338 lines
13 KiB
Python
338 lines
13 KiB
Python
"""Agent配置类，用于管理所有Agent相关的参数"""
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from typing import Optional, List, Dict, Any, TYPE_CHECKING
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from dataclasses import dataclass, field
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import logging
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import json
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import hashlib
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logger = logging.getLogger('app')
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@dataclass
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class AgentConfig:
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"""Agent配置类，包含创建和管理Agent所需的所有参数"""
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# 基础参数
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bot_id: str
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api_key: Optional[str] = None
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model_name: str = "qwen3-next"
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model_server: Optional[str] = None
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language: Optional[str] = "jp"
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# 配置参数
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system_prompt: Optional[str] = None
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mcp_settings: Optional[List[Dict]] = field(default_factory=list)
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generate_cfg: Optional[Dict] = None
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enable_thinking: bool = False
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# 上下文参数
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project_dir: Optional[str] = None
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user_identifier: Optional[str] = None
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session_id: Optional[str] = None
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dataset_ids: Optional[List[str]] = field(default_factory=list)
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trace_id: Optional[str] = None # 请求追踪ID，从 X-Request-ID header 获取
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# 响应控制参数
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stream: bool = False
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tool_response: bool = True
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preamble_text: Optional[str] = None
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messages: Optional[List] = field(default_factory=list)
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_origin_messages: Optional[List] = field(default_factory=list)
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logging_handler: Optional['LoggingCallbackHandler'] = None
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# Mem0 长期记忆配置
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enable_memori: bool = False # 保留名称以兼容 API，实际使用 Mem0
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memori_semantic_search_top_k: int = 20
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_mem0_context: Optional[str] = None # Mem0 召回的记忆上下文，供中间件间传递使用
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# Checkpointer 会话历史
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_session_history: Optional[List] = field(default_factory=list) # 从 checkpointer 读取的历史聊天记录
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def to_dict(self) -> Dict[str, Any]:
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"""转换为字典格式，用于传递给需要**kwargs的函数"""
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return {
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'bot_id': self.bot_id,
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'api_key': self.api_key,
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'model_name': self.model_name,
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'model_server': self.model_server,
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'language': self.language,
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'system_prompt': self.system_prompt,
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'mcp_settings': self.mcp_settings,
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'generate_cfg': self.generate_cfg,
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'enable_thinking': self.enable_thinking,
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'project_dir': self.project_dir,
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'user_identifier': self.user_identifier,
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'session_id': self.session_id,
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'dataset_ids': self.dataset_ids,
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'stream': self.stream,
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'tool_response': self.tool_response,
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'preamble_text': self.preamble_text,
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'messages': self.messages,
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'enable_memori': self.enable_memori,
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'memori_semantic_search_top_k': self.memori_semantic_search_top_k,
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'trace_id': self.trace_id,
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}
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def safe_print(self):
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"""安全打印配置，避免打印敏感信息"""
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safe_dict = self.to_dict().copy()
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if 'api_key' in safe_dict and isinstance(safe_dict['api_key'], str) and safe_dict['api_key'].startswith('sk-'):
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safe_dict['api_key'] = safe_dict['api_key'][:8] + '***' + safe_dict['api_key'][-6:]
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logger.info(f"config={json.dumps(safe_dict, ensure_ascii=False)}")
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@classmethod
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async def from_v1_request(cls, request, api_key: str, project_dir: Optional[str] = None, generate_cfg: Optional[Dict] = None, messages: Optional[List] = None):
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"""从v1请求创建配置"""
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# 延迟导入避免循环依赖
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from .logging_handler import LoggingCallbackHandler
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from utils.fastapi_utils import get_preamble_text
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from utils.settings import (
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MEM0_ENABLED,
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MEM0_SEMANTIC_SEARCH_TOP_K,
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)
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from .checkpoint_utils import prepare_checkpoint_message
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from .checkpoint_manager import get_checkpointer_manager
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from utils.log_util.context import g
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if messages is None:
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messages = []
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# 从全局上下文获取 trace_id
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trace_id = None
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try:
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trace_id = getattr(g, 'trace_id', None)
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except LookupError:
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pass
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preamble_text, system_prompt = get_preamble_text(request.language, request.system_prompt)
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config = cls(
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bot_id=request.bot_id,
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api_key=api_key,
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model_name=request.model,
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model_server=request.model_server,
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language=request.language,
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system_prompt=system_prompt,
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mcp_settings=request.mcp_settings,
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user_identifier=request.user_identifier,
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session_id=request.session_id,
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enable_thinking=request.enable_thinking,
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project_dir=project_dir,
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stream=request.stream,
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tool_response=request.tool_response,
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generate_cfg=generate_cfg,
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logging_handler=LoggingCallbackHandler(),
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messages=messages,
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_origin_messages=messages,
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preamble_text=preamble_text,
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dataset_ids=request.dataset_ids,
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enable_memori=request.enable_memory,
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memori_semantic_search_top_k=getattr(request, 'memori_semantic_search_top_k', None) or MEM0_SEMANTIC_SEARCH_TOP_K,
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trace_id=trace_id,
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)
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# 在创建 config 时尽早准备 checkpoint 消息
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if config.session_id:
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try:
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manager = get_checkpointer_manager()
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checkpointer = manager.checkpointer
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if checkpointer:
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await prepare_checkpoint_message(config, checkpointer)
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except Exception as e:
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logger.warning(f"Failed to load checkpointer: {e}")
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config.safe_print()
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return config
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@classmethod
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async def from_v2_request(cls, request, bot_config: Dict, project_dir: Optional[str] = None, messages: Optional[List] = None):
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"""从v2请求创建配置"""
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# 延迟导入避免循环依赖
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from .logging_handler import LoggingCallbackHandler
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from utils.fastapi_utils import get_preamble_text
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from utils.settings import (
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MEM0_ENABLED,
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MEM0_SEMANTIC_SEARCH_TOP_K,
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)
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from .checkpoint_utils import prepare_checkpoint_message
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from .checkpoint_manager import get_checkpointer_manager
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from utils.log_util.context import g
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if messages is None:
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messages = []
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# 从全局上下文获取 trace_id
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trace_id = None
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try:
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trace_id = getattr(g, 'trace_id', None)
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except LookupError:
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pass
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language = request.language or bot_config.get("language", "zh")
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preamble_text, system_prompt = get_preamble_text(language, bot_config.get("system_prompt"))
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enable_thinking = bot_config.get("enable_thinking", False)
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enable_memori = bot_config.get("enable_memory", False)
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config = cls(
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bot_id=request.bot_id,
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api_key=bot_config.get("api_key"),
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model_name=bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"),
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model_server=bot_config.get("model_server", ""),
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language=language,
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system_prompt=system_prompt,
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mcp_settings=bot_config.get("mcp_settings", []),
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user_identifier=request.user_identifier,
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session_id=request.session_id,
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enable_thinking=enable_thinking,
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project_dir=project_dir,
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stream=request.stream,
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tool_response=request.tool_response,
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generate_cfg={}, # v2接口不传递额外的generate_cfg
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logging_handler=LoggingCallbackHandler(),
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messages=messages,
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_origin_messages=messages,
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preamble_text=preamble_text,
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dataset_ids=bot_config.get("dataset_ids", []), # 从后端配置获取dataset_ids
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enable_memori=enable_memori,
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memori_semantic_search_top_k=bot_config.get("memori_semantic_search_top_k", MEM0_SEMANTIC_SEARCH_TOP_K),
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trace_id=trace_id,
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)
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# 在创建 config 时尽早准备 checkpoint 消息
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if config.session_id:
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try:
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manager = get_checkpointer_manager()
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checkpointer = manager.checkpointer
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if checkpointer:
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await prepare_checkpoint_message(config, checkpointer)
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except Exception as e:
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logger.warning(f"Failed to load checkpointer: {e}")
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config.safe_print()
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return config
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@classmethod
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async def from_v3_request(cls, request, bot_config: Dict, project_dir: Optional[str] = None, messages: Optional[List] = None, language: Optional[str] = None):
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"""从v3请求创建配置 - 从数据库读取所有配置"""
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# 延迟导入避免循环依赖
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from .logging_handler import LoggingCallbackHandler
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from utils.fastapi_utils import get_preamble_text
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from utils.settings import (
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MEM0_ENABLED,
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MEM0_SEMANTIC_SEARCH_TOP_K,
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)
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from .checkpoint_utils import prepare_checkpoint_message
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from .checkpoint_manager import get_checkpointer_manager
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from utils.log_util.context import g
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if messages is None:
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messages = []
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# 从全局上下文获取 trace_id
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trace_id = None
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try:
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trace_id = getattr(g, 'trace_id', None)
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except LookupError:
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pass
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# 从数据库配置获取语言（如果没有传递）
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if language is None:
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language = bot_config.get("language", "zh")
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# 处理 system_prompt 和 preamble
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system_prompt_from_db = bot_config.get("system_prompt", "")
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preamble_text, system_prompt = get_preamble_text(language, system_prompt_from_db)
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# 获取 robot_type
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robot_type = bot_config.get("robot_type", "general_agent")
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if robot_type == "catalog_agent":
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robot_type = "deep_agent"
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# 从数据库配置获取其他参数
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enable_thinking = bot_config.get("enable_thinking", False)
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enable_memori = bot_config.get("enable_memori", False)
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config = cls(
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bot_id=request.bot_id,
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api_key=bot_config.get("api_key", ""),
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model_name=bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"),
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model_server=bot_config.get("model_server", ""),
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language=language,
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system_prompt=system_prompt,
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mcp_settings=bot_config.get("mcp_settings", []),
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robot_type=robot_type,
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user_identifier=bot_config.get("user_identifier", ""),
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session_id=request.session_id,
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enable_thinking=enable_thinking,
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project_dir=project_dir,
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stream=request.stream,
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tool_response=bot_config.get("tool_response", True),
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generate_cfg={}, # v3接口不传递额外的generate_cfg
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logging_handler=LoggingCallbackHandler(),
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messages=messages,
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_origin_messages=messages,
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preamble_text=preamble_text,
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dataset_ids=bot_config.get("dataset_ids", []),
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enable_memori=enable_memori,
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memori_semantic_search_top_k=bot_config.get("memori_semantic_search_top_k", MEM0_SEMANTIC_SEARCH_TOP_K),
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trace_id=trace_id,
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)
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# 在创建 config 时尽早准备 checkpoint 消息
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if config.session_id:
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try:
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manager = get_checkpointer_manager()
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checkpointer = manager.checkpointer
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if checkpointer:
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await prepare_checkpoint_message(config, checkpointer)
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except Exception as e:
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logger.warning(f"Failed to load checkpointer: {e}")
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config.safe_print()
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return config
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def invoke_config(self):
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"""返回Langchain需要的配置字典"""
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config = {}
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if self.logging_handler:
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config["callbacks"] = [self.logging_handler]
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if self.session_id:
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config["configurable"] = {"thread_id": self.session_id}
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return config
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def get_unique_cache_id(self) -> Optional[str]:
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"""
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生成唯一的缓存键
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基于session_id, bot_id, system_prompt, mcp_settings, dataset_ids等生成唯一的缓存键。
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如果没有session_id，返回None，表示不使用缓存。
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Returns:
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str: 唯一的缓存键，如果没有session_id则返回None
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"""
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# 创建用于生成哈希的数据
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cache_components = {
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'bot_id': self.bot_id,
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'system_prompt': self.system_prompt,
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'mcp_settings': self.mcp_settings,
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'model_name': self.model_name,
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'language': self.language,
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'generate_cfg': self.generate_cfg,
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'enable_thinking': self.enable_thinking,
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'user_identifier': self.user_identifier,
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'session_id': self.session_id,
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'dataset_ids': self.dataset_ids, # 添加dataset_ids到缓存键生成
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'project_dir': self.project_dir, # 也应该包含project_dir因为dataset_ids影响project_dir
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}
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# 将组件转换为字符串并连接
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cache_string = json.dumps(cache_components, sort_keys=True)
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# 生成SHA256哈希作为缓存键
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cache_hash = hashlib.sha256(cache_string.encode('utf-8')).hexdigest()
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# 返回带有前缀的缓存键，便于调试
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return f"agent_cache_{cache_hash[:16]}" # 使用前16位哈希值
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