"""Agent configuration class for managing all agent-related parameters.""" from typing import Optional, List, Dict, Any, TYPE_CHECKING from dataclasses import dataclass, field import logging import json import hashlib logger = logging.getLogger('app') @dataclass class AgentConfig: """Agent configuration class containing all parameters required to create and manage an agent.""" # Basic parameters bot_id: str api_key: Optional[str] = None model_name: str = "qwen3-next" model_server: Optional[str] = None language: Optional[str] = "jp" # Configuration parameters system_prompt: Optional[str] = None mcp_settings: Optional[List[Dict]] = field(default_factory=list) generate_cfg: Optional[Dict] = None enable_thinking: bool = False enable_self_knowledge: bool = False # Context parameters project_dir: Optional[str] = None user_identifier: Optional[str] = None session_id: Optional[str] = None dataset_ids: Optional[List[str]] = field(default_factory=list) trace_id: Optional[str] = None # Request trace ID, obtained from the X-Request-ID header request_started_at: Optional[float] = None # Response control parameters stream: bool = False tool_response: bool = True preamble_text: Optional[str] = None messages: Optional[List] = field(default_factory=list) _origin_messages: Optional[List] = field(default_factory=list) logging_handler: Optional['LoggingCallbackHandler'] = None # Mem0 long-term memory configuration enable_memori: bool = False # Keep the name for API compatibility; Mem0 is used internally memori_semantic_search_top_k: int = 20 _mem0_context: Optional[str] = None # Mem0 recalled memory context, used for passing data between middlewares # Custom shell environment variables shell_env: Optional[Dict[str, str]] = field(default_factory=dict) # Checkpointer session history _session_history: Optional[List] = field(default_factory=list) # Historical chat records loaded from the checkpointer def to_dict(self) -> Dict[str, Any]: """Convert to a dictionary for passing into functions that require **kwargs.""" return { 'bot_id': self.bot_id, 'api_key': self.api_key, 'model_name': self.model_name, 'model_server': self.model_server, 'language': self.language, 'system_prompt': self.system_prompt, 'mcp_settings': self.mcp_settings, 'generate_cfg': self.generate_cfg, 'enable_thinking': self.enable_thinking, 'enable_self_knowledge': self.enable_self_knowledge, 'project_dir': self.project_dir, 'user_identifier': self.user_identifier, 'session_id': self.session_id, 'dataset_ids': self.dataset_ids, 'stream': self.stream, 'tool_response': self.tool_response, 'preamble_text': self.preamble_text, 'messages': self.messages, 'enable_memori': self.enable_memori, 'memori_semantic_search_top_k': self.memori_semantic_search_top_k, 'trace_id': self.trace_id, 'shell_env': self.shell_env, } def safe_print(self): """Safely log the configuration without printing sensitive information.""" safe_dict = self.to_dict().copy() if 'api_key' in safe_dict and isinstance(safe_dict['api_key'], str) and safe_dict['api_key'].startswith('sk-'): safe_dict['api_key'] = safe_dict['api_key'][:8] + '***' + safe_dict['api_key'][-6:] logger.info(f"config={json.dumps(safe_dict, ensure_ascii=False, default=str)}") @classmethod async def from_v1_request(cls, request, api_key: str, project_dir: Optional[str] = None, generate_cfg: Optional[Dict] = None, messages: Optional[List] = None): """Create configuration from a v1 request.""" # Delay imports to avoid circular dependencies. from .logging_handler import LoggingCallbackHandler from utils.fastapi_utils import get_preamble_text from utils.settings import ( MEM0_ENABLED, MEM0_SEMANTIC_SEARCH_TOP_K, ) from .checkpoint_utils import prepare_checkpoint_message from .checkpoint_manager import get_checkpointer_manager from utils.log_util.context import g if messages is None: messages = [] # Get trace_id from the global context. trace_id = None try: trace_id = getattr(g, 'trace_id', None) except LookupError: pass preamble_text, system_prompt = get_preamble_text(request.language, request.system_prompt) config = cls( bot_id=request.bot_id, api_key=api_key, model_name=request.model, model_server=request.model_server, language=request.language, system_prompt=system_prompt, mcp_settings=request.mcp_settings, user_identifier=request.user_identifier, session_id=request.session_id, enable_thinking=request.enable_thinking, enable_self_knowledge=request.enable_self_knowledge, project_dir=project_dir, stream=request.stream, tool_response=request.tool_response, generate_cfg=generate_cfg, logging_handler=LoggingCallbackHandler(), messages=messages, _origin_messages=messages, preamble_text=preamble_text, dataset_ids=request.dataset_ids, enable_memori=request.enable_memory, memori_semantic_search_top_k=getattr(request, 'memori_semantic_search_top_k', None) or MEM0_SEMANTIC_SEARCH_TOP_K, trace_id=trace_id, shell_env=getattr(request, 'shell_env', None) or {}, ) # Prepare checkpoint messages as early as possible when creating config. if config.session_id: try: manager = get_checkpointer_manager() checkpointer = manager.checkpointer if checkpointer: await prepare_checkpoint_message(config, checkpointer) except Exception as e: logger.warning(f"Failed to load checkpointer: {e}") config.safe_print() return config @classmethod async def from_v2_request(cls, request, bot_config: Dict, project_dir: Optional[str] = None, messages: Optional[List] = None, generate_cfg: Optional[Dict] = None, model_name: Optional[str] = None, model_server: Optional[str] = None, api_key: Optional[str] = None): """Create configuration from a v2 request.""" # Delay imports to avoid circular dependencies. from .logging_handler import LoggingCallbackHandler from utils.fastapi_utils import get_preamble_text from utils.settings import ( MEM0_ENABLED, MEM0_SEMANTIC_SEARCH_TOP_K, ) from .checkpoint_utils import prepare_checkpoint_message from .checkpoint_manager import get_checkpointer_manager from utils.log_util.context import g if messages is None: messages = [] # Get trace_id from the global context. trace_id = None try: trace_id = getattr(g, 'trace_id', None) except LookupError: pass language = request.language or bot_config.get("language", "zh") preamble_text, system_prompt = get_preamble_text(language, bot_config.get("system_prompt")) enable_thinking = bot_config.get("enable_thinking", False) enable_memori = bot_config.get("enable_memory", False) enable_self_knowledge = bot_config.get("enable_self_knowledge", False) config = cls( bot_id=request.bot_id, api_key=api_key or bot_config.get("api_key"), model_name=model_name or bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"), model_server=model_server or bot_config.get("model_server", ""), language=language, system_prompt=system_prompt, mcp_settings=bot_config.get("mcp_settings", []), user_identifier=request.user_identifier, session_id=request.session_id, enable_thinking=enable_thinking, enable_self_knowledge=enable_self_knowledge, project_dir=project_dir, stream=request.stream, tool_response=request.tool_response, generate_cfg=generate_cfg or {}, # The v2 API also supports passing extra generate_cfg. logging_handler=LoggingCallbackHandler(), messages=messages, _origin_messages=messages, preamble_text=preamble_text, dataset_ids=bot_config.get("dataset_ids", []), # Get dataset_ids from backend configuration. enable_memori=enable_memori, memori_semantic_search_top_k=bot_config.get("memori_semantic_search_top_k", MEM0_SEMANTIC_SEARCH_TOP_K), trace_id=trace_id, shell_env=bot_config.get("shell_env") or {}, ) # Prepare checkpoint messages as early as possible when creating config. if config.session_id: try: manager = get_checkpointer_manager() checkpointer = manager.checkpointer if checkpointer: await prepare_checkpoint_message(config, checkpointer) except Exception as e: logger.warning(f"Failed to load checkpointer: {e}") config.safe_print() return config @classmethod async def from_v3_request(cls, request, bot_config: Dict, project_dir: Optional[str] = None, messages: Optional[List] = None, language: Optional[str] = None): """从v3请求创建配置 - 从数据库读取所有配置""" # 延迟导入避免循环依赖 from .logging_handler import LoggingCallbackHandler from utils.fastapi_utils import get_preamble_text from utils.settings import ( MEM0_ENABLED, MEM0_SEMANTIC_SEARCH_TOP_K, ) from .checkpoint_utils import prepare_checkpoint_message from .checkpoint_manager import get_checkpointer_manager from utils.log_util.context import g if messages is None: messages = [] # 从全局上下文获取 trace_id trace_id = None try: trace_id = getattr(g, 'trace_id', None) except LookupError: pass # 从数据库配置获取语言（如果没有传递） if language is None: language = bot_config.get("language", "zh") # 处理 system_prompt 和 preamble system_prompt_from_db = bot_config.get("system_prompt", "") preamble_text, system_prompt = get_preamble_text(language, system_prompt_from_db) # 从数据库配置获取其他参数 enable_thinking = bot_config.get("enable_thinking", False) enable_memori = bot_config.get("enable_memori", False) config = cls( bot_id=request.bot_id, api_key=bot_config.get("api_key", ""), model_name=bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"), model_server=bot_config.get("model_server", ""), language=language, system_prompt=system_prompt, mcp_settings=bot_config.get("mcp_settings", []), user_identifier=bot_config.get("user_identifier", ""), session_id=request.session_id, enable_thinking=enable_thinking, project_dir=project_dir, stream=request.stream, tool_response=bot_config.get("tool_response", True), generate_cfg={}, # v3接口不传递额外的generate_cfg logging_handler=LoggingCallbackHandler(), messages=messages, _origin_messages=messages, preamble_text=preamble_text, dataset_ids=bot_config.get("dataset_ids", []), enable_memori=enable_memori, memori_semantic_search_top_k=bot_config.get("memori_semantic_search_top_k", MEM0_SEMANTIC_SEARCH_TOP_K), trace_id=trace_id, shell_env=bot_config.get("shell_env") or {}, ) # 在创建 config 时尽早准备 checkpoint 消息 if config.session_id: try: manager = get_checkpointer_manager() checkpointer = manager.checkpointer if checkpointer: await prepare_checkpoint_message(config, checkpointer) except Exception as e: logger.warning(f"Failed to load checkpointer: {e}") config.safe_print() return config def invoke_config(self): """Return the configuration dictionary required by LangChain.""" config = {} callbacks = [] if self.logging_handler: callbacks.append(self.logging_handler) from utils.settings import LANGFUSE_ENABLED if LANGFUSE_ENABLED: from langfuse.langchain import CallbackHandler as LangfuseCallbackHandler trace_context = {"trace_id": self.trace_id} if self.trace_id else None langfuse_handler = LangfuseCallbackHandler( trace_context=trace_context, ) callbacks.append(langfuse_handler) langfuse_metadata = {} if LANGFUSE_ENABLED: if self.session_id: langfuse_metadata["langfuse_session_id"] = self.session_id if self.user_identifier: langfuse_metadata["langfuse_user_id"] = self.user_identifier if self.bot_id: langfuse_metadata["bot_id"] = self.bot_id if self.model_name: langfuse_metadata["model"] = self.model_name if callbacks: config["callbacks"] = callbacks if langfuse_metadata: config["metadata"] = langfuse_metadata if self.session_id: config["configurable"] = {"thread_id": self.session_id} return config def get_unique_cache_id(self) -> Optional[str]: """ Generate a unique cache key. Generates a unique cache key based on session_id, bot_id, system_prompt, mcp_settings, dataset_ids, and related fields. If session_id is missing, returns None to indicate that caching should not be used. Returns: str: The unique cache key, or None if session_id is missing. """ # Create the data used for generating the hash. cache_components = { 'bot_id': self.bot_id, 'system_prompt': self.system_prompt, 'mcp_settings': self.mcp_settings, 'model_name': self.model_name, 'language': self.language, 'generate_cfg': self.generate_cfg, 'enable_thinking': self.enable_thinking, 'enable_self_knowledge': self.enable_self_knowledge, 'user_identifier': self.user_identifier, 'session_id': self.session_id, 'dataset_ids': self.dataset_ids, # Include dataset_ids in cache key generation. 'project_dir': self.project_dir, # project_dir should also be included because dataset_ids affect it. } # Convert components to a string and concatenate them. cache_string = json.dumps(cache_components, sort_keys=True) # Generate a SHA256 hash to use as the cache key. cache_hash = hashlib.sha256(cache_string.encode('utf-8')).hexdigest() # Return a prefixed cache key to make debugging easier. return f"agent_cache_{cache_hash[:16]}" # Use the first 16 characters of the hash.