v2 实现shell_env支持
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朱潮
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32fd8c8656
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5a7aa06681
@ -203,7 +203,7 @@ class AgentConfig:
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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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shell_env=getattr(request, 'shell_env', None) or bot_config.get("shell_env") or {},
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shell_env=bot_config.get("shell_env") or {},
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)
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# 在创建 config 时尽早准备 checkpoint 消息
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@ -218,7 +218,82 @@ class AgentConfig:
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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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# 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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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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@ -12,10 +12,10 @@ logger = logging.getLogger('app')
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from utils import (
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Message, ChatRequest, ChatResponse, BatchSaveChatRequest, BatchSaveChatResponse
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)
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from utils.api_models import ChatRequestV2
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from utils.api_models import ChatRequestV2, ChatRequestV3
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from utils.fastapi_utils import (
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process_messages,
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create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config,
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create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config, fetch_bot_config_from_db,
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call_preamble_llm,
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create_stream_chunk
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)
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@ -768,6 +768,95 @@ async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[st
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raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
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@router.post("/api/v3/chat/completions")
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async def chat_completions_v3(request: ChatRequestV3, authorization: Optional[str] = Header(None)):
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"""
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Chat completions API v3 - 从数据库读取配置
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与 v2 相比，v3 从本地数据库读取所有配置参数，而不是从后端 API。
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前端只需要传递 bot_id 和 messages，其他配置从数据库自动读取。
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Args:
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request: ChatRequestV3 包含 bot_id, messages, stream, session_id
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authorization: 可选的认证头
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Returns:
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Union[ChatResponse, StreamingResponse]: Chat completion response or stream
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Required Parameters:
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- bot_id: str - 目标机器人ID（用户创建时填写的ID）
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- messages: List[Message] - 对话消息列表
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Optional Parameters:
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- stream: bool - 是否流式输出，默认false
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- session_id: str - 会话ID，用于保存聊天历史
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Configuration (from database):
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- model: 模型名称
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- api_key: API密钥
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- model_server: 模型服务器地址
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- language: 回复语言
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- tool_response: 是否包含工具响应
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- system_prompt: 系统提示词
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- dataset_ids: 数据集ID列表
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- mcp_settings: MCP服务器配置
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- user_identifier: 用户标识符
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Authentication:
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- 可选的 Authorization header（如果需要验证）
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"""
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try:
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# 获取bot_id（必需参数）
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bot_id = request.bot_id
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if not bot_id:
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raise HTTPException(status_code=400, detail="bot_id is required")
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# 可选的鉴权验证（如果传递了 authorization header）
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if authorization:
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expected_token = generate_v2_auth_token(bot_id)
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provided_token = extract_api_key_from_auth(authorization)
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if provided_token and provided_token != expected_token:
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logger.warning(f"Invalid auth token provided for v3 API, but continuing anyway")
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# 从数据库获取机器人配置
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bot_config = await fetch_bot_config_from_db(bot_id, request.user_identifier)
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# 构造类 v2 的请求格式
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# 从数据库配置中提取参数
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language = bot_config.get("language", "zh")
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# 创建项目目录（从数据库配置获取）
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project_dir = create_project_directory(
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bot_config.get("dataset_ids", []),
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bot_id,
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bot_config.get("skills", [])
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)
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# 处理消息
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messages = process_messages(request.messages, language)
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# 创建 AgentConfig 对象
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# 需要构造一个兼容 v2 的配置对象
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config = await AgentConfig.from_v3_request(
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request,
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bot_config,
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project_dir,
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messages,
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language
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)
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# 调用公共的agent创建和响应生成逻辑
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return await create_agent_and_generate_response(config)
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except HTTPException:
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raise
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except Exception as e:
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import traceback
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error_details = traceback.format_exc()
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logger.error(f"Error in chat_completions_v3: {str(e)}")
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logger.error(f"Full traceback: {error_details}")
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raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
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# ============================================================================
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# 聊天历史查询接口
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# ============================================================================
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@ -68,11 +68,27 @@ class ChatRequestV2(BaseModel):
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language: Optional[str] = "zh"
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user_identifier: Optional[str] = ""
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session_id: Optional[str] = None
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shell_env: Optional[Dict[str, str]] = None
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model_config = ConfigDict(extra='allow')
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class ChatRequestV3(BaseModel):
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"""
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v3 API 请求模型 - 从数据库读取配置
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所有配置参数从数据库读取，前端只需传递：
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- bot_id: Bot 的用户ID（用于从数据库查找配置）
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- messages: 对话消息列表
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- session_id: 可选的会话ID
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- user_identifier: 当前登录用户的用户名，用于标识用户身份
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"""
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messages: List[Message]
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bot_id: str
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stream: Optional[bool] = False
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session_id: Optional[str] = None
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user_identifier: Optional[str] = None
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class FileProcessRequest(BaseModel):
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unique_id: str
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files: Optional[Dict[str, List[str]]] = Field(default=None, description="Files organized by key groups. Each key maps to a list of file paths (supports zip files)")
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