Merge pull request #2571 from 6vision/master

update readme and adjust some dependency packages.

.gitignore

@@ -14,6 +14,7 @@ tmp
 plugins.json
 itchat.pkl
 *.log
+logs/
 user_datas.pkl
 chatgpt_tool_hub/
 plugins/**/

README.md

@@ -1,11 +1,19 @@
-# 简介
+<p align="center"><img src= "https://github.com/user-attachments/assets/31fb4eab-3be4-477d-aa76-82cf62bfd12c" alt="Chatgpt-on-Wechat" width="600" /></p>
 
-> chatgpt-on-wechat（简称CoW）项目是基于大模型的智能对话机器人，支持微信公众号、企业微信应用、飞书、钉钉接入，可选择GPT3.5/GPT4.0/Claude/Gemini/LinkAI/ChatGLM/KIMI/文心一言/讯飞星火/通义千问/LinkAI，能处理文本、语音和图片，通过插件访问操作系统和互联网等外部资源，支持基于自有知识库定制企业AI应用。
+<p align="center">
+   <a href="https://github.com/zhayujie/chatgpt-on-wechat/releases/latest"><img src="https://img.shields.io/github/v/release/zhayujie/chatgpt-on-wechat" alt="Latest release"></a>
+  <a href="https://github.com/zhayujie/chatgpt-on-wechat/blob/master/LICENSE"><img src="https://img.shields.io/github/license/zhayujie/chatgpt-on-wechat" alt="License: MIT"></a>
+  <a href="https://github.com/zhayujie/chatgpt-on-wechat"><img src="https://img.shields.io/github/stars/zhayujie/chatgpt-on-wechat?style=flat-square" alt="Stars"></a> <br/>
+</p>
+
+chatgpt-on-wechat（简称CoW）项目是基于大模型的智能对话机器人，支持微信公众号、企业微信应用、飞书、钉钉接入，可选择GPT3.5/GPT4.0/Claude/Gemini/LinkAI/ChatGLM/KIMI/文心一言/讯飞星火/通义千问/LinkAI/ModelScope，能处理文本、语音和图片，通过插件访问操作系统和互联网等外部资源，支持基于自有知识库定制企业AI应用。
+
+# 简介
 
 最新版本支持的功能如下：
 
 -  ✅   **多端部署：** 有多种部署方式可选择且功能完备，目前已支持微信公众号、企业微信应用、飞书、钉钉等部署方式
--  ✅   **基础对话：** 私聊及群聊的消息智能回复，支持多轮会话上下文记忆，支持 GPT-3.5, GPT-4o-mini, GPT-4o,  GPT-4, Claude-3.5, Gemini, 文心一言, 讯飞星火, 通义千问，ChatGLM-4，Kimi(月之暗面), MiniMax
+-  ✅   **基础对话：** 私聊及群聊的消息智能回复，支持多轮会话上下文记忆，支持 GPT-3.5, GPT-4o-mini, GPT-4o,  GPT-4, Claude-3.5, Gemini, 文心一言, 讯飞星火, 通义千问，ChatGLM-4，Kimi(月之暗面), MiniMax, GiteeAI, ModelScope(魔搭社区)
 -  ✅   **语音能力：** 可识别语音消息，通过文字或语音回复，支持 azure, baidu, google, openai(whisper/tts) 等多种语音模型
 -  ✅   **图像能力：** 支持图片生成、图片识别、图生图（如照片修复），可选择 Dall-E-3, stable diffusion, replicate, midjourney, CogView-3, vision模型
 -  ✅   **丰富插件：** 支持个性化插件扩展，已实现多角色切换、文字冒险、敏感词过滤、聊天记录总结、文档总结和对话、联网搜索等插件
@@ -45,6 +53,10 @@ DEMO视频：https://cdn.link-ai.tech/doc/cow_demo.mp4
 <br>
 
 # 🏷 更新日志
+>**2025.04.11：** [1.7.5版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.5) 新增支持 [wechatferry](https://github.com/zhayujie/chatgpt-on-wechat/pull/2562) 协议、新增 deepseek 模型、新增支持腾讯云语音能力、新增支持 ModelScope 和 Gitee-AI API接口
+
+>**2024.12.13：** [1.7.4版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.4) 新增 Gemini 2.0 模型、新增web channel、解决内存泄漏问题、解决 `#reloadp` 命令重载不生效问题
+
 >**2024.10.31：** [1.7.3版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.3) 程序稳定性提升、数据库功能、Claude模型优化、linkai插件优化、离线通知
 
 >**2024.09.26：** [1.7.2版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.2)  和 [1.7.1版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.1) 文心，讯飞等模型优化、o1 模型、快速安装和管理脚本

bot/bot_factory.py

@@ -68,5 +68,9 @@ def create_bot(bot_type):
         from bot.minimax.minimax_bot import MinimaxBot
         return MinimaxBot()
 
+    elif bot_type == const.MODELSCOPE:
+        from bot.modelscope.modelscope_bot import ModelScopeBot
+        return ModelScopeBot()
+
 
     raise RuntimeError

bot/chatgpt/chat_gpt_session.py

@@ -83,7 +83,7 @@ def num_tokens_from_messages(messages, model):
         tokens_per_message = 3
         tokens_per_name = 1
     else:
-        logger.warn(f"num_tokens_from_messages() is not implemented for model {model}. Returning num tokens assuming gpt-3.5-turbo.")
+        logger.debug(f"num_tokens_from_messages() is not implemented for model {model}. Returning num tokens assuming gpt-3.5-turbo.")
         return num_tokens_from_messages(messages, model="gpt-3.5-turbo")
     num_tokens = 0
     for message in messages:

bot/linkai/link_ai_bot.py

@@ -147,9 +147,9 @@ class LinkAIBot(Bot):
                 if response["choices"][0].get("img_urls"):
                     thread = threading.Thread(target=self._send_image, args=(context.get("channel"), context, response["choices"][0].get("img_urls")))
                     thread.start()
-                    if response["choices"][0].get("text_content"):
-                        reply_content = response["choices"][0].get("text_content")
-                reply_content = self._process_url(reply_content)
+                    reply_content = response["choices"][0].get("text_content")
+                if reply_content:
+                    reply_content = self._process_url(reply_content)
                 return Reply(ReplyType.TEXT, reply_content)
 
             else:

bot/modelscope/modelscope_bot.py

@@ -0,0 +1,277 @@
+# encoding:utf-8
+
+import time
+import json
+import openai
+import openai.error
+from bot.bot import Bot
+from bot.session_manager import SessionManager
+from bridge.context import ContextType
+from bridge.reply import Reply, ReplyType
+from common.log import logger
+from config import conf, load_config
+from .modelscope_session import ModelScopeSession
+import requests
+
+
+# ModelScope对话模型API
+class ModelScopeBot(Bot):
+    def __init__(self):
+        super().__init__()
+        self.sessions = SessionManager(ModelScopeSession, model=conf().get("model") or "Qwen/Qwen2.5-7B-Instruct")
+        model = conf().get("model") or "Qwen/Qwen2.5-7B-Instruct"
+        if model == "modelscope":
+            model = "Qwen/Qwen2.5-7B-Instruct"
+        self.args = {
+            "model": model,  # 对话模型的名称
+            "temperature": conf().get("temperature", 0.3),  # 如果设置，值域须为 [0, 1] 我们推荐 0.3，以达到较合适的效果。
+            "top_p": conf().get("top_p", 1.0),  # 使用默认值
+        }
+        self.api_key = conf().get("modelscope_api_key")
+        self.base_url = conf().get("modelscope_base_url", "https://api-inference.modelscope.cn/v1/chat/completions")
+        """
+        需要获取ModelScope支持API-inference的模型名称列表，请到魔搭社区官网模型中心查看 https://modelscope.cn/models?filter=inference_type&page=1。
+        或者使用命令 curl https://api-inference.modelscope.cn/v1/models 对模型列表和ID进行获取。查看commend/const.py文件也可以获取模型列表。
+        获取ModelScope的免费API Key，请到魔搭社区官网用户中心查看获取方式 https://modelscope.cn/docs/model-service/API-Inference/intro。
+        """
+    def reply(self, query, context=None):
+        # acquire reply content
+        if context.type == ContextType.TEXT:
+            logger.info("[MODELSCOPE_AI] query={}".format(query))
+
+            session_id = context["session_id"]
+            reply = None
+            clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
+            if query in clear_memory_commands:
+                self.sessions.clear_session(session_id)
+                reply = Reply(ReplyType.INFO, "记忆已清除")
+            elif query == "#清除所有":
+                self.sessions.clear_all_session()
+                reply = Reply(ReplyType.INFO, "所有人记忆已清除")
+            elif query == "#更新配置":
+                load_config()
+                reply = Reply(ReplyType.INFO, "配置已更新")
+            if reply:
+                return reply
+            session = self.sessions.session_query(query, session_id)
+            logger.debug("[MODELSCOPE_AI] session query={}".format(session.messages))
+
+            model = context.get("modelscope_model")
+            new_args = self.args.copy()
+            if model:
+                new_args["model"] = model
+
+            if new_args["model"] == "Qwen/QwQ-32B":
+                reply_content = self.reply_text_stream(session, args=new_args)
+            else:
+                reply_content = self.reply_text(session, args=new_args)
+
+            logger.debug(
+                "[MODELSCOPE_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
+                    session.messages,
+                    session_id,
+                    reply_content["content"],
+                    reply_content["completion_tokens"],
+                )
+            )
+            if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
+                # 只有当 content 为空且 completion_tokens 为 0 时才标记为错误
+                if len(reply_content["content"]) == 0:
+                    reply = Reply(ReplyType.ERROR, reply_content["content"])
+                else:
+                    reply = Reply(ReplyType.TEXT, reply_content["content"])
+            elif reply_content["completion_tokens"] > 0:
+                self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
+                reply = Reply(ReplyType.TEXT, reply_content["content"])
+            else:
+                reply = Reply(ReplyType.ERROR, reply_content["content"])
+                logger.debug("[MODELSCOPE_AI] reply {} used 0 tokens.".format(reply_content))
+            return reply
+        elif context.type == ContextType.IMAGE_CREATE:
+            ok, retstring = self.create_img(query, 0)
+            reply = None
+            if ok:
+                reply = Reply(ReplyType.IMAGE_URL, retstring)
+            else:
+                reply = Reply(ReplyType.ERROR, retstring)
+            return reply
+        else:
+            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
+            return reply
+
+    def reply_text(self, session: ModelScopeSession, args=None, retry_count=0) -> dict:
+        """
+        call openai's ChatCompletion to get the answer
+        :param session: a conversation session
+        :param session_id: session id
+        :param retry_count: retry count
+        :return: {}
+        """
+        try:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": "Bearer " + self.api_key
+            }
+            
+            body = args
+            body["messages"] = session.messages
+            res = requests.post(
+                self.base_url,
+                headers=headers,
+                data=json.dumps(body)
+            )
+
+            if res.status_code == 200:
+                response = res.json()
+                return {
+                    "total_tokens": response["usage"]["total_tokens"],
+                    "completion_tokens": response["usage"]["completion_tokens"],
+                    "content": response["choices"][0]["message"]["content"]
+                }
+            else:
+                response = res.json()
+                if "errors" in response:
+                    error = response.get("errors")
+                elif "error" in response:
+                    error = response.get("error")
+                else:
+                    error = "Unknown error"
+                logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, "
+                             f"msg={error.get('message')}, type={error.get('type')}")
+
+                result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
+                need_retry = False
+                if res.status_code >= 500:
+                    # server error, need retry
+                    logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}")
+                    need_retry = retry_count < 2
+                elif res.status_code == 401:
+                    result["content"] = "授权失败，请检查API Key是否正确"
+                elif res.status_code == 429:
+                    result["content"] = "请求过于频繁，请稍后再试"
+                    need_retry = retry_count < 2
+                else:
+                    need_retry = False
+
+                if need_retry:
+                    time.sleep(3)
+                    return self.reply_text(session, args, retry_count + 1)
+                else:
+                    return result
+        except Exception as e:
+            logger.exception(e)
+            need_retry = retry_count < 2
+            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
+            if need_retry:
+                return self.reply_text(session, args, retry_count + 1)
+            else:
+                return result
+
+    def reply_text_stream(self, session: ModelScopeSession, args=None, retry_count=0) -> dict:
+        """
+        call ModelScope's ChatCompletion to get the answer with stream response
+        :param session: a conversation session
+        :param session_id: session id
+        :param retry_count: retry count
+        :return: {}
+        """
+        try:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": "Bearer " + self.api_key
+            }
+            
+            body = args
+            body["messages"] = session.messages
+            body["stream"] = True  # 启用流式响应
+
+            res = requests.post(
+                self.base_url,
+                headers=headers,
+                data=json.dumps(body),
+                stream=True
+            )
+            if res.status_code == 200:
+                content = ""
+                for line in res.iter_lines():
+                    if line:
+                        decoded_line = line.decode('utf-8')
+                        if decoded_line.startswith("data: "):
+                            try:
+                                json_data = json.loads(decoded_line[6:])
+                                delta_content = json_data.get("choices", [{}])[0].get("delta", {}).get("content", "")
+                                if delta_content:
+                                    content += delta_content
+                            except json.JSONDecodeError as e:
+                                pass
+                return {
+                    "total_tokens": 1,  # 流式响应通常不返回token使用情况
+                    "completion_tokens": 1,
+                    "content": content
+                }
+            else:
+                response = res.json()
+                if "errors" in response:
+                    error = response.get("errors")
+                elif "error" in response:
+                    error = response.get("error")
+                else:
+                    error = "Unknown error"
+                logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, "
+                             f"msg={error.get('message')}, type={error.get('type')}")
+
+                result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
+                need_retry = False
+                if res.status_code >= 500:
+                    # server error, need retry
+                    logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}")
+                    need_retry = retry_count < 2
+                elif res.status_code == 401:
+                    result["content"] = "授权失败，请检查API Key是否正确"
+                elif res.status_code == 429:
+                    result["content"] = "请求过于频繁，请稍后再试"
+                    need_retry = retry_count < 2
+                else:
+                    need_retry = False
+
+                if need_retry:
+                    time.sleep(3)
+                    return self.reply_text_stream(session, args, retry_count + 1)
+                else:
+                    return result
+        except Exception as e:
+            logger.exception(e)
+            need_retry = retry_count < 2
+            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
+            if need_retry:
+                return self.reply_text_stream(session, args, retry_count + 1)
+            else:
+                return result
+    def create_img(self, query, retry_count=0):
+        try:
+            logger.info("[ModelScopeImage] image_query={}".format(query))
+            headers = {
+                "Content-Type": "application/json; charset=utf-8",  # 明确指定编码
+                "Authorization": f"Bearer {self.api_key}"
+            }
+            payload = {
+                "prompt": query,  # required
+                "n": 1,
+                "model": conf().get("text_to_image"),
+            }
+            url = "https://api-inference.modelscope.cn/v1/images/generations"
+            
+            # 手动序列化并保留中文（禁用 ASCII 转义）
+            json_payload = json.dumps(payload, ensure_ascii=False).encode('utf-8')
+            
+            # 使用 data 参数发送原始字符串（requests 会自动处理编码）
+            res = requests.post(url, headers=headers, data=json_payload)
+            
+            response_data = res.json()
+            image_url = response_data['images'][0]['url']
+            logger.info("[ModelScopeImage] image_url={}".format(image_url))
+            return True, image_url
+
+        except Exception as e:
+            logger.error(format(e))
+            return False, "画图出现问题，请休息一下再问我吧"

bot/modelscope/modelscope_session.py

@@ -0,0 +1,51 @@
+from bot.session_manager import Session
+from common.log import logger
+
+
+class ModelScopeSession(Session):
+    def __init__(self, session_id, system_prompt=None, model="Qwen/Qwen2.5-7B-Instruct"):
+        super().__init__(session_id, system_prompt)
+        self.model = model
+        self.reset()
+
+    def discard_exceeding(self, max_tokens, cur_tokens=None):
+        precise = True
+        try:
+            cur_tokens = self.calc_tokens()
+        except Exception as e:
+            precise = False
+            if cur_tokens is None:
+                raise e
+            logger.debug("Exception when counting tokens precisely for query: {}".format(e))
+        while cur_tokens > max_tokens:
+            if len(self.messages) > 2:
+                self.messages.pop(1)
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
+                self.messages.pop(1)
+                if precise:
+                    cur_tokens = self.calc_tokens()
+                else:
+                    cur_tokens = cur_tokens - max_tokens
+                break
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
+                logger.warn("user message exceed max_tokens. total_tokens={}".format(cur_tokens))
+                break
+            else:
+                logger.debug("max_tokens={}, total_tokens={}, len(messages)={}".format(max_tokens, cur_tokens,
+                                                                                       len(self.messages)))
+                break
+            if precise:
+                cur_tokens = self.calc_tokens()
+            else:
+                cur_tokens = cur_tokens - max_tokens
+        return cur_tokens
+
+    def calc_tokens(self):
+        return num_tokens_from_messages(self.messages, self.model)
+
+
+def num_tokens_from_messages(messages, model):
+    tokens = 0
+    for msg in messages:
+        tokens += len(msg["content"])
+    return tokens

bot/xunfei/xunfei_spark_bot.py

@@ -41,7 +41,7 @@ class XunFeiBot(Bot):
         self.api_key = conf().get("xunfei_api_key")
         self.api_secret = conf().get("xunfei_api_secret")
         # 默认使用v2.0版本: "generalv2"
-        # Spark Lite请求地址(spark_url): wss://spark-api.xf-yun.com/v1.1/chat, 对应的domain参数为: "general"
+        # Spark Lite请求地址(spark_url): wss://spark-api.xf-yun.com/v1.1/chat, 对应的domain参数为: "lite"
         # Spark V2.0请求地址(spark_url): wss://spark-api.xf-yun.com/v2.1/chat, 对应的domain参数为: "generalv2"
         # Spark Pro 请求地址(spark_url): wss://spark-api.xf-yun.com/v3.1/chat, 对应的domain参数为: "generalv3"
         # Spark Pro-128K请求地址(spark_url):  wss://spark-api.xf-yun.com/chat/pro-128k, 对应的domain参数为: "pro-128k"

bridge/bridge.py

@@ -49,6 +49,9 @@ class Bridge(object):
             if model_type in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
                 self.btype["chat"] = const.MOONSHOT
 
+            if model_type in [const.MODELSCOPE]:
+                self.btype["chat"] = const.MODELSCOPE
+            
             if model_type in ["abab6.5-chat"]:
                 self.btype["chat"] = const.MiniMax
 

channel/channel_factory.py

@@ -18,6 +18,9 @@ def create_channel(channel_type) -> Channel:
     elif channel_type == "wxy":
         from channel.wechat.wechaty_channel import WechatyChannel
         ch = WechatyChannel()
+    elif channel_type == "wcf":
+        from channel.wechat.wcf_channel import WechatfChannel
+        ch = WechatfChannel()
     elif channel_type == "terminal":
         from channel.terminal.terminal_channel import TerminalChannel
         ch = TerminalChannel()

channel/web/README.md

@@ -3,5 +3,5 @@
 
 #使用方法
 - 在配置文件中channel_type填入web即可
-- 访问地址 http://localhost:9899
+- 访问地址 http://localhost:9899/chat
 - port可以在配置项 web_port中设置

channel/web/web_channel.py

@@ -177,7 +177,7 @@ class WebChannel(ChatChannel):
 
     def startup(self):
         logger.setLevel("WARN")
-        print("\nWeb Channel is running. Send POST requests to /message to send messages.")
+        print("\nWeb Channel is running, please visit http://localhost:9899/chat")
         
         urls = (
             '/sse/(.+)', 'SSEHandler',  # 修改路由以接收用户ID

channel/wechat/wcf_channel.py

@@ -0,0 +1,179 @@
+# encoding:utf-8
+
+"""
+wechat channel
+"""
+
+import io
+import json
+import os
+import threading
+import time
+from queue import Empty
+from typing import Any
+
+from bridge.context import *
+from bridge.reply import *
+from channel.chat_channel import ChatChannel
+from channel.wechat.wcf_message import WechatfMessage
+from common.log import logger
+from common.singleton import singleton
+from common.utils import *
+from config import conf, get_appdata_dir
+from wcferry import Wcf, WxMsg
+
+
+@singleton
+class WechatfChannel(ChatChannel):
+    NOT_SUPPORT_REPLYTYPE = []
+
+    def __init__(self):
+        super().__init__()
+        self.NOT_SUPPORT_REPLYTYPE = []
+        # 使用字典存储最近消息，用于去重
+        self.received_msgs = {}
+        # 初始化wcferry客户端
+        self.wcf = Wcf()
+        self.wxid = None  # 登录后会被设置为当前登录用户的wxid
+
+    def startup(self):
+        """
+        启动通道
+        """
+        try:
+            # wcferry会自动唤起微信并登录
+            self.wxid = self.wcf.get_self_wxid()
+            self.name = self.wcf.get_user_info().get("name")
+            logger.info(f"微信登录成功，当前用户ID: {self.wxid}, 用户名：{self.name}")
+            self.contact_cache = ContactCache(self.wcf)
+            self.contact_cache.update()
+            # 启动消息接收
+            self.wcf.enable_receiving_msg()
+            # 创建消息处理线程
+            t = threading.Thread(target=self._process_messages, name="WeChatThread", daemon=True)
+            t.start()
+
+
+        except Exception as e:
+            logger.error(f"微信通道启动失败: {e}")
+            raise e
+
+    def _process_messages(self):
+        """
+        处理消息队列
+        """
+        while True:
+            try:
+                msg = self.wcf.get_msg()
+                if msg:
+                    self._handle_message(msg)
+            except Empty:
+                continue
+            except Exception as e:
+                logger.error(f"处理消息失败: {e}")
+                continue
+
+    def _handle_message(self, msg: WxMsg):
+        """
+        处理单条消息
+        """
+        try:
+            # 构造消息对象
+            cmsg = WechatfMessage(self, msg)
+            # 消息去重
+            if cmsg.msg_id in self.received_msgs:
+                return
+            self.received_msgs[cmsg.msg_id] = time.time()
+            # 清理过期消息ID
+            self._clean_expired_msgs()
+
+            logger.debug(f"收到消息: {msg}")
+            context = self._compose_context(cmsg.ctype, cmsg.content,
+                                            isgroup=cmsg.is_group,
+                                            msg=cmsg)
+            if context:
+                self.produce(context)
+        except Exception as e:
+            logger.error(f"处理消息失败: {e}")
+
+    def _clean_expired_msgs(self, expire_time: float = 60):
+        """
+        清理过期的消息ID
+        """
+        now = time.time()
+        for msg_id in list(self.received_msgs.keys()):
+            if now - self.received_msgs[msg_id] > expire_time:
+                del self.received_msgs[msg_id]
+
+    def send(self, reply: Reply, context: Context):
+        """
+        发送消息
+        """
+        receiver = context["receiver"]
+        if not receiver:
+            logger.error("receiver is empty")
+            return
+
+        try:
+            if reply.type == ReplyType.TEXT:
+                # 处理@信息
+                at_list = []
+                if context.get("isgroup"):
+                    if context["msg"].actual_user_id:
+                        at_list = [context["msg"].actual_user_id]
+                at_str = ",".join(at_list) if at_list else ""
+                self.wcf.send_text(reply.content, receiver, at_str)
+
+            elif reply.type == ReplyType.ERROR or reply.type == ReplyType.INFO:
+                self.wcf.send_text(reply.content, receiver)
+            else:
+                logger.error(f"暂不支持的消息类型: {reply.type}")
+
+        except Exception as e:
+            logger.error(f"发送消息失败: {e}")
+
+    def close(self):
+        """
+        关闭通道
+        """
+        try:
+            self.wcf.cleanup()
+        except Exception as e:
+            logger.error(f"关闭通道失败: {e}")
+
+
+class ContactCache:
+    def __init__(self, wcf):
+        """
+        wcf: 一个 wcfferry.client.Wcf 实例
+        """
+        self.wcf = wcf
+        self._contact_map = {}  # 形如 {wxid: {完整联系人信息}}
+
+    def update(self):
+        """
+        更新缓存：调用 get_contacts()，
+        再把 wcf.contacts 构建成 {wxid: {完整信息}} 的字典
+        """
+        self.wcf.get_contacts()
+        self._contact_map.clear()
+        for item in self.wcf.contacts:
+            wxid = item.get('wxid')
+            if wxid:  # 确保有 wxid 字段
+                self._contact_map[wxid] = item
+
+    def get_contact(self, wxid: str) -> dict:
+        """
+        返回该 wxid 对应的完整联系人 dict，
+        如果没找到就返回 None
+        """
+        return self._contact_map.get(wxid)
+
+    def get_name_by_wxid(self, wxid: str) -> str:
+        """
+        通过wxid，获取成员/群名称
+        """
+        contact = self.get_contact(wxid)
+        if contact:
+            return contact.get('name', '')
+        return ''

channel/wechat/wcf_message.py

@@ -0,0 +1,58 @@
+# encoding:utf-8
+
+"""
+wechat channel message
+"""
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from common.log import logger
+from wcferry import WxMsg
+
+
+class WechatfMessage(ChatMessage):
+    """
+    微信消息封装类
+    """
+
+    def __init__(self, channel, wcf_msg: WxMsg, is_group=False):
+        """
+        初始化消息对象
+        :param wcf_msg: wcferry消息对象
+        :param is_group: 是否是群消息
+        """
+        super().__init__(wcf_msg)
+        self.msg_id = wcf_msg.id
+        self.create_time = wcf_msg.ts  # 使用消息时间戳
+        self.is_group = is_group or wcf_msg._is_group
+        self.wxid = channel.wxid
+        self.name = channel.name
+
+        # 解析消息类型
+        if wcf_msg.is_text():
+            self.ctype = ContextType.TEXT
+            self.content = wcf_msg.content
+        else:
+            raise NotImplementedError(f"Unsupported message type: {wcf_msg.type}")
+
+        # 设置发送者和接收者信息
+        self.from_user_id = self.wxid if wcf_msg.sender == self.wxid else wcf_msg.sender
+        self.from_user_nickname = self.name if wcf_msg.sender == self.wxid else channel.contact_cache.get_name_by_wxid(wcf_msg.sender)
+        self.to_user_id = self.wxid
+        self.to_user_nickname = self.name
+        self.other_user_id = wcf_msg.sender
+        self.other_user_nickname = channel.contact_cache.get_name_by_wxid(wcf_msg.sender)
+
+        # 群消息特殊处理
+        if self.is_group:
+            self.other_user_id = wcf_msg.roomid
+            self.other_user_nickname = channel.contact_cache.get_name_by_wxid(wcf_msg.roomid)
+            self.actual_user_id = wcf_msg.sender
+            self.actual_user_nickname = channel.wcf.get_alias_in_chatroom(wcf_msg.sender, wcf_msg.roomid)
+            if not self.actual_user_nickname:  # 群聊获取不到企微号成员昵称，这里尝试从联系人缓存去获取
+                self.actual_user_nickname = channel.contact_cache.get_name_by_wxid(wcf_msg.sender)
+            self.room_id = wcf_msg.roomid
+            self.is_at = wcf_msg.is_at(self.wxid)  # 是否被@当前登录用户
+
+        # 判断是否是自己发送的消息
+        self.my_msg = wcf_msg.from_self()

channel/wechat/wechat_channel.py

@@ -117,23 +117,35 @@ class WechatChannel(ChatChannel):
 
     def startup(self):
         try:
-            itchat.instance.receivingRetryCount = 600  # 修改断线超时时间
-            # login by scan QRCode
-            hotReload = conf().get("hot_reload", False)
-            status_path = os.path.join(get_appdata_dir(), "itchat.pkl")
-            itchat.auto_login(
-                enableCmdQR=2,
-                hotReload=hotReload,
-                statusStorageDir=status_path,
-                qrCallback=qrCallback,
-                exitCallback=self.exitCallback,
-                loginCallback=self.loginCallback
-            )
-            self.user_id = itchat.instance.storageClass.userName
-            self.name = itchat.instance.storageClass.nickName
-            logger.info("Wechat login success, user_id: {}, nickname: {}".format(self.user_id, self.name))
-            # start message listener
-            itchat.run()
+            time.sleep(3)
+            logger.error("""[WechatChannel] 当前channel暂不可用，目前支持的channel有:
+                1. terminal: 终端
+                2. wechatmp: 个人公众号
+                3. wechatmp_service: 企业公众号
+                4. wechatcom_app: 企微自建应用
+                5. dingtalk: 钉钉
+                6. feishu: 飞书
+                7. web: 网页
+                8. wcf: wechat (需Windows环境，参考 https://github.com/zhayujie/chatgpt-on-wechat/pull/2562 )
+            可修改 config.json 配置文件的 channel_type 字段进行切换""")
+
+            # itchat.instance.receivingRetryCount = 600  # 修改断线超时时间
+            # # login by scan QRCode
+            # hotReload = conf().get("hot_reload", False)
+            # status_path = os.path.join(get_appdata_dir(), "itchat.pkl")
+            # itchat.auto_login(
+            #     enableCmdQR=2,
+            #     hotReload=hotReload,
+            #     statusStorageDir=status_path,
+            #     qrCallback=qrCallback,
+            #     exitCallback=self.exitCallback,
+            #     loginCallback=self.loginCallback
+            # )
+            # self.user_id = itchat.instance.storageClass.userName
+            # self.name = itchat.instance.storageClass.nickName
+            # logger.info("Wechat login success, user_id: {}, nickname: {}".format(self.user_id, self.name))
+            # # start message listener
+            # itchat.run()
         except Exception as e:
             logger.exception(e)
 

common/const.py

@@ -15,7 +15,7 @@ GEMINI = "gemini"  # gemini-1.0-pro
 ZHIPU_AI = "glm-4"
 MOONSHOT = "moonshot"
 MiniMax = "minimax"
-
+MODELSCOPE = "modelscope"
 
 # model
 CLAUDE3 = "claude-3-opus-20240229"
@@ -74,14 +74,23 @@ GLM_4_AIRX = "glm-4-airx"
 
 CLAUDE_3_OPUS = "claude-3-opus-latest"
 CLAUDE_3_OPUS_0229 = "claude-3-opus-20240229"
-
 CLAUDE_35_SONNET = "claude-3-5-sonnet-latest"  # 带 latest 标签的模型名称，会不断更新指向最新发布的模型
 CLAUDE_35_SONNET_1022 = "claude-3-5-sonnet-20241022"  # 带具体日期的模型名称，会固定为该日期发布的模型
 CLAUDE_35_SONNET_0620 = "claude-3-5-sonnet-20240620"
 CLAUDE_3_SONNET = "claude-3-sonnet-20240229"
-
 CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
 
+DEEPSEEK_CHAT = "deepseek-chat"  # DeepSeek-V3对话模型
+DEEPSEEK_REASONER = "deepseek-reasoner"  # DeepSeek-R1模型
+
+GITEE_AI_MODEL_LIST = ["Yi-34B-Chat", "InternVL2-8B", "deepseek-coder-33B-instruct", "InternVL2.5-26B", "Qwen2-VL-72B", "Qwen2.5-32B-Instruct", "glm-4-9b-chat", "codegeex4-all-9b", "Qwen2.5-Coder-32B-Instruct", "Qwen2.5-72B-Instruct", "Qwen2.5-7B-Instruct", "Qwen2-72B-Instruct", "Qwen2-7B-Instruct", "code-raccoon-v1", "Qwen2.5-14B-Instruct"]
+
+MODELSCOPE_MODEL_LIST = ["LLM-Research/c4ai-command-r-plus-08-2024","mistralai/Mistral-Small-Instruct-2409","mistralai/Ministral-8B-Instruct-2410","mistralai/Mistral-Large-Instruct-2407",
+                          "Qwen/Qwen2.5-Coder-32B-Instruct","Qwen/Qwen2.5-Coder-14B-Instruct","Qwen/Qwen2.5-Coder-7B-Instruct","Qwen/Qwen2.5-72B-Instruct","Qwen/Qwen2.5-32B-Instruct","Qwen/Qwen2.5-14B-Instruct","Qwen/Qwen2.5-7B-Instruct","Qwen/QwQ-32B-Preview",
+                          "LLM-Research/Llama-3.3-70B-Instruct","opencompass/CompassJudger-1-32B-Instruct","Qwen/QVQ-72B-Preview","LLM-Research/Meta-Llama-3.1-405B-Instruct","LLM-Research/Meta-Llama-3.1-8B-Instruct","Qwen/Qwen2-VL-7B-Instruct","LLM-Research/Meta-Llama-3.1-70B-Instruct",
+                          "Qwen/Qwen2.5-14B-Instruct-1M","Qwen/Qwen2.5-7B-Instruct-1M","Qwen/Qwen2.5-VL-3B-Instruct","Qwen/Qwen2.5-VL-7B-Instruct","Qwen/Qwen2.5-VL-72B-Instruct","deepseek-ai/DeepSeek-R1-Distill-Llama-70B","deepseek-ai/DeepSeek-R1-Distill-Llama-8B","deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+                          "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B","deepseek-ai/DeepSeek-R1-Distill-Qwen-7B","deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B","deepseek-ai/DeepSeek-R1","deepseek-ai/DeepSeek-V3","Qwen/QwQ-32B"]
+
 MODEL_LIST = [
               GPT35, GPT35_0125, GPT35_1106, "gpt-3.5-turbo-16k",
               O1, O1_MINI, GPT_4o, GPT_4O_0806, GPT_4o_MINI, GPT4_TURBO, GPT4_TURBO_PREVIEW, GPT4_TURBO_01_25, GPT4_TURBO_11_06, GPT4, GPT4_32k, GPT4_06_13, GPT4_32k_06_13,
@@ -93,9 +102,12 @@ MODEL_LIST = [
               CLAUDE_3_OPUS, CLAUDE_3_OPUS_0229, CLAUDE_35_SONNET, CLAUDE_35_SONNET_1022, CLAUDE_35_SONNET_0620, CLAUDE_3_SONNET, CLAUDE_3_HAIKU, "claude", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3.5-sonnet",
               "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k",
               QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX,
-              LINKAI_35, LINKAI_4_TURBO, LINKAI_4o
+              LINKAI_35, LINKAI_4_TURBO, LINKAI_4o,
+              DEEPSEEK_CHAT, DEEPSEEK_REASONER,
+              MODELSCOPE
             ]
 
+MODEL_LIST = MODEL_LIST + GITEE_AI_MODEL_LIST + MODELSCOPE_MODEL_LIST
 # channel
 FEISHU = "feishu"
 DINGTALK = "dingtalk"

config.py

@@ -171,6 +171,9 @@ available_setting = {
     "zhipu_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
     "moonshot_api_key": "",
     "moonshot_base_url": "https://api.moonshot.cn/v1/chat/completions",
+    #魔搭社区 平台配置
+    "modelscope_api_key": "",
+    "modelscope_base_url": "https://api-inference.modelscope.cn/v1/chat/completions",
     # LinkAI平台配置
     "use_linkai": False,
     "linkai_api_key": "",

plugins/godcmd/godcmd.py

@@ -339,7 +339,8 @@ class Godcmd(Plugin):
                             ok, result = True, "配置已重载"
                         elif cmd == "resetall":
                             if bottype in [const.OPEN_AI, const.CHATGPT, const.CHATGPTONAZURE, const.LINKAI,
-                                           const.BAIDU, const.XUNFEI, const.QWEN, const.GEMINI, const.ZHIPU_AI, const.MOONSHOT]:
+                                           const.BAIDU, const.XUNFEI, const.QWEN, const.GEMINI, const.ZHIPU_AI, const.MOONSHOT,
+                                           const.MODELSCOPE]:
                                 channel.cancel_all_session()
                                 bot.sessions.clear_all_session()
                                 ok, result = True, "重置所有会话成功"

plugins/role/role.py

@@ -99,7 +99,7 @@ class Role(Plugin):
         if e_context["context"].type != ContextType.TEXT:
             return
         btype = Bridge().get_bot_type("chat")
-        if btype not in [const.OPEN_AI, const.CHATGPT, const.CHATGPTONAZURE, const.QWEN_DASHSCOPE, const.XUNFEI, const.BAIDU, const.ZHIPU_AI, const.MOONSHOT, const.MiniMax, const.LINKAI]:
+        if btype not in [const.OPEN_AI, const.CHATGPT, const.CHATGPTONAZURE, const.QWEN_DASHSCOPE, const.XUNFEI, const.BAIDU, const.ZHIPU_AI, const.MOONSHOT, const.MiniMax, const.LINKAI,const.MODELSCOPE]:
             logger.debug(f'不支持的bot: {btype}')
             return
         bot = Bridge().get_bot("chat")

requirements-optional.txt

@@ -44,3 +44,6 @@ zhipuai>=2.0.1
 
 # tongyi qwen new sdk
 dashscope
+
+# tencentcloud sdk
+tencentcloud-sdk-python>=3.0.0

requirements.txt

@@ -8,3 +8,4 @@ Pillow
 pre-commit
 web.py
 linkai>=0.0.6.0
+

voice/factory.py

@@ -50,4 +50,8 @@ def create_voice(voice_type):
         from voice.xunfei.xunfei_voice import XunfeiVoice
 
         return XunfeiVoice()
+    elif voice_type == "tencent":
+        from voice.tencent.tencent_voice import TencentVoice
+
+        return TencentVoice()
     raise RuntimeError

voice/tencent/config.json.template

@@ -0,0 +1,5 @@
+{
+    "voice_type": 1003,
+    "secret_id": "YOUR_SECRET_ID",
+    "secret_key": "YOUR_SECRET_KEY"
+}

voice/tencent/tencent_voice.py

@@ -0,0 +1,119 @@
+import json
+import base64
+import os
+import time
+from voice.voice import Voice
+from common.log import logger
+from tencentcloud.common import credential
+from tencentcloud.asr.v20190614 import asr_client, models as asr_models
+from tencentcloud.tts.v20190823 import tts_client, models as tts_models
+from bridge.reply import Reply, ReplyType
+from common.tmp_dir import TmpDir
+
+class TencentVoice(Voice):
+    def __init__(self):
+        super().__init__()
+        self.secret_id = None
+        self.secret_key = None
+        self.voice_type = 1003
+        self._load_config()
+        
+    def _load_config(self):
+        """
+        从本地配置文件加载配置
+        """
+        try:
+            config_path = os.path.join(os.path.dirname(__file__), 'config.json')
+            with open(config_path, 'r') as f:
+                config = json.load(f)
+            self.secret_id = config.get('secret_id')
+            self.secret_key = config.get('secret_key')
+            self.voice_type = config.get('voice_type', self.voice_type)
+            if not self.secret_id or not self.secret_key:
+                logger.error("[Tencent] Missing credentials in config.json")
+        except Exception as e:
+            logger.error(f"[Tencent] Failed to load config: {e}")
+    
+    def setup(self, config):
+        """
+        设置配置信息（保留此方法用于向后兼容）
+        """
+        pass
+        
+    def voiceToText(self, voice_file):
+        """
+        将语音文件转换为文本
+        """
+        try:
+            # 实例化认证对象
+            cred = credential.Credential(self.secret_id, self.secret_key)
+            
+            # 实例化客户端
+            client = asr_client.AsrClient(cred, "ap-guangzhou")
+            
+            # 读取音频文件
+            with open(voice_file, 'rb') as f:
+                audio_data = f.read()
+            
+            # 进行base64编码
+            base64_audio = base64.b64encode(audio_data).decode('utf-8')
+            
+            # 构造请求对象
+            req = asr_models.SentenceRecognitionRequest()
+            req.ProjectId = 0
+            req.SubServiceType = 2
+            req.EngSerViceType = "16k_zh"
+            req.SourceType = 1
+            req.VoiceFormat = "wav"
+            req.UsrAudioKey = "voice_recognition"
+            req.Data = base64_audio
+            
+            # 发起请求
+            resp = client.SentenceRecognition(req)
+            
+            # 解析结果
+            if resp.Result:
+                logger.info("[Tencent] Voice to text success: {}".format(resp.Result))
+                return Reply(ReplyType.TEXT, resp.Result)
+            else:
+                logger.warning("[Tencent] Voice to text failed")
+                return Reply(ReplyType.ERROR, "腾讯语音识别失败")
+            
+        except Exception as e:
+            logger.error("[Tencent] Voice to text error: {}".format(e))
+            return Reply(ReplyType.ERROR, "腾讯语音识别出错：{}".format(str(e)))
+
+    def textToVoice(self, text):
+        """
+        将文本转换为语音
+        """
+        try:
+            cred = credential.Credential(self.secret_id, self.secret_key)
+            client = tts_client.TtsClient(cred, "ap-guangzhou")
+
+            req = tts_models.TextToVoiceRequest()
+            req.Text = text
+            req.SessionId = str(int(time.time()))
+            req.Volume = 5
+            req.Speed = 0
+            req.ProjectId = 0
+            req.ModelType = 1
+            req.PrimaryLanguage = 1
+            req.SampleRate = 16000
+            req.VoiceType = self.voice_type  # 客服女声
+
+            response = client.TextToVoice(req)
+            
+            if response.Audio:
+                fileName = TmpDir().path() + "reply-" + str(int(time.time())) + "-" + str(hash(text) & 0x7FFFFFFF) + ".mp3"
+                with open(fileName, "wb") as f:
+                    f.write(base64.b64decode(response.Audio))
+                logger.info("[Tencent] textToVoice text={} voice file name={}".format(text, fileName))
+                return Reply(ReplyType.VOICE, fileName)
+            else:
+                logger.error("[Tencent] textToVoice failed")
+                return Reply(ReplyType.ERROR, "腾讯语音合成失败")
+
+        except Exception as e:
+            logger.error("[Tencent] Text to voice error: {}".format(e))
+            return Reply(ReplyType.ERROR, "腾讯语音合成出错：{}".format(str(e)))