mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
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669 lines
27 KiB
Python
669 lines
27 KiB
Python
# encoding:utf-8
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"""
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小米 MiMo Bot —— OpenAI 兼容协议，使用独立 API key / base 配置。
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支持模型：
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- mimo-v2.5-pro (旗舰，长上下文，默认开启思考)
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- mimo-v2.5 (多模态：文/图/音/视频，默认开启思考)
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- mimo-v2-pro (V2 Pro，默认开启思考)
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- mimo-v2-omni (V2 多模态，默认开启思考)
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- mimo-v2-flash (V2 极速版，默认关闭思考)
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思考模式说明：
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- 开关参数：``{"thinking": {"type": "enabled" | "disabled"}}``
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- mimo-v2.5-pro / mimo-v2.5 在思考模式下 ``temperature`` 会被强制为 1.0，
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本地直接剥离 ``temperature`` / ``top_p`` 等参数避免歧义。
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- 多轮工具调用过程中，若历史包含 tool_calls，所有后续 assistant 消息必须回传
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``reasoning_content``，否则 API 返回 400 错误。
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- 文档：https://platform.xiaomimimo.com/docs/zh-CN/usage-guide/passing-back-reasoning_content
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"""
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import json
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import time
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from typing import Optional
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import requests
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from bridge.context import ContextType
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from bridge.reply import Reply, ReplyType
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from common import const
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from common.log import logger
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from config import conf, load_config
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from models.bot import Bot
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from models.openai_compatible_bot import OpenAICompatibleBot
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from models.session_manager import SessionManager
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from .mimo_session import MimoSession
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DEFAULT_API_BASE = "https://api.xiaomimimo.com/v1"
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DEFAULT_MODEL = const.MIMO_V2_5_PRO
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# 支持多模态输入（图/音/视频）的模型
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MULTIMODAL_MODELS = {const.MIMO_V2_5_PRO, const.MIMO_V2_5, const.MIMO_V2_OMNI}
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class MimoBot(Bot, OpenAICompatibleBot):
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def __init__(self):
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super().__init__()
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self.sessions = SessionManager(
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MimoSession,
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model=conf().get("model") or DEFAULT_MODEL,
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)
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conf_model = conf().get("model") or DEFAULT_MODEL
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self.args = {
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"model": conf_model,
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"temperature": conf().get("temperature", 1.0),
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"top_p": conf().get("top_p", 0.95),
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}
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# ---------- config helpers ----------
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@property
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def api_key(self):
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return conf().get("mimo_api_key")
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@property
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def api_base(self):
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url = conf().get("mimo_api_base") or DEFAULT_API_BASE
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return url.rstrip("/")
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def get_api_config(self):
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"""OpenAICompatibleBot 接口 —— 供 call_with_tools() 使用。"""
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return {
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"api_key": self.api_key,
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"api_base": self.api_base,
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"model": conf().get("model", DEFAULT_MODEL),
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"default_temperature": conf().get("temperature", 1.0),
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"default_top_p": conf().get("top_p", 0.95),
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}
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@property
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def supports_vision(self) -> bool:
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"""主模型为多模态模型时，允许 vision tool 走主 bot 通道。"""
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model_name = (conf().get("model") or "").lower()
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return model_name in MULTIMODAL_MODELS
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@staticmethod
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def _model_supports_thinking(model_name: str) -> bool:
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"""全部 mimo 系列模型都支持 thinking 开关。"""
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if not model_name:
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return False
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return model_name.lower().startswith("mimo-")
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@staticmethod
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def _thinking_default_enabled(model_name: str) -> bool:
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"""各模型的思考模式默认值。mimo-v2-flash 默认关闭，其他默认开启。"""
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if not model_name:
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return False
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return model_name.lower() != const.MIMO_V2_FLASH
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def _build_headers(self) -> dict:
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return {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}",
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}
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# ---------- simple chat (non-agent mode) ----------
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def reply(self, query, context=None):
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if context.type == ContextType.TEXT:
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logger.info("[MIMO] query={}".format(query))
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session_id = context["session_id"]
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reply = None
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clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
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if query in clear_memory_commands:
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self.sessions.clear_session(session_id)
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reply = Reply(ReplyType.INFO, "记忆已清除")
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elif query == "#清除所有":
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self.sessions.clear_all_session()
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reply = Reply(ReplyType.INFO, "所有人记忆已清除")
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elif query == "#更新配置":
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load_config()
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reply = Reply(ReplyType.INFO, "配置已更新")
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if reply:
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return reply
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session = self.sessions.session_query(query, session_id)
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logger.debug("[MIMO] session query={}".format(session.messages))
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new_args = self.args.copy()
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reply_content = self.reply_text(session, args=new_args)
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logger.debug(
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"[MIMO] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
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session.messages, session_id,
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reply_content["content"], reply_content["completion_tokens"],
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)
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)
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if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
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reply = Reply(ReplyType.ERROR, reply_content["content"])
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elif reply_content["completion_tokens"] > 0:
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self.sessions.session_reply(
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reply_content["content"], session_id, reply_content["total_tokens"],
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)
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reply = Reply(ReplyType.TEXT, reply_content["content"])
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else:
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reply = Reply(ReplyType.ERROR, reply_content["content"])
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logger.debug("[MIMO] reply {} used 0 tokens.".format(reply_content))
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return reply
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else:
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reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
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return reply
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def reply_text(self, session, args=None, retry_count: int = 0) -> dict:
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try:
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headers = self._build_headers()
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body = dict(args) if args else dict(self.args)
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body["messages"] = session.messages
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model_name = str(body.get("model", ""))
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# 思考模式下 mimo-v2.5-pro / mimo-v2.5 不支持自定义 temperature/top_p,
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# 简单起见，所有支持思考的模型按默认配置走，剥离这些参数。
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if self._model_supports_thinking(model_name) and self._thinking_default_enabled(model_name):
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for k in ("temperature", "top_p", "presence_penalty", "frequency_penalty"):
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body.pop(k, None)
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res = requests.post(
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f"{self.api_base}/chat/completions",
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headers=headers,
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json=body,
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timeout=180,
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)
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if res.status_code == 200:
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response = res.json()
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return {
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"total_tokens": response["usage"]["total_tokens"],
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"completion_tokens": response["usage"]["completion_tokens"],
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"content": response["choices"][0]["message"]["content"],
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}
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else:
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try:
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response = res.json()
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error = response.get("error", {})
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except Exception:
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error = {"message": res.text[:300]}
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logger.error(
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f"[MIMO] chat failed, status_code={res.status_code}, "
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f"msg={error.get('message')}, type={error.get('type')}"
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)
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result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
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need_retry = False
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if res.status_code >= 500:
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need_retry = retry_count < 2
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elif res.status_code == 401:
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result["content"] = "授权失败，请检查API Key是否正确"
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elif res.status_code == 429:
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result["content"] = "请求过于频繁，请稍后再试"
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need_retry = retry_count < 2
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if need_retry:
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time.sleep(3)
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return self.reply_text(session, args, retry_count + 1)
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return result
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except Exception as e:
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logger.exception(e)
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if retry_count < 2:
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return self.reply_text(session, args, retry_count + 1)
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return {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
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# ==================== Agent mode support ====================
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def call_with_tools(self, messages, tools=None, stream: bool = False, **kwargs):
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"""
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带工具调用支持的 MiMo API 调用 (供 agent 集成使用)。
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处理逻辑：
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- Claude 格式 → OpenAI 格式 转换（含 reasoning_content 全量回传）
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- System prompt 注入
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- SSE 流式响应（包含 tool_calls 与 reasoning_content 增量）
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- 思考模式开关传递
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"""
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try:
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converted_messages = self._convert_messages_to_openai_format(messages)
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system_prompt = kwargs.pop("system", None)
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if system_prompt:
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if not converted_messages or converted_messages[0].get("role") != "system":
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converted_messages.insert(0, {"role": "system", "content": system_prompt})
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else:
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converted_messages[0] = {"role": "system", "content": system_prompt}
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converted_tools = None
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if tools:
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converted_tools = self._convert_tools_to_openai_format(tools)
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model = kwargs.pop("model", None) or self.args["model"]
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max_tokens = kwargs.pop("max_tokens", None)
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request_body = {
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"model": model,
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"messages": converted_messages,
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"stream": stream,
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}
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if max_tokens is not None:
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# MiMo 使用 max_completion_tokens 命名（含可见输出 + 推理 token）
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request_body["max_completion_tokens"] = max_tokens
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if converted_tools:
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request_body["tools"] = converted_tools
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request_body["tool_choice"] = kwargs.pop("tool_choice", "auto")
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# 思考模式：默认遵循各模型的官方默认值；caller 可显式覆盖
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thinking_param = kwargs.pop("thinking", None)
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thinking_active = False
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if self._model_supports_thinking(model):
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if thinking_param is None:
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default_on = self._thinking_default_enabled(model)
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thinking_param = {"type": "enabled" if default_on else "disabled"}
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request_body["thinking"] = thinking_param
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thinking_active = thinking_param.get("type") == "enabled"
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# 思考模式下 v2.5-pro / v2.5 不支持自定义 temperature；干脆全部剥离避免被静默忽略
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if thinking_active:
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for k in ("temperature", "top_p", "presence_penalty", "frequency_penalty"):
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request_body.pop(k, None)
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kwargs.pop(k, None)
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else:
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temperature = kwargs.pop("temperature", None)
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if temperature is not None:
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request_body["temperature"] = temperature
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top_p = kwargs.pop("top_p", None)
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if top_p is not None:
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request_body["top_p"] = top_p
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logger.debug(
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f"[MIMO] API call: model={model}, "
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f"tools={len(converted_tools) if converted_tools else 0}, "
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f"stream={stream}, thinking={thinking_active}"
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)
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if stream:
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return self._handle_stream_response(request_body)
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else:
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return self._handle_sync_response(request_body)
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except Exception as e:
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logger.error(f"[MIMO] call_with_tools error: {e}")
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import traceback
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logger.error(traceback.format_exc())
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def error_generator():
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yield {"error": True, "message": str(e), "status_code": 500}
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return error_generator()
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# -------------------- streaming --------------------
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def _handle_stream_response(self, request_body: dict):
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"""SSE 流式 chunk 转为 OpenAI 标准 delta 输出（含 reasoning_content）。"""
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try:
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headers = self._build_headers()
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url = f"{self.api_base}/chat/completions"
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response = requests.post(url, headers=headers, json=request_body, stream=True, timeout=180)
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if response.status_code != 200:
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error_msg = response.text
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logger.error(f"[MIMO] API error: status={response.status_code}, msg={error_msg}")
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yield {"error": True, "message": error_msg, "status_code": response.status_code}
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return
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current_tool_calls = {}
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finish_reason = None
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for line in response.iter_lines():
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if not line:
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continue
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line = line.decode("utf-8")
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if line.startswith("data: "):
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data_str = line[6:]
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elif line.startswith("data:"):
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data_str = line[5:]
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else:
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continue
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if data_str.strip() == "[DONE]":
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break
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try:
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chunk = json.loads(data_str)
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except json.JSONDecodeError as e:
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logger.warning(f"[MIMO] JSON decode error: {e}, data: {data_str[:200]}")
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continue
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if chunk.get("error"):
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error_data = chunk["error"]
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error_msg = error_data.get("message", "Unknown error") if isinstance(error_data, dict) else str(error_data)
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logger.error(f"[MIMO] stream error: {error_msg}")
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yield {"error": True, "message": error_msg, "status_code": 500}
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return
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if not chunk.get("choices"):
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continue
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choice = chunk["choices"][0]
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delta = choice.get("delta", {})
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if choice.get("finish_reason"):
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finish_reason = choice["finish_reason"]
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# 推理内容（思考模式）：单独 delta 透传给 agent_stream
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if delta.get("reasoning_content"):
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yield {
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"choices": [{
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"index": 0,
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"delta": {
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"role": "assistant",
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"reasoning_content": delta["reasoning_content"],
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},
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"finish_reason": None,
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}]
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}
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|
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if delta.get("content"):
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yield {
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"choices": [{
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"index": 0,
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"delta": {
|
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"role": "assistant",
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"content": delta["content"],
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},
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}]
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}
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if "tool_calls" in delta and delta["tool_calls"]:
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for tool_call_chunk in delta["tool_calls"]:
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index = tool_call_chunk.get("index", 0)
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if index not in current_tool_calls:
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current_tool_calls[index] = {
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"id": tool_call_chunk.get("id", ""),
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"name": tool_call_chunk.get("function", {}).get("name", ""),
|
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"arguments": "",
|
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}
|
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if "function" in tool_call_chunk and "arguments" in tool_call_chunk["function"]:
|
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current_tool_calls[index]["arguments"] += tool_call_chunk["function"]["arguments"]
|
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|
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yield {
|
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"choices": [{
|
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"index": 0,
|
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"delta": {"tool_calls": [tool_call_chunk]},
|
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}]
|
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}
|
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|
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yield {
|
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"choices": [{
|
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"index": 0,
|
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"delta": {},
|
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"finish_reason": finish_reason,
|
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}]
|
||
}
|
||
|
||
except requests.exceptions.Timeout:
|
||
logger.error("[MIMO] Request timeout")
|
||
yield {"error": True, "message": "Request timeout", "status_code": 500}
|
||
except Exception as e:
|
||
logger.error(f"[MIMO] stream response error: {e}")
|
||
import traceback
|
||
logger.error(traceback.format_exc())
|
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yield {"error": True, "message": str(e), "status_code": 500}
|
||
|
||
# -------------------- sync --------------------
|
||
|
||
def _handle_sync_response(self, request_body: dict):
|
||
"""非流式响应；统一 yield 一份 Claude 格式 dict 与流式路径对齐。"""
|
||
try:
|
||
headers = self._build_headers()
|
||
request_body.pop("stream", None)
|
||
url = f"{self.api_base}/chat/completions"
|
||
response = requests.post(url, headers=headers, json=request_body, timeout=180)
|
||
|
||
if response.status_code != 200:
|
||
error_msg = response.text
|
||
logger.error(f"[MIMO] API error: status={response.status_code}, msg={error_msg}")
|
||
yield {"error": True, "message": error_msg, "status_code": response.status_code}
|
||
return
|
||
|
||
result = response.json()
|
||
message = result["choices"][0]["message"]
|
||
finish_reason = result["choices"][0]["finish_reason"]
|
||
|
||
response_data = {"role": "assistant", "content": []}
|
||
|
||
# 推理内容包装成 thinking block，便于 agent 层持久化并在工具调用时回传
|
||
if message.get("reasoning_content"):
|
||
response_data["content"].append({
|
||
"type": "thinking",
|
||
"thinking": message["reasoning_content"],
|
||
})
|
||
|
||
if message.get("content"):
|
||
response_data["content"].append({
|
||
"type": "text",
|
||
"text": message["content"],
|
||
})
|
||
|
||
if message.get("tool_calls"):
|
||
for tool_call in message["tool_calls"]:
|
||
try:
|
||
tool_input = json.loads(tool_call["function"]["arguments"])
|
||
except (json.JSONDecodeError, TypeError):
|
||
tool_input = {}
|
||
response_data["content"].append({
|
||
"type": "tool_use",
|
||
"id": tool_call["id"],
|
||
"name": tool_call["function"]["name"],
|
||
"input": tool_input,
|
||
})
|
||
|
||
if finish_reason == "tool_calls":
|
||
response_data["stop_reason"] = "tool_use"
|
||
elif finish_reason == "stop":
|
||
response_data["stop_reason"] = "end_turn"
|
||
else:
|
||
response_data["stop_reason"] = finish_reason
|
||
|
||
yield response_data
|
||
|
||
except requests.exceptions.Timeout:
|
||
logger.error("[MIMO] Request timeout")
|
||
yield {"error": True, "message": "Request timeout", "status_code": 500}
|
||
except Exception as e:
|
||
logger.error(f"[MIMO] sync response error: {e}")
|
||
import traceback
|
||
logger.error(traceback.format_exc())
|
||
yield {"error": True, "message": str(e), "status_code": 500}
|
||
|
||
# -------------------- format conversion --------------------
|
||
|
||
def _convert_messages_to_openai_format(self, messages):
|
||
"""
|
||
将 Claude 格式（content blocks）转为 OpenAI 格式。
|
||
|
||
关键约束：MiMo 思考模式下，一旦历史包含 tool_calls 的 assistant 轮次，
|
||
所有后续 assistant 消息（含工具调用轮）必须回传 reasoning_content，
|
||
否则 API 返回 400。本地无 trace 时用空字符串回填，MiMo 接受字段存在
|
||
即可。
|
||
"""
|
||
if not messages:
|
||
return []
|
||
|
||
has_tool_call_history = False
|
||
for msg in messages:
|
||
if msg.get("role") != "assistant":
|
||
continue
|
||
if msg.get("tool_calls"):
|
||
has_tool_call_history = True
|
||
break
|
||
content = msg.get("content")
|
||
if isinstance(content, list) and any(
|
||
isinstance(b, dict) and b.get("type") == "tool_use" for b in content
|
||
):
|
||
has_tool_call_history = True
|
||
break
|
||
|
||
converted = []
|
||
|
||
for msg in messages:
|
||
role = msg.get("role")
|
||
content = msg.get("content")
|
||
|
||
if not isinstance(content, list):
|
||
if (
|
||
role == "assistant"
|
||
and isinstance(msg, dict)
|
||
and has_tool_call_history
|
||
and "reasoning_content" not in msg
|
||
):
|
||
patched = dict(msg)
|
||
patched["reasoning_content"] = ""
|
||
converted.append(patched)
|
||
else:
|
||
converted.append(msg)
|
||
continue
|
||
|
||
if role == "user":
|
||
has_tool_result = any(
|
||
isinstance(b, dict) and b.get("type") == "tool_result" for b in content
|
||
)
|
||
if has_tool_result:
|
||
text_parts = []
|
||
tool_results = []
|
||
|
||
for block in content:
|
||
if not isinstance(block, dict):
|
||
continue
|
||
if block.get("type") == "text":
|
||
text_parts.append(block.get("text", ""))
|
||
elif block.get("type") == "tool_result":
|
||
tool_call_id = block.get("tool_use_id") or ""
|
||
result_content = block.get("content", "")
|
||
if not isinstance(result_content, str):
|
||
result_content = json.dumps(result_content, ensure_ascii=False)
|
||
tool_results.append({
|
||
"role": "tool",
|
||
"tool_call_id": tool_call_id,
|
||
"content": result_content,
|
||
})
|
||
|
||
converted.extend(tool_results)
|
||
|
||
if text_parts:
|
||
converted.append({"role": "user", "content": "\n".join(text_parts)})
|
||
else:
|
||
# 多模态原样保留（image_url / input_audio / video_url 等 block）
|
||
converted.append(msg)
|
||
|
||
elif role == "assistant":
|
||
openai_msg = {"role": "assistant"}
|
||
text_parts = []
|
||
tool_calls = []
|
||
reasoning_parts = []
|
||
|
||
for block in content:
|
||
if not isinstance(block, dict):
|
||
continue
|
||
btype = block.get("type")
|
||
if btype == "text":
|
||
text_parts.append(block.get("text", ""))
|
||
elif btype == "tool_use":
|
||
tool_calls.append({
|
||
"id": block.get("id"),
|
||
"type": "function",
|
||
"function": {
|
||
"name": block.get("name"),
|
||
"arguments": json.dumps(block.get("input", {})),
|
||
},
|
||
})
|
||
elif btype == "thinking":
|
||
reasoning_parts.append(block.get("thinking", ""))
|
||
|
||
if text_parts:
|
||
openai_msg["content"] = "\n".join(text_parts)
|
||
elif not tool_calls:
|
||
openai_msg["content"] = ""
|
||
|
||
if tool_calls:
|
||
openai_msg["tool_calls"] = tool_calls
|
||
if not text_parts:
|
||
openai_msg["content"] = None
|
||
|
||
if reasoning_parts:
|
||
openai_msg["reasoning_content"] = "\n".join(reasoning_parts)
|
||
elif has_tool_call_history:
|
||
openai_msg["reasoning_content"] = ""
|
||
|
||
converted.append(openai_msg)
|
||
else:
|
||
converted.append(msg)
|
||
|
||
return converted
|
||
|
||
def _convert_tools_to_openai_format(self, tools):
|
||
"""工具定义 Claude 格式 → OpenAI 格式。"""
|
||
if not tools:
|
||
return None
|
||
|
||
converted = []
|
||
for tool in tools:
|
||
if "type" in tool and tool["type"] == "function":
|
||
converted.append(tool)
|
||
else:
|
||
converted.append({
|
||
"type": "function",
|
||
"function": {
|
||
"name": tool.get("name"),
|
||
"description": tool.get("description"),
|
||
"parameters": tool.get("input_schema", {}),
|
||
},
|
||
})
|
||
return converted
|
||
|
||
# -------------------- vision --------------------
|
||
|
||
def call_vision(self, image_url: str, question: str,
|
||
model: Optional[str] = None,
|
||
max_tokens: int = 1000) -> dict:
|
||
"""通过 MiMo OpenAI 兼容的 /chat/completions 端点进行图像理解。"""
|
||
try:
|
||
# 主模型若不支持视觉（如 mimo-v2-flash），自动切到 mimo-v2.5-pro
|
||
vision_model = model
|
||
if not vision_model:
|
||
cur = self.args.get("model") or DEFAULT_MODEL
|
||
vision_model = cur if cur in MULTIMODAL_MODELS else const.MIMO_V2_5_PRO
|
||
|
||
payload = {
|
||
"model": vision_model,
|
||
"max_completion_tokens": max_tokens,
|
||
"messages": [{
|
||
"role": "user",
|
||
"content": [
|
||
{"type": "text", "text": question},
|
||
{"type": "image_url", "image_url": {"url": image_url}},
|
||
],
|
||
}],
|
||
}
|
||
headers = self._build_headers()
|
||
resp = requests.post(
|
||
f"{self.api_base}/chat/completions",
|
||
headers=headers, json=payload, timeout=60,
|
||
)
|
||
if resp.status_code != 200:
|
||
return {"error": True, "message": f"HTTP {resp.status_code}: {resp.text[:300]}"}
|
||
data = resp.json()
|
||
if "error" in data:
|
||
return {"error": True, "message": data["error"].get("message", str(data["error"]))}
|
||
choice = data.get("choices", [{}])[0].get("message", {})
|
||
# 部分模型在多模态下会把答案塞在 reasoning_content 而非 content
|
||
content = choice.get("content") or choice.get("reasoning_content") or ""
|
||
usage = data.get("usage", {})
|
||
return {
|
||
"model": vision_model,
|
||
"content": content,
|
||
"usage": {
|
||
"prompt_tokens": usage.get("prompt_tokens", 0),
|
||
"completion_tokens": usage.get("completion_tokens", 0),
|
||
"total_tokens": usage.get("total_tokens", 0),
|
||
},
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"[MIMO] call_vision error: {e}")
|
||
return {"error": True, "message": str(e)}
|