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♻️ 大幅重构，拆分模块，增加一些MCP相关限制
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FuQuan233 2026-07-29 17:00:22 +08:00
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20
README.md
View file

@ -120,6 +120,9 @@ _✨ 支持多API预设、MCP协议、内置工具、联网搜索、视觉模型
| LLMCHAT__HISTORY_SIZE | 否 | 20 | LLM上下文消息保留数量（1-40），越大token消耗量越多 | | LLMCHAT__HISTORY_SIZE | 否 | 20 | LLM上下文消息保留数量（1-40），越大token消耗量越多 |
| LLMCHAT__PAST_EVENTS_SIZE | 否 | 10 | 触发回复时发送的群消息数量（1-20），越大token消耗量越多 | | LLMCHAT__PAST_EVENTS_SIZE | 否 | 10 | 触发回复时发送的群消息数量（1-20），越大token消耗量越多 |
| LLMCHAT__REQUEST_TIMEOUT | 否 | 30 | API请求超时时间（秒） | | LLMCHAT__REQUEST_TIMEOUT | 否 | 30 | API请求超时时间（秒） |
| LLMCHAT__MAX_TOOL_ROUNDS | 否 | 8 | 单次对话最大工具调用轮数，达到后强制生成最终回答 |
| LLMCHAT__MAX_REPEATED_TOOL_CALLS | 否 | 2 | 工具名及参数完全相同时允许实际执行的最大次数 |
| LLMCHAT__MCP_TIMEOUT | 否 | 30 | MCP建连、工具发现及执行的超时时间（秒） |
| LLMCHAT__DEFAULT_PRESET | 否 | off | 默认使用的预设名称，配置为off则为关闭 | | LLMCHAT__DEFAULT_PRESET | 否 | off | 默认使用的预设名称，配置为off则为关闭 |
| LLMCHAT__RANDOM_TRIGGER_PROB | 否 | 0.05 | 默认随机触发概率 [0, 1] | | LLMCHAT__RANDOM_TRIGGER_PROB | 否 | 0.05 | 默认随机触发概率 [0, 1] |
| LLMCHAT__DEFAULT_PROMPT | 否 | 你的回答应该尽量简洁、幽默、可以使用一些语气词、颜文字。你应该拒绝回答任何政治相关的问题。 | 默认提示词 | | LLMCHAT__DEFAULT_PROMPT | 否 | 你的回答应该尽量简洁、幽默、可以使用一些语气词、颜文字。你应该拒绝回答任何政治相关的问题。 | 默认提示词 |
@ -267,6 +270,23 @@ LLMCHAT__MCP_SERVERS同样为一个dict，key为服务器名称，value配置的
</details> </details>
## 🧩 代码结构
核心代码按职责拆分：
- `__init__.py`：仅负责 NoneBot 注册、依赖装配和生命周期。
- `state.py`：群聊/私聊状态与预设选择。
- `dispatcher.py`：每会话单 worker 的可靠队列调度。
- `conversation.py`：LLM 请求、有限工具循环和回复发送。
- `message_utils.py` 与 `prompts.py`：消息转换、图片处理和提示词构建。
- `mcpclient.py` 与 `onebottools.py`：外部 MCP 和 OneBot 工具适配。
- `persistence.py`：带锁和原子替换的状态持久化。
- `commands.py`：管理命令注册。
运行回归测试：
python -m unittest discover -s tests -v
## 🎉 使用 ## 🎉 使用
**如果`LLMCHAT__DEFAULT_PRESET`没有配置，则插件默认为关闭状态，请使用`API预设+[预设名]`开启插件, 私聊同理。** **如果`LLMCHAT__DEFAULT_PRESET`没有配置，则插件默认为关闭状态，请使用`API预设+[预设名]`开启插件, 私聊同理。**

915
nonebot_plugin_llmchat/__init__.py
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@ -1,36 +1,17 @@
import asyncio
import base64
from collections import defaultdict, deque
from datetime import datetime
import json
import os
import random import random
import re
import ssl
import time
from typing import TYPE_CHECKING
import aiofiles from nonebot import get_driver, get_plugin_config, logger, on_message, require
import httpx from nonebot.adapters.onebot.v11 import GroupMessageEvent, PrivateMessageEvent
from nonebot import (
get_bot,
get_driver,
get_plugin_config,
logger,
on_command,
on_message,
require,
)
from nonebot.adapters.onebot.v11 import GroupMessageEvent, Message, MessageSegment, PrivateMessageEvent
from nonebot.adapters.onebot.v11.permission import GROUP_ADMIN, GROUP_OWNER, PRIVATE
from nonebot.params import CommandArg
from nonebot.permission import SUPERUSER
from nonebot.plugin import PluginMetadata from nonebot.plugin import PluginMetadata
from nonebot.rule import Rule from nonebot.rule import Rule
from openai import AsyncOpenAI
from .config import Config, PresetConfig from .commands import register_commands
from .config import Config
from .conversation import ConversationService
from .dispatcher import MessageDispatcher
from .mcpclient import MCPClient from .mcpclient import MCPClient
from .persistence import StatePersistence
from .state import StateStore
require("nonebot_plugin_localstore") require("nonebot_plugin_localstore")
import nonebot_plugin_localstore as store import nonebot_plugin_localstore as store
@ -38,16 +19,10 @@ import nonebot_plugin_localstore as store
require("nonebot_plugin_apscheduler") require("nonebot_plugin_apscheduler")
from nonebot_plugin_apscheduler import scheduler from nonebot_plugin_apscheduler import scheduler
if TYPE_CHECKING:
from openai.types.chat import (
ChatCompletionContentPartParam,
ChatCompletionMessageParam,
)
__plugin_meta__ = PluginMetadata( __plugin_meta__ = PluginMetadata(
name="llmchat", name="llmchat",
description="支持多API预设、MCP协议、联网搜索、视觉模型、Nano Banana（生图模型）的AI群聊插件", description="支持多API预设、MCP协议、联网搜索、视觉模型的AI群聊插件",
usage="""@机器人 + 消息 开启对话""", usage="@机器人 + 消息 开启对话",
type="application", type="application",
homepage="https://github.com/FuQuan233/nonebot-plugin-llmchat", homepage="https://github.com/FuQuan233/nonebot-plugin-llmchat",
config=Config, config=Config,
@ -56,852 +31,76 @@ __plugin_meta__ = PluginMetadata(
plugin_config = get_plugin_config(Config).llmchat plugin_config = get_plugin_config(Config).llmchat
driver = get_driver() driver = get_driver()
tasks: set["asyncio.Task"] = set() states = StateStore(plugin_config)
# 保留旧版公开名称，避免依赖插件内部状态的代码立即失效。
def pop_reasoning_content( group_states = states.group_states
content: str | None, private_chat_states = states.private_states
) -> tuple[str | None, str | None]:
if content is None:
return None, None
# 如果找到了 <think> 标签内容，返回过滤后的文本和标签内的内容，否则只返回过滤后的文本和None
if matched := re.match(r"<think>(.*?)</think>", content, flags=re.DOTALL):
reasoning_element = matched.group(0)
reasoning_content = matched.group(1).strip()
filtered_content = content.replace(reasoning_element, "").strip()
return filtered_content, reasoning_content
else:
return content, None
# 初始化群组状态
class GroupState:
def __init__(self):
self.preset_name = plugin_config.default_preset
self.history = deque(maxlen=plugin_config.history_size * 2)
self.queue = asyncio.Queue()
self.processing = False
self.last_active = time.time()
self.past_events = deque(maxlen=plugin_config.past_events_size)
self.group_prompt: str | None = None
self.user_prompt: str | None = None
self.output_reasoning_content = False
self.random_trigger_prob = plugin_config.random_trigger_prob
# 初始化私聊状态
class PrivateChatState:
def __init__(self):
self.preset_name = plugin_config.private_chat_preset
self.history = deque(maxlen=plugin_config.history_size * 2)
self.queue = asyncio.Queue()
self.processing = False
self.last_active = time.time()
self.past_events = deque(maxlen=plugin_config.past_events_size)
self.group_prompt: str | None = None
self.output_reasoning_content = False
group_states: dict[int, GroupState] = defaultdict(GroupState)
private_chat_states: dict[int, PrivateChatState] = defaultdict(PrivateChatState)
# 获取当前预设配置
def get_preset(context_id: int, is_group: bool = True) -> PresetConfig:
if is_group:
state = group_states[context_id]
else:
state = private_chat_states[context_id]
for preset in plugin_config.api_presets:
if preset.name == state.preset_name:
return preset
return plugin_config.api_presets[0] # 默认返回第一个预设
# 消息格式转换
def format_message(event: GroupMessageEvent | PrivateMessageEvent) -> str:
text_message = ""
if isinstance(event, GroupMessageEvent) and event.reply is not None:
text_message += f"[回复 {event.reply.sender.nickname} 的消息 {event.reply.message.extract_plain_text()}]\n"
if isinstance(event, GroupMessageEvent) and event.is_tome():
text_message += f"@{next(iter(driver.config.nickname))} "
for msgseg in event.get_message():
if msgseg.type == "at":
text_message += msgseg.data.get("name", "")
elif msgseg.type == "image":
text_message += "[图片]"
elif msgseg.type == "voice":
text_message += "[语音]"
elif msgseg.type == "face":
pass
elif msgseg.type == "text":
text_message += msgseg.data.get("text", "")
if isinstance(event, GroupMessageEvent):
message = {
"SenderNickname": str(event.sender.card or event.sender.nickname),
"SenderUserId": str(event.user_id),
"Message": text_message,
"MessageID": event.message_id,
"SendTime": datetime.fromtimestamp(event.time).isoformat(),
}
else: # PrivateMessageEvent
message = {
"SenderNickname": str(event.sender.nickname),
"SenderUserId": str(event.user_id),
"Message": text_message,
"MessageID": event.message_id,
"SendTime": datetime.fromtimestamp(event.time).isoformat(),
}
return json.dumps(message, ensure_ascii=False)
def build_reasoning_forward_nodes(self_id: str, reasoning_content: str):
self_nickname = next(iter(driver.config.nickname))
nodes = [
{
"type": "node",
"data": {
"nickname": self_nickname,
"user_id": self_id,
"content": f"{self_nickname}的内心OS:",
},
},
{
"type": "node",
"data": {
"nickname": self_nickname,
"user_id": self_id,
"content": reasoning_content,
},
},
]
return nodes
async def is_triggered(event: GroupMessageEvent | PrivateMessageEvent) -> bool: async def is_triggered(event: GroupMessageEvent | PrivateMessageEvent) -> bool:
"""扩展后的消息处理规则""" is_group = isinstance(event, GroupMessageEvent)
if not is_group and not plugin_config.enable_private_chat:
if isinstance(event, GroupMessageEvent): return False
state = group_states[event.group_id] state = states.get(event.group_id if is_group else event.user_id, is_group)
if state.preset_name == "off" or event.user_id in plugin_config.blacklist_user_ids:
if state.preset_name == "off": return False
text = event.get_plaintext().strip()
if any(text.startswith(prefix) for prefix in plugin_config.ignore_prefixes):
return False return False
# 黑名单用户 state.pending_events.append(event)
if event.user_id in plugin_config.blacklist_user_ids: if not is_group:
return False
# 忽略特定前缀的消息
msg_text = event.get_plaintext().strip()
for prefix in plugin_config.ignore_prefixes:
if msg_text.startswith(prefix):
return False
state.past_events.append(event)
# 原有@触发条件
if event.is_tome():
return True return True
return event.is_tome() or random.random() < state.random_trigger_prob
# 随机触发条件
if random.random() < state.random_trigger_prob:
return True
return False
elif isinstance(event, PrivateMessageEvent):
# 检查私聊功能是否启用
if not plugin_config.enable_private_chat:
return False
state = private_chat_states[event.user_id]
if state.preset_name == "off":
return False
# 黑名单用户
if event.user_id in plugin_config.blacklist_user_ids:
return False
# 忽略特定前缀的消息
msg_text = event.get_plaintext().strip()
for prefix in plugin_config.ignore_prefixes:
if msg_text.startswith(prefix):
return False
state.past_events.append(event)
# 私聊默认触发
return True
return False
# 消息处理器 handler = on_message(rule=Rule(is_triggered), priority=99, block=False)
handler = on_message( conversation = ConversationService(
rule=Rule(is_triggered), config=plugin_config,
priority=99, states=states,
block=False, bot_names=set(driver.config.nickname),
sender=handler.send,
) )
message_dispatcher = MessageDispatcher(conversation.process_event)
@handler.handle() @handler.handle()
async def handle_message(event: GroupMessageEvent | PrivateMessageEvent): async def handle_message(event: GroupMessageEvent | PrivateMessageEvent) -> None:
if isinstance(event, GroupMessageEvent):
group_id = event.group_id
logger.debug(
f"收到群聊消息 群号：{group_id} 用户：{event.user_id} 内容：{event.get_plaintext()}"
)
state = group_states[group_id]
context_id = group_id
else: # PrivateMessageEvent
user_id = event.user_id
logger.debug(
f"收到私聊消息 用户：{user_id} 内容：{event.get_plaintext()}"
)
state = private_chat_states[user_id]
context_id = user_id
await state.queue.put(event)
if not state.processing:
state.processing = True
is_group = isinstance(event, GroupMessageEvent) is_group = isinstance(event, GroupMessageEvent)
task = asyncio.create_task(process_messages(context_id, is_group)) context_id = event.group_id if is_group else event.user_id
task.add_done_callback(tasks.discard) state = states.get(context_id, is_group)
tasks.add(task)
async def process_images(event: GroupMessageEvent | PrivateMessageEvent) -> list[str]:
base64_images = []
for segement in event.get_message():
if segement.type == "image":
image_url = segement.data.get("url") or segement.data.get("file")
if image_url:
try:
# 处理高版本 httpx 的 [SSL: SSLV3_ALERT_HANDSHAKE_FAILURE] 报错
ssl_context = ssl.create_default_context()
ssl_context.check_hostname = False
ssl_context.verify_mode = ssl.CERT_NONE
ssl_context.set_ciphers("DEFAULT@SECLEVEL=2")
# 下载图片并将图片转换为base64
async with httpx.AsyncClient(verify=ssl_context) as client:
response = await client.get(image_url, timeout=10.0)
if response.status_code != 200:
logger.error(f"下载图片失败: {image_url}, 状态码: {response.status_code}")
continue
image_data = response.content
base64_data = base64.b64encode(image_data).decode("utf-8")
base64_images.append(base64_data)
except Exception as e:
logger.error(f"处理图片时出错: {e}")
logger.debug(f"共处理 {len(base64_images)} 张图片")
return base64_images
async def send_split_messages(message_handler, content: str):
"""
将消息按分隔符<botbr>分段并发送
"""
logger.info(f"准备发送分段消息，分段数：{len(content.split('<botbr>'))}")
for segment in content.split("<botbr>"):
# 跳过空消息
if not segment.strip():
continue
segment = segment.strip() # 删除前后多余的换行和空格
await asyncio.sleep(2) # 避免发送过快
logger.debug(f"发送消息分段 内容：{segment[:50]}...") # 只记录前50个字符避免日志过大
await message_handler.send(Message(segment))
async def process_messages(context_id: int, is_group: bool = True):
if is_group:
group_id = context_id
state = group_states[group_id]
else:
user_id = context_id
state = private_chat_states[user_id]
group_id = None
preset = get_preset(context_id, is_group)
# 初始化OpenAI客户端
if preset.proxy != "":
client = AsyncOpenAI(
base_url=preset.api_base,
api_key=preset.api_key,
timeout=plugin_config.request_timeout,
http_client=httpx.AsyncClient(proxy=preset.proxy),
)
else:
client = AsyncOpenAI(
base_url=preset.api_base,
api_key=preset.api_key,
timeout=plugin_config.request_timeout,
)
chat_type = "群聊" if is_group else "私聊"
context_type = "群号" if is_group else "用户"
logger.info(
f"开始处理{chat_type}消息 {context_type}：{context_id} 当前队列长度：{state.queue.qsize()}"
)
try:
while not state.queue.empty():
event = await state.queue.get()
if is_group:
logger.debug(f"从队列获取消息 群号：{context_id} 消息ID：{event.message_id}")
group_id = context_id
else:
logger.debug(f"从队列获取消息 用户：{context_id} 消息ID：{event.message_id}")
group_id = None
past_events_snapshot = []
mcp_client = MCPClient.get_instance(
plugin_config.mcp_servers,
plugin_config.mcp_server_cwd,
)
try:
# 构建系统提示，分成多行以满足行长限制
chat_type = "群聊" if is_group else "私聊"
bot_names = "、".join(list(driver.config.nickname))
default_prompt = (state.group_prompt) or plugin_config.default_prompt
system_lines = [
f"我想要你帮我在{chat_type}中闲聊，大家一般叫你{bot_names}。",
"我将会在后面的信息中告诉你每条信息的发送者和发送时间，你可以直接称呼发送者为他对应的昵称。",
"你的回复需要遵守以下几点规则：",
"- 你可以使用多条消息回复，每两条消息之间使用<botbr>分隔，<botbr>前后不需要包含额外的换行和空格。",
"- 除<botbr>外，消息中不应该包含其他类似的标记。",
"- 不要使用markdown或者html，聊天软件不支持解析，换行请用换行符。",
"- 你应该以普通人的方式发送消息，每条消息字数要尽量少一些，应该倾向于使用更多条的消息回复。",
"- 代码则不需要分段，用单独的一条消息发送。",
"- 请使用发送者的昵称称呼发送者，你可以礼貌地问候发送者，但只需要在"
"第一次回答这位发送者的问题时问候他。",
"- 你有引用某条消息的能力，使用[CQ:reply,id=（消息id）]来引用。",
"- 如果有多条消息，你应该优先回复提到你的，一段时间之前的就不要回复了，也可以直接选择不回复。",
"- 如果你选择完全不回复，你只需要直接输出一个<botbr>。",
"- 如果你需要思考的话，你应该尽量少思考，以节省时间。",
]
if is_group:
system_lines += [
"- 你有at群成员的能力，只需要在某条消息中插入[CQ:at,qq=（QQ号）]，"
"也就是CQ码。at发送者是非必要的，你可以根据你自己的想法at某个人。",
]
system_lines += [
"下面是关于你性格的设定，如果设定中提到让你扮演某个人，或者设定中有提到名字，则优先使用设定中的名字。",
default_prompt,
]
systemPrompt = "\n".join(system_lines)
if preset.support_mcp:
systemPrompt += "\n你也可以使用一些工具，下面是关于这些工具的额外说明：\n"
for mcp_name, mcp_config in plugin_config.mcp_servers.items():
if mcp_config.additional_prompt:
systemPrompt += f"{mcp_name}：{mcp_config.additional_prompt}"
systemPrompt += "\n"
logger.debug(f"构建系统提示词：\n{systemPrompt}")
messages: list[ChatCompletionMessageParam] = [
{"role": "system", "content": systemPrompt}
]
while len(state.history) > 0 and state.history[0]["role"] != "user":
state.history.popleft()
messages += list(state.history)[-plugin_config.history_size * 2 :]
# 没有未处理的消息说明已经被处理了，跳过
if state.past_events.__len__() < 1:
break
content: list[ChatCompletionContentPartParam] = []
# 将机器人错过的消息推送给LLM
past_events_snapshot = list(state.past_events)
state.past_events.clear()
for ev in past_events_snapshot:
text_content = format_message(ev)
content.append({"type": "text", "text": text_content})
# 将消息中的图片转成 base64
if preset.support_image:
base64_images = await process_images(ev)
for base64_image in base64_images:
content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}})
new_messages: list[ChatCompletionMessageParam] = [
{"role": "user", "content": content}
]
logger.debug( logger.debug(
f"发送API请求 模型：{preset.model_name} 历史消息数：{len(messages)}" f"收到{'群聊' if is_group else '私聊'}消息 " f"会话：{context_id} 用户：{event.user_id} 内容：{event.get_plaintext()}"
) )
await message_dispatcher.enqueue(context_id, is_group, state, event)
client_config = {
"model": preset.model_name,
"max_tokens": preset.max_tokens,
"temperature": preset.temperature,
"timeout": 60,
"extra_body": preset.extra_body,
}
if preset.support_mcp:
available_tools = await mcp_client.get_available_tools(is_group)
client_config["tools"] = available_tools
response = await client.chat.completions.create(
**client_config,
messages=messages + new_messages,
)
if response.usage is not None:
logger.debug(f"收到API响应 使用token数：{response.usage.total_tokens}")
message = response.choices[0].message
# 处理响应并处理工具调用
while preset.support_mcp and message and message.tool_calls:
llm_reply: ChatCompletionMessageParam = {
"role": "assistant",
"content": message.content,
"tool_calls": [tool_call.model_dump() for tool_call in message.tool_calls]
}
if preset.request_with_reasoning_content:
llm_reply["reasoning_content"] = message.reasoning_content # pyright: ignore[reportGeneralTypeIssues]
# 发送LLM调用工具时的回复，一般没有
if message.content:
await send_split_messages(handler, message.content)
# 处理每个工具调用
new_messages.append(llm_reply)
for tool_call in message.tool_calls:
logger.debug(f"处理工具调用：{tool_call.function.name} 参数：{tool_call.function.arguments}")
tool_name = tool_call.function.name
try:
tool_args = json.loads(tool_call.function.arguments)
except (json.JSONDecodeError, TypeError, ValueError) as e:
error_message = (
f"工具调用参数格式错误，无法解析 {tool_name} 的 arguments: {e!s}. "
f"原始参数: {tool_call.function.arguments}"
)
logger.warning(error_message)
new_messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": error_message,
})
continue
# 发送工具调用提示
await handler.send(Message(f"正在使用{mcp_client.get_friendly_name(tool_name)}"))
if is_group:
result = await mcp_client.call_tool(
tool_name,
tool_args,
group_id=event.group_id,
bot_id=str(event.self_id)
)
else:
result = await mcp_client.call_tool(
tool_name,
tool_args,
bot_id=str(event.self_id)
)
new_messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": str(result)
})
# 将工具调用的结果交给 LLM
response = await client.chat.completions.create(
**client_config,
messages=messages + new_messages,
)
message = response.choices[0].message
# 安全检查：确保 message 不为 None
if not message:
logger.error("API 响应中的 message 为 None")
await handler.send(Message("服务暂时不可用，请稍后再试"))
return
reply, matched_reasoning_content = pop_reasoning_content(
message.content
)
reasoning_content: str | None = (
getattr(message, "reasoning_content", None)
or matched_reasoning_content
)
llm_reply: ChatCompletionMessageParam = {
"role": "assistant",
"content": reply,
}
reply_images = getattr(message, "images", None)
if reply_images:
# openai的sdk里的assistant消息暂时没有images字段，需要单独处理
llm_reply["images"] = reply_images # pyright: ignore[reportGeneralTypeIssues]
if preset.request_with_reasoning_content:
llm_reply["reasoning_content"] = reasoning_content # pyright: ignore[reportGeneralTypeIssues]
new_messages.append(llm_reply)
# 请求成功后再保存历史记录，保证user和assistant穿插，防止R1模型报错
for message in new_messages:
state.history.append(message)
if state.output_reasoning_content and reasoning_content:
try:
bot = get_bot(str(event.self_id))
if is_group:
await bot.send_group_forward_msg(
group_id=group_id,
messages=build_reasoning_forward_nodes(
bot.self_id, reasoning_content
),
)
else:
await bot.send_private_forward_msg(
user_id=context_id,
messages=build_reasoning_forward_nodes(
bot.self_id, reasoning_content
),
)
except Exception as e:
logger.error(f"合并转发消息发送失败：\n{e!s}\n")
assert reply is not None
await send_split_messages(handler, reply)
if reply_images:
logger.debug(f"API响应 图片数：{len(reply_images)}")
for i, image in enumerate(reply_images, start=1):
logger.debug(f"正在发送第{i}张图片")
image_base64 = image["image_url"]["url"].removeprefix("data:image/png;base64,")
image_msg = MessageSegment.image(base64.b64decode(image_base64))
await handler.send(image_msg)
except Exception as e:
logger.opt(exception=e).error(f"API请求失败 {'群号' if is_group else '用户'}：{context_id}")
# 如果在处理过程中出现异常，恢复未处理的消息到state中
state.past_events.extendleft(reversed(past_events_snapshot))
await handler.send(Message(f"服务暂时不可用，请稍后再试\n{e!s}"))
finally:
state.queue.task_done()
# 不再需要每次都清理MCPClient，因为它现在是单例
# await mcp_client.cleanup()
finally:
state.processing = False
# 预设切换命令 register_commands(plugin_config, states)
preset_handler = on_command("API预设", priority=1, block=True, permission=SUPERUSER) state_persistence = StatePersistence(
plugin_config,
states,
@preset_handler.handle() store.get_plugin_data_file("llmchat_state.json"),
async def handle_preset(event: GroupMessageEvent | PrivateMessageEvent, args: Message = CommandArg()): store.get_plugin_data_file("llmchat_private_state.json"),
# 解析命令参数
args_text = args.extract_plain_text().strip()
args_parts = args_text.split(maxsplit=1)
target_id = None
preset_name = None
# 可用预设列表
available_presets = {p.name for p in plugin_config.api_presets}
# 只在私聊中允许 SUPERUSER 修改他人预设
if isinstance(event, PrivateMessageEvent) and args_parts and args_parts[0].isdigit():
# 第一个参数是纯数字，且不是预设名
if args_parts[0] not in available_presets:
target_id = int(args_parts[0])
# 判断目标是群聊还是私聊
if target_id in group_states:
state = group_states[target_id]
is_group_target = True
elif target_id in private_chat_states:
state = private_chat_states[target_id]
is_group_target = False
else:
# 默认创建私聊状态
state = private_chat_states[target_id]
is_group_target = False
# 如果只有目标 ID，没有预设名，返回当前预设
if len(args_parts) == 1:
context_type = "群聊" if is_group_target else "私聊"
available_presets_str = "\n- ".join(available_presets)
await preset_handler.finish(
f"{context_type} {target_id} 当前API预设：{state.preset_name}\n可用API预设：\n- {available_presets_str}"
)
# 有预设名，进行修改
preset_name = args_parts[1]
context_id = target_id
else:
# 第一个参数虽然是数字但也是预设名，按普通流程处理
target_id = None
preset_name = args_text
if not plugin_config.enable_private_chat:
return
context_id = event.user_id
state = private_chat_states[context_id]
is_group_target = False
else:
# 普通情况：修改自己的预设
preset_name = args_text
if isinstance(event, GroupMessageEvent):
context_id = event.group_id
state = group_states[context_id]
is_group_target = True
else: # PrivateMessageEvent
if not plugin_config.enable_private_chat:
return
context_id = event.user_id
state = private_chat_states[context_id]
is_group_target = False
# 处理关闭功能
if preset_name == "off":
state.preset_name = preset_name
if target_id:
context_type = "群聊" if is_group_target else "私聊"
await preset_handler.finish(f"已关闭 {context_type} {context_id} 的llmchat功能")
elif isinstance(event, GroupMessageEvent):
await preset_handler.finish("已关闭llmchat群聊功能")
else:
await preset_handler.finish("已关闭llmchat私聊功能")
# 检查预设是否存在
if preset_name not in available_presets:
available_presets_str = "\n- ".join(available_presets)
await preset_handler.finish(
f"当前API预设：{state.preset_name}\n可用API预设：\n- {available_presets_str}"
)
# 切换预设
state.preset_name = preset_name
if target_id:
context_type = "群聊" if is_group_target else "私聊"
await preset_handler.finish(f"已将 {context_type} {context_id} 切换至API预设：{preset_name}")
else:
await preset_handler.finish(f"已切换至API预设：{preset_name}")
edit_preset_handler = on_command(
"修改设定",
priority=1,
block=True,
permission=(SUPERUSER | GROUP_ADMIN | GROUP_OWNER | PRIVATE),
) )
@edit_preset_handler.handle()
async def handle_edit_preset(event: GroupMessageEvent | PrivateMessageEvent, args: Message = CommandArg()):
if isinstance(event, GroupMessageEvent):
context_id = event.group_id
state = group_states[context_id]
else: # PrivateMessageEvent
if not plugin_config.enable_private_chat:
return
context_id = event.user_id
state = private_chat_states[context_id]
group_prompt = args.extract_plain_text().strip()
state.group_prompt = group_prompt
await edit_preset_handler.finish("修改成功")
reset_handler = on_command(
"记忆清除",
priority=1,
block=True,
permission=(SUPERUSER | GROUP_ADMIN | GROUP_OWNER | PRIVATE),
)
@reset_handler.handle()
async def handle_reset(event: GroupMessageEvent | PrivateMessageEvent, args: Message = CommandArg()):
if isinstance(event, GroupMessageEvent):
context_id = event.group_id
state = group_states[context_id]
else: # PrivateMessageEvent
if not plugin_config.enable_private_chat:
return
context_id = event.user_id
state = private_chat_states[context_id]
state.past_events.clear()
state.history.clear()
await reset_handler.finish("记忆已清空")
set_prob_handler = on_command(
"设置主动回复概率",
priority=1,
block=True,
permission=(SUPERUSER | GROUP_ADMIN | GROUP_OWNER),
)
@set_prob_handler.handle()
async def handle_set_prob(event: GroupMessageEvent, args: Message = CommandArg()):
context_id = event.group_id
state = group_states[context_id]
try:
prob = float(args.extract_plain_text().strip())
if prob < 0 or prob > 1:
raise ValueError("概率值必须在0-1之间")
except ValueError as e:
await set_prob_handler.finish(f"输入有误，请使用 [0,1] 的浮点数\n{e!s}")
return
state.random_trigger_prob = prob
await set_prob_handler.finish(f"主动回复概率已设为 {prob}")
# 思维输出切换命令
think_handler = on_command(
"切换思维输出",
priority=1,
block=True,
permission=(SUPERUSER | GROUP_ADMIN | GROUP_OWNER | PRIVATE),
)
@think_handler.handle()
async def handle_think(event: GroupMessageEvent | PrivateMessageEvent, args: Message = CommandArg()):
if isinstance(event, GroupMessageEvent):
state = group_states[event.group_id]
else: # PrivateMessageEvent
if not plugin_config.enable_private_chat:
return
state = private_chat_states[event.user_id]
state.output_reasoning_content = not state.output_reasoning_content
await think_handler.finish(
f"已{(state.output_reasoning_content and '开启') or '关闭'}思维输出"
)
# region 持久化与定时任务
# 获取插件数据目录
data_dir = store.get_plugin_data_dir()
# 获取插件数据文件
data_file = store.get_plugin_data_file("llmchat_state.json")
private_data_file = store.get_plugin_data_file("llmchat_private_state.json")
async def save_state():
"""保存群组状态到文件"""
logger.info(f"开始保存群组状态到文件：{data_file}")
data = {
gid: {
"preset": state.preset_name,
"history": list(state.history),
"last_active": state.last_active,
"group_prompt": state.group_prompt,
"output_reasoning_content": state.output_reasoning_content,
"random_trigger_prob": state.random_trigger_prob,
}
for gid, state in group_states.items()
}
os.makedirs(os.path.dirname(data_file), exist_ok=True)
async with aiofiles.open(data_file, "w", encoding="utf8") as f:
await f.write(json.dumps(data, ensure_ascii=False))
# 保存私聊状态
if plugin_config.enable_private_chat:
logger.info(f"开始保存私聊状态到文件：{private_data_file}")
private_data = {
uid: {
"preset": state.preset_name,
"history": list(state.history),
"last_active": state.last_active,
"group_prompt": state.group_prompt,
"output_reasoning_content": state.output_reasoning_content,
}
for uid, state in private_chat_states.items()
}
os.makedirs(os.path.dirname(private_data_file), exist_ok=True)
async with aiofiles.open(private_data_file, "w", encoding="utf8") as f:
await f.write(json.dumps(private_data, ensure_ascii=False))
async def load_state():
"""从文件加载群组状态"""
logger.info(f"从文件加载群组状态：{data_file}")
if not os.path.exists(data_file):
return
async with aiofiles.open(data_file, encoding="utf8") as f:
data = json.loads(await f.read())
for gid, state_data in data.items():
state = GroupState()
state.preset_name = state_data["preset"]
state.history = deque(
state_data["history"], maxlen=plugin_config.history_size * 2
)
state.last_active = state_data["last_active"]
state.group_prompt = state_data["group_prompt"]
state.output_reasoning_content = state_data["output_reasoning_content"]
state.random_trigger_prob = state_data.get("random_trigger_prob", plugin_config.random_trigger_prob)
group_states[int(gid)] = state
# 加载私聊状态
if plugin_config.enable_private_chat:
logger.info(f"从文件加载私聊状态：{private_data_file}")
if os.path.exists(private_data_file):
async with aiofiles.open(private_data_file, encoding="utf8") as f:
private_data = json.loads(await f.read())
for uid, state_data in private_data.items():
state = PrivateChatState()
state.preset_name = state_data["preset"]
state.history = deque(
state_data["history"], maxlen=plugin_config.history_size * 2
)
state.last_active = state_data["last_active"]
state.group_prompt = state_data["group_prompt"]
state.output_reasoning_content = state_data["output_reasoning_content"]
private_chat_states[int(uid)] = state
# 注册生命周期事件
@driver.on_startup @driver.on_startup
async def init_plugin(): async def init_plugin() -> None:
logger.info("插件启动初始化") logger.info("llmchat插件启动初始化")
await load_state() await state_persistence.load()
# 每5分钟保存状态 scheduler.add_job(
scheduler.add_job(save_state, "interval", minutes=5) state_persistence.save,
"interval",
minutes=5,
id="llmchat_save_state",
replace_existing=True,
)
@driver.on_shutdown @driver.on_shutdown
async def cleanup_plugin(): async def cleanup_plugin() -> None:
logger.info("插件关闭清理") logger.info("llmchat插件关闭清理")
await save_state() await message_dispatcher.shutdown()
# 销毁MCPClient单例 await state_persistence.save()
await MCPClient.destroy_instance() await MCPClient.destroy_instance()

112
nonebot_plugin_llmchat/commands.py Normal file
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@ -0,0 +1,112 @@
from nonebot import on_command
from nonebot.adapters.onebot.v11 import GroupMessageEvent, Message, PrivateMessageEvent
from nonebot.adapters.onebot.v11.permission import GROUP_ADMIN, GROUP_OWNER, PRIVATE
from nonebot.params import CommandArg
from nonebot.permission import SUPERUSER
from .config import ScopedConfig
from .state import ChatState, StateStore
def _event_state(config: ScopedConfig, states: StateStore, event) -> ChatState | None:
if isinstance(event, GroupMessageEvent):
return states.group_states[event.group_id]
if config.enable_private_chat:
return states.private_states[event.user_id]
return None
def register_commands(config: ScopedConfig, states: StateStore) -> None:
preset_handler = on_command("API预设", priority=1, block=True, permission=SUPERUSER)
@preset_handler.handle()
async def handle_preset(event: GroupMessageEvent | PrivateMessageEvent, args: Message = CommandArg()):
text = args.extract_plain_text().strip()
parts = text.split(maxsplit=1)
target_id: int | None = None
is_group_target = isinstance(event, GroupMessageEvent)
state = _event_state(config, states, event)
if state is None:
return
preset_name = text
if isinstance(event, PrivateMessageEvent) and parts and parts[0].isdigit():
target_id = int(parts[0])
is_group_target = target_id in states.group_states
state = states.get(target_id, is_group_target)
preset_name = parts[1] if len(parts) > 1 else ""
available = {preset.name for preset in config.api_presets}
if not preset_name or preset_name not in available | {"off"}:
names = "\n- ".join(sorted(available))
await preset_handler.finish(f"当前API预设：{state.preset_name}\n可用API预设：\n- {names}")
state.preset_name = preset_name
if target_id is not None:
kind = "群聊" if is_group_target else "私聊"
await preset_handler.finish(f"已将 {kind} {target_id} 切换至API预设：{preset_name}")
await preset_handler.finish(f"已切换至API预设：{preset_name}")
edit_handler = on_command(
"修改设定",
priority=1,
block=True,
permission=SUPERUSER | GROUP_ADMIN | GROUP_OWNER | PRIVATE,
)
@edit_handler.handle()
async def handle_edit(event: GroupMessageEvent | PrivateMessageEvent, args: Message = CommandArg()):
state = _event_state(config, states, event)
if state is None:
return
state.prompt = args.extract_plain_text().strip()
await edit_handler.finish("修改成功")
reset_handler = on_command(
"记忆清除",
priority=1,
block=True,
permission=SUPERUSER | GROUP_ADMIN | GROUP_OWNER | PRIVATE,
)
@reset_handler.handle()
async def handle_reset(event: GroupMessageEvent | PrivateMessageEvent):
state = _event_state(config, states, event)
if state is None:
return
state.pending_events.clear()
state.history.clear()
await reset_handler.finish("记忆已清空")
probability_handler = on_command(
"设置主动回复概率",
priority=1,
block=True,
permission=SUPERUSER | GROUP_ADMIN | GROUP_OWNER,
)
@probability_handler.handle()
async def handle_probability(event: GroupMessageEvent, args: Message = CommandArg()):
try:
probability = float(args.extract_plain_text().strip())
if not 0 <= probability <= 1:
raise ValueError("概率必须在0到1之间")
except ValueError as error:
await probability_handler.finish(f"输入有误，请使用 [0,1] 的浮点数\n{error!s}")
return
states.group_states[event.group_id].random_trigger_prob = probability
await probability_handler.finish(f"主动回复概率已设为 {probability}")
think_handler = on_command(
"切换思维输出",
priority=1,
block=True,
permission=SUPERUSER | GROUP_ADMIN | GROUP_OWNER | PRIVATE,
)
@think_handler.handle()
async def handle_think(event: GroupMessageEvent | PrivateMessageEvent):
state = _event_state(config, states, event)
if state is None:
return
state.output_reasoning_content = not state.output_reasoning_content
status = "开启" if state.output_reasoning_content else "关闭"
await think_handler.finish(f"已{status}思维输出")

104
nonebot_plugin_llmchat/config.py
View file

@ -1,66 +1,82 @@
from pydantic import BaseModel, Field from pydantic import BaseModel, Field, model_validator
class PresetConfig(BaseModel): class PresetConfig(BaseModel):
"""API预设配置""" """API预设配置。"""
name: str = Field(..., description="预设名称（唯一标识）") name: str = Field(..., description="预设名称（唯一标识）")
api_base: str = Field(..., description="API基础地址") api_base: str = Field(..., description="API基础地址")
api_key: str = Field(..., description="API密钥") api_key: str = Field(..., description="API密钥")
model_name: str = Field(..., description="模型名称") model_name: str = Field(..., description="模型名称")
max_tokens: int = Field(2048, description="最大响应token数") max_tokens: int = Field(default=2048, description="最大响应token数")
temperature: float = Field(0.7, description="生成温度（0-2]") temperature: float = Field(default=0.7, description="生成温度（0-2]")
proxy: str = Field("", description="HTTP代理服务器") proxy: str = Field(default="", description="HTTP代理服务器")
support_mcp: bool = Field(False, description="是否支持MCP") support_mcp: bool = Field(default=False, description="是否支持MCP")
support_image: bool = Field(False, description="是否支持图片输入") support_image: bool = Field(default=False, description="是否支持图片输入")
extra_body: dict = Field({}, description="额外的请求体字段，用于兼容不同API的特殊参数") extra_body: dict = Field(default_factory=dict, description="额外请求体字段")
request_with_reasoning_content: bool = Field( request_with_reasoning_content: bool = Field(
False, default=False,
description="请求中是否包含推理过程内容（部分模型要求进行了工具调用后，必须完整回传推理过程给API）" description="工具调用后是否向API回传推理内容",
) )
class MCPServerConfig(BaseModel): class MCPServerConfig(BaseModel):
"""MCP服务器配置""" """MCP服务器配置。"""
command: str | None = Field(None, description="stdio模式下MCP命令")
args: list[str] | None = Field([], description="stdio模式下MCP命令参数") command: str | None = Field(default=None, description="stdio模式下MCP命令")
env: dict[str, str] | None = Field({}, description="stdio模式下MCP命令环境变量") args: list[str] | None = Field(default_factory=list, description="stdio命令参数")
url: str | None = Field(None, description="远程MCP服务器地址") env: dict[str, str] | None = Field(default_factory=dict, description="stdio环境变量")
headers: dict[str, str] | None = Field({}, description="远程MCP服务器http请求头，用于认证或其他设置") url: str | None = Field(default=None, description="远程MCP服务器地址")
transport: str | None = Field(None, description="远程MCP传输协议类型，可选 'sse' 或 'streamable_http'，默认自动检测") headers: dict[str, str] | None = Field(default_factory=dict, description="HTTP请求头")
transport: str | None = Field(default=None, description="sse或streamable_http")
friendly_name: str | None = Field(default=None, description="MCP服务器友好名称")
additional_prompt: str | None = Field(default=None, description="额外提示词")
@model_validator(mode="after")
def validate_transport(self):
if bool(self.command) == bool(self.url):
raise ValueError("MCP服务器必须且只能配置 command 或 url 其中之一")
if self.transport not in {None, "sse", "streamable_http"}:
raise ValueError("transport 必须是 sse 或 streamable_http")
return self
# 额外字段
friendly_name: str | None = Field(None, description="MCP服务器友好名称")
additional_prompt: str | None = Field(None, description="额外提示词")
class ScopedConfig(BaseModel): class ScopedConfig(BaseModel):
"""LLM Chat Plugin配置""" """LLM Chat Plugin配置。"""
api_presets: list[PresetConfig] = Field( api_presets: list[PresetConfig] = Field(..., description="API预设列表")
..., description="API预设列表（至少配置1个预设）" history_size: int = Field(default=20, ge=1, description="LLM上下文消息保留数量")
) past_events_size: int = Field(default=10, ge=1, description="触发时发送的消息数量")
history_size: int = Field(20, description="LLM上下文消息保留数量") request_timeout: int = Field(default=30, ge=1, description="API请求超时时间（秒）")
past_events_size: int = Field(10, description="触发回复时发送的群消息数量") max_tool_rounds: int = Field(default=8, ge=1, le=50, description="最大工具调用轮数")
request_timeout: int = Field(30, description="API请求超时时间（秒）") max_repeated_tool_calls: int = Field(default=2, ge=1, le=10, description="相同工具调用最多执行次数")
default_preset: str = Field("off", description="默认使用的预设名称") mcp_timeout: int = Field(default=30, ge=1, description="MCP操作超时时间（秒）")
random_trigger_prob: float = Field( default_preset: str = Field(default="off", description="默认预设名称")
0.05, ge=0.0, le=1.0, description="随机触发概率（0-1]" random_trigger_prob: float = Field(default=0.05, ge=0.0, le=1.0, description="随机触发概率")
)
default_prompt: str = Field( default_prompt: str = Field(
"你的回答应该尽量简洁、幽默、可以使用一些语气词、颜文字。你应该拒绝回答任何政治相关的问题。", default="你的回答应该尽量简洁、幽默、可以使用一些语气词、颜文字。你应该拒绝回答任何政治相关的问题。",
description="默认提示词", description="默认提示词",
) )
mcp_server_cwd: str | None = Field( mcp_server_cwd: str | None = Field(default=None, description="stdio MCP服务器全局工作目录")
None, mcp_servers: dict[str, MCPServerConfig] = Field(default_factory=dict, description="MCP服务器配置")
description="command类型MCP服务器的全局工作目录（cwd）" blacklist_user_ids: set[int] = Field(default_factory=set, description="黑名单用户ID")
) ignore_prefixes: list[str] = Field(default_factory=list, description="忽略的消息前缀")
mcp_servers: dict[str, MCPServerConfig] = Field({}, description="MCP服务器配置") enable_private_chat: bool = Field(default=False, description="是否启用私聊")
blacklist_user_ids: set[int] = Field(set(), description="黑名单用户ID列表") private_chat_preset: str = Field(default="off", description="私聊默认预设")
ignore_prefixes: list[str] = Field(
default_factory=list, @model_validator(mode="after")
description="需要忽略的消息前缀列表，匹配到这些前缀的消息不会处理" def validate_presets(self):
) names = [preset.name for preset in self.api_presets]
enable_private_chat: bool = Field(False, description="是否启用私聊功能") if not names:
private_chat_preset: str = Field("off", description="私聊默认使用的预设名称") raise ValueError("api_presets 至少需要一个预设")
if len(names) != len(set(names)):
raise ValueError("api_presets 中的预设名称不能重复")
available = set(names) | {"off"}
if self.default_preset not in available:
raise ValueError(f"default_preset 不存在: {self.default_preset}")
if self.private_chat_preset not in available:
raise ValueError(f"private_chat_preset 不存在: {self.private_chat_preset}")
return self
class Config(BaseModel): class Config(BaseModel):

270
nonebot_plugin_llmchat/conversation.py Normal file
View file

@ -0,0 +1,270 @@
import asyncio
import base64
from collections import Counter
import json
from typing import Any, cast
import httpx
from nonebot import get_bot, logger
from nonebot.adapters.onebot.v11 import GroupMessageEvent, Message, MessageSegment
from openai import AsyncOpenAI
from .config import PresetConfig, ScopedConfig
from .mcpclient import MCPClient
from .message_utils import (
ChatEvent,
MessageSender,
build_reasoning_forward_nodes,
download_images,
format_message,
pop_reasoning_content,
send_split_messages,
)
from .prompts import build_system_prompt
from .state import ChatState, StateStore
class ConversationService:
def __init__(
self,
config: ScopedConfig,
states: StateStore,
bot_names: set[str],
sender: MessageSender,
) -> None:
self.config = config
self.states = states
self.bot_names = bot_names
self.sender = sender
async def process_event(
self,
context_id: int,
is_group: bool,
state: ChatState,
event: ChatEvent,
) -> None:
snapshot = list(state.pending_events)
if not snapshot:
logger.debug(f"会话 {context_id} 没有待处理上下文，跳过重复触发")
return
state.pending_events.clear()
state.last_active = event.time
try:
await self._process_snapshot(context_id, is_group, state, event, snapshot)
except asyncio.CancelledError:
state.pending_events.extendleft(reversed(snapshot))
raise
except Exception as error:
state.pending_events.extendleft(reversed(snapshot))
logger.opt(exception=error).error(f"API请求失败 会话：{context_id}")
await self.sender(Message(f"服务暂时不可用，请稍后再试\n{error!s}"))
def _create_client(self, preset: PresetConfig) -> AsyncOpenAI:
options: dict[str, Any] = {
"base_url": preset.api_base,
"api_key": preset.api_key,
"timeout": self.config.request_timeout,
}
if preset.proxy:
options["http_client"] = httpx.AsyncClient(proxy=preset.proxy)
return AsyncOpenAI(**options)
async def _process_snapshot(
self,
context_id: int,
is_group: bool,
state: ChatState,
event: ChatEvent,
events: list[ChatEvent],
) -> None:
preset = self.states.get_preset(state)
mcp_client = MCPClient.get_instance(
self.config.mcp_servers,
self.config.mcp_server_cwd,
self.config.mcp_timeout,
)
system_prompt = build_system_prompt(
config=self.config,
state=state,
bot_names=self.bot_names,
is_group=is_group,
support_tools=preset.support_mcp,
)
while state.history and state.history[0].get("role") != "user":
state.history.popleft()
messages: list[dict[str, Any]] = [
{"role": "system", "content": system_prompt},
*list(state.history)[-self.config.history_size * 2 :],
]
content: list[dict[str, Any]] = []
bot_name = next(iter(sorted(self.bot_names)), "机器人")
for pending_event in events:
content.append({"type": "text", "text": format_message(pending_event, bot_name)})
if preset.support_image:
for image in await download_images(pending_event):
content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image}"}})
transcript: list[dict[str, Any]] = [{"role": "user", "content": content}]
request_config: dict[str, Any] = {
"model": preset.model_name,
"max_tokens": preset.max_tokens,
"temperature": preset.temperature,
"timeout": self.config.request_timeout,
"extra_body": preset.extra_body,
}
if preset.support_mcp:
request_config["tools"] = await mcp_client.get_available_tools(is_group)
client = self._create_client(preset)
try:
message = await self._run_tool_loop(
client=client,
preset=preset,
mcp_client=mcp_client,
request_config=request_config,
messages=messages,
transcript=transcript,
event=event,
is_group=is_group,
)
finally:
await client.close()
reply, tagged_reasoning = pop_reasoning_content(getattr(message, "content", None))
reasoning = getattr(message, "reasoning_content", None) or tagged_reasoning
assistant_message: dict[str, Any] = {"role": "assistant", "content": reply or ""}
reply_images = getattr(message, "images", None)
if reply_images:
assistant_message["images"] = reply_images
if preset.request_with_reasoning_content:
assistant_message["reasoning_content"] = reasoning
transcript.append(assistant_message)
state.history.extend(transcript)
if state.output_reasoning_content and reasoning:
await self._send_reasoning(context_id, is_group, event, reasoning)
if reply:
await send_split_messages(self.sender, reply)
await self._send_images(reply_images)
async def _complete(
self,
client: AsyncOpenAI,
request_config: dict[str, Any],
messages: list[dict[str, Any]],
) -> Any:
response = await cast(Any, client.chat.completions.create)(**request_config, messages=messages)
if not response.choices:
raise RuntimeError("API响应中没有choices")
if response.usage is not None:
logger.debug(f"API响应token数：{response.usage.total_tokens}")
return response.choices[0].message
async def _run_tool_loop(
self,
*,
client: AsyncOpenAI,
preset: PresetConfig,
mcp_client: MCPClient,
request_config: dict[str, Any],
messages: list[dict[str, Any]],
transcript: list[dict[str, Any]],
event: ChatEvent,
is_group: bool,
) -> Any:
message = await self._complete(client, request_config, messages + transcript)
if not preset.support_mcp:
return message
call_counts: Counter[str] = Counter()
for round_number in range(1, self.config.max_tool_rounds + 1):
tool_calls = getattr(message, "tool_calls", None)
if not tool_calls:
return message
logger.info(f"处理第 {round_number}/{self.config.max_tool_rounds} 轮工具调用")
assistant_reply: dict[str, Any] = {
"role": "assistant",
"content": getattr(message, "content", None),
"tool_calls": [tool_call.model_dump() for tool_call in tool_calls],
}
reasoning = getattr(message, "reasoning_content", None)
if preset.request_with_reasoning_content and reasoning is not None:
assistant_reply["reasoning_content"] = reasoning
transcript.append(assistant_reply)
if getattr(message, "content", None):
await send_split_messages(self.sender, message.content)
for tool_call in tool_calls:
await self._handle_tool_call(
mcp_client,
transcript,
tool_call,
call_counts,
event,
is_group,
)
message = await self._complete(client, request_config, messages + transcript)
if not getattr(message, "tool_calls", None):
return message
logger.warning(f"工具调用达到上限 {self.config.max_tool_rounds}，强制要求模型总结")
final_config = {key: value for key, value in request_config.items() if key not in {"tools", "tool_choice"}}
final_messages = [
*messages,
*transcript,
{
"role": "system",
"content": "工具调用次数已达上限。不得再调用工具，请根据已有结果直接给出最终回答。",
},
]
return await self._complete(client, final_config, final_messages)
async def _handle_tool_call(
self,
mcp_client: MCPClient,
transcript: list[dict[str, Any]],
tool_call: Any,
call_counts: Counter[str],
event: ChatEvent,
is_group: bool,
) -> None:
name = tool_call.function.name
raw_arguments = tool_call.function.arguments
try:
arguments = json.loads(raw_arguments)
if not isinstance(arguments, dict):
raise TypeError("arguments必须是JSON对象")
except (json.JSONDecodeError, TypeError, ValueError) as error:
result = f"工具参数格式错误: {error!s}"
else:
signature = f"{name}:{json.dumps(arguments, sort_keys=True, ensure_ascii=False)}"
call_counts[signature] += 1
if call_counts[signature] > self.config.max_repeated_tool_calls:
result = "相同工具和参数的调用已达到上限，请使用已有结果继续回答。"
logger.warning(f"阻止重复工具调用: {signature}")
else:
await self.sender(Message(f"正在使用{mcp_client.get_friendly_name(name)}"))
result = await mcp_client.call_tool(
name,
arguments,
group_id=event.group_id if is_group and isinstance(event, GroupMessageEvent) else None,
bot_id=str(event.self_id),
)
transcript.append({"role": "tool", "tool_call_id": tool_call.id, "content": str(result)})
async def _send_reasoning(self, context_id: int, is_group: bool, event: ChatEvent, content: str) -> None:
try:
bot = get_bot(str(event.self_id))
nickname = next(iter(sorted(self.bot_names)), "机器人")
nodes = build_reasoning_forward_nodes(bot.self_id, nickname, content)
if is_group:
await bot.send_group_forward_msg(group_id=context_id, messages=nodes)
else:
await bot.send_private_forward_msg(user_id=context_id, messages=nodes)
except Exception:
logger.exception("合并转发思维内容失败")
async def _send_images(self, images: Any) -> None:
for image in images or []:
encoded = image["image_url"]["url"].split(",", maxsplit=1)[-1]
await self.sender(Message(MessageSegment.image(base64.b64decode(encoded))))

66
nonebot_plugin_llmchat/dispatcher.py Normal file
View file

@ -0,0 +1,66 @@
import asyncio
from collections.abc import Awaitable, Callable
from typing import Any
from nonebot import logger
from .state import ChatState
EventProcessor = Callable[[int, bool, ChatState, Any], Awaitable[None]]
class MessageDispatcher:
"""每个会话只运行一个worker，并可靠接管竞态窗口中新入队的消息。"""
def __init__(self, processor: EventProcessor):
self._processor = processor
self._tasks: set[asyncio.Task[None]] = set()
self._closing = False
async def enqueue(self, context_id: int, is_group: bool, state: ChatState, event: Any) -> None:
if self._closing:
return
await state.queue.put(event)
async with state.worker_lock:
if state.worker_task is None or state.worker_task.done():
self._start_worker(context_id, is_group, state)
def _start_worker(self, context_id: int, is_group: bool, state: ChatState) -> None:
task = asyncio.create_task(
self._run_worker(context_id, is_group, state),
name=f"llmchat:{'group' if is_group else 'private'}:{context_id}",
)
state.worker_task = task
self._tasks.add(task)
task.add_done_callback(self._tasks.discard)
async def _run_worker(self, context_id: int, is_group: bool, state: ChatState) -> None:
current_task = asyncio.current_task()
try:
while True:
try:
event = state.queue.get_nowait()
except asyncio.QueueEmpty:
break
try:
await self._processor(context_id, is_group, state, event)
except asyncio.CancelledError:
raise
except Exception:
logger.exception(f"处理会话 {context_id} 的消息失败")
finally:
state.queue.task_done()
finally:
async with state.worker_lock:
if state.worker_task is current_task:
state.worker_task = None
if not self._closing and not state.queue.empty():
self._start_worker(context_id, is_group, state)
async def shutdown(self) -> None:
self._closing = True
tasks = list(self._tasks)
for task in tasks:
task.cancel()
if tasks:
await asyncio.gather(*tasks, return_exceptions=True)

55
nonebot_plugin_llmchat/mcpclient.py
View file

@ -24,6 +24,7 @@ class MCPClient:
cls, cls,
server_config: dict[str, MCPServerConfig] | None = None, server_config: dict[str, MCPServerConfig] | None = None,
default_command_cwd: str | None = None, default_command_cwd: str | None = None,
operation_timeout: int = 30,
): ):
if cls._instance is None: if cls._instance is None:
cls._instance = super().__new__(cls) cls._instance = super().__new__(cls)
@ -33,6 +34,7 @@ class MCPClient:
self, self,
server_config: dict[str, MCPServerConfig] | None = None, server_config: dict[str, MCPServerConfig] | None = None,
default_command_cwd: str | None = None, default_command_cwd: str | None = None,
operation_timeout: int = 30,
): ):
if self._initialized: if self._initialized:
return return
@ -43,6 +45,7 @@ class MCPClient:
logger.info(f"正在初始化MCPClient单例，共有{len(server_config)}个服务器配置") logger.info(f"正在初始化MCPClient单例，共有{len(server_config)}个服务器配置")
self.server_config = server_config self.server_config = server_config
self.default_command_cwd = default_command_cwd self.default_command_cwd = default_command_cwd
self.operation_timeout = operation_timeout
self.sessions = {} self.sessions = {}
self.exit_stack = AsyncExitStack() self.exit_stack = AsyncExitStack()
self._session_exit_stacks: dict[str, AsyncExitStack] = {} self._session_exit_stacks: dict[str, AsyncExitStack] = {}
@ -62,12 +65,13 @@ class MCPClient:
cls, cls,
server_config: dict[str, MCPServerConfig] | None = None, server_config: dict[str, MCPServerConfig] | None = None,
default_command_cwd: str | None = None, default_command_cwd: str | None = None,
operation_timeout: int = 30,
): ):
"""获取MCPClient实例""" """获取MCPClient实例"""
if cls._instance is None: if cls._instance is None:
if server_config is None: if server_config is None:
raise ValueError("server_config must be provided for first initialization") raise ValueError("server_config must be provided for first initialization")
cls._instance = cls(server_config, default_command_cwd) cls._instance = cls(server_config, default_command_cwd, operation_timeout)
return cls._instance return cls._instance
@classmethod @classmethod
@ -85,10 +89,24 @@ class MCPClient:
await self._get_or_create_session(server_name) await self._get_or_create_session(server_name)
logger.info(f"已成功连接到MCP服务器[{server_name}]") logger.info(f"已成功连接到MCP服务器[{server_name}]")
async def _create_server_session(self, server_name: str) -> tuple[ClientSession, AsyncExitStack]: async def _open_server_session(self, server_name: str) -> tuple[ClientSession, AsyncExitStack]:
session_stack = AsyncExitStack()
try:
return await self._initialize_server_session(server_name, session_stack)
except BaseException:
try:
await asyncio.wait_for(session_stack.aclose(), timeout=self.operation_timeout)
except (asyncio.TimeoutError, RuntimeError):
logger.error(f"清理未完成的MCP会话[{server_name}]失败或超时")
raise
async def _initialize_server_session(
self,
server_name: str,
session_stack: AsyncExitStack,
) -> tuple[ClientSession, AsyncExitStack]:
"""创建并初始化一个新的服务器会话。""" """创建并初始化一个新的服务器会话。"""
config = self.server_config[server_name] config = self.server_config[server_name]
session_stack = AsyncExitStack()
if config.url: if config.url:
transport_type = config.transport transport_type = config.transport
if transport_type == "streamable_http": if transport_type == "streamable_http":
@ -137,6 +155,15 @@ class MCPClient:
await session.initialize() await session.initialize()
return session, session_stack return session, session_stack
async def _create_server_session(self, server_name: str) -> tuple[ClientSession, AsyncExitStack]:
try:
return await asyncio.wait_for(
self._open_server_session(server_name),
timeout=self.operation_timeout,
)
except asyncio.TimeoutError as error:
raise TimeoutError(f"连接MCP服务器[{server_name}]超时") from error
async def _close_server_session(self, server_name: str): async def _close_server_session(self, server_name: str):
"""关闭指定服务器会话。""" """关闭指定服务器会话。"""
session_stack = self._session_exit_stacks.pop(server_name, None) session_stack = self._session_exit_stacks.pop(server_name, None)
@ -144,7 +171,10 @@ class MCPClient:
self._session_last_used.pop(server_name, None) self._session_last_used.pop(server_name, None)
if session_stack is not None: if session_stack is not None:
await session_stack.aclose() try:
await asyncio.wait_for(session_stack.aclose(), timeout=self.operation_timeout)
except asyncio.TimeoutError:
logger.error(f"关闭MCP会话[{server_name}]超时")
async def _get_or_create_session(self, server_name: str) -> ClientSession: async def _get_or_create_session(self, server_name: str) -> ClientSession:
"""获取可复用会话；若不存在或已过期则新建。""" """获取可复用会话；若不存在或已过期则新建。"""
@ -204,7 +234,7 @@ class MCPClient:
for server_name in self.server_config.keys(): for server_name in self.server_config.keys():
logger.debug(f"正在从服务器[{server_name}]获取工具列表") logger.debug(f"正在从服务器[{server_name}]获取工具列表")
session = await self._get_or_create_session(server_name) session = await self._get_or_create_session(server_name)
response = await session.list_tools() response = await asyncio.wait_for(session.list_tools(), timeout=self.operation_timeout)
tools = response.tools tools = response.tools
logger.debug(f"在服务器[{server_name}]中找到{len(tools)}个工具") logger.debug(f"在服务器[{server_name}]中找到{len(tools)}个工具")
@ -243,7 +273,13 @@ class MCPClient:
if group_id is None or bot_id is None: if group_id is None or bot_id is None:
return "QQ工具需要提供group_id和bot_id参数" return "QQ工具需要提供group_id和bot_id参数"
logger.info(f"调用OneBot工具[{tool_name}]") logger.info(f"调用OneBot工具[{tool_name}]")
return await self.onebot_tools.call_tool(tool_name, tool_args, group_id, bot_id) try:
return await asyncio.wait_for(
self.onebot_tools.call_tool(tool_name, tool_args, group_id, bot_id),
timeout=self.operation_timeout,
)
except asyncio.TimeoutError:
return f"调用OneBot工具[{tool_name}]超时"
# 检查是否是MCP工具 # 检查是否是MCP工具
if tool_name.startswith("mcp__"): if tool_name.startswith("mcp__"):
@ -254,12 +290,14 @@ class MCPClient:
server_name = parts[1] server_name = parts[1]
real_tool_name = parts[2] real_tool_name = parts[2]
if server_name not in self.server_config:
return f"未知的MCP服务器: {server_name}"
logger.info(f"按需连接到服务器[{server_name}]调用工具[{real_tool_name}]") logger.info(f"按需连接到服务器[{server_name}]调用工具[{real_tool_name}]")
try: try:
await self._ensure_cleanup_task() await self._ensure_cleanup_task()
session = await self._get_or_create_session(server_name) session = await self._get_or_create_session(server_name)
response = await asyncio.wait_for(session.call_tool(real_tool_name, tool_args), timeout=30) response = await asyncio.wait_for(session.call_tool(real_tool_name, tool_args), timeout=self.operation_timeout)
logger.debug(f"工具[{real_tool_name}]调用完成，响应: {response}") logger.debug(f"工具[{real_tool_name}]调用完成，响应: {response}")
return response.content return response.content
except asyncio.TimeoutError: except asyncio.TimeoutError:
@ -289,7 +327,8 @@ class MCPClient:
server_name = parts[1] server_name = parts[1]
real_tool_name = parts[2] real_tool_name = parts[2]
return (self.server_config[server_name].friendly_name or server_name) + " - " + real_tool_name server = self.server_config.get(server_name)
return ((server.friendly_name if server else None) or server_name) + " - " + real_tool_name
# 未知工具类型，返回原名称 # 未知工具类型，返回原名称
return tool_name return tool_name

93
nonebot_plugin_llmchat/message_utils.py Normal file
View file

@ -0,0 +1,93 @@
import asyncio
import base64
from collections.abc import Awaitable, Callable
from datetime import datetime
import json
import re
import ssl
import httpx
from nonebot import logger
from nonebot.adapters.onebot.v11 import GroupMessageEvent, Message, PrivateMessageEvent
ChatEvent = GroupMessageEvent | PrivateMessageEvent
MessageSender = Callable[[Message], Awaitable[object]]
def pop_reasoning_content(content: str | None) -> tuple[str | None, str | None]:
if content is None:
return None, None
if matched := re.match(r"<think>(.*?)</think>", content, flags=re.DOTALL):
return content.replace(matched.group(0), "").strip(), matched.group(1).strip()
return content, None
def format_message(event: ChatEvent, bot_name: str) -> str:
text = ""
if isinstance(event, GroupMessageEvent) and event.reply is not None:
text += f"[回复 {event.reply.sender.nickname} 的消息 {event.reply.message.extract_plain_text()}]\n"
if isinstance(event, GroupMessageEvent) and event.is_tome():
text += f"@{bot_name} "
for segment in event.get_message():
if segment.type == "at":
text += segment.data.get("name", "")
elif segment.type == "image":
text += "[图片]"
elif segment.type == "voice":
text += "[语音]"
elif segment.type == "text":
text += segment.data.get("text", "")
nickname = event.sender.card or event.sender.nickname if isinstance(event, GroupMessageEvent) else event.sender.nickname
return json.dumps(
{
"SenderNickname": str(nickname),
"SenderUserId": str(event.user_id),
"Message": text,
"MessageID": event.message_id,
"SendTime": datetime.fromtimestamp(event.time).isoformat(),
},
ensure_ascii=False,
)
async def download_images(event: ChatEvent) -> list[str]:
urls = [segment.data.get("url") or segment.data.get("file") for segment in event.get_message() if segment.type == "image"]
urls = [url for url in urls if url]
if not urls:
return []
ssl_context = ssl.create_default_context()
ssl_context.check_hostname = False
ssl_context.verify_mode = ssl.CERT_NONE
ssl_context.set_ciphers("DEFAULT@SECLEVEL=2")
images: list[str] = []
async with httpx.AsyncClient(verify=ssl_context, timeout=10.0) as client:
for url in urls:
try:
response = await client.get(url)
response.raise_for_status()
images.append(base64.b64encode(response.content).decode())
except Exception:
logger.exception(f"下载图片失败: {url}")
return images
async def send_split_messages(sender: MessageSender, content: str) -> None:
segments = [segment.strip() for segment in content.split("<botbr>") if segment.strip()]
logger.info(f"准备发送分段消息，分段数：{len(segments)}")
for index, segment in enumerate(segments):
if index:
await asyncio.sleep(2)
await sender(Message(segment))
def build_reasoning_forward_nodes(self_id: str, nickname: str, content: str) -> list[dict]:
return [
{
"type": "node",
"data": {"nickname": nickname, "user_id": self_id, "content": f"{nickname}的内心OS:"},
},
{
"type": "node",
"data": {"nickname": nickname, "user_id": self_id, "content": content},
},
]

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import asyncio
from collections import deque
import json
import os
from pathlib import Path
import aiofiles
from nonebot import logger
from .config import ScopedConfig
from .state import ChatState, StateStore
class StatePersistence:
def __init__(self, config: ScopedConfig, states: StateStore, group_file: Path, private_file: Path):
self.config = config
self.states = states
self.group_file = group_file
self.private_file = private_file
self._lock = asyncio.Lock()
@staticmethod
def _serialize(state: ChatState, include_probability: bool) -> dict:
data = {
"preset": state.preset_name,
"history": list(state.history),
"last_active": state.last_active,
"group_prompt": state.prompt,
"output_reasoning_content": state.output_reasoning_content,
}
if include_probability:
data["random_trigger_prob"] = state.random_trigger_prob
return data
async def _write_json(self, path: Path, data: dict) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_suffix(path.suffix + ".tmp")
async with aiofiles.open(temporary, "w", encoding="utf8") as file:
await file.write(json.dumps(data, ensure_ascii=False))
os.replace(temporary, path)
async def save(self) -> None:
async with self._lock:
await self._write_json(
self.group_file,
{key: self._serialize(state, True) for key, state in self.states.group_states.items()},
)
if self.config.enable_private_chat:
await self._write_json(
self.private_file,
{key: self._serialize(state, False) for key, state in self.states.private_states.items()},
)
async def _read_json(self, path: Path) -> dict:
if not path.exists():
return {}
try:
async with aiofiles.open(path, encoding="utf8") as file:
value = json.loads(await file.read())
return value if isinstance(value, dict) else {}
except (OSError, json.JSONDecodeError):
logger.exception(f"读取llmchat状态失败: {path}")
return {}
def _restore(self, raw: dict, *, is_group: bool) -> ChatState:
state = ChatState(
preset_name=raw.get(
"preset",
self.config.default_preset if is_group else self.config.private_chat_preset,
),
history_size=self.config.history_size,
past_events_size=self.config.past_events_size,
random_trigger_prob=raw.get("random_trigger_prob", self.config.random_trigger_prob) if is_group else 0.0,
)
state.history = deque(raw.get("history", []), maxlen=self.config.history_size * 2)
state.last_active = raw.get("last_active", state.last_active)
state.prompt = raw.get("group_prompt", raw.get("prompt"))
state.output_reasoning_content = raw.get("output_reasoning_content", False)
return state
async def load(self) -> None:
groups = await self._read_json(self.group_file)
for context_id, raw in groups.items():
self.states.group_states[int(context_id)] = self._restore(raw, is_group=True)
if self.config.enable_private_chat:
private = await self._read_json(self.private_file)
for context_id, raw in private.items():
self.states.private_states[int(context_id)] = self._restore(raw, is_group=False)

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from .config import ScopedConfig
from .state import ChatState
def build_system_prompt(
*,
config: ScopedConfig,
state: ChatState,
bot_names: set[str],
is_group: bool,
support_tools: bool,
) -> str:
chat_type = "群聊" if is_group else "私聊"
names = "、".join(sorted(bot_names))
lines = [
f"我想要你帮我在{chat_type}中闲聊，大家一般叫你{names}。",
"我会在后面的信息中告诉你每条信息的发送者和发送时间，你可以直接称呼发送者的昵称。",
"你的回复需要遵守以下规则：",
"- 多条消息之间使用<botbr>分隔，标记前后不需要额外换行或空格。",
"- 除<botbr>外，不要输出其他类似标记。",
"- 不要使用Markdown或HTML，换行请直接使用换行符。",
"- 以普通人的方式聊天，每条消息尽量简短；代码应放在单独一条消息中。",
"- 只在第一次回答某位发送者时礼貌问候。",
"- 使用[CQ:reply,id=消息id]引用消息。",
"- 优先回复提到你的新消息；也可以选择不回复。",
"- 完全不回复时只输出<botbr>。",
"- 尽量减少不必要的思考。",
]
if is_group:
lines.append("- 使用[CQ:at,qq=QQ号]提及群成员，是否提及由你决定。")
lines.extend(
[
"下面是你的性格设定；若其中指定了身份或名字，应优先遵守：",
state.prompt or config.default_prompt,
]
)
if support_tools:
additions = [
f"{name}：{server.additional_prompt}" for name, server in config.mcp_servers.items() if server.additional_prompt
]
if additions:
lines.extend(["你可以使用工具，额外说明如下：", *additions])
return "\n".join(lines)

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import asyncio
from collections import defaultdict, deque
from dataclasses import dataclass, field
import time
from typing import Any
from .config import PresetConfig, ScopedConfig
@dataclass
class ChatState:
preset_name: str
history_size: int
past_events_size: int
random_trigger_prob: float = 0.0
history: deque[dict[str, Any]] = field(init=False)
pending_events: deque[Any] = field(init=False)
queue: asyncio.Queue[Any] = field(default_factory=asyncio.Queue)
worker_lock: asyncio.Lock = field(default_factory=asyncio.Lock)
worker_task: asyncio.Task[None] | None = None
last_active: float = field(default_factory=time.time)
prompt: str | None = None
output_reasoning_content: bool = False
def __post_init__(self) -> None:
self.history = deque(maxlen=self.history_size * 2)
self.pending_events = deque(maxlen=self.past_events_size)
class StateStore:
def __init__(self, config: ScopedConfig):
self.config = config
self.group_states: dict[int, ChatState] = defaultdict(self._new_group_state)
self.private_states: dict[int, ChatState] = defaultdict(self._new_private_state)
def _new_group_state(self) -> ChatState:
return ChatState(
self.config.default_preset,
self.config.history_size,
self.config.past_events_size,
self.config.random_trigger_prob,
)
def _new_private_state(self) -> ChatState:
return ChatState(
self.config.private_chat_preset,
self.config.history_size,
self.config.past_events_size,
)
def get(self, context_id: int, is_group: bool) -> ChatState:
return self.group_states[context_id] if is_group else self.private_states[context_id]
def get_preset(self, state: ChatState) -> PresetConfig:
return next(
(preset for preset in self.config.api_presets if preset.name == state.preset_name),
self.config.api_presets[0],
)

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tests/__init__.py Normal file
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"""加载子模块而不执行NoneBot插件注册入口。"""
from pathlib import Path
import sys
from types import ModuleType
PACKAGE_NAME = "nonebot_plugin_llmchat"
if PACKAGE_NAME not in sys.modules:
package = ModuleType(PACKAGE_NAME)
package.__path__ = [str(Path(__file__).parents[1] / PACKAGE_NAME)]
sys.modules[PACKAGE_NAME] = package

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# ruff: noqa: I001
import unittest
from pydantic import ValidationError
import tests.bootstrap # noqa: F401
from nonebot_plugin_llmchat.config import MCPServerConfig, PresetConfig, ScopedConfig
from nonebot_plugin_llmchat.message_utils import pop_reasoning_content
def assert_validation_error(factory):
try:
factory()
except ValidationError:
return
raise AssertionError("expected ValidationError")
class ConfigurationTests(unittest.TestCase):
def test_rejects_duplicate_presets(self):
preset = PresetConfig(name="same", api_base="x", api_key="x", model_name="x")
assert_validation_error(lambda: ScopedConfig(api_presets=[preset, preset]))
def test_mcp_requires_exactly_one_transport_target(self):
assert_validation_error(MCPServerConfig)
assert_validation_error(lambda: MCPServerConfig(command="cmd", url="https://example.invalid"))
def test_reasoning_tag_is_removed(self):
reply, reasoning = pop_reasoning_content("<think>secret</think>answer")
assert reply == "answer"
assert reasoning == "secret"

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import asyncio
import unittest
# ruff: noqa: I001
import tests.bootstrap # noqa: F401
from nonebot_plugin_llmchat.dispatcher import MessageDispatcher
from nonebot_plugin_llmchat.state import ChatState
class DispatcherTests(unittest.IsolatedAsyncioTestCase):
async def test_messages_enqueued_while_worker_runs_are_not_stranded(self):
started = asyncio.Event()
release = asyncio.Event()
processed = []
async def process(_context_id, _is_group, _state, event):
processed.append(event)
if event == "first":
started.set()
await release.wait()
state = ChatState("test", 10, 10)
dispatcher = MessageDispatcher(process)
await dispatcher.enqueue(1, True, state, "first")
await asyncio.wait_for(started.wait(), timeout=1)
await dispatcher.enqueue(1, True, state, "second")
release.set()
await asyncio.wait_for(state.queue.join(), timeout=1)
assert processed, ["first", "second"]
assert state.queue.empty()
await dispatcher.shutdown()
async def test_processor_failure_does_not_stop_the_queue(self):
processed = []
async def process(_context_id, _is_group, _state, event):
processed.append(event)
if event == "bad":
raise RuntimeError("boom")
state = ChatState("test", 10, 10)
dispatcher = MessageDispatcher(process)
await dispatcher.enqueue(1, True, state, "bad")
await dispatcher.enqueue(1, True, state, "good")
await asyncio.wait_for(state.queue.join(), timeout=1)
assert processed, ["bad", "good"]
await dispatcher.shutdown()

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# ruff: noqa: I001
import tempfile
from pathlib import Path
import unittest
import tests.bootstrap # noqa: F401
from nonebot_plugin_llmchat.config import PresetConfig, ScopedConfig
from nonebot_plugin_llmchat.persistence import StatePersistence
from nonebot_plugin_llmchat.state import StateStore
class PersistenceTests(unittest.IsolatedAsyncioTestCase):
async def test_private_state_loads_even_when_group_file_is_missing(self):
preset = PresetConfig(name="test", api_base="x", api_key="x", model_name="x")
config = ScopedConfig(
api_presets=[preset],
default_preset="test",
enable_private_chat=True,
private_chat_preset="test",
)
with tempfile.TemporaryDirectory() as directory:
group_file = Path(directory) / "groups.json"
private_file = Path(directory) / "private.json"
source = StateStore(config)
source.private_states[42].prompt = "remember me"
persistence = StatePersistence(config, source, group_file, private_file)
await persistence.save()
group_file.unlink()
restored = StateStore(config)
await StatePersistence(config, restored, group_file, private_file).load()
assert restored.private_states[42].prompt == "remember me"
assert not group_file.exists()

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from types import SimpleNamespace
from typing import Any, cast
import unittest
# ruff: noqa: I001
import tests.bootstrap # noqa: F401
from nonebot_plugin_llmchat.config import PresetConfig, ScopedConfig
from nonebot_plugin_llmchat.conversation import ConversationService
from nonebot_plugin_llmchat.state import StateStore
class FakeToolCall:
def __init__(self):
self.id = "call-1"
self.function = SimpleNamespace(name="mcp__demo__search", arguments='{"query":"same"}')
def model_dump(self):
return {
"id": self.id,
"type": "function",
"function": {"name": self.function.name, "arguments": self.function.arguments},
}
class FakeMessage:
def __init__(self, content=None, tool_calls=None):
self.content = content
self.tool_calls = tool_calls
self.reasoning_content = None
class FakeCompletions:
def __init__(self):
self.calls = []
async def create(self, **kwargs):
self.calls.append(kwargs)
if "tools" not in kwargs:
message = FakeMessage("final answer")
else:
message = FakeMessage(tool_calls=[FakeToolCall()])
return SimpleNamespace(choices=[SimpleNamespace(message=message)], usage=None)
class FakeClient:
def __init__(self):
self.chat = SimpleNamespace(completions=FakeCompletions())
class FakeMCPClient:
def __init__(self):
self.call_count = 0
def get_friendly_name(self, _name):
return "测试工具"
async def call_tool(self, *_args, **_kwargs):
self.call_count += 1
return "result"
class ToolLoopTests(unittest.IsolatedAsyncioTestCase):
async def test_repeated_tool_calls_are_bounded_and_forced_to_finish(self):
preset = PresetConfig(
name="test",
api_base="https://example.invalid/v1",
api_key="test",
model_name="test",
support_mcp=True,
)
config = ScopedConfig(
api_presets=[preset],
default_preset="test",
max_tool_rounds=3,
max_repeated_tool_calls=2,
)
sent = []
async def sender(message):
sent.append(message)
service = ConversationService(config, StateStore(config), {"bot"}, sender)
client = FakeClient()
mcp_client = FakeMCPClient()
transcript = [{"role": "user", "content": "hello"}]
message = await service._run_tool_loop(
client=cast(Any, client),
preset=preset,
mcp_client=cast(Any, mcp_client),
request_config={"model": "test", "tools": [{}]},
messages=[{"role": "system", "content": "system"}],
transcript=transcript,
event=cast(Any, SimpleNamespace(self_id=1)),
is_group=False,
)
assert message.content == "final answer"
assert mcp_client.call_count == 2
assert len(client.chat.completions.calls) == 5
assert "tools" not in client.chat.completions.calls[-1]
assert "达到上限" in transcript[-1]["content"]