perf: 强化ltm异常处理

long_term_memory.py, line 69

README.md

@@ -1,14 +1,12 @@
-
 <p align="center">
+    
+![logo](https://github.com/user-attachments/assets/07649e07-3b8e-4feb-9aa9-bf13af4f3476)
 
-<img src="https://github.com/user-attachments/assets/de10f24d-cd64-433a-90b8-16c0a60de24a" width=500>
 
 </p>
 
 <div align="center">
 
-<h1>AstrBot</h1>
-
 _✨ 易上手的多平台 LLM 聊天机器人及开发框架 ✨_
 
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/Soulter/AstrBot)](https://github.com/Soulter/AstrBot/releases/latest)
@@ -72,7 +70,7 @@ AstrBot 是一个松耦合、异步、支持多消息平台部署、具有易用
 | -------- | ------- | ------- | ------ |
 | QQ      | ✔    | 私聊、群聊 | 文字、图片、语音 |
 | QQ 官方API | ✔    | 私聊、群聊，QQ 频道私聊、群聊 | 文字、图片 |
-| 微信    | ✔    | [Gewechat](https://github.com/Devo919/Gewechat)。微信个人号私聊、群聊 | 文字 |
+| 微信    | ✔    | [Gewechat](https://github.com/Devo919/Gewechat)。微信个人号私聊、群聊 | 文字、图片、语音 |
 | [Telegram](https://github.com/Soulter/astrbot_plugin_telegram)   | ✔    | 私聊、群聊 | 文字、图片 |
 | 微信对话开放平台 | 🚧    | 计划内 | - |
 | 飞书   | 🚧    | 计划内 | - |

astrbot/api/provider/__init__.py

@@ -1,2 +1,2 @@
 from astrbot.core.provider import Provider, STTProvider, Personality
-from astrbot.core.provider.entites import ProviderRequest, ProviderType, ProviderMetaData
+from astrbot.core.provider.entites import ProviderRequest, ProviderType, ProviderMetaData, LLMResponse

astrbot/core/config/default.py

@@ -2,7 +2,7 @@
 如需修改配置，请在 `data/cmd_config.json` 中修改或者在管理面板中可视化修改。
 """
 
-VERSION = "3.4.12"
+VERSION = "3.4.14"
 DB_PATH = "data/data_v3.db"
 
 # 默认配置
@@ -24,7 +24,13 @@ DEFAULT_CONFIG = {
         "wl_ignore_admin_on_friend": True,
         "reply_with_mention": False,
         "reply_with_quote": False,
-        "path_mapping": []
+        "path_mapping": [],
+        "segmented_reply": {
+            "enable": False,
+            "only_llm_result": True,
+            "interval": "1.5,3.5",
+            "regex": ".*?[。？！~…]+|.+$"
+        }
     },
     "provider": [],
     "provider_settings": {
@@ -44,6 +50,12 @@ DEFAULT_CONFIG = {
         "enable": False,
         "provider_id": "",
     },
+    "provider_ltm_settings": {
+        "group_icl_enable": False,
+        "group_message_max_cnt": 300,
+        "image_caption": False,
+        "image_caption_prompt": "Please describe the image using Chinese.",
+    },
     "content_safety": {
         "internal_keywords": {"enable": True, "extra_keywords": []},
         "baidu_aip": {"enable": False, "app_id": "", "api_key": "", "secret_key": ""},
@@ -182,6 +194,31 @@ CONFIG_METADATA_2 = {
                             },
                         },
                     },
+                    "segmented_reply": {
+                        "description": "分段回复",
+                        "type": "object",
+                        "items": {
+                            "enable": {
+                                "description": "启用分段回复",
+                                "type": "bool",
+                            },
+                            "only_llm_result": {
+                                "description": "仅对 LLM 结果分段",
+                                "type": "bool",
+                            },
+                            "interval": {
+                                "description": "随机间隔时间(秒)",
+                                "type": "string",
+                                "hint": "每一段回复的间隔时间，格式为 `最小时间,最大时间`。如 `0.75,2.5`",
+                            },
+                            "regex": {
+                                "description": "正则表达式",
+                                "type": "string",
+                                "obvious_hint": True,
+                                "hint": "用于分隔一段消息。默认情况下会根据句号、问号等标点符号分隔。re.findall(r'<regex>', text)",
+                            },
+                        },
+                    },
                     "reply_prefix": {
                         "description": "回复前缀",
                         "type": "string",
@@ -599,6 +636,34 @@ CONFIG_METADATA_2 = {
                     },
                 },
             },
+            "provider_ltm_settings": {
+                "description": "聊天记忆增强(Beta)",
+                "type": "object",
+                "items": {
+                    "group_icl_enable": {
+                        "description": "群聊内记录各群员对话",
+                        "type": "bool",
+                        "obvious-hint": True,
+                        "hint": "启用后，会记录群聊内各群员的对话。使用 /reset 命令清除记录。推荐使用 gpt-4o-mini 模型。",
+                    },
+                    "group_message_max_cnt": {
+                        "description": "群聊消息最大数量",
+                        "type": "int",
+                        "obvious-hint": True,
+                        "hint": "群聊消息最大数量。超过此数量后，会自动清除旧消息。",
+                    },
+                    "image_caption": {
+                        "description": "启用图像转述(需要模型支持)",
+                        "type": "bool",
+                        "obvious-hint": True,
+                        "hint": "启用后，当接收到图片消息时，会使用模型先将图片转述为文字再进行后续处理。推荐使用 gpt-4o-mini 模型。",
+                    },
+                    "image_caption_prompt": {
+                        "description": "图像转述提示词",
+                        "type": "string"
+                    },
+                },
+            },
         },
     },
     "misc_config_group": {
@@ -608,7 +673,8 @@ CONFIG_METADATA_2 = {
                 "description": "机器人唤醒前缀",
                 "type": "list",
                 "items": {"type": "string"},
-                "hint": "在不 @ 机器人的情况下，可以通过外加消息前缀来唤醒机器人。",
+                "obvious_hint": True,
+                "hint": "在不 @ 机器人的情况下，可以通过外加消息前缀来唤醒机器人。更改此配置将影响整个 Bot 的功能唤醒，包括所有指令。如果您不保留 `/`，则内置指令（help等）将需要通过您的唤醒前缀来触发。",
             },
             "t2i": {
                 "description": "文本转图像",

astrbot/core/message/message_event_result.py

@@ -13,12 +13,10 @@ class MessageChain():
     Attributes:
         `chain` (list): 用于顺序存储各个组件。
         `use_t2i_` (bool): 用于标记是否使用文本转图片服务。默认为 None，即跟随用户的设置。当设置为 True 时，将会使用文本转图片服务。
-        `is_split_` (bool): 用于标记是否分条发送消息。默认为 False。启用后，将会依次发送 chain 中的每个 component。
     '''
     
     chain: List[BaseMessageComponent] = field(default_factory=list)
     use_t2i_: Optional[bool] = None # None 为跟随用户设置
-    is_split_: Optional[bool] = False # 是否将消息分条发送。默认为 False。启用后，将会依次发送 chain 中的每个 component。
     
     def message(self, message: str):
         '''添加一条文本消息到消息链 `chain` 中。
@@ -77,16 +75,6 @@ class MessageChain():
         '''
         self.use_t2i_ = use_t2i
         return self
-    
-    def is_split(self, is_split: bool):
-        '''设置是否分条发送消息。默认为 False。启用后，将会依次发送 chain 中的每个 component。
-        
-        Note:
-            具体的效果以各适配器实现为准。
-            
-        '''
-        self.is_split_ = is_split
-        return self
 
 class EventResultType(enum.Enum):
     '''用于描述事件处理的结果类型。
@@ -113,7 +101,6 @@ class MessageEventResult(MessageChain):
     Attributes:
         `chain` (list): 用于顺序存储各个组件。
         `use_t2i_` (bool): 用于标记是否使用文本转图片服务。默认为 None，即跟随用户的设置。当设置为 True 时，将会使用文本转图片服务。
-        `is_split_` (bool): 用于标记是否分条发送消息。默认为 False。启用后，将会依次发送 chain 中的每个 component。
         `result_type` (EventResultType): 事件处理的结果类型。
     '''
     
@@ -153,5 +140,10 @@ class MessageEventResult(MessageChain):
         '''
         return self.result_content_type == ResultContentType.LLM_RESULT
     
+    def get_plain_text(self) -> str:
+        '''获取纯文本消息。这个方法将获取所有 Plain 组件的文本并拼接成一条消息。空格分隔。
+        '''
+        return " ".join([comp.text for comp in self.chain if isinstance(comp, Plain)])
+    
     
 CommandResult = MessageEventResult

astrbot/core/pipeline/process_stage/method/star_request.py

@@ -39,8 +39,11 @@ class StarRequestSubStage(Stage):
             except Exception as e:
                 logger.error(traceback.format_exc())
                 logger.error(f"Star {handler.handler_full_name} handle error: {e}")
-                ret = f":(\n\n在调用插件 {star_map.get(handler.handler_module_path).name} 的处理函数 {handler.handler_name} 时出现异常：{e}"
-                event.set_result(MessageEventResult().message(ret))
-                yield
-                event.clear_result()
+                
+                if event.is_at_or_wake_command:
+                    ret = f":(\n\n在调用插件 {star_map.get(handler.handler_module_path).name} 的处理函数 {handler.handler_name} 时出现异常：{e}"
+                    event.set_result(MessageEventResult().message(ret))
+                    yield
+                    event.clear_result()
+                
                 event.stop_event()

astrbot/core/pipeline/respond/stage.py

@@ -1,7 +1,10 @@
+import random
+import asyncio
 from typing import Union, AsyncGenerator
 from ..stage import register_stage, Stage
 from ..context import PipelineContext
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
+from astrbot.core.message.message_event_result import MessageChain
 from astrbot.core import logger
 from astrbot.core.star.star_handler import star_handlers_registry, EventType
 
@@ -9,6 +12,17 @@ from astrbot.core.star.star_handler import star_handlers_registry, EventType
 class RespondStage(Stage):
     async def initialize(self, ctx: PipelineContext):
         self.ctx = ctx
+        
+        # 分段回复
+        self.enable_seg: bool = ctx.astrbot_config['platform_settings']['segmented_reply']['enable']
+        interval_str: str = ctx.astrbot_config['platform_settings']['segmented_reply']['interval']
+        interval_str_ls = interval_str.replace(" ", "").split(",")
+        try:
+            self.interval = [float(t) for t in interval_str_ls]
+        except BaseException as e:
+            logger.error(f'解析分段回复的间隔时间失败。{e}')
+            self.interval = [1.5, 3.5]
+
 
     async def process(self, event: AstrMessageEvent) -> Union[None, AsyncGenerator[None, None]]:
         result = event.get_result()
@@ -16,7 +30,15 @@ class RespondStage(Stage):
             return
 
         if len(result.chain) > 0:
-            await event.send(result)
+            await event._pre_send()
+            if self.enable_seg:
+                # 分段回复
+                for comp in result.chain:
+                    await event.send(MessageChain([comp]))
+                await asyncio.sleep(random.uniform(self.interval[0], self.interval[1]))
+            else:
+                await event.send(result)
+            await event._post_send()
             logger.info(f"AstrBot -> {event.get_sender_name()}/{event.get_sender_id()}: {event._outline_chain(result.chain)}")
         
         handlers = star_handlers_registry.get_handlers_by_event_type(EventType.OnAfterMessageSentEvent)

astrbot/core/pipeline/result_decorate/stage.py

@@ -1,4 +1,5 @@
 import time
+import re
 import traceback
 from typing import Union, AsyncGenerator
 from ..stage import register_stage
@@ -18,7 +19,11 @@ class ResultDecorateStage:
         self.reply_with_mention = ctx.astrbot_config['platform_settings']['reply_with_mention']
         self.reply_with_quote = ctx.astrbot_config['platform_settings']['reply_with_quote']
         self.use_tts = ctx.astrbot_config['provider_tts_settings']['enable']
-        self.t2i = ctx.astrbot_config['t2i']
+        
+        # 分段回复
+        self.enable_segmented_reply = ctx.astrbot_config['platform_settings']['segmented_reply']['enable']
+        self.only_llm_result = ctx.astrbot_config['platform_settings']['segmented_reply']['only_llm_result']
+        self.regex = ctx.astrbot_config['platform_settings']['segmented_reply']['regex']
 
     async def process(self, event: AstrMessageEvent) -> Union[None, AsyncGenerator[None, None]]:
         result = event.get_result()
@@ -33,29 +38,53 @@ class ResultDecorateStage:
         if len(result.chain) > 0:
             # 回复前缀
             if self.reply_prefix:
-                result.chain.insert(0, Plain(self.reply_prefix))
+                for comp in result.chain:
+                    if isinstance(comp, Plain):
+                        comp.text = self.reply_prefix + comp.text
+                        break
+            
+             # 分段回复 
+            if self.enable_segmented_reply:
+                if (self.only_llm_result and result.is_llm_result()) or not self.only_llm_result:
+                    new_chain = []
+                    for comp in result.chain:
+                        if isinstance(comp, Plain):
+                            split_response = re.findall(r".*?[。？！~…]+|.+$", comp.text)
+                            if not split_response:
+                                new_chain.append(comp)
+                                continue
+                            for seg in split_response:
+                                new_chain.append(Plain(seg))
+                        else:
+                            # 非 Plain 类型的消息段不分段
+                            new_chain.append(comp)
+                    result.chain = new_chain
                 
             # TTS
             if self.use_tts and result.is_llm_result():
                 tts_provider = self.ctx.plugin_manager.context.provider_manager.curr_tts_provider_inst
-                plain_str = ""
+                new_chain = []
                 for comp in result.chain:
-                    if isinstance(comp, Plain):
-                        plain_str += " " + comp.text
+                    if isinstance(comp, Plain) and len(comp.text) > 1:
+                        try:
+                            logger.info("TTS 请求: " + comp.text)
+                            audio_path = await tts_provider.get_audio(comp.text)
+                            logger.info("TTS 结果: " + audio_path)
+                            if audio_path:
+                                new_chain.append(Record(file=audio_path, url=audio_path))
+                            else:
+                                logger.error(f"由于 TTS 音频文件没找到，消息段转语音失败: {comp.text}")
+                                new_chain.append(comp)
+                        except BaseException:
+                            traceback.print_exc()
+                            logger.error("TTS 失败，使用文本发送。")
+                            new_chain.append(comp)
                     else:
-                        break
-                if plain_str:
-                    try:
-                        audio_path = await tts_provider.get_audio(plain_str)
-                        logger.info("TTS 结果: " + audio_path)
-                        if audio_path:
-                            result.chain = [Record(file=audio_path, url=audio_path)]
-                    except BaseException:
-                        traceback.print_exc()
-                        logger.error("TTS 失败，使用文本发送。")
+                        new_chain.append(comp)
+                result.chain = new_chain
             
             # 文本转图片
-            elif (result.use_t2i_ is None and self.t2i) or result.use_t2i_:
+            elif (result.use_t2i_ is None and self.ctx.astrbot_config['t2i']) or result.use_t2i_:
                 plain_str = ""
                 for comp in result.chain:
                     if isinstance(comp, Plain):

astrbot/core/platform/astr_message_event.py

@@ -179,6 +179,15 @@ class AstrMessageEvent(abc.ABC):
         await Metric.upload(msg_event_tick = 1, adapter_name = self.platform_meta.name)
         self._has_send_oper = True
         
+    async def _pre_send(self):
+        '''调度器会在执行 send() 前调用该方法'''
+        pass
+    
+    async def _post_send(self):
+        '''调度器会在执行 send() 后调用该方法'''
+        pass
+        
+        
     def set_result(self, result: Union[MessageEventResult, str]):
         '''设置消息事件的结果。
         

astrbot/core/platform/sources/aiocqhttp/aiocqhttp_message_event.py

@@ -40,11 +40,5 @@ class AiocqhttpMessageEvent(AstrMessageEvent):
         ret = await AiocqhttpMessageEvent._parse_onebot_json(message)
         if os.environ.get('TEST_MODE', 'off') == 'on':
             return
-        
-        if message.is_split_: # 分条发送
-            for m in ret:
-                await self.bot.send(self.message_obj.raw_message, [m])
-                await asyncio.sleep(random.uniform(0.75, 2.5))
-        else:
-            await self.bot.send(self.message_obj.raw_message, ret)
+        await self.bot.send(self.message_obj.raw_message, ret)
         await super().send(message)

astrbot/core/platform/sources/gewechat/client.py

@@ -66,6 +66,7 @@ class SimpleGewechatClient():
         if type_name == "Offline":
             logger.critical("收到 gewechat 下线通知。")
             return
+        
         abm = AstrBotMessage()
         d = data['Data']
     
@@ -102,7 +103,7 @@ class SimpleGewechatClient():
         if at_me:
             abm.message.insert(0, At(qq=abm.self_id))
         
-        user_real_name = d['PushContent'].split(' : ')[0] \
+        user_real_name = d.get('PushContent', 'unknown : ').split(' : ')[0] \
             .replace('在群聊中@了你', '') \
             .replace('在群聊中发了一段语音', '') # 真实昵称
         abm.sender = MessageMember(user_id, user_real_name)
@@ -153,13 +154,17 @@ class SimpleGewechatClient():
         if data.get('testMsg', None):
             return quart.jsonify({"r": "AstrBot ACK"})
         
-        abm = await self._convert(data)
-        
+        abm = None
+        try:
+            abm = await self._convert(data)
+        except BaseException as e:
+            logger.warning(f"尝试解析 GeweChat 下发的消息时遇到问题: {e}。下发消息内容: {data}。")
+            
         if abm:
             coro = getattr(self, "on_event_received")
             if coro:
                 await coro(abm)
-            
+        
         return quart.jsonify({"r": "AstrBot ACK"})
     
     async def handle_file(self, file_id):

astrbot/core/platform/sources/qqofficial/qqofficial_message_event.py

@@ -14,12 +14,20 @@ class QQOfficialMessageEvent(AstrMessageEvent):
     def __init__(self, message_str: str, message_obj: AstrBotMessage, platform_meta: PlatformMetadata, session_id: str, bot: Client):
         super().__init__(message_str, message_obj, platform_meta, session_id)
         self.bot = bot
+        self.send_buffer = None
         
     async def send(self, message: MessageChain):
+        if not self.send_buffer:
+            self.send_buffer = message
+        else:
+            self.send_buffer.chain.extend(message.chain)
+
+    async def _post_send(self):
+        '''QQ 官方 API 仅支持回复一次'''
         source = self.message_obj.raw_message
         assert isinstance(source, (botpy.message.Message, botpy.message.GroupMessage, botpy.message.DirectMessage, botpy.message.C2CMessage))
         
-        plain_text, image_base64, image_path = await QQOfficialMessageEvent._parse_to_qqofficial(message)
+        plain_text, image_base64, image_path = await QQOfficialMessageEvent._parse_to_qqofficial(self.send_buffer)
         
         payload = {
             'content': plain_text,
@@ -48,7 +56,9 @@ class QQOfficialMessageEvent(AstrMessageEvent):
                     payload['file_image'] = image_path
                 await self.bot.api.post_dms(guild_id=source.guild_id, **payload)
 
-        await super().send(message)
+        await super().send(self.send_buffer)
+        
+        self.send_buffer = None
             
     async def upload_group_and_c2c_image(self, image_base64: str, file_type: int, **kwargs) -> botpy.types.message.Media:
         payload = {

astrbot/core/provider/manager.py

@@ -117,19 +117,21 @@ class ProviderManager():
                 continue
             
     async def initialize(self):
+        
+        selected_provider_id = sp.get("curr_provider")
+        selected_stt_provider_id = self.provider_stt_settings.get("provider_id")
+        selected_tts_provider_id = self.provider_settings.get("provider_id")
+        provider_enabled = self.provider_settings.get("enable", False)
+        stt_enabled = self.provider_stt_settings.get("enable", False)
+        tts_enabled = self.provider_settings.get("enable", False)
+            
         for provider_config in self.providers_config:
             if not provider_config['enable']:
                 continue
             if provider_config['type'] not in provider_cls_map:
                 logger.error(f"未找到适用于 {provider_config['type']}({provider_config['id']}) 的提供商适配器，请检查是否已经安装或者名称填写错误。已跳过。")
                 continue
-            selected_provider_id = sp.get("curr_provider")
-            selected_stt_provider_id = self.provider_stt_settings.get("provider_id")
-            selected_tts_provider_id = self.provider_settings.get("provider_id")
-            provider_enabled = self.provider_settings.get("enable", False)
-            stt_enabled = self.provider_stt_settings.get("enable", False)
-            tts_enabled = self.provider_settings.get("enable", False)
-            
+
             provider_metadata = provider_cls_map[provider_config['type']]
             logger.info(f"尝试实例化 {provider_config['type']}({provider_config['id']}) 提供商适配器 ...")
             try:

astrbot/core/provider/sources/openai_source.py

@@ -1,5 +1,6 @@
 import base64
 import json
+import re
 
 from openai import AsyncOpenAI, NOT_GIVEN
 from openai.types.chat.chat_completion import ChatCompletion
@@ -103,10 +104,20 @@ class ProviderOpenAIOfficial(Provider):
             if tool_list:
                 payloads['tools'] = tool_list
         
-        completion = await self.client.chat.completions.create(
-            **payloads,
-            stream=False
-        )
+        try:
+            completion = await self.client.chat.completions.create(
+                **payloads,
+                stream=False
+            )
+        except BaseException as e:
+            if 'does not support Function Calling' \
+                or 'does not support tools' in e: # ollama 
+                    del payloads['tools']
+                    logger.debug(f"模型 {self.model_name} 不支持 tools，已自动移除")
+                    completion = await self.client.chat.completions.create(
+                        **payloads,
+                        stream=False
+                    )
         
         assert isinstance(completion, ChatCompletion)
         logger.debug(f"completion: {completion}")
@@ -118,6 +129,12 @@ class ProviderOpenAIOfficial(Provider):
         if choice.message.content:
             # text completion
             completion_text = str(choice.message.content).strip()
+            
+            # 适配 deepseek-r1 模型
+            if r'<think>' in completion_text:
+                completion_text = re.sub(r'<think>.*?<think/>', '', completion_text).strip()
+                completion_text = completion_text.replace(r'<think>', '').replace(r'</think>', '').strip()
+            
             return LLMResponse("assistant", completion_text)
         elif choice.message.tool_calls:
             # tools call (function calling)
@@ -163,7 +180,8 @@ class ProviderOpenAIOfficial(Provider):
 
         try:
             llm_response = await self._query(payloads, func_tool)
-            await self.save_history(contexts, new_record, session_id, llm_response)
+            if kwargs.get("persist", True):
+                await self.save_history(contexts, new_record, session_id, llm_response)
             return llm_response
         except Exception as e:
             if "maximum context length" in str(e):

changelogs/v3.4.13.md

@@ -0,0 +1,4 @@
+# What's Changed
+
+- 修复 astrbot_updator 属性缺失与stt_enabled 未初始化 #252
+- 支持消息分段回复

changelogs/v3.4.14.md

@@ -0,0 +1,8 @@
+# What's Changed
+
+- 修复: TTS 问题
+- 新增: **支持记录非唤醒状态下群聊历史记录(beta)**
+- 优化: 自动删除 deepseek-r1 模型自带的 think 标签
+- 优化: 自动移除 ollama 不支持 tool 的模型的 tool 请求
+- 优化: /t2i 即时生效
+- 优化: gewechat 消息下发异常处理

packages/astrbot/long_term_memory.py

@@ -0,0 +1,88 @@
+import datetime
+import uuid
+import astrbot.api.star as star
+from astrbot.api.event import AstrMessageEvent
+from astrbot.api.platform import MessageType
+from astrbot.api.provider import ProviderRequest
+from astrbot.api.message_components import Plain, Image
+from astrbot import logger
+from collections import defaultdict
+
+
+class LongTermMemory:
+    def __init__(self, config: dict, context: star.Context):
+        self.config = config
+        self.context = context
+        self.session_chats = defaultdict(list)
+        """记录群成员的群聊记录"""
+        try:
+            self.max_cnt = int(self.config["group_message_max_cnt"])
+        except BaseException as e:
+            logger.error(e)
+            self.max_cnt = 300
+        self.image_caption = self.config["image_caption"]
+        self.image_caption_prompt = self.config["image_caption_prompt"]
+        
+    async def remove_session(self, event: AstrMessageEvent) -> int:
+        cnt = 0
+        if event.unified_msg_origin in self.session_chats:
+            cnt = len(self.session_chats[event.unified_msg_origin])
+            del self.session_chats[event.unified_msg_origin]
+        return cnt
+
+    async def get_image_caption(self, image_url: str) -> str:
+        provider = self.context.get_using_provider()
+        response = await provider.text_chat(
+            prompt=self.image_caption_prompt,
+            session_id=uuid.uuid4().hex,
+            image_urls=[image_url],
+            persist=False,
+        )
+        return response.completion_text
+
+    async def handle_message(self, event: AstrMessageEvent):
+        if event.get_message_type() == MessageType.GROUP_MESSAGE:
+            datetime_str = datetime.datetime.now().strftime("%H:%M:%S")
+
+            final_message = f"[{event.message_obj.sender.nickname}/{datetime_str}]: "
+
+            for comp in event.get_messages():
+                if isinstance(comp, Plain):
+                    final_message += f" {comp.text}"
+                elif isinstance(comp, Image):
+                    # image_urls.append(comp.url if comp.url else comp.file)
+                    if self.image_caption:
+                        try:
+                            caption = await self.get_image_caption(
+                                comp.url if comp.url else comp.file
+                            )
+                            final_message += f" [Image: {caption}]"
+                        except Exception as e:
+                            logger.error(f"获取图片描述失败: {e}")
+            logger.debug(f"ltm | {event.unified_msg_origin} | {final_message}")
+            self.session_chats[event.unified_msg_origin].append(final_message)
+            if len(self.session_chats[event.unified_msg_origin]) > self.max_cnt:
+                self.session_chats[event.unified_msg_origin].pop(0)
+
+    async def on_req_llm(self, event: AstrMessageEvent, req: ProviderRequest):
+        if event.unified_msg_origin not in self.session_chats:
+            return
+        
+        chats_str = '\n---\n'.join(self.session_chats[event.unified_msg_origin])
+        req.system_prompt += "You are now in a chatroom. The chat history is as follows: \n"
+        req.system_prompt += chats_str
+        if self.image_caption:
+            req.system_prompt += (
+                "The images sent by the members are displayed in text form above."
+            )
+
+    async def after_req_llm(self, event: AstrMessageEvent):
+        if event.unified_msg_origin not in self.session_chats:
+            return
+
+        if event.get_result() and event.get_result().is_llm_result():
+            final_message = f"[AstrBot/{datetime.datetime.now().strftime('%H:%M:%S')}]: {event.get_result().get_plain_text()}"
+            logger.debug(f"ltm | {event.unified_msg_origin} | {final_message}")
+            self.session_chats[event.unified_msg_origin].append(final_message)
+            if len(self.session_chats[event.unified_msg_origin]) > self.max_cnt:
+                self.session_chats[event.unified_msg_origin].pop(0)

packages/astrbot/main.py

@@ -6,12 +6,16 @@ import astrbot.api.event.filter as filter
 from astrbot.api.event import AstrMessageEvent, MessageEventResult
 from astrbot.api import sp
 from astrbot.api.provider import Personality, ProviderRequest
+from astrbot.api.platform import MessageType
 from astrbot.core.utils.io import download_dashboard, get_dashboard_version
 from astrbot.core.config.default import VERSION
+from collections import defaultdict
+from .long_term_memory import LongTermMemory
+from astrbot.core import logger
 
 from typing import Union
 
-@star.register(name="astrbot", desc="AstrBot 基础指令集合", author="Soulter", version="4.0.0")
+@star.register(name="astrbot", desc="AstrBot 基础指令结合 + 拓展功能", author="Soulter", version="4.0.0")
 class Main(star.Star):
     def __init__(self, context: star.Context) -> None:
         self.context = context
@@ -20,7 +24,12 @@ class Main(star.Star):
         self.identifier = cfg['provider_settings']['identifier']
         self.enable_datetime = cfg['provider_settings']["datetime_system_prompt"]
         
-        self.kdb_enabled = False
+        self.ltm = None
+        if self.context.get_config()['provider_ltm_settings']['group_icl_enable']:
+            try:
+                self.ltm = LongTermMemory(self.context.get_config()['provider_ltm_settings'], self.context)
+            except BaseException as e:
+                logger.error(f"聊天增强 err: {e}")
     
     async def _query_astrbot_notice(self):
         try:
@@ -219,7 +228,12 @@ UID: {user_id} 此 ID 可用于设置管理员。/op <UID> 授权管理员, /deo
     @filter.command("reset")
     async def reset(self, message: AstrMessageEvent):
         await self.context.get_using_provider().forget(message.session_id)
-        message.set_result(MessageEventResult().message("重置成功"))
+        ret = "清除会话 LLM 聊天历史成功。"
+        if self.ltm:
+            cnt = await self.ltm.remove_session(event=message)
+            ret += f"\n聊天增强: 已清除 {cnt} 条聊天记录。"
+        
+        message.set_result(MessageEventResult().message(ret))
 
     @filter.command("model")
     async def model_ls(self, message: AstrMessageEvent, idx_or_name: Union[int, str] = None):
@@ -355,9 +369,9 @@ UID: {user_id} 此 ID 可用于设置管理员。/op <UID> 授权管理员, /deo
                 self.context.provider_manager.personas
             ), None):
                 self.context.get_using_provider().curr_personality = persona
-                message.set_result(MessageEventResult().message(f"设置成功。如果您正在切换到不同的人格，请注意使用 /reset 来清空上下文，防止原人格对话影响现人格。"))
+                message.set_result(MessageEventResult().message("设置成功。如果您正在切换到不同的人格，请注意使用 /reset 来清空上下文，防止原人格对话影响现人格。"))
             else:
-                message.set_result(MessageEventResult().message(f"不存在该人格情景。使用 /persona list 查看所有。"))
+                message.set_result(MessageEventResult().message("不存在该人格情景。使用 /persona list 查看所有。"))
     
     @filter.permission_type(filter.PermissionType.ADMIN)
     @filter.command("dashboard_update")
@@ -366,31 +380,6 @@ UID: {user_id} 此 ID 可用于设置管理员。/op <UID> 授权管理员, /deo
         await download_dashboard()
         yield event.plain_result("管理面板更新完成。")
 
-    @filter.on_llm_request()
-    async def decorate_llm_req(self, event: AstrMessageEvent, req: ProviderRequest):
-        provider = self.context.get_using_provider()
-        if self.prompt_prefix:
-            req.prompt = self.prompt_prefix + req.prompt
-        if self.identifier:
-            user_id = event.message_obj.sender.user_id
-            user_nickname = event.message_obj.sender.nickname
-            user_info = f"\n[User ID: {user_id}, Nickname: {user_nickname}]\n"
-            req.prompt = user_info + req.prompt
-        if self.enable_datetime:
-            req.system_prompt += f"\nCurrent datetime: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M')}\n"
-        
-        if persona := provider.curr_personality:
-            if prompt := persona['prompt']:
-                req.system_prompt += prompt
-            if mood_dialogs := persona['_mood_imitation_dialogs_processed']:
-                req.system_prompt += "\nHere are few shots of dialogs, you need to imitate the tone of 'B' in the following dialogs to respond:\n"
-                req.system_prompt += mood_dialogs
-            if begin_dialogs := persona["_begin_dialogs_processed"]:
-                req.contexts[:0] = begin_dialogs
-
-        # if provider.curr_personality['prompt']:
-        #     req.system_prompt += f"\n{provider.curr_personality['prompt']}"
-        
     @filter.command("set")
     async def set_variable(self, event: AstrMessageEvent, key: str, value: str):
         session_id = event.get_session_id()
@@ -428,32 +417,84 @@ UID: {user_id} 此 ID 可用于设置管理员。/op <UID> 授权管理员, /deo
                 await platform.logout()
                 yield event.plain_result("已登出 gewechat")
                 return
-                
-    @filter.command_group("kdb")
-    def kdb(self):
-        pass
-        
-    @kdb.command("on")
-    async def on_kdb(self, event: AstrMessageEvent):
-        self.kdb_enabled = True
-        curr_kdb_name = self.context.provider_manager.curr_kdb_name
-        if not curr_kdb_name:
-            yield event.plain_result("未载入任何知识库")
-        else:
-            yield event.plain_result(f"知识库已打开。当前载入的知识库: {curr_kdb_name}")
-        
-    @kdb.command("off")
-    async def off_kdb(self, event: AstrMessageEvent):
-        self.kdb_enabled = False
-        yield event.plain_result("知识库已关闭")
-        
+            
+            
+    @filter.platform_adapter_type(filter.PlatformAdapterType.ALL)
+    async def on_message(self, event: AstrMessageEvent):
+        '''长期记忆'''
+        if self.ltm:
+            try:
+                await self.ltm.handle_message(event)
+            except BaseException as e:
+                logger.error(e)
+    
+    
     @filter.on_llm_request()
-    async def on_llm_response(self, event: AstrMessageEvent, req: ProviderRequest):
-        curr_kdb_name = self.context.provider_manager.curr_kdb_name
-        if self.kdb_enabled and curr_kdb_name:
-            mgr = self.context.knowledge_db_manager
-            results = await mgr.retrive_records(curr_kdb_name, req.prompt)
-            if results:
-                req.system_prompt += "\nHere are documents that related to user's query: \n"
-                for result in results:
-                    req.system_prompt += f"- {result}\n"
+    async def decorate_llm_req(self, event: AstrMessageEvent, req: ProviderRequest):
+        '''在请求 LLM 前注入人格信息、Identifier、时间等 System Prompt'''
+        provider = self.context.get_using_provider()
+        if self.prompt_prefix:
+            req.prompt = self.prompt_prefix + req.prompt
+            
+        if self.identifier:
+            user_id = event.message_obj.sender.user_id
+            user_nickname = event.message_obj.sender.nickname
+            user_info = f"\n[User ID: {user_id}, Nickname: {user_nickname}]\n"
+            req.prompt = user_info + req.prompt
+            
+        if self.enable_datetime:
+            req.system_prompt += f"\nCurrent datetime: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M')}\n"
+        
+        if persona := provider.curr_personality:
+            if prompt := persona['prompt']:
+                req.system_prompt += prompt
+            if mood_dialogs := persona['_mood_imitation_dialogs_processed']:
+                req.system_prompt += "\nHere are few shots of dialogs, you need to imitate the tone of 'B' in the following dialogs to respond:\n"
+                req.system_prompt += mood_dialogs
+            if begin_dialogs := persona["_begin_dialogs_processed"]:
+                req.contexts[:0] = begin_dialogs
+                
+        if self.ltm:
+            try:
+                await self.ltm.on_req_llm(event, req)
+            except BaseException as e:
+                logger.error(f"ltm: {e}")
+
+    
+    @filter.after_message_sent()
+    async def after_llm_req(self, event: AstrMessageEvent):
+        '''在 LLM 请求后记录对话'''
+        if self.ltm:
+            try:
+                await self.ltm.after_req_llm(event)
+            except BaseException as e:
+                logger.error(f"ltm: {e}")
+
+    # @filter.command_group("kdb")
+    # def kdb(self):
+    #     pass
+        
+    # @kdb.command("on")
+    # async def on_kdb(self, event: AstrMessageEvent):
+    #     self.kdb_enabled = True
+    #     curr_kdb_name = self.context.provider_manager.curr_kdb_name
+    #     if not curr_kdb_name:
+    #         yield event.plain_result("未载入任何知识库")
+    #     else:
+    #         yield event.plain_result(f"知识库已打开。当前载入的知识库: {curr_kdb_name}")
+        
+    # @kdb.command("off")
+    # async def off_kdb(self, event: AstrMessageEvent):
+    #     self.kdb_enabled = False
+    #     yield event.plain_result("知识库已关闭")
+        
+    # @filter.on_llm_request()
+    # async def on_llm_response(self, event: AstrMessageEvent, req: ProviderRequest):
+    #     curr_kdb_name = self.context.provider_manager.curr_kdb_name
+    #     if self.kdb_enabled and curr_kdb_name:
+    #         mgr = self.context.knowledge_db_manager
+    #         results = await mgr.retrive_records(curr_kdb_name, req.prompt)
+    #         if results:
+    #             req.system_prompt += "\nHere are documents that related to user's query: \n"
+    #             for result in results:
+    #                 req.system_prompt += f"- {result}\n"

requirements.txt

@@ -1,5 +1,4 @@
 pydantic~=2.10.3
-vchat
 aiohttp
 openai
 qq-botpy