Agent/agent/trace/models.py

"""
Trace 和 Message 数据模型

Trace: 一次完整的 LLM 交互（单次调用或 Agent 任务）
Message: Trace 中的 LLM 消息，对应 LLM API 格式
"""

from dataclasses import dataclass, field
from datetime import datetime
from typing import Dict, Any, List, Optional, Literal, Union
import uuid

# ===== 消息线格式类型别名 =====
# 轻量 wire-format 类型，用于工具参数和 runner/LLM API 接口。
# 内部存储使用下方的 Message dataclass。

ChatMessage = Dict[str, Any]                          # 单条 OpenAI 格式消息
Messages = List[ChatMessage]                          # 消息列表
MessageContent = Union[str, List[Dict[str, str]]]     # content 字段（文本或多模态）


# 导入 TokenUsage（延迟导入避免循环依赖）
def _get_token_usage_class():
    from ..llm.usage import TokenUsage
    return TokenUsage


@dataclass
class Trace:
    """
    执行轨迹 - 一次完整的 LLM 交互

    单次调用: mode="call"
    Agent 模式: mode="agent"

    主 Trace 和 Sub-Trace 使用相同的数据结构。
    Sub-Trace 通过 parent_trace_id 和 parent_goal_id 关联父 Trace。
    """
    trace_id: str
    mode: Literal["call", "agent"]

    # Prompt 标识（可选）
    prompt_name: Optional[str] = None

    # Agent 模式特有
    task: Optional[str] = None
    agent_type: Optional[str] = None

    # 父子关系（Sub-Trace 特有）
    parent_trace_id: Optional[str] = None     # 父 Trace ID
    parent_goal_id: Optional[str] = None      # 哪个 Goal 启动的

    # 状态
    status: Literal["running", "completed", "failed", "stopped"] = "running"

    # 统计
    total_messages: int = 0      # 消息总数（改名自 total_steps）
    total_tokens: int = 0        # 总 tokens（向后兼容，= prompt + completion）
    total_prompt_tokens: int = 0      # 总输入 tokens
    total_completion_tokens: int = 0  # 总输出 tokens
    total_reasoning_tokens: int = 0   # 总推理 tokens（o1/o3, DeepSeek R1, Gemini thinking）
    total_cache_creation_tokens: int = 0  # 总缓存创建 tokens（Claude）
    total_cache_read_tokens: int = 0      # 总缓存读取 tokens（Claude）
    total_cost: float = 0.0
    total_duration_ms: int = 0   # 总耗时（毫秒）

    # 进度追踪（head）
    last_sequence: int = 0      # 最新 message 的 sequence
    head_sequence: int = 0      # 当前主路径的头节点 sequence（用于 build_llm_messages）
    last_event_id: int = 0      # 最新事件 ID（用于 WS 续传）

    # 配置
    uid: Optional[str] = None
    model: Optional[str] = None              # 默认模型
    tools: Optional[List[Dict]] = None       # 工具定义（整个 trace 共享）
    llm_params: Dict[str, Any] = field(default_factory=dict)  # LLM 参数（temperature 等）
    context: Dict[str, Any] = field(default_factory=dict)     # 其他元数据

    # 当前焦点 goal
    current_goal_id: Optional[str] = None

    # 结果
    result_summary: Optional[str] = None     # 执行结果摘要
    error_message: Optional[str] = None      # 错误信息

    # 时间
    created_at: datetime = field(default_factory=datetime.now)
    completed_at: Optional[datetime] = None

    @classmethod
    def create(
        cls,
        mode: Literal["call", "agent"],
        **kwargs
    ) -> "Trace":
        """创建新的 Trace"""
        return cls(
            trace_id=str(uuid.uuid4()),
            mode=mode,
            **kwargs
        )

    def to_dict(self) -> Dict[str, Any]:
        """转换为字典"""
        return {
            "trace_id": self.trace_id,
            "mode": self.mode,
            "prompt_name": self.prompt_name,
            "task": self.task,
            "agent_type": self.agent_type,
            "parent_trace_id": self.parent_trace_id,
            "parent_goal_id": self.parent_goal_id,
            "status": self.status,
            "total_messages": self.total_messages,
            "total_tokens": self.total_tokens,
            "total_prompt_tokens": self.total_prompt_tokens,
            "total_completion_tokens": self.total_completion_tokens,
            "total_reasoning_tokens": self.total_reasoning_tokens,
            "total_cache_creation_tokens": self.total_cache_creation_tokens,
            "total_cache_read_tokens": self.total_cache_read_tokens,
            "total_cost": self.total_cost,
            "total_duration_ms": self.total_duration_ms,
            "last_sequence": self.last_sequence,
            "head_sequence": self.head_sequence,
            "last_event_id": self.last_event_id,
            "uid": self.uid,
            "model": self.model,
            "tools": self.tools,
            "llm_params": self.llm_params,
            "context": self.context,
            "current_goal_id": self.current_goal_id,
            "result_summary": self.result_summary,
            "error_message": self.error_message,
            "created_at": self.created_at.isoformat() if self.created_at else None,
            "completed_at": self.completed_at.isoformat() if self.completed_at else None,
        }


@dataclass
class Message:
    """
    执行消息 - Trace 中的 LLM 消息

    对应 LLM API 消息格式（system/user/assistant/tool），通过 goal_id 关联 Goal。

    description 字段自动生成规则：
    - system: 取 content 前 200 字符
    - user: 取 content 前 200 字符
    - assistant: 优先取 content，若无 content 则生成 "tool call: XX, XX"
    - tool: 使用 tool name
    """
    message_id: str
    trace_id: str
    role: Literal["system", "user", "assistant", "tool"]   # 和 LLM API 一致
    sequence: int                        # 全局顺序
    parent_sequence: Optional[int] = None  # 父消息的 sequence（构成消息树）
    status: Literal["active", "abandoned"] = "active"  # [已弃用] 由 parent_sequence 树结构替代
    goal_id: Optional[str] = None        # 关联的 Goal 内部 ID（None = 还没有创建 Goal）
    description: str = ""                # 消息描述（系统自动生成）
    tool_call_id: Optional[str] = None   # tool 消息关联对应的 tool_call
    content: Any = None                  # 消息内容（和 LLM API 格式一致）

    # 元数据
    prompt_tokens: Optional[int] = None  # 输入 tokens
    completion_tokens: Optional[int] = None  # 输出 tokens
    reasoning_tokens: Optional[int] = None   # 推理 tokens（o1/o3, DeepSeek R1, Gemini thinking）
    cache_creation_tokens: Optional[int] = None  # 缓存创建 tokens（Claude）
    cache_read_tokens: Optional[int] = None      # 缓存读取 tokens（Claude）
    cost: Optional[float] = None
    duration_ms: Optional[int] = None
    created_at: datetime = field(default_factory=datetime.now)
    abandoned_at: Optional[datetime] = None  # [已弃用] 由 parent_sequence 树结构替代

    # LLM 响应信息（仅 role="assistant" 时使用）
    finish_reason: Optional[str] = None  # stop, length, tool_calls, content_filter 等

    @property
    def tokens(self) -> int:
        """动态计算总 tokens（向后兼容，input + output）"""
        return (self.prompt_tokens or 0) + (self.completion_tokens or 0)

    @property
    def all_tokens(self) -> int:
        """所有 tokens（包括 reasoning）"""
        return self.tokens + (self.reasoning_tokens or 0)

    def get_usage(self):
        """获取 TokenUsage 对象"""
        TokenUsage = _get_token_usage_class()
        return TokenUsage(
            input_tokens=self.prompt_tokens or 0,
            output_tokens=self.completion_tokens or 0,
            reasoning_tokens=self.reasoning_tokens or 0,
            cache_creation_tokens=self.cache_creation_tokens or 0,
            cache_read_tokens=self.cache_read_tokens or 0,
        )

    def to_llm_dict(self) -> Dict[str, Any]:
        """转换为 OpenAI SDK 格式的消息字典（用于 LLM 调用）"""
        msg: Dict[str, Any] = {"role": self.role}

        if self.role == "tool":
            # tool message: tool_call_id + name + content
            if self.tool_call_id:
                msg["tool_call_id"] = self.tool_call_id
                msg["name"] = self.description or "unknown"
            if isinstance(self.content, dict):
                result = self.content.get("result", self.content)
                # result 可能是 list（含图片的多模态内容）或字符串
                msg["content"] = result if isinstance(result, list) else str(result)
            else:
                msg["content"] = str(self.content) if self.content is not None else ""

        elif self.role == "assistant":
            # assistant message: content(text) + tool_calls
            if isinstance(self.content, dict):
                msg["content"] = self.content.get("text", "") or ""
                if self.content.get("tool_calls"):
                    msg["tool_calls"] = self.content["tool_calls"]
            elif isinstance(self.content, str):
                msg["content"] = self.content
            else:
                msg["content"] = ""

        else:
            # system / user message: content 直接传
            msg["content"] = self.content

        return msg

    @classmethod
    def from_llm_dict(
        cls,
        d: Dict[str, Any],
        trace_id: str,
        sequence: int,
        goal_id: Optional[str] = None,
        parent_sequence: Optional[int] = None,
    ) -> "Message":
        """从 OpenAI SDK 格式创建 Message"""
        role = d["role"]

        if role == "assistant":
            content = {"text": d.get("content", ""), "tool_calls": d.get("tool_calls")}
        elif role == "tool":
            content = {"tool_name": d.get("name", "unknown"), "result": d.get("content", "")}
        else:
            content = d.get("content", "")

        return cls.create(
            trace_id=trace_id,
            role=role,
            sequence=sequence,
            goal_id=goal_id,
            parent_sequence=parent_sequence,
            content=content,
            tool_call_id=d.get("tool_call_id"),
        )

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> "Message":
        """从字典创建 Message（处理向后兼容）"""
        # 过滤掉已删除的字段
        filtered_data = {k: v for k, v in data.items() if k not in ["tokens", "available_tools"]}

        # 解析 datetime
        if filtered_data.get("created_at") and isinstance(filtered_data["created_at"], str):
            filtered_data["created_at"] = datetime.fromisoformat(filtered_data["created_at"])
        if filtered_data.get("abandoned_at") and isinstance(filtered_data["abandoned_at"], str):
            filtered_data["abandoned_at"] = datetime.fromisoformat(filtered_data["abandoned_at"])

        # 向后兼容：旧消息没有 status 字段，默认 active
        if "status" not in filtered_data:
            filtered_data["status"] = "active"

        # 向后兼容：旧消息没有 parent_sequence 字段
        if "parent_sequence" not in filtered_data:
            filtered_data["parent_sequence"] = None

        return cls(**filtered_data)

    @classmethod
    def create(
        cls,
        trace_id: str,
        role: Literal["system", "user", "assistant", "tool"],
        sequence: int,
        goal_id: Optional[str] = None,
        content: Any = None,
        tool_call_id: Optional[str] = None,
        parent_sequence: Optional[int] = None,
        prompt_tokens: Optional[int] = None,
        completion_tokens: Optional[int] = None,
        reasoning_tokens: Optional[int] = None,
        cache_creation_tokens: Optional[int] = None,
        cache_read_tokens: Optional[int] = None,
        cost: Optional[float] = None,
        duration_ms: Optional[int] = None,
        finish_reason: Optional[str] = None,
    ) -> "Message":
        """创建新的 Message，自动生成 description"""
        description = cls._generate_description(role, content)

        return cls(
            message_id=f"{trace_id}-{sequence:04d}",
            trace_id=trace_id,
            role=role,
            sequence=sequence,
            parent_sequence=parent_sequence,
            goal_id=goal_id,
            content=content,
            description=description,
            tool_call_id=tool_call_id,
            prompt_tokens=prompt_tokens,
            completion_tokens=completion_tokens,
            reasoning_tokens=reasoning_tokens,
            cache_creation_tokens=cache_creation_tokens,
            cache_read_tokens=cache_read_tokens,
            cost=cost,
            duration_ms=duration_ms,
            finish_reason=finish_reason,
        )

    @staticmethod
    def _generate_description(role: str, content: Any) -> str:
        """
        自动生成 description

        - system: 取 content 前 200 字符
        - user: 取 content 前 200 字符
        - assistant: 优先取 content，若无 content 则生成 "tool call: XX, XX"
        - tool: 使用 tool name
        """
        if role == "system":
            # system 消息：直接截取文本
            if isinstance(content, str):
                return content[:200] + "..." if len(content) > 200 else content
            return "system prompt"

        elif role == "user":
            # user 消息：直接截取文本
            if isinstance(content, str):
                return content[:200] + "..." if len(content) > 200 else content
            return "user message"

        elif role == "assistant":
            # assistant 消息：content 是字典，可能包含 text 和 tool_calls
            if isinstance(content, dict):
                # 优先返回文本内容
                if content.get("text"):
                    text = content["text"]
                    # 截断过长的文本
                    return text[:200] + "..." if len(text) > 200 else text

                # 如果没有文本，检查 tool_calls
                if content.get("tool_calls"):
                    tool_calls = content["tool_calls"]
                    if isinstance(tool_calls, list):
                        tool_descriptions = []
                        for tc in tool_calls:
                            if isinstance(tc, dict) and tc.get("function", {}).get("name"):
                                tool_name = tc["function"]["name"]
                                # 提取参数并截断到 100 字符
                                tool_args = tc["function"].get("arguments", "{}")
                                if isinstance(tool_args, str):
                                    args_str = tool_args
                                else:
                                    import json
                                    args_str = json.dumps(tool_args, ensure_ascii=False)
                                args_display = args_str[:100] + "..." if len(args_str) > 100 else args_str
                                tool_descriptions.append(f"{tool_name}({args_display})")
                        if tool_descriptions:
                            return "tool call: " + ", ".join(tool_descriptions)

            # 如果 content 是字符串
            if isinstance(content, str):
                return content[:200] + "..." if len(content) > 200 else content

            return "assistant message"

        elif role == "tool":
            # tool 消息：从 content 中提取 tool name
            if isinstance(content, dict):
                if content.get("tool_name"):
                    return content["tool_name"]

            # 如果是字符串，尝试解析
            if isinstance(content, str):
                return content[:100] + "..." if len(content) > 100 else content

            return "tool result"

        return ""

    def to_dict(self) -> Dict[str, Any]:
        """转换为字典"""
        result = {
            "message_id": self.message_id,
            "trace_id": self.trace_id,
            "role": self.role,
            "sequence": self.sequence,
            "parent_sequence": self.parent_sequence,
            "status": self.status,
            "goal_id": self.goal_id,
            "tool_call_id": self.tool_call_id,
            "content": self.content,
            "description": self.description,
            "tokens": self.tokens,  # 使用 @property 动态计算
            "prompt_tokens": self.prompt_tokens,
            "completion_tokens": self.completion_tokens,
            "cost": self.cost,
            "duration_ms": self.duration_ms,
            "finish_reason": self.finish_reason,
            "created_at": self.created_at.isoformat() if self.created_at else None,
        }
        # 只添加非空的可选字段
        if self.abandoned_at:
            result["abandoned_at"] = self.abandoned_at.isoformat()
        if self.reasoning_tokens is not None:
            result["reasoning_tokens"] = self.reasoning_tokens
        if self.cache_creation_tokens is not None:
            result["cache_creation_tokens"] = self.cache_creation_tokens
        if self.cache_read_tokens is not None:
            result["cache_read_tokens"] = self.cache_read_tokens
        return result


# ===== 已弃用：Step 模型（保留用于向后兼容）=====

# Step 类型
StepType = Literal[
    "goal", "thought", "evaluation", "response",
    "action", "result", "memory_read", "memory_write",
]

# Step 状态
StepStatus = Literal[
    "planned", "in_progress", "awaiting_approval",
    "completed", "failed", "skipped",
]


@dataclass
class Step:
    """
    [已弃用] 执行步骤 - 使用 Message 模型替代

    保留用于向后兼容
    """
    step_id: str
    trace_id: str
    step_type: StepType
    status: StepStatus
    sequence: int
    parent_id: Optional[str] = None
    description: str = ""
    data: Dict[str, Any] = field(default_factory=dict)
    summary: Optional[str] = None
    has_children: bool = False
    children_count: int = 0
    duration_ms: Optional[int] = None
    tokens: Optional[int] = None
    cost: Optional[float] = None
    created_at: datetime = field(default_factory=datetime.now)

    @classmethod
    def create(
        cls,
        trace_id: str,
        step_type: StepType,
        sequence: int,
        status: StepStatus = "completed",
        description: str = "",
        data: Dict[str, Any] = None,
        parent_id: Optional[str] = None,
        summary: Optional[str] = None,
        duration_ms: Optional[int] = None,
        tokens: Optional[int] = None,
        cost: Optional[float] = None,
    ) -> "Step":
        """创建新的 Step"""
        return cls(
            step_id=str(uuid.uuid4()),
            trace_id=trace_id,
            step_type=step_type,
            status=status,
            sequence=sequence,
            parent_id=parent_id,
            description=description,
            data=data or {},
            summary=summary,
            duration_ms=duration_ms,
            tokens=tokens,
            cost=cost,
        )

    def to_dict(self, view: str = "full") -> Dict[str, Any]:
        """
        转换为字典

        Args:
            view: "compact" - 不返回大字段
                  "full" - 返回完整数据
        """
        result = {
            "step_id": self.step_id,
            "trace_id": self.trace_id,
            "step_type": self.step_type,
            "status": self.status,
            "sequence": self.sequence,
            "parent_id": self.parent_id,
            "description": self.description,
            "summary": self.summary,
            "has_children": self.has_children,
            "children_count": self.children_count,
            "duration_ms": self.duration_ms,
            "tokens": self.tokens,
            "cost": self.cost,
            "created_at": self.created_at.isoformat() if self.created_at else None,
        }

        # 处理 data 字段
        if view == "compact":
            data_copy = self.data.copy()
            for key in ["output", "content", "full_output", "full_content"]:
                data_copy.pop(key, None)
            result["data"] = data_copy
        else:
            result["data"] = self.data

        return result