mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-02-20 01:30:01 +08:00
522 lines
21 KiB
Python
522 lines
21 KiB
Python
# encoding:utf-8
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import json
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import time
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import requests
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from models.bot import Bot
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from models.session_manager import SessionManager
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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.log import logger
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from config import conf, load_config
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from .moonshot_session import MoonshotSession
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# Moonshot (Kimi) API Bot
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class MoonshotBot(Bot):
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def __init__(self):
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super().__init__()
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self.sessions = SessionManager(MoonshotSession, model=conf().get("model") or "moonshot-v1-128k")
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model = conf().get("model") or "moonshot-v1-128k"
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if model == "moonshot":
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model = "moonshot-v1-32k"
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self.args = {
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"model": model,
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"temperature": conf().get("temperature", 0.3),
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"top_p": conf().get("top_p", 1.0),
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}
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self.api_key = conf().get("moonshot_api_key")
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self.base_url = conf().get("moonshot_base_url", "https://api.moonshot.cn/v1")
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# Ensure base_url does not end with /chat/completions (backward compat)
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if self.base_url.endswith("/chat/completions"):
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self.base_url = self.base_url.rsplit("/chat/completions", 1)[0]
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if self.base_url.endswith("/"):
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self.base_url = self.base_url.rstrip("/")
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def reply(self, query, context=None):
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# acquire reply content
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if context.type == ContextType.TEXT:
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logger.info("[MOONSHOT] 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("[MOONSHOT] session query={}".format(session.messages))
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model = context.get("moonshot_model")
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new_args = self.args.copy()
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if model:
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new_args["model"] = model
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reply_content = self.reply_text(session, args=new_args)
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logger.debug(
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"[MOONSHOT] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
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session.messages,
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session_id,
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reply_content["content"],
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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(reply_content["content"], session_id, reply_content["total_tokens"])
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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("[MOONSHOT] 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: MoonshotSession, args=None, retry_count: int = 0) -> dict:
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"""
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Call Moonshot chat completion API to get the answer
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:param session: a conversation session
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:param args: model args
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:param retry_count: retry count
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:return: {}
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"""
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try:
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer " + self.api_key
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}
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body = args
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body["messages"] = session.messages
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res = requests.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=body
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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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response = res.json()
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error = response.get("error")
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logger.error(f"[MOONSHOT] chat failed, status_code={res.status_code}, "
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f"msg={error.get('message')}, type={error.get('type')}")
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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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logger.warn(f"[MOONSHOT] do retry, times={retry_count}")
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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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else:
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need_retry = False
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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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else:
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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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need_retry = retry_count < 2
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result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
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if need_retry:
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return self.reply_text(session, args, retry_count + 1)
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else:
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return result
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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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Call Moonshot API with tool support for agent integration.
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This method handles:
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1. Format conversion (Claude format -> OpenAI format)
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2. System prompt injection
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3. Streaming SSE response with tool_calls
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4. Thinking (reasoning) is disabled by default to avoid tool_choice conflicts
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Args:
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messages: List of messages (may be in Claude format from agent)
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tools: List of tool definitions (may be in Claude format from agent)
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stream: Whether to use streaming
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**kwargs: Additional parameters (max_tokens, temperature, system, model, etc.)
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Returns:
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Generator yielding OpenAI-format chunks (for streaming)
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"""
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try:
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# Convert messages from Claude format to OpenAI format
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converted_messages = self._convert_messages_to_openai_format(messages)
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# Inject system prompt if provided
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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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# Convert tools from Claude format to OpenAI format
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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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# Resolve model / temperature
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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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# Don't pop temperature, just ignore it
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kwargs.pop("temperature", None)
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# Build request body (omit temperature, let the API use its own default)
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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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request_body["max_tokens"] = max_tokens
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# Add tools
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if converted_tools:
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request_body["tools"] = converted_tools
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request_body["tool_choice"] = "auto"
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# Explicitly disable thinking to avoid reasoning_content issues in multi-turn tool calls.
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# kimi-k2.5 may enable thinking by default; without preserving reasoning_content
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# in conversation history the API will reject subsequent requests.
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request_body["thinking"] = {"type": "disabled"}
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logger.debug(f"[MOONSHOT] API call: model={model}, "
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f"tools={len(converted_tools) if converted_tools else 0}, stream={stream}")
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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"[MOONSHOT] 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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"""Handle streaming SSE response from Moonshot API and yield OpenAI-format chunks."""
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try:
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headers = {
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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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url = f"{self.base_url}/chat/completions"
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response = requests.post(url, headers=headers, json=request_body, stream=True, timeout=120)
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if response.status_code != 200:
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error_msg = response.text
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logger.error(f"[MOONSHOT] 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 not line.startswith("data: "):
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continue
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data_str = line[6:] # Remove "data: " prefix
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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"[MOONSHOT] JSON decode error: {e}, data: {data_str[:200]}")
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continue
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# Check for error in chunk
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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"[MOONSHOT] 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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# Skip reasoning_content (thinking) – don't log or forward
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if delta.get("reasoning_content"):
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continue
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# Handle text content
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if "content" in delta and delta["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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# Handle tool_calls (streamed incrementally)
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if "tool_calls" in delta:
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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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"type": "tool_use",
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"name": tool_call_chunk.get("function", {}).get("name", ""),
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"input": ""
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}
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# Accumulate arguments
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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]["input"] += tool_call_chunk["function"]["arguments"]
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# Yield OpenAI-format tool call delta
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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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"tool_calls": [tool_call_chunk]
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}
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}]
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}
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# Capture finish_reason
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if choice.get("finish_reason"):
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finish_reason = choice["finish_reason"]
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# Final chunk with finish_reason
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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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}]
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}
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except requests.exceptions.Timeout:
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logger.error("[MOONSHOT] Request timeout")
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yield {"error": True, "message": "Request timeout", "status_code": 500}
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except Exception as e:
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logger.error(f"[MOONSHOT] stream response error: {e}")
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import traceback
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logger.error(traceback.format_exc())
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yield {"error": True, "message": str(e), "status_code": 500}
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# -------------------- sync --------------------
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def _handle_sync_response(self, request_body: dict):
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"""Handle synchronous API response and yield a single result dict."""
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try:
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headers = {
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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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request_body.pop("stream", None)
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url = f"{self.base_url}/chat/completions"
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response = requests.post(url, headers=headers, json=request_body, timeout=120)
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if response.status_code != 200:
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error_msg = response.text
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logger.error(f"[MOONSHOT] 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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result = response.json()
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message = result["choices"][0]["message"]
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finish_reason = result["choices"][0]["finish_reason"]
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response_data = {"role": "assistant", "content": []}
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# Add text content
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if message.get("content"):
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response_data["content"].append({
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"type": "text",
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"text": message["content"]
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})
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# Add tool calls
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if message.get("tool_calls"):
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for tool_call in message["tool_calls"]:
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response_data["content"].append({
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"type": "tool_use",
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"id": tool_call["id"],
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"name": tool_call["function"]["name"],
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"input": json.loads(tool_call["function"]["arguments"])
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})
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# Map finish_reason
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if finish_reason == "tool_calls":
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response_data["stop_reason"] = "tool_use"
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elif finish_reason == "stop":
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response_data["stop_reason"] = "end_turn"
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else:
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response_data["stop_reason"] = finish_reason
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yield response_data
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except requests.exceptions.Timeout:
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logger.error("[MOONSHOT] Request timeout")
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yield {"error": True, "message": "Request timeout", "status_code": 500}
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except Exception as e:
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logger.error(f"[MOONSHOT] sync response error: {e}")
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import traceback
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logger.error(traceback.format_exc())
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yield {"error": True, "message": str(e), "status_code": 500}
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# -------------------- format conversion --------------------
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def _convert_messages_to_openai_format(self, messages):
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"""
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Convert messages from Claude format to OpenAI format.
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Claude format uses content blocks: tool_use / tool_result / text
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OpenAI format uses tool_calls in assistant, role=tool for results
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"""
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if not messages:
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return []
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converted = []
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for msg in messages:
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role = msg.get("role")
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content = msg.get("content")
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# Already a simple string – pass through
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if isinstance(content, str):
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converted.append(msg)
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continue
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if not isinstance(content, list):
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converted.append(msg)
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continue
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if role == "user":
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text_parts = []
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tool_results = []
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for block in content:
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if not isinstance(block, dict):
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continue
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if block.get("type") == "text":
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text_parts.append(block.get("text", ""))
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elif block.get("type") == "tool_result":
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tool_call_id = block.get("tool_use_id") or ""
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result_content = block.get("content", "")
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if not isinstance(result_content, str):
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result_content = json.dumps(result_content, ensure_ascii=False)
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tool_results.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"content": result_content
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})
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# Tool results first (must come right after assistant with tool_calls)
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for tr in tool_results:
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converted.append(tr)
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if text_parts:
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converted.append({"role": "user", "content": "\n".join(text_parts)})
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elif role == "assistant":
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openai_msg = {"role": "assistant"}
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text_parts = []
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tool_calls = []
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for block in content:
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if not isinstance(block, dict):
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continue
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if block.get("type") == "text":
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text_parts.append(block.get("text", ""))
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elif block.get("type") == "tool_use":
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tool_calls.append({
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"id": block.get("id"),
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"type": "function",
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"function": {
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"name": block.get("name"),
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"arguments": json.dumps(block.get("input", {}))
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}
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})
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if text_parts:
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openai_msg["content"] = "\n".join(text_parts)
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elif not tool_calls:
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openai_msg["content"] = ""
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if tool_calls:
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openai_msg["tool_calls"] = tool_calls
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if not text_parts:
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openai_msg["content"] = None
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converted.append(openai_msg)
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else:
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converted.append(msg)
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return converted
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def _convert_tools_to_openai_format(self, tools):
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"""
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Convert tools from Claude format to OpenAI format.
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Claude: {name, description, input_schema}
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OpenAI: {type: "function", function: {name, description, parameters}}
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"""
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if not tools:
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return None
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converted = []
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for tool in tools:
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# Already in OpenAI format
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if "type" in tool and tool["type"] == "function":
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converted.append(tool)
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else:
|
||
converted.append({
|
||
"type": "function",
|
||
"function": {
|
||
"name": tool.get("name"),
|
||
"description": tool.get("description"),
|
||
"parameters": tool.get("input_schema", {})
|
||
}
|
||
})
|
||
|
||
return converted
|