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feat：优化maisaka

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This commit is contained in:
SengokuCola
2026-03-24 01:30:36 +08:00
parent 620f682c38
commit f431d78bff
9 changed files with 999 additions and 1099 deletions
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108
src/maisaka/llm_service.py
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@@ -1,11 +1,14 @@
"""
"""
MaiSaka LLM 服务 - 使用主项目 LLM 系统
将主项目的 LLMRequest 适配为 MaiSaka 需要的接口
"""
from datetime import datetime
import json
import random
from dataclasses import dataclass
from typing import Any, List, Literal, Optional
import json
from rich.console import Group
from rich.panel import Panel
@@ -13,7 +16,7 @@ from rich.pretty import Pretty
from rich.text import Text
from src.common.logger import get_logger
from src.config.config import config_manager
from src.config.config import config_manager, global_config
from src.llm_models.payload_content.message import MessageBuilder, RoleType
from src.llm_models.payload_content.tool_option import ToolCall as ToolCallOption, ToolOption
from src.llm_models.utils_model import LLMRequest
@@ -58,7 +61,13 @@ class ChatResponse:
def build_message(role: str, content: str, msg_type: MessageType = "user", **kwargs) -> dict:
"""构建消息字典，包含消息类型标记。"""
msg = {"role": role, "content": content, MSG_TYPE_FIELD: msg_type, **kwargs}
msg = {
"role": role,
"content": content,
MSG_TYPE_FIELD: msg_type,
"_time": datetime.now().strftime("%H:%M:%S"),
**kwargs,
}
return msg
@@ -107,8 +116,8 @@ class MaiSakaLLMService:
# 初始化 LLMRequest 实例（只使用 tool_use 和 replyer）
self._llm_tool_use = LLMRequest(model_set=self._model_configs.tool_use, request_type="maisaka_tool_use")
# 主对话也使用 tool_use 模型（因为需要工具调用支持）
self._llm_chat = self._llm_tool_use
# 分析模块也使用 tool_use 模型
self._llm_planner = LLMRequest(model_set=self._model_configs.planner, request_type="maisaka_planner")
self._llm_chat = self._llm_planner
self._llm_utils = self._llm_tool_use
# 回复生成使用 replyer 模型
self._llm_replyer = LLMRequest(model_set=self._model_configs.replyer, request_type="maisaka_replyer")
@@ -116,6 +125,9 @@ class MaiSakaLLMService:
# 尝试修复数据库 schema（忽略错误）
self._try_fix_database_schema()
# 构建人设信息
personality_prompt = self._build_personality_prompt()
# 加载系统提示词
if chat_system_prompt is None:
try:
@@ -130,6 +142,7 @@ class MaiSakaLLMService:
tools_section += "\n• list_files() — 获取 mai_files 目录下所有文件的元信息列表。"
chat_prompt.add_context("file_tools_section", tools_section if tools_section else "")
chat_prompt.add_context("identity", personality_prompt)
import asyncio
loop = asyncio.new_event_loop()
@@ -141,15 +154,15 @@ class MaiSakaLLMService:
loop.close()
except Exception as e:
logger.error(f"加载系统提示词失败: {e}")
self._chat_system_prompt = "你是一个友好的 AI 助手。"
self._chat_system_prompt = f"{personality_prompt}\n\n你是一个友好的 AI 助手。"
else:
self._chat_system_prompt = chat_system_prompt
# 获取模型名称用于显示
self._model_name = (
self._model_configs.tool_use.model_list[0] if self._model_configs.tool_use.model_list else "未配置"
self._model_configs.planner.model_list[0] if self._model_configs.planner.model_list else "未配置"
)
# 加载子模块提示词
self._emotion_prompt: Optional[str] = None
self._cognition_prompt: Optional[str] = None
@@ -200,6 +213,37 @@ class MaiSakaLLMService:
# 静默忽略任何错误，不影响正常流程
pass
def _build_personality_prompt(self) -> str:
"""构建人设信息，参考 replyer 的做法"""
try:
bot_name = global_config.bot.nickname
if global_config.bot.alias_names:
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
else:
bot_nickname = ""
# 获取基础personality
prompt_personality = global_config.personality.personality
# 检查是否需要随机替换为状态（personality 本体）
if (
hasattr(global_config.personality, "states")
and global_config.personality.states
and hasattr(global_config.personality, "state_probability")
and global_config.personality.state_probability > 0
and random.random() < global_config.personality.state_probability
):
# 随机选择一个状态替换personality
selected_state = random.choice(global_config.personality.states)
prompt_personality = selected_state
prompt_personality = f"{prompt_personality};"
return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
except Exception as e:
logger.warning(f"构建人设信息失败: {e}")
# 返回默认人设
return "你的名字是麦麦，你是一个活泼可爱的AI助手。"
def set_extra_tools(self, tools: List[dict]) -> None:
"""设置额外的工具定义（如 MCP 工具）"""
self._extra_tools = list(tools)
@@ -390,14 +434,34 @@ class MaiSakaLLMService:
# 打印消息列表
built_messages = message_factory(None)
console.print(
Panel(
Group(*[self._render_message_panel(msg, index + 1) for index, msg in enumerate(built_messages)]),
title="MaiSaka LLM Request - chat_loop_step",
border_style="cyan",
padding=(0, 1),
# 将消息分为普通消息和 tool 消息
non_tool_panels = []
tool_panels = []
for index, msg in enumerate(built_messages):
panel = self._render_message_panel(msg, index + 1)
role = msg.role.value if hasattr(msg.role, "value") else str(msg.role)
if role == "tool":
tool_panels.append(panel)
else:
non_tool_panels.append(panel)
# 先显示普通消息（group 在一个 panel 内）
if non_tool_panels:
console.print(
Panel(
Group(*non_tool_panels),
title="MaiSaka LLM Request - chat_loop_step",
border_style="cyan",
padding=(0, 1),
)
)
)
# tool 消息作为单独的块展示
for panel in tool_panels:
console.print(panel)
response, (reasoning, model, tool_calls) = await self._llm_chat.generate_response_with_message_async(
message_factory=message_factory,
@@ -424,7 +488,11 @@ class MaiSakaLLMService:
)
# 构建原始消息格式（MaiSaka 风格）
raw_message = {"role": "assistant", "content": response}
raw_message = {
"role": "assistant",
"content": response,
"_time": datetime.now().strftime("%H:%M:%S"),
}
if converted_tool_calls:
raw_message["tool_calls"] = [
{
@@ -660,8 +728,12 @@ class MaiSakaLLMService:
temperature=0.8,
max_tokens=512,
)
return response.strip() if response else "..."
except Exception as e:
logger.error(f"回复生成 LLM 调用出错: {e}")
return "..."