feat：使用url阅览图片，标记表情包类消息
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SengokuCola
@@ -1,22 +1,17 @@
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"""Maisaka 对话循环服务。"""
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from base64 import b64decode
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from dataclasses import dataclass
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from datetime import datetime
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from io import BytesIO
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from time import perf_counter
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from typing import Any, Dict, List, Optional, Sequence
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from typing import List, Optional, Sequence
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import asyncio
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import json
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import random
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from PIL import Image as PILImage
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from pydantic import BaseModel, Field as PydanticField
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from rich.console import Group, RenderableType
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from rich.console import Group
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from rich.panel import Panel
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from rich.pretty import Pretty
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from rich.text import Text
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from src.cli.console import console
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from src.common.data_models.llm_service_data_models import LLMGenerationOptions
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@@ -35,6 +30,7 @@ from src.services.llm_service import LLMServiceClient
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from .builtin_tools import get_builtin_tools
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from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage
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from .message_adapter import format_speaker_content
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from .prompt_cli_renderer import PromptCLIVisualizer
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@dataclass(slots=True)
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@@ -44,6 +40,11 @@ class ChatResponse:
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content: Optional[str]
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tool_calls: List[ToolCall]
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raw_message: AssistantMessage
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selected_history_count: int
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prompt_tokens: int
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built_message_count: int
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completion_tokens: int
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total_tokens: int
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class ToolFilterSelection(BaseModel):
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@@ -468,259 +469,6 @@ class MaisakaChatLoopService:
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return extract_category_ids_from_result(generation_result.response or "")
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@staticmethod
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def _get_role_badge_style(role: str) -> str:
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"""返回终端中角色标签的样式。
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Args:
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role: 消息角色名称。
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Returns:
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str: Rich 可识别的样式字符串。
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"""
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if role == "system":
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return "bold white on blue"
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if role == "user":
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return "bold black on green"
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if role == "assistant":
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return "bold black on yellow"
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if role == "tool":
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return "bold white on magenta"
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return "bold white on bright_black"
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@staticmethod
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def _get_role_badge_label(role: str) -> str:
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"""返回终端中角色标签的中文名称。
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Args:
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role: 消息角色名称。
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Returns:
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str: 用于展示的中文角色名称。
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"""
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if role == "system":
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return "系统"
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if role == "user":
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return "用户"
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if role == "assistant":
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return "助手"
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if role == "tool":
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return "工具"
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return "未知"
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@staticmethod
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def _build_terminal_image_preview(image_base64: str) -> Optional[str]:
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"""构造终端图片预览字符画。
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Args:
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image_base64: 图片的 Base64 编码。
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Returns:
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Optional[str]: 生成成功时返回字符画文本，否则返回 ``None``。
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"""
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ascii_chars = " .:-=+*#%@"
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try:
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image_bytes = b64decode(image_base64)
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with PILImage.open(BytesIO(image_bytes)) as image:
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grayscale = image.convert("L")
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width, height = grayscale.size
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if width <= 0 or height <= 0:
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return None
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preview_width = max(8, int(global_config.maisaka.terminal_image_preview_width))
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preview_height = max(1, int(height * (preview_width / width) * 0.5))
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resized = grayscale.resize((preview_width, preview_height))
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pixels = list(resized.tobytes())
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except Exception:
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return None
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rows: List[str] = []
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for row_index in range(preview_height):
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row_pixels = pixels[row_index * preview_width : (row_index + 1) * preview_width]
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row = "".join(ascii_chars[min(len(ascii_chars) - 1, pixel * len(ascii_chars) // 256)] for pixel in row_pixels)
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rows.append(row)
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return "\n".join(rows)
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@classmethod
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def _render_message_content(cls, content: Any) -> RenderableType:
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"""将消息内容渲染为终端可展示对象。
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Args:
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content: 原始消息内容。
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Returns:
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RenderableType: Rich 可渲染对象。
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"""
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if isinstance(content, str):
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return Text(content)
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if isinstance(content, list):
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parts: List[RenderableType] = []
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for item in content:
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if isinstance(item, str):
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parts.append(Text(item))
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continue
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if isinstance(item, tuple) and len(item) == 2:
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image_format, image_base64 = item
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if isinstance(image_format, str) and isinstance(image_base64, str):
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approx_size = max(0, len(image_base64) * 3 // 4)
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size_text = f"{approx_size / 1024:.1f} KB" if approx_size >= 1024 else f"{approx_size} B"
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preview_parts: List[RenderableType] = [
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Text(f"图片格式 image/{image_format} {size_text}\nbase64 内容已省略", style="magenta")
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]
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if global_config.maisaka.terminal_image_preview:
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preview_text = cls._build_terminal_image_preview(image_base64)
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if preview_text:
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preview_parts.append(Text(preview_text, style="white"))
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parts.append(
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Panel(
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Group(*preview_parts),
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border_style="magenta",
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padding=(0, 1),
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)
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)
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continue
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if isinstance(item, dict) and item.get("type") == "text" and isinstance(item.get("text"), str):
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parts.append(Text(item["text"]))
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else:
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parts.append(Pretty(item, expand_all=True))
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return Group(*parts) if parts else Text("")
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if content is None:
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return Text("")
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return Pretty(content, expand_all=True)
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@staticmethod
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def _format_tool_call_for_display(tool_call: Any) -> Dict[str, Any]:
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"""将工具调用对象格式化为易读字典。
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Args:
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tool_call: 原始工具调用对象或字典。
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Returns:
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Dict[str, Any]: 适合终端展示的工具调用字典。
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"""
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if isinstance(tool_call, dict):
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function_info = tool_call.get("function", {})
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return {
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"id": tool_call.get("id"),
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"name": function_info.get("name", tool_call.get("name")),
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"arguments": function_info.get("arguments", tool_call.get("arguments")),
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}
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return {
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"id": getattr(tool_call, "call_id", getattr(tool_call, "id", None)),
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"name": getattr(tool_call, "func_name", getattr(tool_call, "name", None)),
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"arguments": getattr(tool_call, "args", getattr(tool_call, "arguments", None)),
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}
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def _render_tool_call_panel(self, tool_call: Any, index: int, parent_index: int) -> Panel:
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"""渲染单个工具调用面板。
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Args:
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tool_call: 原始工具调用对象。
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index: 工具调用在当前消息中的序号。
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parent_index: 所属消息的序号。
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Returns:
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Panel: 工具调用展示面板。
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"""
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title = Text.assemble(
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Text(" 工具调用 ", style="bold white on magenta"),
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Text(f" #{parent_index}.{index}", style="muted"),
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)
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return Panel(
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Pretty(self._format_tool_call_for_display(tool_call), expand_all=True),
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title=title,
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border_style="magenta",
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padding=(0, 1),
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)
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def _render_message_panel(self, message: Any, index: int) -> Panel:
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"""渲染单条消息面板。
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Args:
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message: 原始消息对象或字典。
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index: 消息序号。
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Returns:
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Panel: 终端展示面板。
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"""
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if isinstance(message, dict):
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raw_role = message.get("role", "unknown")
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content = message.get("content")
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tool_call_id = message.get("tool_call_id")
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else:
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raw_role = getattr(message, "role", "unknown")
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content = getattr(message, "content", None)
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tool_call_id = getattr(message, "tool_call_id", None)
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role = raw_role.value if isinstance(raw_role, RoleType) else str(raw_role)
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title = Text.assemble(
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Text(f" {self._get_role_badge_label(role)} ", style=self._get_role_badge_style(role)),
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Text(f" #{index}", style="muted"),
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)
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parts: List[RenderableType] = []
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if content not in (None, "", []):
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parts.append(Text(" 消息 ", style="bold cyan"))
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parts.append(self._render_message_content(content))
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if tool_call_id:
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parts.append(
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Text.assemble(
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Text(" 工具调用编号 ", style="bold magenta"),
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Text(" "),
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Text(str(tool_call_id), style="magenta"),
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)
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)
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if not parts:
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parts.append(Text("[空消息]", style="muted"))
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return Panel(
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Group(*parts),
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title=title,
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border_style="dim",
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padding=(0, 1),
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)
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@staticmethod
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def _format_token_count(token_count: int) -> str:
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"""格式化 token 数量展示文本。"""
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if token_count >= 10_000:
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return f"{token_count / 1000:.1f}k"
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return str(token_count)
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@classmethod
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def _build_prompt_stats_text(
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cls,
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*,
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selected_history_count: int,
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built_message_count: int,
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prompt_tokens: int,
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completion_tokens: int,
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total_tokens: int,
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) -> str:
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"""构造本轮 prompt 的统计信息文本。"""
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return (
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f"已选上下文消息数={selected_history_count} "
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f"大模型消息数={built_message_count} "
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f"实际输入Token={cls._format_token_count(prompt_tokens)} "
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f"输出Token={cls._format_token_count(completion_tokens)} "
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f"总Token={cls._format_token_count(total_tokens)}"
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)
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async def chat_loop_step(self, chat_history: List[LLMContextMessage]) -> ChatResponse:
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"""执行一轮 Maisaka 规划器请求。
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@@ -756,13 +504,10 @@ class MaisakaChatLoopService:
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else:
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all_tools = [*get_builtin_tools(), *self._extra_tools]
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ordered_panels: List[Panel] = []
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for index, msg in enumerate(built_messages, start=1):
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ordered_panels.append(self._render_message_panel(msg, index))
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tool_calls = getattr(msg, "tool_calls", None)
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if tool_calls:
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for tool_call_index, tool_call in enumerate(tool_calls, start=1):
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ordered_panels.append(self._render_tool_call_panel(tool_call, tool_call_index, index))
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ordered_panels = PromptCLIVisualizer.build_prompt_panels(
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built_messages,
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image_display_mode=global_config.maisaka.terminal_image_display_mode,
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)
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if global_config.maisaka.show_thinking and ordered_panels:
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console.print(
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@@ -795,7 +540,7 @@ class MaisakaChatLoopService:
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request_elapsed = perf_counter() - request_started_at
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logger.info(f"规划器请求完成，耗时={request_elapsed:.3f} 秒")
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prompt_stats_text = self._build_prompt_stats_text(
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prompt_stats_text = PromptCLIVisualizer.build_prompt_stats_text(
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selected_history_count=len(selected_history),
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built_message_count=len(built_messages),
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prompt_tokens=generation_result.prompt_tokens,
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@@ -826,6 +571,11 @@ class MaisakaChatLoopService:
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content=generation_result.response,
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tool_calls=generation_result.tool_calls or [],
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raw_message=raw_message,
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selected_history_count=len(selected_history),
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prompt_tokens=generation_result.prompt_tokens,
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built_message_count=len(built_messages),
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completion_tokens=generation_result.completion_tokens,
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total_tokens=generation_result.total_tokens,
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)
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@staticmethod
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