feat：重构maisaka的消息类型，添加打断功能
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SengokuCola
275
src/maisaka/context_messages.py
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275
src/maisaka/context_messages.py
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"""Maisaka 内部上下文消息抽象。"""
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from datetime import datetime
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from enum import Enum
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from io import BytesIO
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from typing import Optional
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import base64
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from PIL import Image as PILImage
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from src.chat.message_receive.message import SessionMessage
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from src.common.data_models.message_component_data_model import EmojiComponent, ImageComponent, MessageSequence, TextComponent
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from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
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from src.llm_models.payload_content.tool_option import ToolCall
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def _guess_image_format(image_bytes: bytes) -> Optional[str]:
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if not image_bytes:
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return None
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try:
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with PILImage.open(BytesIO(image_bytes)) as image:
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return image.format.lower() if image.format else None
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except Exception:
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return None
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def _build_message_from_sequence(
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role: RoleType,
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message_sequence: MessageSequence,
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fallback_text: str,
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*,
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tool_call_id: Optional[str] = None,
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tool_calls: Optional[list[ToolCall]] = None,
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) -> Optional[Message]:
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"""根据消息片段构造统一 LLM 消息。"""
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builder = MessageBuilder().set_role(role)
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if role == RoleType.Assistant and tool_calls:
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builder.set_tool_calls(tool_calls)
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if role == RoleType.Tool and tool_call_id:
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builder.add_tool_call(tool_call_id)
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has_content = False
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for component in message_sequence.components:
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if isinstance(component, TextComponent):
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if component.text:
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builder.add_text_content(component.text)
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has_content = True
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continue
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if isinstance(component, (EmojiComponent, ImageComponent)):
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image_format = _guess_image_format(component.binary_data)
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if image_format and component.binary_data:
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builder.add_image_content(image_format, base64.b64encode(component.binary_data).decode("utf-8"))
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has_content = True
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continue
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if component.content:
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builder.add_text_content(component.content)
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has_content = True
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if not has_content and fallback_text:
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builder.add_text_content(fallback_text)
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has_content = True
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if not has_content and not (role == RoleType.Assistant and tool_calls):
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return None
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return builder.build()
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class ReferenceMessageType(str, Enum):
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"""参考消息类型。"""
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CUSTOM = "custom"
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JARGON = "jargon"
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KNOWLEDGE = "knowledge"
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MEMORY = "memory"
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TOOL_HINT = "tool_hint"
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class LLMContextMessage(ABC):
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"""Maisaka 内部用于组织 LLM 上下文的统一消息抽象。"""
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timestamp: datetime
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@property
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@abstractmethod
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def role(self) -> str:
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"""返回 LLM 消息角色。"""
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@property
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@abstractmethod
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def processed_plain_text(self) -> str:
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"""返回可读的纯文本内容。"""
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@property
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def count_in_context(self) -> bool:
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"""是否占用普通 user/assistant 上下文窗口。"""
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return True
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@property
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def source(self) -> str:
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"""返回消息来源。"""
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return self.__class__.__name__
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@abstractmethod
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def to_llm_message(self) -> Optional[Message]:
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"""转换为统一 LLM 消息。"""
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def consume_once(self) -> bool:
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"""消费一次生命周期，返回是否继续保留。"""
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return True
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@dataclass(slots=True)
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class SessionBackedMessage(LLMContextMessage):
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"""真实会话上下文消息。"""
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raw_message: MessageSequence
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visible_text: str
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timestamp: datetime
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message_id: Optional[str] = None
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original_message: Optional[SessionMessage] = None
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source_kind: str = "user"
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@property
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def role(self) -> str:
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return RoleType.User.value
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@property
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def processed_plain_text(self) -> str:
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return self.visible_text
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@property
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def source(self) -> str:
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return self.source_kind
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def to_llm_message(self) -> Optional[Message]:
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return _build_message_from_sequence(
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RoleType.User,
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self.raw_message,
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self.processed_plain_text,
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)
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@classmethod
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def from_session_message(
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cls,
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session_message: SessionMessage,
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*,
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raw_message: MessageSequence,
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visible_text: str,
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source_kind: str = "user",
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) -> "SessionBackedMessage":
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"""从真实 SessionMessage 构造上下文消息。"""
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return cls(
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raw_message=raw_message,
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visible_text=visible_text,
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timestamp=session_message.timestamp,
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message_id=session_message.message_id,
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original_message=session_message,
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source_kind=source_kind,
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)
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@dataclass(slots=True)
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class ReferenceMessage(LLMContextMessage):
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"""参考消息。"""
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content: str
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timestamp: datetime
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reference_type: ReferenceMessageType = ReferenceMessageType.CUSTOM
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remaining_uses_value: Optional[int] = 1
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display_prefix: str = "[参考消息]"
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@property
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def role(self) -> str:
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return RoleType.User.value
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@property
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def processed_plain_text(self) -> str:
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return f"{self.display_prefix}\n{self.content}".strip()
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@property
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def count_in_context(self) -> bool:
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return False
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@property
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def source(self) -> str:
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return self.reference_type.value
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def to_llm_message(self) -> Optional[Message]:
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message_sequence = MessageSequence([TextComponent(self.processed_plain_text)])
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return _build_message_from_sequence(RoleType.User, message_sequence, self.processed_plain_text)
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def consume_once(self) -> bool:
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if self.remaining_uses_value is None:
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return True
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self.remaining_uses_value -= 1
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return self.remaining_uses_value > 0
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@dataclass(slots=True)
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class AssistantMessage(LLMContextMessage):
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"""内部 assistant 消息。"""
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content: str
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timestamp: datetime
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tool_calls: list[ToolCall] = field(default_factory=list)
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source_kind: str = "assistant"
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@property
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def role(self) -> str:
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return RoleType.Assistant.value
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@property
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def processed_plain_text(self) -> str:
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return self.content
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@property
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def count_in_context(self) -> bool:
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return self.source_kind != "perception"
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@property
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def source(self) -> str:
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return self.source_kind
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def to_llm_message(self) -> Optional[Message]:
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message_sequence = MessageSequence([])
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if self.content:
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message_sequence.text(self.content)
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return _build_message_from_sequence(
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RoleType.Assistant,
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message_sequence,
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self.content,
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tool_calls=self.tool_calls or None,
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)
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@dataclass(slots=True)
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class ToolResultMessage(LLMContextMessage):
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"""工具返回结果消息。"""
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content: str
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timestamp: datetime
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tool_call_id: str
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tool_name: str = ""
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success: bool = True
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@property
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def role(self) -> str:
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return RoleType.Tool.value
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@property
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def processed_plain_text(self) -> str:
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return self.content
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@property
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def count_in_context(self) -> bool:
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return False
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@property
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def source(self) -> str:
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return self.tool_name or "tool"
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def to_llm_message(self) -> Optional[Message]:
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message_sequence = MessageSequence([TextComponent(self.content)])
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return _build_message_from_sequence(
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RoleType.Tool,
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message_sequence,
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self.content,
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tool_call_id=self.tool_call_id,
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)
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