v2 实现shell_env支持

agent/agent_config.py

@@ -203,7 +203,82 @@ class AgentConfig:
             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=getattr(request, 'shell_env', None) or bot_config.get("shell_env") or {},
+            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
+
+    @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,
         )
 
         # 在创建 config 时尽早准备 checkpoint 消息

routes/chat.py

@@ -12,10 +12,10 @@ logger = logging.getLogger('app')
 from utils import (
     Message, ChatRequest, ChatResponse, BatchSaveChatRequest, BatchSaveChatResponse
 )
-from utils.api_models import ChatRequestV2
+from utils.api_models import ChatRequestV2, ChatRequestV3
 from utils.fastapi_utils import (
     process_messages,
-    create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config,
+    create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config, fetch_bot_config_from_db,
     call_preamble_llm,
     create_stream_chunk
 )
@@ -768,6 +768,95 @@ async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[st
         raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
 
 
+@router.post("/api/v3/chat/completions")
+async def chat_completions_v3(request: ChatRequestV3, authorization: Optional[str] = Header(None)):
+    """
+    Chat completions API v3 - 从数据库读取配置
+
+    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。
+    前端只需要传递 bot_id 和 messages，其他配置从数据库自动读取。
+
+    Args:
+        request: ChatRequestV3 包含 bot_id, messages, stream, session_id
+        authorization: 可选的认证头
+
+    Returns:
+        Union[ChatResponse, StreamingResponse]: Chat completion response or stream
+
+    Required Parameters:
+        - bot_id: str - 目标机器人ID（用户创建时填写的ID）
+        - messages: List[Message] - 对话消息列表
+
+    Optional Parameters:
+        - stream: bool - 是否流式输出，默认false
+        - session_id: str - 会话ID，用于保存聊天历史
+
+    Configuration (from database):
+        - model: 模型名称
+        - api_key: API密钥
+        - model_server: 模型服务器地址
+        - language: 回复语言
+        - tool_response: 是否包含工具响应
+        - system_prompt: 系统提示词
+        - dataset_ids: 数据集ID列表
+        - mcp_settings: MCP服务器配置
+        - user_identifier: 用户标识符
+
+    Authentication:
+        - 可选的 Authorization header（如果需要验证）
+    """
+    try:
+        # 获取bot_id（必需参数）
+        bot_id = request.bot_id
+        if not bot_id:
+            raise HTTPException(status_code=400, detail="bot_id is required")
+
+        # 可选的鉴权验证（如果传递了 authorization header）
+        if authorization:
+            expected_token = generate_v2_auth_token(bot_id)
+            provided_token = extract_api_key_from_auth(authorization)
+            if provided_token and provided_token != expected_token:
+                logger.warning(f"Invalid auth token provided for v3 API, but continuing anyway")
+
+        # 从数据库获取机器人配置
+        bot_config = await fetch_bot_config_from_db(bot_id, request.user_identifier)
+
+        # 构造类 v2 的请求格式
+        # 从数据库配置中提取参数
+        language = bot_config.get("language", "zh")
+        # 创建项目目录（从数据库配置获取）
+        project_dir = create_project_directory(
+            bot_config.get("dataset_ids", []),
+            bot_id,
+            bot_config.get("skills", [])
+        )
+
+        # 处理消息
+        messages = process_messages(request.messages, language)
+
+        # 创建 AgentConfig 对象
+        # 需要构造一个兼容 v2 的配置对象
+        config = await AgentConfig.from_v3_request(
+            request,
+            bot_config,
+            project_dir,
+            messages,
+            language
+        )
+
+        # 调用公共的agent创建和响应生成逻辑
+        return await create_agent_and_generate_response(config)
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        import traceback
+        error_details = traceback.format_exc()
+        logger.error(f"Error in chat_completions_v3: {str(e)}")
+        logger.error(f"Full traceback: {error_details}")
+        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
+
+
 # ============================================================================
 # 聊天历史查询接口
 # ============================================================================

utils/api_models.py

@@ -68,11 +68,27 @@ class ChatRequestV2(BaseModel):
     language: Optional[str] = "zh"
     user_identifier: Optional[str] = ""
     session_id: Optional[str] = None
-    shell_env: Optional[Dict[str, str]] = None
 
     model_config = ConfigDict(extra='allow')
 
 
+class ChatRequestV3(BaseModel):
+    """
+    v3 API 请求模型 - 从数据库读取配置
+
+    所有配置参数从数据库读取，前端只需传递：
+    - bot_id: Bot 的用户ID（用于从数据库查找配置）
+    - messages: 对话消息列表
+    - session_id: 可选的会话ID
+    - user_identifier: 当前登录用户的用户名，用于标识用户身份
+    """
+    messages: List[Message]
+    bot_id: str
+    stream: Optional[bool] = False
+    session_id: Optional[str] = None
+    user_identifier: Optional[str] = None
+
+
 class FileProcessRequest(BaseModel):
     unique_id: str
     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)")