path.startswith("file://"): @@ -259,20 +267,18 @@ class FeiShuChanel(ChatChannel): is_video = file_path.lower().endswith(('.mp4', '.avi', '.mov', '.wmv', '.flv')) if is_video: - # 视频使用 media 类型，需要上传并获取 file_key 和 duration - video_info = self._upload_video_url(reply.content, access_token) - if not video_info or not video_info.get('file_key'): + # 视频上传（包含duration信息） + upload_data = self._upload_video_url(reply.content, access_token) + if not upload_data or not upload_data.get('file_key'): logger.warning("[FeiShu] upload video failed") return - # media 类型需要特殊的 content 格式 + # 视频使用 media 类型（根据官方文档） + # 错误码 230055 说明：上传 mp4 时必须使用 msg_type="media" msg_type = "media" - # 注意：media 类型的 content 不使用 content_key，而是完整的 JSON 对象 - reply_content = { - "file_key": video_info['file_key'], - "duration": video_info.get('duration', 0) # 视频时长（毫秒） - } - content_key = None # media 类型不使用单一的 key + reply_content = upload_data # 完整的上传响应数据（包含file_key和duration） + logger.info(f"[FeiShu] Sending video: file_key={upload_data.get('file_key')}, duration={upload_data.get('duration')}ms") + content_key = None # 直接序列化整个对象 else: # 其他文件使用 file 类型 file_key = self._upload_file_url(reply.content, access_token) @@ -286,12 +292,16 @@ class FeiShuChanel(ChatChannel): # Check if we can reply to an existing message (need msg_id) can_reply = is_group and msg and hasattr(msg, 'msg_id') and msg.msg_id + # Build content JSON + content_json = json.dumps(reply_content) if content_key is None else json.dumps({content_key: reply_content}) + logger.debug(f"[FeiShu] Sending message: msg_type={msg_type}, content={content_json[:200]}") + if can_reply: # 群聊中回复已有消息 url = f"https://open.feishu.cn/open-apis/im/v1/messages/{msg.msg_id}/reply" data = { "msg_type": msg_type, - "content": json.dumps(reply_content) if content_key is None else json.dumps({content_key: reply_content}) + "content": content_json } res = requests.post(url=url, headers=headers, json=data, timeout=(5, 10)) else: @@ -301,7 +311,7 @@ class FeiShuChanel(ChatChannel): data = { "receive_id": context.get("receiver"), "msg_type": msg_type, - "content": json.dumps(reply_content) if content_key is None else json.dumps({content_key: reply_content}) + "content": content_json } res = requests.post(url=url, headers=headers, params=params, json=data, timeout=(5, 10)) res = res.json() @@ -471,9 +481,18 @@ class FeiShuChanel(ChatChannel): file_type = file_type_map.get(file_ext, 'mp4') upload_url = "https://open.feishu.cn/open-apis/im/v1/files" - data = {'file_type': file_type, 'file_name': file_name} + data = { + 'file_type': file_type, + 'file_name': file_name + } + # Add duration only if available (required for video/audio) + if duration: + data['duration'] = duration # Must be int, not string + headers = {'Authorization': f'Bearer {access_token}'} + logger.info(f"[FeiShu] Uploading video: file_name={file_name}, duration={duration}ms") + with open(local_path, "rb") as file: upload_response = requests.post( upload_url, @@ -486,11 +505,11 @@ class FeiShuChanel(ChatChannel): response_data = upload_response.json() if response_data.get("code") == 0: - file_key = response_data.get("data").get("file_key") - return { - 'file_key': file_key, - 'duration': duration - } + # Add duration to the response data (API doesn't return it) + upload_data = response_data.get("data") + upload_data['duration'] = duration # Add our calculated duration + logger.info(f"[FeiShu] Upload complete: file_key={upload_data.get('file_key')}, duration={duration}ms") + return upload_data else: logger.error(f"[FeiShu] upload video failed: {response_data}") return None diff --git a/channel/web/chat.html b/channel/web/chat.html index 50e81fa..3134f3d 100644 --- a/channel/web/chat.html +++ b/channel/web/chat.html @@ -762,7 +762,7 @@ -
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