Merge branch 'dev' into patch-2

.gitignore

@@ -321,6 +321,7 @@ run_pet.bat
 /plugins/*
 !/plugins
 !/plugins/hello_world_plugin
+!/plugins/emoji_manage_plugin
 !/plugins/take_picture_plugin
 
 config.toml

changelogs/changelog.md

@@ -1,7 +1,8 @@
 # Changelog
 
-0.10.4饼 表达方式优化
+## [0.10.4] - 2025-9-22
-无了
+表达方式优化
+
 
 ## [0.10.3] - 2025-9-22
 ### 🌟 主要功能更改

docs/model_configuration_guide.md

@@ -28,7 +28,7 @@ version = "1.1.1"
 ```toml
 [[api_providers]]
 name = "DeepSeek"                       # 服务商名称（自定义）
-base_url = "https://api.deepseek.cn/v1" # API服务的基础URL
+base_url = "https://api.deepseek.com/v1" # API服务的基础URL
 api_key = "your-api-key-here"           # API密钥
 client_type = "openai"                  # 客户端类型
 max_retry = 2                           # 最大重试次数
@@ -43,19 +43,19 @@ retry_interval = 10                     # 重试间隔（秒）
 | `name` | ✅ | 服务商名称，需要在模型配置中引用 | - |
 | `base_url` | ✅ | API服务的基础URL | - |
 | `api_key` | ✅ | API密钥，请替换为实际密钥 | - |
-| `client_type` | ❌ | 客户端类型：`openai`（OpenAI格式）或 `gemini`（Gemini格式，现在支持不良好） | `openai` |
+| `client_type` | ❌ | 客户端类型：`openai`（OpenAI格式）或 `gemini`（Gemini格式） | `openai` |
 | `max_retry` | ❌ | API调用失败时的最大重试次数 | 2 |
 | `timeout` | ❌ | API请求超时时间（秒） | 30 |
 | `retry_interval` | ❌ | 重试间隔时间（秒） | 10 |
 
-**请注意，对于`client_type`为`gemini`的模型，`base_url`字段无效。**
+**请注意，对于`client_type`为`gemini`的模型，`retry`字段由`gemini`自己决定。**
 ### 2.3 支持的服务商示例
 
 #### DeepSeek
 ```toml
 [[api_providers]]
 name = "DeepSeek"
-base_url = "https://api.deepseek.cn/v1"
+base_url = "https://api.deepseek.com/v1"
 api_key = "your-deepseek-api-key"
 client_type = "openai"
 ```
@@ -73,7 +73,7 @@ client_type = "openai"
 ```toml
 [[api_providers]]
 name = "Google"
-base_url = "https://api.google.com/v1"
+base_url = "https://generativelanguage.googleapis.com/v1beta"
 api_key = "your-google-api-key"
 client_type = "gemini"  # 注意：Gemini需要使用特殊客户端
 ```
@@ -131,9 +131,20 @@ enable_thinking = false # 禁用思考
 [models.extra_params]
 thinking = {type = "disabled"} # 禁用思考
 ```
+
+而对于`gemini`需要单独进行配置
+```toml
+[[models]]
+model_identifier = "gemini-2.5-flash"
+name = "gemini-2.5-flash"
+api_provider = "Google"
+[models.extra_params]
+thinking_budget = 0 # 禁用思考
+# thinking_budget = -1 由模型自己决定
+```
+
 请注意，`extra_params` 的配置应该构成一个合法的TOML字典结构，具体内容取决于API服务商的要求。
 
-**请注意，对于`client_type`为`gemini`的模型，此字段无效。**
 ### 3.3 配置参数说明
 
 | 参数 | 必填 | 说明 |

plugins/emoji_manage_plugin/_manifest.json

@@ -0,0 +1,53 @@
+{
+  "manifest_version": 1,
+  "name": "BetterEmoji",
+  "version": "1.0.0",
+  "description": "更好的表情包管理插件",
+  "author": {
+    "name": "SengokuCola",
+    "url": "https://github.com/SengokuCola"
+  },
+  "license": "GPL-v3.0-or-later",
+  
+  "host_application": {
+    "min_version": "0.10.4"
+  },
+  "homepage_url": "https://github.com/SengokuCola/BetterEmoji",
+  "repository_url": "https://github.com/SengokuCola/BetterEmoji",
+  "keywords": ["emoji", "manage", "plugin"],
+  "categories": ["Examples", "Tutorial"],
+  
+  "default_locale": "zh-CN",
+  "locales_path": "_locales",
+  
+  "plugin_info": {
+    "is_built_in": false,
+    "plugin_type": "emoji_manage",
+    "components": [
+      {
+        "type": "action",
+        "name": "hello_greeting",
+        "description": "向用户发送问候消息"
+      },
+      {
+        "type": "action",
+        "name": "bye_greeting", 
+        "description": "向用户发送告别消息",
+        "activation_modes": ["keyword"],
+        "keywords": ["再见", "bye", "88", "拜拜"]
+      },
+      {
+        "type": "command",
+        "name": "time",
+        "description": "查询当前时间",
+        "pattern": "/time"
+      }
+    ],
+    "features": [
+      "问候和告别功能",
+      "时间查询命令",
+      "配置文件示例",
+      "新手教程代码"
+    ]
+  }
+}

plugins/emoji_manage_plugin/plugin.py

@@ -0,0 +1,404 @@
+import random
+from typing import List, Tuple, Type, Any
+from src.plugin_system import (
+    BasePlugin,
+    register_plugin,
+    BaseAction,
+    BaseCommand,
+    BaseTool,
+    ComponentInfo,
+    ActionActivationType,
+    ConfigField,
+    BaseEventHandler,
+    EventType,
+    MaiMessages,
+    ToolParamType,
+    ReplyContentType,
+    emoji_api,
+)
+from maim_message import Seg
+from src.config.config import global_config
+from src.common.logger import get_logger
+logger = get_logger("emoji_manage_plugin")
+
+class AddEmojiCommand(BaseCommand):
+    command_name = "add_emoji"
+    command_description = "添加表情包"
+    command_pattern = r".*/emoji add.*"
+
+    async def execute(self) -> Tuple[bool, str, bool]:
+        # 查找消息中的表情包
+        # logger.info(f"查找消息中的表情包: {self.message.message_segment}")
+        
+        emoji_base64_list = self.find_and_return_emoji_in_message(self.message.message_segment)
+
+        if not emoji_base64_list:
+            return False, "未在消息中找到表情包或图片", False
+
+        # 注册找到的表情包
+        success_count = 0
+        fail_count = 0
+        results = []
+
+        for i, emoji_base64 in enumerate(emoji_base64_list):
+            try:
+                # 使用emoji_api注册表情包（让API自动生成唯一文件名）
+                result = await emoji_api.register_emoji(emoji_base64)
+
+                if result["success"]:
+                    success_count += 1
+                    description = result.get("description", "未知描述")
+                    emotions = result.get("emotions", [])
+                    replaced = result.get("replaced", False)
+
+                    result_msg = f"表情包 {i+1} 注册成功{'(替换旧表情包)' if replaced else '(新增表情包)'}"
+                    if description:
+                        result_msg += f"\n描述: {description}"
+                    if emotions:
+                        result_msg += f"\n情感标签: {', '.join(emotions)}"
+
+                    results.append(result_msg)
+                else:
+                    fail_count += 1
+                    error_msg = result.get("message", "注册失败")
+                    results.append(f"表情包 {i+1} 注册失败: {error_msg}")
+
+            except Exception as e:
+                fail_count += 1
+                results.append(f"表情包 {i+1} 注册时发生错误: {str(e)}")
+
+        # 构建返回消息
+        total_count = success_count + fail_count
+        summary_msg = f"表情包注册完成: 成功 {success_count} 个，失败 {fail_count} 个，共处理 {total_count} 个"
+
+        # 如果有结果详情，添加到返回消息中
+        details_msg = ""
+        if results:
+            details_msg = "\n" + "\n".join(results)
+            final_msg = summary_msg + details_msg
+        else:
+            final_msg = summary_msg
+
+        # 使用表达器重写回复
+        try:
+            from src.plugin_system.apis import generator_api
+
+            # 构建重写数据
+            rewrite_data = {
+                "raw_reply": summary_msg,
+                "reason": f"注册了表情包：{details_msg}\n",
+            }
+
+            # 调用表达器重写
+            result_status, data = await generator_api.rewrite_reply(
+                chat_stream=self.message.chat_stream,
+                reply_data=rewrite_data,
+            )
+
+            if result_status:
+                # 发送重写后的回复
+                for reply_seg in data.reply_set.reply_data:
+                    send_data = reply_seg.content
+                    await self.send_text(send_data)
+
+                return success_count > 0, final_msg, success_count > 0
+            else:
+                # 如果重写失败，发送原始消息
+                await self.send_text(final_msg)
+                return success_count > 0, final_msg, success_count > 0
+
+        except Exception as e:
+            # 如果表达器调用失败，发送原始消息
+            logger.error(f"[add_emoji] 表达器重写失败: {e}")
+            await self.send_text(final_msg)
+            return success_count > 0, final_msg, success_count > 0
+
+    def find_and_return_emoji_in_message(self, message_segments) -> List[str]:
+        emoji_base64_list = []
+
+        # 处理单个Seg对象的情况
+        if isinstance(message_segments, Seg):
+            if message_segments.type == "emoji":
+                emoji_base64_list.append(message_segments.data)
+            elif message_segments.type == "image":
+                # 假设图片数据是base64编码的
+                emoji_base64_list.append(message_segments.data)
+            elif message_segments.type == "seglist":
+                # 递归处理嵌套的Seg列表
+                emoji_base64_list.extend(self.find_and_return_emoji_in_message(message_segments.data))
+            return emoji_base64_list
+
+        # 处理Seg列表的情况
+        for seg in message_segments:
+            if seg.type == "emoji":
+                emoji_base64_list.append(seg.data)
+            elif seg.type == "image":
+                # 假设图片数据是base64编码的
+                emoji_base64_list.append(seg.data)
+            elif seg.type == "seglist":
+                # 递归处理嵌套的Seg列表
+                emoji_base64_list.extend(self.find_and_return_emoji_in_message(seg.data))
+        return emoji_base64_list
+
+class ListEmojiCommand(BaseCommand):
+    """列表表情包Command - 响应/emoji list命令"""
+
+    command_name = "emoji_list"
+    command_description = "列表表情包"
+
+    # === 命令设置（必须填写）===
+    command_pattern = r"^/emoji list(\s+\d+)?$"  # 匹配 "/emoji list" 或 "/emoji list 数量"
+
+    async def execute(self) -> Tuple[bool, str, bool]:
+        """执行列表表情包"""
+        from src.plugin_system.apis import emoji_api
+        import datetime
+
+        # 解析命令参数
+        import re
+        match = re.match(r"^/emoji list(?:\s+(\d+))?$", self.message.raw_message)
+        max_count = 10  # 默认显示10个
+        if match and match.group(1):
+            max_count = min(int(match.group(1)), 50)  # 最多显示50个
+
+        # 获取当前时间
+        time_format: str = self.get_config("time.format", "%Y-%m-%d %H:%M:%S")  # type: ignore
+        now = datetime.datetime.now()
+        time_str = now.strftime(time_format)
+
+        # 获取表情包信息
+        emoji_count = emoji_api.get_count()
+        emoji_info = emoji_api.get_info()
+
+        # 构建返回消息
+        message_lines = [
+            f"📊 表情包统计信息 ({time_str})",
+            f"• 总数: {emoji_count} / {emoji_info['max_count']}",
+            f"• 可用: {emoji_info['available_emojis']}",
+        ]
+
+        if emoji_count == 0:
+            message_lines.append("\n❌ 暂无表情包")
+            final_message = "\n".join(message_lines)
+            await self.send_text(final_message)
+            return True, final_message, True
+
+        # 获取所有表情包
+        all_emojis = await emoji_api.get_all()
+        if not all_emojis:
+            message_lines.append("\n❌ 无法获取表情包列表")
+            final_message = "\n".join(message_lines)
+            await self.send_text(final_message)
+            return False, final_message, True
+
+        # 显示前N个表情包
+        display_emojis = all_emojis[:max_count]
+        message_lines.append(f"\n📋 显示前 {len(display_emojis)} 个表情包:")
+
+        for i, (emoji_base64, description, emotion) in enumerate(display_emojis, 1):
+            # 截断过长的描述
+            short_desc = description[:50] + "..." if len(description) > 50 else description
+            message_lines.append(f"{i}. {short_desc} [{emotion}]")
+
+        # 如果还有更多表情包，显示总数
+        if len(all_emojis) > max_count:
+            message_lines.append(f"\n💡 还有 {len(all_emojis) - max_count} 个表情包未显示")
+
+        final_message = "\n".join(message_lines)
+
+        # 直接发送文本消息
+        await self.send_text(final_message)
+
+        return True, final_message, True
+
+
+class DeleteEmojiCommand(BaseCommand):
+    command_name = "delete_emoji"
+    command_description = "删除表情包"
+    command_pattern = r".*/emoji delete.*"
+
+    async def execute(self) -> Tuple[bool, str, bool]:
+        # 查找消息中的表情包图片
+        logger.info(f"查找消息中的表情包用于删除: {self.message.message_segment}")
+
+        emoji_base64_list = self.find_and_return_emoji_in_message(self.message.message_segment)
+
+        if not emoji_base64_list:
+            return False, "未在消息中找到表情包或图片", False
+
+        # 删除找到的表情包
+        success_count = 0
+        fail_count = 0
+        results = []
+
+        for i, emoji_base64 in enumerate(emoji_base64_list):
+            try:
+                # 计算图片的哈希值来查找对应的表情包
+                import base64
+                import hashlib
+
+                # 确保base64字符串只包含ASCII字符
+                if isinstance(emoji_base64, str):
+                    emoji_base64_clean = emoji_base64.encode("ascii", errors="ignore").decode("ascii")
+                else:
+                    emoji_base64_clean = str(emoji_base64)
+
+                # 计算哈希值
+                image_bytes = base64.b64decode(emoji_base64_clean)
+                emoji_hash = hashlib.md5(image_bytes).hexdigest()
+
+                # 使用emoji_api删除表情包
+                result = await emoji_api.delete_emoji(emoji_hash)
+
+                if result["success"]:
+                    success_count += 1
+                    description = result.get("description", "未知描述")
+                    count_before = result.get("count_before", 0)
+                    count_after = result.get("count_after", 0)
+                    emotions = result.get("emotions", [])
+
+                    result_msg = f"表情包 {i+1} 删除成功"
+                    if description:
+                        result_msg += f"\n描述: {description}"
+                    if emotions:
+                        result_msg += f"\n情感标签: {', '.join(emotions)}"
+                    result_msg += f"\n表情包数量: {count_before} → {count_after}"
+
+                    results.append(result_msg)
+                else:
+                    fail_count += 1
+                    error_msg = result.get("message", "删除失败")
+                    results.append(f"表情包 {i+1} 删除失败: {error_msg}")
+
+            except Exception as e:
+                fail_count += 1
+                results.append(f"表情包 {i+1} 删除时发生错误: {str(e)}")
+
+        # 构建返回消息
+        total_count = success_count + fail_count
+        summary_msg = f"表情包删除完成: 成功 {success_count} 个，失败 {fail_count} 个，共处理 {total_count} 个"
+
+        # 如果有结果详情，添加到返回消息中
+        details_msg = ""
+        if results:
+            details_msg = "\n" + "\n".join(results)
+            final_msg = summary_msg + details_msg
+        else:
+            final_msg = summary_msg
+
+        # 使用表达器重写回复
+        try:
+            from src.plugin_system.apis import generator_api
+
+            # 构建重写数据
+            rewrite_data = {
+                "raw_reply": summary_msg,
+                "reason": f"删除了表情包：{details_msg}\n",
+            }
+
+            # 调用表达器重写
+            result_status, data = await generator_api.rewrite_reply(
+                chat_stream=self.message.chat_stream,
+                reply_data=rewrite_data,
+            )
+
+            if result_status:
+                # 发送重写后的回复
+                for reply_seg in data.reply_set.reply_data:
+                    send_data = reply_seg.content
+                    await self.send_text(send_data)
+
+                return success_count > 0, final_msg, success_count > 0
+            else:
+                # 如果重写失败，发送原始消息
+                await self.send_text(final_msg)
+                return success_count > 0, final_msg, success_count > 0
+
+        except Exception as e:
+            # 如果表达器调用失败，发送原始消息
+            logger.error(f"[delete_emoji] 表达器重写失败: {e}")
+            await self.send_text(final_msg)
+            return success_count > 0, final_msg, success_count > 0
+
+    def find_and_return_emoji_in_message(self, message_segments) -> List[str]:
+        emoji_base64_list = []
+
+        # 处理单个Seg对象的情况
+        if isinstance(message_segments, Seg):
+            if message_segments.type == "emoji":
+                emoji_base64_list.append(message_segments.data)
+            elif message_segments.type == "image":
+                # 假设图片数据是base64编码的
+                emoji_base64_list.append(message_segments.data)
+            elif message_segments.type == "seglist":
+                # 递归处理嵌套的Seg列表
+                emoji_base64_list.extend(self.find_and_return_emoji_in_message(message_segments.data))
+            return emoji_base64_list
+
+        # 处理Seg列表的情况
+        for seg in message_segments:
+            if seg.type == "emoji":
+                emoji_base64_list.append(seg.data)
+            elif seg.type == "image":
+                # 假设图片数据是base64编码的
+                emoji_base64_list.append(seg.data)
+            elif seg.type == "seglist":
+                # 递归处理嵌套的Seg列表
+                emoji_base64_list.extend(self.find_and_return_emoji_in_message(seg.data))
+        return emoji_base64_list
+
+
+class RandomEmojis(BaseCommand):
+    command_name = "random_emojis"
+    command_description = "发送多张随机表情包"
+    command_pattern = r"^/random_emojis$"
+
+    async def execute(self):
+        emojis = await emoji_api.get_random(5)
+        if not emojis:
+            return False, "未找到表情包", False
+        emoji_base64_list = []
+        for emoji in emojis:
+            emoji_base64_list.append(emoji[0])
+        return await self.forward_images(emoji_base64_list)
+
+    async def forward_images(self, images: List[str]):
+        """
+        把多张图片用合并转发的方式发给用户
+        """
+        success = await self.send_forward([("0", "神秘用户", [(ReplyContentType.IMAGE, img)]) for img in images])
+        return (True, "已发送随机表情包", True) if success else (False, "发送随机表情包失败", False)
+
+
+# ===== 插件注册 =====
+
+
+@register_plugin
+class EmojiManagePlugin(BasePlugin):
+    """表情包管理插件 - 管理表情包"""
+
+    # 插件基本信息
+    plugin_name: str = "emoji_manage_plugin"  # 内部标识符
+    enable_plugin: bool = False
+    dependencies: List[str] = []  # 插件依赖列表
+    python_dependencies: List[str] = []  # Python包依赖列表
+    config_file_name: str = "config.toml"  # 配置文件名
+
+    # 配置节描述
+    config_section_descriptions = {"plugin": "插件基本信息", "emoji": "表情包功能配置"}
+
+    # 配置Schema定义
+    config_schema: dict = {
+        "plugin": {
+            "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
+            "config_version": ConfigField(type=str, default="1.0.1", description="配置文件版本"),
+        },
+    }
+
+    def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
+        return [
+            (RandomEmojis.get_command_info(), RandomEmojis),
+            (AddEmojiCommand.get_command_info(), AddEmojiCommand),
+            (ListEmojiCommand.get_command_info(), ListEmojiCommand),
+            (DeleteEmojiCommand.get_command_info(), DeleteEmojiCommand),
+        ]

plugins/hello_world_plugin/plugin.py

@@ -237,8 +237,7 @@ class HelloWorldPlugin(BasePlugin):
     # 配置Schema定义
     config_schema: dict = {
         "plugin": {
-            "name": ConfigField(type=str, default="hello_world_plugin", description="插件名称"),
+            "config_version": ConfigField(type=str, default="1.0.0", description="配置文件版本"),
-            "version": ConfigField(type=str, default="1.0.0", description="插件版本"),
             "enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
         },
         "greeting": {

pyproject.toml

@@ -15,7 +15,7 @@ dependencies = [
     "jieba>=0.42.1",
     "json-repair>=0.47.6",
     "jsonlines>=4.0.0",
-    "maim-message>=0.3.8",
+    "maim-message>=0.5.1",
     "matplotlib>=3.10.3",
     "networkx>=3.4.2",
     "numpy>=2.2.6",

requirements.lock

@@ -1,34 +1,36 @@
 # This file was autogenerated by uv via the following command:
 #    uv pip compile requirements.txt -o requirements.lock
-aenum==3.1.16
-    # via reportportal-client
 aiohappyeyeballs==2.6.1
     # via aiohttp
 aiohttp==3.12.14
     # via
     #   -r requirements.txt
+    #   aiohttp-cors
     #   maim-message
-    #   reportportal-client
+aiohttp-cors==0.8.1
+    # via -r requirements.txt
 aiosignal==1.4.0
     # via aiohttp
 annotated-types==0.7.0
     # via pydantic
 anyio==4.9.0
     # via
+    #   google-genai
     #   httpx
     #   openai
     #   starlette
-apscheduler==3.11.0
+async-timeout==5.0.1
-    # via -r requirements.txt
+    # via aiohttp
 attrs==25.3.0
     # via
     #   aiohttp
     #   jsonlines
+cachetools==5.5.2
+    # via google-auth
 certifi==2025.7.9
     # via
     #   httpcore
     #   httpx
-    #   reportportal-client
     #   requests
 cffi==1.17.1
     # via cryptography
@@ -41,24 +43,16 @@ colorama==0.4.6
     #   -r requirements.txt
     #   click
     #   tqdm
-contourpy==1.3.2
-    # via matplotlib
 cryptography==45.0.5
-    # via
+    # via maim-message
-    #   -r requirements.txt
-    #   maim-message
-customtkinter==5.2.2
-    # via -r requirements.txt
-cycler==0.12.1
-    # via matplotlib
-darkdetect==0.8.0
-    # via customtkinter
 distro==1.9.0
     # via openai
 dnspython==2.7.0
     # via pymongo
 dotenv==0.9.9
     # via -r requirements.txt
+exceptiongroup==1.3.0
+    # via anyio
 faiss-cpu==1.11.0
     # via -r requirements.txt
 fastapi==0.116.0
@@ -66,12 +60,14 @@ fastapi==0.116.0
     #   -r requirements.txt
     #   maim-message
     #   strawberry-graphql
-fonttools==4.58.5
-    # via matplotlib
 frozenlist==1.7.0
     # via
     #   aiohttp
     #   aiosignal
+google-auth==2.40.3
+    # via google-genai
+google-genai==1.38.0
+    # via -r requirements.txt
 graphql-core==3.2.6
     # via strawberry-graphql
 h11==0.16.0
@@ -81,86 +77,70 @@ h11==0.16.0
 httpcore==1.0.9
     # via httpx
 httpx==0.28.1
-    # via openai
+    # via
+    #   google-genai
+    #   openai
 idna==3.10
     # via
     #   anyio
     #   httpx
     #   requests
     #   yarl
-igraph==0.11.9
-    # via python-igraph
 jieba==0.42.1
     # via -r requirements.txt
 jiter==0.10.0
     # via openai
-joblib==1.5.1
-    # via scikit-learn
 json-repair==0.47.6
     # via -r requirements.txt
 jsonlines==4.0.0
     # via -r requirements.txt
-kiwisolver==1.4.8
+maim-message==0.5.1
-    # via matplotlib
-maim-message==0.3.8
     # via -r requirements.txt
 markdown-it-py==3.0.0
     # via rich
-matplotlib==3.10.3
-    # via
-    #   -r requirements.txt
-    #   seaborn
 mdurl==0.1.2
     # via markdown-it-py
 multidict==6.6.3
     # via
     #   aiohttp
     #   yarl
-networkx==3.5
+networkx==3.4.2
     # via -r requirements.txt
-numpy==2.3.1
+numpy==2.2.6
     # via
     #   -r requirements.txt
-    #   contourpy
     #   faiss-cpu
-    #   matplotlib
-    #   pandas
-    #   scikit-learn
     #   scipy
-    #   seaborn
 openai==1.95.0
     # via -r requirements.txt
 packaging==25.0
     # via
     #   -r requirements.txt
-    #   customtkinter
     #   faiss-cpu
-    #   matplotlib
     #   strawberry-graphql
-pandas==2.3.1
-    # via
-    #   -r requirements.txt
-    #   seaborn
 peewee==3.18.2
     # via -r requirements.txt
 pillow==11.3.0
-    # via
+    # via -r requirements.txt
-    #   -r requirements.txt
-    #   matplotlib
 propcache==0.3.2
     # via
     #   aiohttp
     #   yarl
 psutil==7.0.0
     # via -r requirements.txt
-pyarrow==20.0.0
+pyasn1==0.6.1
-    # via -r requirements.txt
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.4.2
+    # via google-auth
 pycparser==2.22
     # via cffi
 pydantic==2.11.7
     # via
     #   -r requirements.txt
     #   fastapi
+    #   google-genai
     #   maim-message
     #   openai
 pydantic-core==2.33.2
@@ -169,45 +149,27 @@ pygments==2.19.2
     # via rich
 pymongo==4.13.2
     # via -r requirements.txt
-pyparsing==3.2.3
-    # via matplotlib
 pypinyin==0.54.0
     # via -r requirements.txt
 python-dateutil==2.9.0.post0
-    # via
+    # via strawberry-graphql
-    #   -r requirements.txt
-    #   matplotlib
-    #   pandas
-    #   strawberry-graphql
 python-dotenv==1.1.1
     # via
     #   -r requirements.txt
     #   dotenv
-python-igraph==0.11.9
-    # via -r requirements.txt
 python-multipart==0.0.20
     # via strawberry-graphql
-pytz==2025.2
-    # via pandas
 quick-algo==0.1.3
     # via -r requirements.txt
-reportportal-client==5.6.5
-    # via -r requirements.txt
 requests==2.32.4
-    # via
+    # via google-genai
-    #   -r requirements.txt
-    #   reportportal-client
 rich==14.0.0
     # via -r requirements.txt
+rsa==4.9.1
+    # via google-auth
 ruff==0.12.2
     # via -r requirements.txt
-scikit-learn==1.7.0
+scipy==1.15.3
-    # via -r requirements.txt
-scipy==1.16.0
-    # via
-    #   -r requirements.txt
-    #   scikit-learn
-seaborn==0.13.2
     # via -r requirements.txt
 setuptools==80.9.0
     # via -r requirements.txt
@@ -223,10 +185,8 @@ strawberry-graphql==0.275.5
     # via -r requirements.txt
 structlog==25.4.0
     # via -r requirements.txt
-texttable==1.7.0
+tenacity==9.1.2
-    # via igraph
+    # via google-genai
-threadpoolctl==3.6.0
-    # via scikit-learn
 toml==0.10.2
     # via -r requirements.txt
 tomli==2.2.1
@@ -236,25 +196,25 @@ tomli-w==1.2.0
 tomlkit==0.13.3
     # via -r requirements.txt
 tqdm==4.67.1
-    # via
+    # via openai
-    #   -r requirements.txt
-    #   openai
 typing-extensions==4.14.1
     # via
+    #   aiosignal
+    #   anyio
+    #   exceptiongroup
     #   fastapi
+    #   google-genai
+    #   multidict
     #   openai
     #   pydantic
     #   pydantic-core
+    #   rich
     #   strawberry-graphql
+    #   structlog
     #   typing-inspection
+    #   uvicorn
 typing-inspection==0.4.1
     # via pydantic
-tzdata==2025.2
-    # via
-    #   pandas
-    #   tzlocal
-tzlocal==5.3.1
-    # via apscheduler
 urllib3==2.5.0
     # via
     #   -r requirements.txt
@@ -266,6 +226,7 @@ uvicorn==0.35.0
 websockets==15.0.1
     # via
     #   -r requirements.txt
+    #   google-genai
     #   maim-message
 yarl==1.20.1
     # via aiohttp

requirements.txt

@@ -1,30 +1,22 @@
-APScheduler
 Pillow
 aiohttp
 aiohttp-cors
 colorama
-customtkinter
 dotenv
 faiss-cpu
 fastapi
 jieba
 jsonlines
-maim_message
+maim-message>=0
 quick_algo
-matplotlib
 networkx
 numpy
 openai
-pandas
 peewee
-pyarrow
 pydantic
 pypinyin
-python-dateutil
 python-dotenv
-python-igraph
 pymongo
-requests
 ruff
 scipy
 setuptools
@@ -32,7 +24,6 @@ toml
 tomli
 tomli_w
 tomlkit
-tqdm
 urllib3
 uvicorn
 websockets
@@ -40,10 +31,6 @@ strawberry-graphql[fastapi]
 packaging
 rich
 psutil
-cryptography
 json-repair
-reportportal-client
-scikit-learn
-seaborn
 structlog
 google.genai

src/chat/express/expression_learner.py

@@ -62,6 +62,7 @@ class ExpressionLearner:
             model_set=model_config.model_task_config.replyer, request_type="expression.learner"
         )
         self.chat_id = chat_id
+        self.chat_stream = get_chat_manager().get_stream(chat_id)
         self.chat_name = get_chat_manager().get_stream_name(chat_id) or chat_id
 
         # 维护每个chat的上次学习时间
@@ -69,24 +70,8 @@ class ExpressionLearner:
 
         # 学习参数
         self.min_messages_for_learning = 25  # 触发学习所需的最少消息数
-        self.min_learning_interval = 300  # 最短学习时间间隔（秒）
+        _, self.enable_learning, self.learning_intensity = global_config.expression.get_expression_config_for_chat(self.chat_id)
-
+        self.min_learning_interval = 300 / self.learning_intensity
-    def can_learn_for_chat(self) -> bool:
-        """
-        检查指定聊天流是否允许学习表达
-
-        Args:
-            chat_id: 聊天流ID
-
-        Returns:
-            bool: 是否允许学习
-        """
-        try:
-            use_expression, enable_learning, _ = global_config.expression.get_expression_config_for_chat(self.chat_id)
-            return enable_learning
-        except Exception as e:
-            logger.error(f"检查学习权限失败: {e}")
-            return False
 
     def should_trigger_learning(self) -> bool:
         """
@@ -98,27 +83,13 @@ class ExpressionLearner:
         Returns:
             bool: 是否应该触发学习
         """
-        current_time = time.time()
-
-        # 获取该聊天流的学习强度
-        try:
-            _, enable_learning, learning_intensity = global_config.expression.get_expression_config_for_chat(
-                self.chat_id
-            )
-        except Exception as e:
-            logger.error(f"获取聊天流 {self.chat_id} 的学习配置失败: {e}")
-            return False
-
         # 检查是否允许学习
-        if not enable_learning:
+        if not self.enable_learning:
             return False
 
-        # 根据学习强度计算最短学习时间间隔
-        min_interval = self.min_learning_interval / learning_intensity
-
         # 检查时间间隔
-        time_diff = current_time - self.last_learning_time
+        time_diff = time.time() - self.last_learning_time
-        if time_diff < min_interval:
+        if time_diff < self.min_learning_interval:
             return False
 
         # 检查消息数量（只检查指定聊天流的消息）
@@ -228,32 +199,17 @@ class ExpressionLearner:
         """
         学习并存储表达方式
         """
-        # 检查是否允许在此聊天流中学习（在函数最前面检查）
-        if not self.can_learn_for_chat():
-            logger.debug(f"聊天流 {self.chat_name} 不允许学习表达，跳过学习")
-            return []
-
         res = await self.learn_expression(num)
 
         if res is None:
+            logger.info("没有学习到表达风格")
             return []
         learnt_expressions, chat_id = res
-
-        chat_stream = get_chat_manager().get_stream(chat_id)
-        if chat_stream is None:
-            group_name = f"聊天流 {chat_id}"
-        elif chat_stream.group_info:
-            group_name = chat_stream.group_info.group_name
-        else:
-            group_name = f"{chat_stream.user_info.user_nickname}的私聊"
         learnt_expressions_str = ""
         for _chat_id, situation, style in learnt_expressions:
             learnt_expressions_str += f"{situation}->{style}\n"
-        logger.info(f"在 {group_name} 学习到表达风格:\n{learnt_expressions_str}")
             
-        if not learnt_expressions:
+        logger.info(f"在 {self.chat_name} 学习到表达风格:\n{learnt_expressions_str}")
-            logger.info("没有学习到表达风格")
-            return []
 
         # 按chat_id分组
         chat_dict: Dict[str, List[Dict[str, Any]]] = {}
@@ -316,7 +272,7 @@ class ExpressionLearner:
 
         current_time = time.time()
 
-        # 获取上次学习时间
+        # 获取上次学习之后的消息
         random_msg = get_raw_msg_by_timestamp_with_chat_inclusive(
             chat_id=self.chat_id,
             timestamp_start=self.last_learning_time,
@@ -330,14 +286,15 @@ class ExpressionLearner:
         chat_id: str = random_msg[0].chat_id
         # random_msg_str: str = build_readable_messages(random_msg, timestamp_mode="normal")
         random_msg_str: str = await build_anonymous_messages(random_msg)
-        # print(f"random_msg_str:{random_msg_str}")
+        
 
         prompt: str = await global_prompt_manager.format_prompt(
             prompt,
             chat_str=random_msg_str,
         )
 
-        logger.debug(f"学习{type_str}的prompt: {prompt}")
+        print(f"random_msg_str:{random_msg_str}")
+        logger.info(f"学习{type_str}的prompt: {prompt}")
 
         try:
             response, _ = await self.express_learn_model.generate_response_async(prompt, temperature=0.3)

src/chat/heart_flow/heartFC_chat.py

@@ -207,7 +207,7 @@ class HeartFChatting:
                 await self._observe(recent_messages_list=recent_messages_list)
             else:
                 # 没有提到，继续保持沉默，等待5秒防止频繁触发
-                await asyncio.sleep(5)
+                await asyncio.sleep(10)
                 return True
         else:
             await asyncio.sleep(0.2)
@@ -345,6 +345,10 @@ class HeartFChatting:
                     )
                 )
                 
+            logger.info(
+            f"{self.log_prefix}决定执行{len(action_to_use_info)}个动作: {' '.join([a.action_type for a in action_to_use_info])}"
+        )
+
             # 3. 并行执行所有动作
             action_tasks = [
                 asyncio.create_task(

src/chat/message_receive/bot.py

@@ -3,7 +3,7 @@ import os
 import re
 
 from typing import Dict, Any, Optional
-from maim_message import UserInfo, Seg
+from maim_message import UserInfo, Seg, GroupInfo
 
 from src.common.logger import get_logger
 from src.config.config import global_config
@@ -27,7 +27,7 @@ PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..
 logger = get_logger("chat")
 
 
-def _check_ban_words(text: str, chat: ChatStream, userinfo: UserInfo) -> bool:
+def _check_ban_words(text: str, userinfo: UserInfo, group_info: Optional[GroupInfo] = None) -> bool:
     """检查消息是否包含过滤词
 
     Args:
@@ -40,14 +40,14 @@ def _check_ban_words(text: str, chat: ChatStream, userinfo: UserInfo) -> bool:
     """
     for word in global_config.message_receive.ban_words:
         if word in text:
-            chat_name = chat.group_info.group_name if chat.group_info else "私聊"
+            chat_name = group_info.group_name if group_info else "私聊"
             logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
             logger.info(f"[过滤词识别]消息中含有{word}，filtered")
             return True
     return False
 
 
-def _check_ban_regex(text: str, chat: ChatStream, userinfo: UserInfo) -> bool:
+def _check_ban_regex(text: str, userinfo: UserInfo, group_info: Optional[GroupInfo] = None) -> bool:
     """检查消息是否匹配过滤正则表达式
 
     Args:
@@ -64,7 +64,7 @@ def _check_ban_regex(text: str, chat: ChatStream, userinfo: UserInfo) -> bool:
 
     for pattern in global_config.message_receive.ban_msgs_regex:
         if re.search(pattern, text):
-            chat_name = chat.group_info.group_name if chat.group_info else "私聊"
+            chat_name = group_info.group_name if group_info else "私聊"
             logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
             logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
             return True
@@ -251,6 +251,21 @@ class ChatBot:
                 # return
                 pass
 
+            # 处理消息内容，生成纯文本
+            await message.process()
+
+            # 过滤检查
+            if _check_ban_words(
+                message.processed_plain_text,
+                user_info,  # type: ignore
+                group_info,
+            ) or _check_ban_regex(
+                message.raw_message,  # type: ignore
+                user_info,  # type: ignore
+                group_info,
+            ):
+                return
+
             get_chat_manager().register_message(message)
 
             chat = await get_chat_manager().get_or_create_stream(
@@ -261,21 +276,10 @@ class ChatBot:
 
             message.update_chat_stream(chat)
 
-            # 处理消息内容，生成纯文本
-            await message.process()
-
             # if await self.check_ban_content(message):
             #     logger.warning(f"检测到消息中含有违法，色情，暴力，反动，敏感内容，消息内容：{message.processed_plain_text}，发送者：{message.message_info.user_info.user_nickname}")
             #     return
 
-            # 过滤检查
-            if _check_ban_words(message.processed_plain_text, chat, user_info) or _check_ban_regex(  # type: ignore
-                message.raw_message,  # type: ignore
-                chat,
-                user_info,  # type: ignore
-            ):
-                return
-
             # 命令处理 - 使用新插件系统检查并处理命令
             is_command, cmd_result, continue_process = await self._process_commands_with_new_system(message)
 

src/chat/planner_actions/planner.py

@@ -499,8 +499,8 @@ class ActionPlanner:
             action.action_data = action.action_data or {}
             action.action_data["loop_start_time"] = loop_start_time
 
-        logger.info(
+        logger.debug(
-            f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
+            f"{self.log_prefix}规划器选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
         )
 
         return actions

src/chat/utils/utils_image.py

@@ -623,3 +623,41 @@ def image_path_to_base64(image_path: str) -> str:
             return base64.b64encode(image_data).decode("utf-8")
         else:
             raise IOError(f"读取图片文件失败: {image_path}")
+
+
+def base64_to_image(image_base64: str, output_path: str) -> bool:
+    """将base64编码的图片保存为文件
+
+    Args:
+        image_base64: 图片的base64编码
+        output_path: 输出文件路径
+
+    Returns:
+        bool: 是否成功保存
+
+    Raises:
+        ValueError: 当base64编码无效时
+        IOError: 当保存文件失败时
+    """
+    try:
+        # 确保base64字符串只包含ASCII字符
+        if isinstance(image_base64, str):
+            image_base64 = image_base64.encode("ascii", errors="ignore").decode("ascii")
+
+        # 解码base64
+        image_bytes = base64.b64decode(image_base64)
+
+        # 确保输出目录存在
+        output_dir = os.path.dirname(output_path)
+        if output_dir:
+            os.makedirs(output_dir, exist_ok=True)
+
+        # 保存文件
+        with open(output_path, "wb") as f:
+            f.write(image_bytes)
+
+        return True
+
+    except Exception as e:
+        logger.error(f"保存base64图片失败: {e}")
+        return False

src/config/config.py

@@ -54,7 +54,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
 
 # 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
 # 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
-MMC_VERSION = "0.10.3"
+MMC_VERSION = "0.10.4-snapshot.1"
 
 
 def get_key_comment(toml_table, key):

src/llm_models/model_client/gemini_client.py

@@ -1,7 +1,7 @@
 import asyncio
 import io
 import base64
-from typing import Callable, AsyncIterator, Optional, Coroutine, Any, List
+from typing import Callable, AsyncIterator, Optional, Coroutine, Any, List, Dict
 
 from google import genai
 from google.genai.types import (
@@ -17,6 +17,7 @@ from google.genai.types import (
     EmbedContentResponse,
     EmbedContentConfig,
     SafetySetting,
+    HttpOptions,
     HarmCategory,
     HarmBlockThreshold,
 )
@@ -345,7 +346,24 @@ class GeminiClient(BaseClient):
 
     def __init__(self, api_provider: APIProvider):
         super().__init__(api_provider)
+
+        # 增加传入参数处理
+        http_options_kwargs: Dict[str, Any] = {}
+
+        # 秒转换为毫秒传入
+        if api_provider.timeout is not None:
+            http_options_kwargs["timeout"] = int(api_provider.timeout * 1000)
+
+        # 传入并处理地址和版本(必须为Gemini格式)
+        if api_provider.base_url:
+            parts = api_provider.base_url.rstrip("/").rsplit("/", 1)
+            if len(parts) == 2 and parts[1].startswith("v"):
+                http_options_kwargs["base_url"] = f"{parts[0]}/"
+                http_options_kwargs["api_version"] = parts[1]
+            else:
+                http_options_kwargs["base_url"] = api_provider.base_url
         self.client = genai.Client(
+            http_options=HttpOptions(**http_options_kwargs),
             api_key=api_provider.api_key,
         )  # 这里和openai不一样，gemini会自己决定自己是否需要retry
 
@@ -368,20 +386,29 @@ class GeminiClient(BaseClient):
                     limits = THINKING_BUDGET_LIMITS[key]
                     break
 
-        # 特殊值处理
+        # 预算值处理
         if tb == THINKING_BUDGET_AUTO:
             return THINKING_BUDGET_AUTO
         if tb == THINKING_BUDGET_DISABLED:
             if limits and limits.get("can_disable", False):
                 return THINKING_BUDGET_DISABLED
-            return limits["min"] if limits else THINKING_BUDGET_AUTO
-
-        # 已知模型裁剪到范围
             if limits:
-            return max(limits["min"], min(tb, limits["max"]))
+                logger.warning(f"模型 {model_id} 不支持禁用思考预算，已回退到最小值 {limits['min']}")
+                return limits["min"]
+            return THINKING_BUDGET_AUTO
 
-        # 未知模型，返回动态模式
+        # 已知模型范围裁剪 + 提示
-        logger.warning(f"模型 {model_id} 未在 THINKING_BUDGET_LIMITS 中定义，将使用动态模式 tb=-1 兼容。")
+        if limits:
+            if tb < limits["min"]:
+                logger.warning(f"模型 {model_id} 的 thinking_budget={tb} 过小，已调整为最小值 {limits['min']}")
+                return limits["min"]
+            if tb > limits["max"]:
+                logger.warning(f"模型 {model_id} 的 thinking_budget={tb} 过大，已调整为最大值 {limits['max']}")
+                return limits["max"]
+            return tb
+
+        # 未知模型 → 默认自动模式
+        logger.warning(f"模型 {model_id} 未在 THINKING_BUDGET_LIMITS 中定义，已启用模型自动预算兼容")
         return THINKING_BUDGET_AUTO
 
     async def get_response(
@@ -436,7 +463,9 @@ class GeminiClient(BaseClient):
             try:
                 tb = int(extra_params["thinking_budget"])
             except (ValueError, TypeError):
-                logger.warning(f"无效的 thinking_budget 值 {extra_params['thinking_budget']}，将使用默认动态模式 {tb}")
+                logger.warning(
+                    f"无效的 thinking_budget 值 {extra_params['thinking_budget']}，将使用模型自动预算模式 {tb}"
+                )
         # 裁剪到模型支持的范围
         tb = self.clamp_thinking_budget(tb, model_info.model_identifier)
 

src/llm_models/utils_model.py

@@ -26,18 +26,6 @@ install(extra_lines=3)
 
 logger = get_logger("model_utils")
 
-# 常见Error Code Mapping
-error_code_mapping = {
-    400: "参数不正确",
-    401: "API key 错误，认证失败，请检查 config/model_config.toml 中的配置是否正确",
-    402: "账号余额不足",
-    403: "需要实名,或余额不足",
-    404: "Not Found",
-    429: "请求过于频繁，请稍后再试",
-    500: "服务器内部故障",
-    503: "服务器负载过高",
-}
-
 
 class RequestType(Enum):
     """请求类型枚举"""
@@ -267,14 +255,14 @@ class LLMRequest:
                         extra_params=model_info.extra_params,
                     )
                 elif request_type == RequestType.EMBEDDING:
-                    assert embedding_input is not None
+                    assert embedding_input is not None, "嵌入输入不能为空"
                     return await client.get_embedding(
                         model_info=model_info,
                         embedding_input=embedding_input,
                         extra_params=model_info.extra_params,
                     )
                 elif request_type == RequestType.AUDIO:
-                    assert audio_base64 is not None
+                    assert audio_base64 is not None, "音频Base64不能为空"
                     return await client.get_audio_transcriptions(
                         model_info=model_info,
                         audio_base64=audio_base64,
@@ -365,24 +353,23 @@ class LLMRequest:
                     embedding_input=embedding_input,
                     audio_base64=audio_base64,
                 )
+                total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
+                if response_usage := response.usage:
+                    total_tokens += response_usage.total_tokens
+                self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty - 1)
                 return response, model_info
 
             except ModelAttemptFailed as e:
                 last_exception = e.original_exception or e
                 logger.warning(f"模型 '{model_info.name}' 尝试失败，切换到下一个模型。原因: {e}")
                 total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
-                self.model_usage[model_info.name] = (total_tokens, penalty + 1, usage_penalty)
+                self.model_usage[model_info.name] = (total_tokens, penalty + 1, usage_penalty - 1)
                 failed_models_this_request.add(model_info.name)
 
                 if isinstance(last_exception, RespNotOkException) and last_exception.status_code == 400:
                     logger.error("收到不可恢复的客户端错误 (400)，中止所有尝试。")
                     raise last_exception from e
 
-            finally:
-                total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
-                if usage_penalty > 0:
-                    self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty - 1)
-
         logger.error(f"所有 {max_attempts} 个模型均尝试失败。")
         if last_exception:
             raise last_exception

src/plugin_system/apis/emoji_api.py

@@ -9,11 +9,15 @@
 """
 
 import random
+import base64
+import os
+import uuid
+import time
 
-from typing import Optional, Tuple, List
+from typing import Optional, Tuple, List, Dict, Any
 from src.common.logger import get_logger
-from src.chat.emoji_system.emoji_manager import get_emoji_manager
+from src.chat.emoji_system.emoji_manager import get_emoji_manager, EMOJI_DIR
-from src.chat.utils.utils_image import image_path_to_base64
+from src.chat.utils.utils_image import image_path_to_base64, base64_to_image
 
 logger = get_logger("emoji_api")
 
@@ -245,6 +249,42 @@ def get_emotions() -> List[str]:
         return []
 
 
+async def get_all() -> List[Tuple[str, str, str]]:
+    """获取所有表情包
+
+    Returns:
+        List[Tuple[str, str, str]]: 包含(base64编码, 表情包描述, 随机情感标签)的元组列表
+    """
+    try:
+        emoji_manager = get_emoji_manager()
+        all_emojis = emoji_manager.emoji_objects
+
+        if not all_emojis:
+            logger.warning("[EmojiAPI] 没有可用的表情包")
+            return []
+
+        results = []
+        for emoji_obj in all_emojis:
+            if emoji_obj.is_deleted:
+                continue
+
+            emoji_base64 = image_path_to_base64(emoji_obj.full_path)
+
+            if not emoji_base64:
+                logger.error(f"[EmojiAPI] 无法转换表情包为base64: {emoji_obj.full_path}")
+                continue
+
+            matched_emotion = random.choice(emoji_obj.emotion) if emoji_obj.emotion else "随机表情"
+            results.append((emoji_base64, emoji_obj.description, matched_emotion))
+
+        logger.debug(f"[EmojiAPI] 成功获取 {len(results)} 个表情包")
+        return results
+
+    except Exception as e:
+        logger.error(f"[EmojiAPI] 获取所有表情包失败: {e}")
+        return []
+
+
 def get_descriptions() -> List[str]:
     """获取所有表情包描述
 
@@ -264,3 +304,400 @@ def get_descriptions() -> List[str]:
     except Exception as e:
         logger.error(f"[EmojiAPI] 获取表情包描述失败: {e}")
         return []
+
+
+# =============================================================================
+# 表情包注册API函数
+# =============================================================================
+
+
+async def register_emoji(image_base64: str, filename: Optional[str] = None) -> Dict[str, Any]:
+    """注册新的表情包
+
+    Args:
+        image_base64: 图片的base64编码
+        filename: 可选的文件名，如果未提供则自动生成
+
+    Returns:
+        Dict[str, Any]: 注册结果，包含以下字段：
+            - success: bool, 是否成功注册
+            - message: str, 结果消息
+            - description: Optional[str], 表情包描述（成功时）
+            - emotions: Optional[List[str]], 情感标签列表（成功时）
+            - replaced: Optional[bool], 是否替换了旧表情包（成功时）
+            - hash: Optional[str], 表情包哈希值（成功时）
+
+    Raises:
+        ValueError: 如果base64为空或无效
+        TypeError: 如果参数类型不正确
+    """
+    if not image_base64:
+        raise ValueError("图片base64编码不能为空")
+    if not isinstance(image_base64, str):
+        raise TypeError("image_base64必须是字符串类型")
+    if filename is not None and not isinstance(filename, str):
+        raise TypeError("filename必须是字符串类型或None")
+
+    try:
+        logger.info(f"[EmojiAPI] 开始注册表情包，文件名: {filename or '自动生成'}")
+
+        # 1. 获取emoji管理器并检查容量
+        emoji_manager = get_emoji_manager()
+        count_before = emoji_manager.emoji_num
+        max_count = emoji_manager.emoji_num_max
+
+        # 2. 检查是否可以注册（未达到上限或启用替换）
+        can_register = count_before < max_count or (
+            count_before >= max_count and emoji_manager.emoji_num_max_reach_deletion
+        )
+
+        if not can_register:
+            return {
+                "success": False,
+                "message": f"表情包数量已达上限({count_before}/{max_count})且未启用替换功能",
+                "description": None,
+                "emotions": None,
+                "replaced": None,
+                "hash": None
+            }
+
+        # 3. 确保emoji目录存在
+        os.makedirs(EMOJI_DIR, exist_ok=True)
+
+        # 4. 生成文件名
+        if not filename:
+            # 基于时间戳、微秒和短base64生成唯一文件名
+            import time
+            timestamp = int(time.time())
+            microseconds = int(time.time() * 1000000) % 1000000  # 添加微秒级精度
+
+            # 生成12位随机标识符，使用base64编码（增加随机性）
+            import random
+            random_bytes = random.getrandbits(72).to_bytes(9, 'big')  # 72位 = 9字节 = 12位base64
+            short_id = base64.b64encode(random_bytes).decode('ascii')[:12].rstrip('=')
+            # 确保base64编码适合文件名（替换/和-）
+            short_id = short_id.replace('/', '_').replace('+', '-')
+            filename = f"emoji_{timestamp}_{microseconds}_{short_id}"
+
+        # 确保文件名有扩展名
+        if not filename.lower().endswith(('.jpg', '.jpeg', '.png', '.gif')):
+            filename = f"{filename}.png"  # 默认使用png格式
+
+        # 检查文件名是否已存在，如果存在则重新生成短标识符
+        temp_file_path = os.path.join(EMOJI_DIR, filename)
+        attempts = 0
+        max_attempts = 10
+        while os.path.exists(temp_file_path) and attempts < max_attempts:
+            # 重新生成短标识符
+            import random
+            random_bytes = random.getrandbits(48).to_bytes(6, 'big')
+            short_id = base64.b64encode(random_bytes).decode('ascii')[:8].rstrip('=')
+            short_id = short_id.replace('/', '_').replace('+', '-')
+
+            # 分离文件名和扩展名，重新生成文件名
+            name_part, ext = os.path.splitext(filename)
+            # 去掉原来的标识符，添加新的
+            base_name = name_part.rsplit('_', 1)[0]  # 移除最后一个_后的部分
+            filename = f"{base_name}_{short_id}{ext}"
+            temp_file_path = os.path.join(EMOJI_DIR, filename)
+            attempts += 1
+
+        # 如果还是冲突，使用UUID作为备用方案
+        if os.path.exists(temp_file_path):
+            uuid_short = str(uuid.uuid4())[:8]
+            name_part, ext = os.path.splitext(filename)
+            base_name = name_part.rsplit('_', 1)[0]
+            filename = f"{base_name}_{uuid_short}{ext}"
+            temp_file_path = os.path.join(EMOJI_DIR, filename)
+
+            # 如果UUID方案也冲突，添加序号
+            counter = 1
+            original_filename = filename
+            while os.path.exists(temp_file_path):
+                name_part, ext = os.path.splitext(original_filename)
+                filename = f"{name_part}_{counter}{ext}"
+                temp_file_path = os.path.join(EMOJI_DIR, filename)
+                counter += 1
+
+                # 防止无限循环，最多尝试100次
+                if counter > 100:
+                    logger.error(f"[EmojiAPI] 无法生成唯一文件名，尝试次数过多: {original_filename}")
+                    return {
+                        "success": False,
+                        "message": "无法生成唯一文件名，请稍后重试",
+                        "description": None,
+                        "emotions": None,
+                        "replaced": None,
+                        "hash": None
+                    }
+
+        # 5. 保存base64图片到emoji目录
+
+        try:
+            # 解码base64并保存图片
+            if not base64_to_image(image_base64, temp_file_path):
+                logger.error(f"[EmojiAPI] 无法保存base64图片到文件: {temp_file_path}")
+                return {
+                    "success": False,
+                    "message": "无法保存图片文件",
+                    "description": None,
+                    "emotions": None,
+                    "replaced": None,
+                    "hash": None
+                }
+
+            logger.debug(f"[EmojiAPI] 图片已保存到临时文件: {temp_file_path}")
+
+        except Exception as save_error:
+            logger.error(f"[EmojiAPI] 保存图片文件失败: {save_error}")
+            return {
+                "success": False,
+                "message": f"保存图片文件失败: {str(save_error)}",
+                "description": None,
+                "emotions": None,
+                "replaced": None,
+                "hash": None
+            }
+
+        # 6. 调用注册方法
+        register_success = await emoji_manager.register_emoji_by_filename(filename)
+
+        # 7. 清理临时文件（如果注册失败但文件还存在）
+        if not register_success and os.path.exists(temp_file_path):
+            try:
+                os.remove(temp_file_path)
+                logger.debug(f"[EmojiAPI] 已清理临时文件: {temp_file_path}")
+            except Exception as cleanup_error:
+                logger.warning(f"[EmojiAPI] 清理临时文件失败: {cleanup_error}")
+
+        # 8. 构建返回结果
+        if register_success:
+            count_after = emoji_manager.emoji_num
+            replaced = count_after <= count_before  # 如果数量没增加，说明是替换
+
+            # 尝试获取新注册的表情包信息
+            new_emoji_info = None
+            if count_after > count_before or replaced:
+                # 获取最新的表情包信息
+                try:
+                    # 通过文件名查找新注册的表情包（注意：文件名在注册后可能已经改变）
+                    for emoji_obj in reversed(emoji_manager.emoji_objects):
+                        if not emoji_obj.is_deleted and (
+                            emoji_obj.filename == filename or  # 直接匹配
+                            (hasattr(emoji_obj, 'full_path') and filename in emoji_obj.full_path)  # 路径包含匹配
+                        ):
+                            new_emoji_info = emoji_obj
+                            break
+                except Exception as find_error:
+                    logger.warning(f"[EmojiAPI] 查找新注册表情包信息失败: {find_error}")
+
+            description = new_emoji_info.description if new_emoji_info else None
+            emotions = new_emoji_info.emotion if new_emoji_info else None
+            emoji_hash = new_emoji_info.hash if new_emoji_info else None
+
+            return {
+                "success": True,
+                "message": f"表情包注册成功 {'(替换旧表情包)' if replaced else '(新增表情包)'}",
+                "description": description,
+                "emotions": emotions,
+                "replaced": replaced,
+                "hash": emoji_hash
+            }
+        else:
+            return {
+                "success": False,
+                "message": "表情包注册失败，可能因为重复、格式不支持或审核未通过",
+                "description": None,
+                "emotions": None,
+                "replaced": None,
+                "hash": None
+            }
+
+    except Exception as e:
+        logger.error(f"[EmojiAPI] 注册表情包时发生异常: {e}")
+        return {
+            "success": False,
+            "message": f"注册过程中发生错误: {str(e)}",
+            "description": None,
+            "emotions": None,
+            "replaced": None,
+            "hash": None
+        }
+
+
+# =============================================================================
+# 表情包删除API函数
+# =============================================================================
+
+
+async def delete_emoji(emoji_hash: str) -> Dict[str, Any]:
+    """删除表情包
+
+    Args:
+        emoji_hash: 要删除的表情包的哈希值
+
+    Returns:
+        Dict[str, Any]: 删除结果，包含以下字段：
+            - success: bool, 是否成功删除
+            - message: str, 结果消息
+            - count_before: Optional[int], 删除前的表情包数量
+            - count_after: Optional[int], 删除后的表情包数量
+            - description: Optional[str], 被删除的表情包描述（成功时）
+            - emotions: Optional[List[str]], 被删除的表情包情感标签（成功时）
+
+    Raises:
+        ValueError: 如果哈希值为空
+        TypeError: 如果哈希值不是字符串类型
+    """
+    if not emoji_hash:
+        raise ValueError("表情包哈希值不能为空")
+    if not isinstance(emoji_hash, str):
+        raise TypeError("emoji_hash必须是字符串类型")
+
+    try:
+        logger.info(f"[EmojiAPI] 开始删除表情包，哈希值: {emoji_hash}")
+
+        # 1. 获取emoji管理器和删除前的数量
+        emoji_manager = get_emoji_manager()
+        count_before = emoji_manager.emoji_num
+
+        # 2. 获取被删除表情包的信息（用于返回结果）
+        try:
+            deleted_emoji = await emoji_manager.get_emoji_from_manager(emoji_hash)
+            description = deleted_emoji.description if deleted_emoji else None
+            emotions = deleted_emoji.emotion if deleted_emoji else None
+        except Exception as info_error:
+            logger.warning(f"[EmojiAPI] 获取被删除表情包信息失败: {info_error}")
+            description = None
+            emotions = None
+
+        # 3. 执行删除操作
+        delete_success = await emoji_manager.delete_emoji(emoji_hash)
+
+        # 4. 获取删除后的数量
+        count_after = emoji_manager.emoji_num
+
+        # 5. 构建返回结果
+        if delete_success:
+            return {
+                "success": True,
+                "message": f"表情包删除成功 (哈希: {emoji_hash[:8]}...)",
+                "count_before": count_before,
+                "count_after": count_after,
+                "description": description,
+                "emotions": emotions
+            }
+        else:
+            return {
+                "success": False,
+                "message": f"表情包删除失败，可能因为哈希值不存在或删除过程出错",
+                "count_before": count_before,
+                "count_after": count_after,
+                "description": None,
+                "emotions": None
+            }
+
+    except Exception as e:
+        logger.error(f"[EmojiAPI] 删除表情包时发生异常: {e}")
+        return {
+            "success": False,
+            "message": f"删除过程中发生错误: {str(e)}",
+            "count_before": None,
+            "count_after": None,
+            "description": None,
+            "emotions": None
+        }
+
+
+async def delete_emoji_by_description(description: str, exact_match: bool = False) -> Dict[str, Any]:
+    """根据描述删除表情包
+
+    Args:
+        description: 表情包描述文本
+        exact_match: 是否精确匹配描述，False则为模糊匹配
+
+    Returns:
+        Dict[str, Any]: 删除结果，包含以下字段：
+            - success: bool, 是否成功删除
+            - message: str, 结果消息
+            - deleted_count: int, 删除的表情包数量
+            - deleted_hashes: List[str], 被删除的表情包哈希列表
+            - matched_count: int, 匹配到的表情包数量
+
+    Raises:
+        ValueError: 如果描述为空
+        TypeError: 如果描述不是字符串类型
+    """
+    if not description:
+        raise ValueError("描述不能为空")
+    if not isinstance(description, str):
+        raise TypeError("description必须是字符串类型")
+
+    try:
+        logger.info(f"[EmojiAPI] 根据描述删除表情包: {description} (精确匹配: {exact_match})")
+
+        emoji_manager = get_emoji_manager()
+        all_emojis = emoji_manager.emoji_objects
+
+        # 筛选匹配的表情包
+        matching_emojis = []
+        for emoji_obj in all_emojis:
+            if emoji_obj.is_deleted:
+                continue
+
+            if exact_match:
+                if emoji_obj.description == description:
+                    matching_emojis.append(emoji_obj)
+            else:
+                if description.lower() in emoji_obj.description.lower():
+                    matching_emojis.append(emoji_obj)
+
+        matched_count = len(matching_emojis)
+        if matched_count == 0:
+            return {
+                "success": False,
+                "message": f"未找到匹配描述 '{description}' 的表情包",
+                "deleted_count": 0,
+                "deleted_hashes": [],
+                "matched_count": 0
+            }
+
+        # 删除匹配的表情包
+        deleted_count = 0
+        deleted_hashes = []
+        for emoji_obj in matching_emojis:
+            try:
+                delete_success = await emoji_manager.delete_emoji(emoji_obj.hash)
+                if delete_success:
+                    deleted_count += 1
+                    deleted_hashes.append(emoji_obj.hash)
+            except Exception as delete_error:
+                logger.error(f"[EmojiAPI] 删除表情包失败 (哈希: {emoji_obj.hash}): {delete_error}")
+
+        # 构建返回结果
+        if deleted_count > 0:
+            return {
+                "success": True,
+                "message": f"成功删除 {deleted_count} 个表情包 (匹配到 {matched_count} 个)",
+                "deleted_count": deleted_count,
+                "deleted_hashes": deleted_hashes,
+                "matched_count": matched_count
+            }
+        else:
+            return {
+                "success": False,
+                "message": f"匹配到 {matched_count} 个表情包，但删除全部失败",
+                "deleted_count": 0,
+                "deleted_hashes": [],
+                "matched_count": matched_count
+            }
+
+    except Exception as e:
+        logger.error(f"[EmojiAPI] 根据描述删除表情包时发生异常: {e}")
+        return {
+            "success": False,
+            "message": f"删除过程中发生错误: {str(e)}",
+            "deleted_count": 0,
+            "deleted_hashes": [],
+            "matched_count": 0
+        }

src/plugins/built_in/emoji_plugin/emoji.py

@@ -28,16 +28,6 @@ class EmojiAction(BaseAction):
     action_name = "emoji"
     action_description = "发送表情包辅助表达情绪"
 
-    # LLM判断提示词
-    llm_judge_prompt = """
-    判定是否需要使用表情动作的条件：
-    1. 用户明确要求使用表情包
-    2. 这是一个适合表达强烈情绪的场合
-    3. 不要发送太多表情包，如果你已经发送过多个表情包则回答"否"
-    
-    请回答"是"或"否"。
-    """
-
     # 动作参数定义
     action_parameters = {}
 
@@ -56,7 +46,8 @@ class EmojiAction(BaseAction):
         """执行表情动作"""
         try:
             # 1. 获取发送表情的原因
-            reason = self.action_data.get("reason", "表达当前情绪")
+            # reason = self.action_data.get("reason", "表达当前情绪")
+            reason = self.reasoning
             
             # 2. 随机获取20个表情包
             sampled_emojis = await emoji_api.get_random(30)
@@ -72,6 +63,9 @@ class EmojiAction(BaseAction):
                 emotion_map[emo].append((b64, desc))
 
             available_emotions = list(emotion_map.keys())
+            available_emotions_str = ""
+            for emotion in available_emotions:
+                available_emotions_str += f"{emotion}\n"
 
             if not available_emotions:
                 logger.warning(f"{self.log_prefix} 获取到的表情包均无情感标签, 将随机发送")
@@ -90,14 +84,15 @@ class EmojiAction(BaseAction):
                     )
 
                 # 4. 构建prompt让LLM选择情感
-                prompt = f"""
+                prompt = f"""你正在进行QQ聊天，你需要根据聊天记录，选出一个合适的情感标签。
-                你是一个正在进行聊天的网友，你需要根据一个理由和最近的聊天记录，从一个情感标签列表中选择最匹配的一个。
+请你根据以下原因和聊天记录进行选择
-                这是最近的聊天记录：
+原因：{reason}
-                {messages_text}
+聊天记录：
+{messages_text}
 
-                这是理由：“{reason}”
+这里是可用的情感标签：
-                这里是可用的情感标签：{available_emotions}
+{available_emotions_str}
-                请直接返回最匹配的那个情感标签，不要进行任何解释或添加其他多余的文字。
+请直接返回最匹配的那个情感标签，不要进行任何解释或添加其他多余的文字。
                 """
 
                 if global_config.debug.show_prompt:
@@ -107,10 +102,10 @@ class EmojiAction(BaseAction):
 
                 # 5. 调用LLM
                 models = llm_api.get_available_models()
-                chat_model_config = models.get("utils_small")  # 使用字典访问方式
+                chat_model_config = models.get("replyer")  # 使用字典访问方式
                 if not chat_model_config:
-                    logger.error(f"{self.log_prefix} 未找到'utils_small'模型配置，无法调用LLM")
+                    logger.error(f"{self.log_prefix} 未找到'replyer'模型配置，无法调用LLM")
-                    return False, "未找到'utils_small'模型配置"
+                    return False, "未找到'replyer'模型配置"
 
                 success, chosen_emotion, _, _ = await llm_api.generate_with_model(
                     prompt, model_config=chat_model_config, request_type="emoji"

template/model_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "1.7.0"
+version = "1.7.2"
 
 # 配置文件版本号迭代规则同bot_config.toml
 
@@ -23,7 +23,7 @@ retry_interval = 5
 
 [[api_providers]] # 特殊：Google的Gimini使用特殊API，与OpenAI格式不兼容，需要配置client为"gemini"
 name = "Google"
-base_url = "https://api.google.com/v1"
+base_url = "https://generativelanguage.googleapis.com/v1beta"
 api_key = "your-google-api-key-1"
 client_type = "gemini"
 max_retry = 2