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feat：maisaka使用主程序的message格式，优化逻辑，移除冗余模块

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SengokuCola
2026-03-24 15:41:58 +08:00
parent 03ed59e388
commit 37f45d48c5
16 changed files with 864 additions and 1292 deletions
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325
src/maisaka/llm_service.py
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@@ -5,78 +5,47 @@ MaiSaka LLM 服务 - 使用主项目 LLM 系统
from datetime import datetime
import json
import random
from dataclasses import dataclass
from typing import Any, List, Literal, Optional
from typing import Any, List, Optional
from rich.console import Group
from rich.panel import Panel
from rich.pretty import Pretty
from rich.text import Text
from src.common.data_models.mai_message_data_model import MaiMessage
from src.common.logger import get_logger
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.payload_content.message import Message, MessageBuilder, RoleType
from src.llm_models.payload_content.tool_option import ToolCall, ToolOption
from src.llm_models.utils_model import LLMRequest
from src.prompt.prompt_manager import prompt_manager
from . import config
from .config import console
from .builtin_tools import get_builtin_tools
from .message_adapter import (
build_message,
format_speaker_content,
get_message_kind,
get_message_role,
get_message_text,
get_tool_call_id,
get_tool_calls,
remove_last_perception,
to_llm_message,
)
logger = get_logger("maisaka_llm")
# ──────────────────── 消息类型 ────────────────────
MessageType = Literal["user", "assistant", "system", "perception"]
# 内部使用的字段前缀，用于标记不应发送给 API 的元数据
INTERNAL_FIELD_PREFIX = "_"
# 消息类型字段名
MSG_TYPE_FIELD = "_type"
@dataclass
class ToolCall:
"""工具调用信息"""
id: str
name: str
arguments: dict
@dataclass
class ChatResponse:
"""LLM 对话循环单步响应"""
content: Optional[str]
tool_calls: List[ToolCall]
raw_message: dict # 可直接追加到对话历史的消息字典
# ──────────────────── 工具函数 ────────────────────
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,
"_time": datetime.now().strftime("%H:%M:%S"),
**kwargs,
}
return msg
def remove_last_perception(messages: list[dict]) -> None:
"""移除最后一条感知消息（直接修改原列表）。"""
for i in range(len(messages) - 1, -1, -1):
if messages[i].get(MSG_TYPE_FIELD) == "perception":
messages.pop(i)
break
raw_message: MaiMessage
class MaiSakaLLMService:
@@ -132,7 +101,6 @@ class MaiSakaLLMService:
if chat_system_prompt is None:
try:
chat_prompt = prompt_manager.get_prompt("maidairy_chat")
logger.info("成功加载 maidairy_chat 提示词模板")
tools_section = ""
if config.ENABLE_WRITE_FILE:
tools_section += "\n• write_file(filename, content) — 在 mai_files 目录下写入文件。"
@@ -142,6 +110,7 @@ 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("bot_name", global_config.bot.nickname)
chat_prompt.add_context("identity", personality_prompt)
import asyncio
@@ -167,8 +136,6 @@ class MaiSakaLLMService:
self._emotion_prompt: Optional[str] = None
self._cognition_prompt: Optional[str] = None
self._timing_prompt: Optional[str] = None
self._context_summarize_prompt: Optional[str] = None
try:
import asyncio
@@ -184,9 +151,6 @@ class MaiSakaLLMService:
self._timing_prompt = loop.run_until_complete(
prompt_manager.render_prompt(prompt_manager.get_prompt("maidairy_timing"))
)
self._context_summarize_prompt = loop.run_until_complete(
prompt_manager.render_prompt(prompt_manager.get_prompt("maidairy_context_summarize"))
)
logger.info("成功加载 MaiSaka 子模块提示词")
finally:
loop.close()
@@ -367,12 +331,12 @@ class MaiSakaLLMService:
params.append((param.name, param.param_type, param.description, param.required, param.enum_values))
return {"name": tool.name, "description": tool.description, "parameters": params}
async def chat_loop_step(self, chat_history: List[dict]) -> ChatResponse:
async def chat_loop_step(self, chat_history: list[MaiMessage]) -> ChatResponse:
"""执行对话循环的一步 - 使用 tool_use 模型"""
def message_factory(client) -> List:
def message_factory(client) -> list[Message]:
"""将 MaiSaka 的 chat_history 转换为主项目的 Message 格式"""
messages = []
messages: list[Message] = []
# 首先添加系统提示词
system_msg = MessageBuilder().set_role(RoleType.System)
@@ -381,48 +345,9 @@ class MaiSakaLLMService:
# 然后添加对话历史
for msg in chat_history:
role = msg.get("role", "")
content = msg.get("content", "")
# 跳过内部字段类型的消息和系统消息（已经有系统提示词了）
if role in ("perception", "system"):
continue
# 映射角色类型
if role == "user":
role_type = RoleType.User
elif role == "assistant":
role_type = RoleType.Assistant
elif role == "tool":
role_type = RoleType.Tool
else:
continue
builder = MessageBuilder().set_role(role_type)
# 处理工具调用
if role == "assistant" and "tool_calls" in msg:
# 转换 tool_calls 格式：从 MaiSaka 格式转为主项目格式
tool_calls_list = []
for tc in msg["tool_calls"]:
tc_func = tc.get("function", {})
# 主项目的 ToolCall: call_id, func_name, args
tool_calls_list.append(
ToolCallOption(
call_id=tc.get("id", ""),
func_name=tc_func.get("name", ""),
args=json.loads(tc_func.get("arguments", "{}")) if tc_func.get("arguments") else {},
)
)
builder.set_tool_calls(tool_calls_list)
elif role == "tool" and "tool_call_id" in msg:
builder.add_tool_call(msg["tool_call_id"])
# 添加文本内容
if content:
builder.add_text_content(content)
messages.append(builder.build())
llm_message = to_llm_message(msg)
if llm_message is not None:
messages.append(llm_message)
return messages
@@ -435,33 +360,18 @@ class MaiSakaLLMService:
# 打印消息列表
built_messages = message_factory(None)
# 将消息分为普通消息和 tool 消息
non_tool_panels = []
tool_panels = []
ordered_panels = [self._render_message_panel(msg, index + 1) for index, msg in enumerate(built_messages)]
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:
if config.SHOW_THINKING and ordered_panels:
console.print(
Panel(
Group(*non_tool_panels),
Group(*ordered_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,
@@ -469,86 +379,60 @@ class MaiSakaLLMService:
temperature=self._temperature,
max_tokens=self._max_tokens,
)
# 转换 tool_calls 格式：从主项目格式转为 MaiSaka 格式
converted_tool_calls = []
if tool_calls:
for tc in tool_calls:
# 主项目的 ToolCall 有 call_id, func_name, args
call_id = tc.call_id if hasattr(tc, "call_id") else ""
func_name = tc.func_name if hasattr(tc, "func_name") else ""
args = tc.args if hasattr(tc, "args") else {}
converted_tool_calls.append(
ToolCall(
id=call_id,
name=func_name,
arguments=args,
)
)
# 构建原始消息格式（MaiSaka 风格）
raw_message = {
"role": "assistant",
"content": response,
"_time": datetime.now().strftime("%H:%M:%S"),
}
if converted_tool_calls:
raw_message["tool_calls"] = [
{
"id": tc.id,
"type": "function",
"function": {
"name": tc.name,
"arguments": json.dumps(tc.arguments),
},
}
for tc in converted_tool_calls
]
raw_message = build_message(
role=RoleType.Assistant.value,
content=response or "",
source="assistant",
tool_calls=tool_calls or None,
)
return ChatResponse(
content=response,
tool_calls=converted_tool_calls,
tool_calls=tool_calls or [],
raw_message=raw_message,
)
def _filter_for_api(self, chat_history: List[dict]) -> str:
def _filter_for_api(self, chat_history: list[MaiMessage]) -> str:
"""过滤对话历史为 API 格式"""
parts = []
for msg in chat_history:
role = msg.get("role", "")
content = msg.get("content", "")
role = get_message_role(msg)
content = get_message_text(msg)
# 跳过内部字段
if role in ("perception", "tool"):
if get_message_kind(msg) == "perception" or role == RoleType.Tool.value:
continue
if role == "system":
if role == RoleType.System.value:
parts.append(f"System: {content}")
elif role == "user":
elif role == RoleType.User.value:
parts.append(f"User: {content}")
elif role == "assistant":
elif role == RoleType.Assistant.value:
# 处理工具调用
if "tool_calls" in msg:
tool_desc = ", ".join([tc.get("name", "") for tc in msg["tool_calls"]])
tool_calls = get_tool_calls(msg)
if tool_calls:
tool_desc = ", ".join([tc.func_name for tc in tool_calls if tc.func_name])
parts.append(f"Assistant (called tools: {tool_desc})")
else:
parts.append(f"Assistant: {content}")
return "\n\n".join(parts)
def build_chat_context(self, user_text: str) -> List[dict]:
def build_chat_context(self, user_text: str) -> list[MaiMessage]:
"""构建对话上下文"""
return [
{"role": "system", "content": self._chat_system_prompt},
{"role": "user", "content": user_text},
build_message(
role=RoleType.User.value,
content=format_speaker_content(config.USER_NAME, user_text),
source="user",
)
]
# ──────── 分析模块（使用 utils 模型） ────────
async def analyze_emotion(self, chat_history: List[dict]) -> str:
async def analyze_emotion(self, chat_history: list[MaiMessage]) -> str:
"""情绪分析 - 使用 utils 模型"""
filtered = [m for m in chat_history if m.get("_type") != "perception"]
filtered = [m for m in chat_history if get_message_kind(m) != "perception"]
recent = filtered[-10:] if len(filtered) > 10 else filtered
# 使用加载的系统提示词
@@ -556,17 +440,20 @@ class MaiSakaLLMService:
prompt_parts = [f"{system_prompt}\n\n【对话内容】\n"]
for msg in recent:
if msg.get("role") == "user":
prompt_parts.append(f"用户: {msg.get('content', '')}")
elif msg.get("role") == "assistant":
prompt_parts.append(f"助手: {msg.get('content', '')}")
role = get_message_role(msg)
content = get_message_text(msg)
if role == RoleType.User.value:
prompt_parts.append(f"{config.USER_NAME}: {content}")
elif role == RoleType.Assistant.value:
prompt_parts.append(f"助手: {content}")
prompt = "\n".join(prompt_parts)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - analyze_emotion:")
print(f" {prompt}")
print("=" * 60 + "\n")
if config.SHOW_THINKING:
print("\n" + "=" * 60)
print("MaiSaka LLM Request - analyze_emotion:")
print(f" {prompt}")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_utils.generate_response_async(
@@ -580,9 +467,9 @@ class MaiSakaLLMService:
logger.error(f"情绪分析 LLM 调用出错: {e}")
return ""
async def analyze_cognition(self, chat_history: List[dict]) -> str:
async def analyze_cognition(self, chat_history: list[MaiMessage]) -> str:
"""认知分析 - 使用 utils 模型"""
filtered = [m for m in chat_history if m.get("_type") != "perception"]
filtered = [m for m in chat_history if get_message_kind(m) != "perception"]
recent = filtered[-10:] if len(filtered) > 10 else filtered
# 使用加载的系统提示词
@@ -590,14 +477,16 @@ class MaiSakaLLMService:
prompt_parts = [f"{system_prompt}\n\n【对话内容】\n"]
for msg in recent:
if msg.get("role") == "user":
prompt_parts.append(f"用户: {msg.get('content', '')}")
elif msg.get("role") == "assistant":
prompt_parts.append(f"助手: {msg.get('content', '')}")
role = get_message_role(msg)
content = get_message_text(msg)
if role == RoleType.User.value:
prompt_parts.append(f"{config.USER_NAME}: {content}")
elif role == RoleType.Assistant.value:
prompt_parts.append(f"助手: {content}")
prompt = "\n".join(prompt_parts)
if config.SHOW_ANALYZE_COGNITION_PROMPT:
if config.SHOW_THINKING and config.SHOW_ANALYZE_COGNITION_PROMPT:
print("\n" + "=" * 60)
print("MaiSaka LLM Request - analyze_cognition:")
print(f" {prompt}")
@@ -615,25 +504,29 @@ class MaiSakaLLMService:
logger.error(f"认知分析 LLM 调用出错: {e}")
return ""
async def analyze_timing(self, chat_history: List[dict], timing_info: str) -> str:
async def analyze_timing(self, chat_history: list[MaiMessage], timing_info: str) -> str:
"""时间分析 - 使用 utils 模型"""
filtered = [m for m in chat_history if m.get("_type") not in ("perception", "system")]
filtered = [
m
for m in chat_history
if get_message_kind(m) != "perception" and get_message_role(m) != RoleType.System.value
]
# 使用加载的系统提示词
system_prompt = self._timing_prompt or "请分析以下对话的时间节奏和用户状态："
prompt_parts = [f"{system_prompt}\n\n【系统时间戳信息】\n{timing_info}\n\n【当前对话记录】\n"]
for msg in filtered:
role = msg.get("role", "")
content = msg.get("content", "")
if role == "user":
prompt_parts.append(f"用户: {content}")
elif role == "assistant":
role = get_message_role(msg)
content = get_message_text(msg)
if role == RoleType.User.value:
prompt_parts.append(f"{config.USER_NAME}: {content}")
elif role == RoleType.Assistant.value:
prompt_parts.append(f"助手: {content}")
prompt = "\n".join(prompt_parts)
if config.SHOW_ANALYZE_TIMING_PROMPT:
if config.SHOW_THINKING and config.SHOW_ANALYZE_TIMING_PROMPT:
print("\n" + "=" * 60)
print("MaiSaka LLM Request - analyze_timing:")
print(f" {prompt}")
@@ -651,44 +544,9 @@ class MaiSakaLLMService:
logger.error(f"时间分析 LLM 调用出错: {e}")
return ""
async def summarize_context(self, context_messages: List[dict]) -> str:
"""上下文总结 - 使用 utils 模型"""
filtered = [m for m in context_messages if m.get("role") != "system"]
# 使用加载的系统提示词
system_prompt = self._context_summarize_prompt or "请对以下对话内容进行总结："
prompt_parts = [f"{system_prompt}\n\n【对话内容】\n"]
for msg in filtered:
role = msg.get("role", "")
content = msg.get("content", "")
if role == "user":
prompt_parts.append(f"用户: {content}")
elif role == "assistant":
prompt_parts.append(f"助手: {content}")
prompt = "\n".join(prompt_parts)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - summarize_context:")
print(f" {prompt}")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_utils.generate_response_async(
prompt=prompt,
temperature=0.3,
max_tokens=1024,
)
return response
except Exception as e:
logger.error(f"上下文总结 LLM 调用出错: {e}")
return ""
# ──────── 回复生成（使用 replyer 模型） ────────
async def generate_reply(self, reason: str, chat_history: List[dict]) -> str:
async def generate_reply(self, reason: str, chat_history: list[MaiMessage]) -> str:
"""
生成回复 - 使用 replyer 模型
可供 Replyer 类直接调用
@@ -700,7 +558,9 @@ class MaiSakaLLMService:
# 格式化对话历史
filtered_history = [
msg for msg in chat_history if msg.get("role") != "system" and msg.get("_type") != "perception"
msg
for msg in chat_history
if get_message_role(msg) != RoleType.System.value and get_message_kind(msg) != "perception"
]
formatted_history = format_chat_history(filtered_history)
@@ -717,10 +577,11 @@ class MaiSakaLLMService:
messages = f"System: {system_prompt}\n\nUser: {user_prompt}"
print("\n" + "=" * 60)
print("MaiSaka LLM Request - generate_reply:")
print(f" {messages}")
print("=" * 60 + "\n")
if config.SHOW_THINKING:
print("\n" + "=" * 60)
print("MaiSaka LLM Request - generate_reply:")
print(f" {messages}")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_replyer.generate_response_async(