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feat：给planner最终展示添加统一结果

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This commit is contained in:
SengokuCola
2026-04-07 18:16:12 +08:00
parent 6968879a04
commit 2233ee1af4
12 changed files with 651 additions and 235 deletions
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5
src/maisaka/builtin_tool/reply.py
View File

@@ -5,6 +5,7 @@ from typing import Optional
from src.chat.replyer.replyer_manager import replyer_manager
from src.cli.maisaka_cli_sender import CLI_PLATFORM_NAME, render_cli_message
from src.common.data_models.reply_generation_data_models import ReplyGenerationResult
from src.common.logger import get_logger
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
from src.services import send_service
@@ -57,10 +58,10 @@ def get_tool_spec() -> ToolSpec:
)
def _build_monitor_metadata(reply_result: object) -> dict[str, object]:
def _build_monitor_metadata(reply_result: ReplyGenerationResult) -> dict[str, object]:
"""从 reply 结果中提取统一监控详情。"""
monitor_detail = getattr(reply_result, "monitor_detail", None)
monitor_detail = reply_result.monitor_detail
if isinstance(monitor_detail, dict):
return {"monitor_detail": monitor_detail}
return {}

183
src/maisaka/builtin_tool/send_emoji.py
View File

@@ -66,6 +66,25 @@ def get_tool_spec() -> ToolSpec:
)
def _normalize_candidate_emotions(emoji: MaiEmoji) -> list[str]:
"""清洗候选表情上的情绪标签。"""
raw_emotions = getattr(emoji, "emotion", None)
if isinstance(raw_emotions, list) and raw_emotions:
return [str(item).strip() for item in raw_emotions if str(item).strip()]
description = str(getattr(emoji, "description", "") or "").strip()
if not description:
return []
normalized_description = (
description.replace("，", ",")
.replace("、", ",")
.replace("；", ",")
)
return [item.strip() for item in normalized_description.split(",") if item.strip()]
async def _load_emoji_bytes(emoji: MaiEmoji) -> bytes:
"""读取单个表情包图片字节。"""
@@ -211,13 +230,126 @@ async def _build_emoji_candidate_message(emojis: list[MaiEmoji]) -> SessionBacke
)
def _build_emoji_candidate_summary(emojis: list[MaiEmoji]) -> str:
"""构建供监控展示使用的候选表情摘要。"""
summary_lines: list[str] = []
for index, emoji in enumerate(emojis, start=1):
description = emoji.description.strip() or "（无描述）"
emotions = "、".join(_normalize_candidate_emotions(emoji)) or "无"
summary_lines.append(f"{index}. 描述：{description}")
summary_lines.append(f" 情绪：{emotions}")
return "\n".join(summary_lines).strip()
def _build_send_emoji_prompt_preview(
*,
system_prompt: str,
requested_emotion: str,
grid_rows: int,
grid_columns: int,
sampled_emojis: list[MaiEmoji],
) -> str:
"""构建表情选择子代理的文本预览。"""
task_text = (
"[选择任务]\n"
f"requested_emotion: {requested_emotion or '未指定'}\n"
f"候选总数: {len(sampled_emojis)}\n"
f"拼图布局: {grid_rows}x{grid_columns}\n"
"请只输出 JSON。"
)
candidate_summary = _build_emoji_candidate_summary(sampled_emojis)
return (
f"[System Prompt]\n{system_prompt}\n\n"
f"{task_text}\n\n"
f"[候选表情摘要]\n{candidate_summary or '无候选表情'}"
).strip()
def _build_send_emoji_monitor_detail(
*,
prompt_text: str = "",
reasoning_text: str = "",
output_text: str = "",
metrics: Optional[Dict[str, Any]] = None,
extra_sections: Optional[list[dict[str, str]]] = None,
) -> Dict[str, Any]:
"""构建 emotion tool 统一监控详情。"""
detail: Dict[str, Any] = {}
if prompt_text.strip():
detail["prompt_text"] = prompt_text.strip()
if reasoning_text.strip():
detail["reasoning_text"] = reasoning_text.strip()
if output_text.strip():
detail["output_text"] = output_text.strip()
if isinstance(metrics, dict) and metrics:
detail["metrics"] = dict(metrics)
normalized_sections = [
{
"title": str(section.get("title") or "").strip(),
"content": str(section.get("content") or "").strip(),
}
for section in extra_sections or []
if isinstance(section, dict)
and str(section.get("title") or "").strip()
and str(section.get("content") or "").strip()
]
if normalized_sections:
detail["extra_sections"] = normalized_sections
return detail
def _build_send_emoji_monitor_metadata(
selection_metadata: Dict[str, Any],
*,
requested_emotion: str,
send_result: Optional[Any] = None,
error_message: str = "",
) -> Dict[str, Any]:
"""根据表情选择与发送结果构建统一监控 metadata。"""
raw_detail = selection_metadata.get("monitor_detail")
detail = dict(raw_detail) if isinstance(raw_detail, dict) else {}
extra_sections = list(detail.get("extra_sections", [])) if isinstance(detail.get("extra_sections"), list) else []
if send_result is not None:
result_lines = [
f"请求情绪：{requested_emotion or '未指定'}",
f"命中情绪：{send_result.matched_emotion or '未命中'}",
f"表情描述：{send_result.description or '无描述'}",
f"情绪标签：{'、'.join(send_result.emotions) if send_result.emotions else '无'}",
f"发送结果：{send_result.message or ('成功' if send_result.success else '失败')}",
]
extra_sections.append({
"title": "表情发送结果",
"content": "\n".join(result_lines),
})
elif error_message.strip():
extra_sections.append({
"title": "表情发送结果",
"content": (
f"请求情绪：{requested_emotion or '未指定'}\n"
f"发送结果：{error_message.strip()}"
),
})
if extra_sections:
detail["extra_sections"] = extra_sections
if detail:
return {"monitor_detail": detail}
return {}
async def _select_emoji_with_sub_agent(
tool_ctx: BuiltinToolRuntimeContext,
requested_emotion: str,
reasoning: str,
context_texts: list[str],
sample_size: int,
selection_metadata: Optional[Dict[str, str]] = None,
selection_metadata: Optional[Dict[str, Any]] = None,
) -> tuple[MaiEmoji | None, str]:
"""通过临时子代理从候选表情包中选出一个结果。"""
@@ -255,7 +387,15 @@ async def _select_emoji_with_sub_agent(
remaining_uses_value=1,
display_prefix="[表情包选择任务]",
)
prompt_preview = _build_send_emoji_prompt_preview(
system_prompt=system_prompt,
requested_emotion=requested_emotion,
grid_rows=grid_rows,
grid_columns=grid_columns,
sampled_emojis=sampled_emojis,
)
selection_started_at = datetime.now()
response = await tool_ctx.runtime.run_sub_agent(
context_message_limit=_EMOJI_SUB_AGENT_CONTEXT_LIMIT,
system_prompt=system_prompt,
@@ -266,16 +406,40 @@ async def _select_emoji_with_sub_agent(
schema=EmojiSelectionResult,
),
)
selection_duration_ms = round((datetime.now() - selection_started_at).total_seconds() * 1000, 2)
selection_metrics: Dict[str, Any] = {
"prompt_tokens": response.prompt_tokens,
"completion_tokens": response.completion_tokens,
"total_tokens": response.total_tokens,
"overall_ms": selection_duration_ms,
}
try:
selection = EmojiSelectionResult.model_validate_json(response.content or "")
except Exception as exc:
logger.warning(f"{tool_ctx.runtime.log_prefix} 表情包子代理结果解析失败，将回退到候选首项: {exc}")
if selection_metadata is not None:
selection_metadata["monitor_detail"] = _build_send_emoji_monitor_detail(
prompt_text=prompt_preview,
output_text=response.content or "",
metrics=selection_metrics,
extra_sections=[{
"title": "解析异常",
"content": str(exc),
}],
)
fallback_emoji = sampled_emojis[0] if sampled_emojis else None
return fallback_emoji, ""
if selection_metadata is not None:
selection_metadata["reason"] = selection.reason.strip()
selection_metadata["monitor_detail"] = _build_send_emoji_monitor_detail(
prompt_text=prompt_preview,
reasoning_text=selection.reason,
output_text=response.content or "",
metrics=selection_metrics,
)
emoji_index = int(selection.emoji_index)
if emoji_index < 1 or emoji_index > len(sampled_emojis):
@@ -310,7 +474,7 @@ async def handle_tool(
"matched_emotion": "",
"reason": "",
}
selection_metadata: Dict[str, str] = {"reason": ""}
selection_metadata: Dict[str, Any] = {"reason": "", "monitor_detail": {}}
logger.info(f"{tool_ctx.runtime.log_prefix} 触发表情包发送工具，请求情绪={emotion!r}")
@@ -336,6 +500,11 @@ async def handle_tool(
invocation.tool_name,
structured_result["message"],
structured_content=structured_result,
metadata=_build_send_emoji_monitor_metadata(
selection_metadata,
requested_emotion=emotion,
error_message=structured_result["message"],
),
)
if send_result.success:
@@ -358,6 +527,11 @@ async def handle_tool(
invocation.tool_name,
selection_metadata["reason"] or _EMOJI_SUCCESS_MESSAGE,
structured_content=structured_result,
metadata=_build_send_emoji_monitor_metadata(
selection_metadata,
requested_emotion=emotion,
send_result=send_result,
),
)
structured_result["description"] = send_result.description
@@ -373,4 +547,9 @@ async def handle_tool(
invocation.tool_name,
structured_result["message"],
structured_content=structured_result,
metadata=_build_send_emoji_monitor_metadata(
selection_metadata,
requested_emotion=emotion,
send_result=send_result,
),
)