Merge branch 'master' into Astrbot_session_manage

README.md

@@ -16,7 +16,7 @@ _✨ 易上手的多平台 LLM 聊天机器人及开发框架 ✨_
 <a  href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
 <a  href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
 [![wakatime](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e.svg?style=for-the-badge&color=76bad9)](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e)
-![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fstats&query=v&label=7%E6%97%A5%E6%B4%BB%E8%B7%83%E9%87%8F&cacheSeconds=3600&style=for-the-badge&color=3b618e)
+![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fstats&query=v&label=7日消息量&cacheSeconds=3600&style=for-the-badge&color=3b618e)
 ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E4%B8%AA&style=for-the-badge&label=%E6%8F%92%E4%BB%B6%E5%B8%82%E5%9C%BA&cacheSeconds=3600)
 
 <a href="https://github.com/Soulter/AstrBot/blob/master/README_en.md">English</a> ｜
@@ -111,10 +111,14 @@ uvx astrbot init
 
 或者请参阅官方文档 [通过源码部署 AstrBot](https://astrbot.app/deploy/astrbot/cli.html) 。
 
-#### Replit 部署
+#### 在 Replit 上部署
 
 [![Run on Repl.it](https://repl.it/badge/github/Soulter/AstrBot)](https://repl.it/github/Soulter/AstrBot)
 
+#### 在 雨云 上部署
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
 ## ⚡ 消息平台支持情况
 
 | 平台    | 支持性 |

astrbot/core/config/default.py

@@ -6,14 +6,14 @@ import os
 
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 
-VERSION = "3.5.17"
+VERSION = "3.5.18"
 DB_PATH = os.path.join(get_astrbot_data_path(), "data_v3.db")
 
 # 默认配置
 DEFAULT_CONFIG = {
     "config_version": 2,
     "platform_settings": {
-        "plugin_enable": [],
+        "plugin_enable": {},
         "unique_session": False,
         "rate_limit": {
             "time": 60,
@@ -54,6 +54,7 @@ DEFAULT_CONFIG = {
         "wake_prefix": "",
         "web_search": False,
         "web_search_link": False,
+        "display_reasoning_text": False,
         "identifier": False,
         "datetime_system_prompt": True,
         "default_personality": "default",
@@ -61,6 +62,7 @@ DEFAULT_CONFIG = {
         "max_context_length": -1,
         "dequeue_context_length": 1,
         "streaming_response": False,
+        "show_tool_use_status": False,
         "streaming_segmented": False,
         "separate_provider": False,
     },
@@ -441,7 +443,7 @@ CONFIG_METADATA_2 = {
                     "ignore_bot_self_message": {
                         "description": "是否忽略机器人自身的消息",
                         "type": "bool",
-                        "hint": "某些平台如 gewechat 会将自身账号在其他 APP 端发送的消息也当做消息事件下发导致给自己发消息时唤醒机器人",
+                        "hint": "某些平台会将自身账号在其他 APP 端发送的消息也当做消息事件下发导致给自己发消息时唤醒机器人",
                     },
                     "ignore_at_all": {
                         "description": "是否忽略 @ 全体成员",
@@ -770,17 +772,6 @@ CONFIG_METADATA_2 = {
                             "model": "deepseek/deepseek-r1",
                         },
                     },
-                    "LLMTuner": {
-                        "id": "llmtuner_default",
-                        "type": "llm_tuner",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "base_model_path": "",
-                        "adapter_model_path": "",
-                        "llmtuner_template": "",
-                        "finetuning_type": "lora",
-                        "quantization_bit": 4,
-                    },
                     "Dify": {
                         "id": "dify_app_default",
                         "type": "dify",
@@ -1662,6 +1653,11 @@ CONFIG_METADATA_2 = {
                         "obvious_hint": True,
                         "hint": "开启后，将会传入网页搜索结果的链接给模型，并引导模型输出引用链接。",
                     },
+                    "display_reasoning_text": {
+                        "description": "显示思考内容",
+                        "type": "bool",
+                        "hint": "开启后，将在回复中显示模型的思考过程。",
+                    },
                     "identifier": {
                         "description": "启动识别群员",
                         "type": "bool",
@@ -1699,10 +1695,15 @@ CONFIG_METADATA_2 = {
                         "type": "bool",
                         "hint": "启用后，将会流式输出 LLM 的响应。目前仅支持 OpenAI API提供商 以及 Telegram、QQ Official 私聊 两个平台",
                     },
+                    "show_tool_use_status": {
+                        "description": "函数调用状态输出",
+                        "type": "bool",
+                        "hint": "在触发函数调用时输出其函数名和内容。",
+                    },
                     "streaming_segmented": {
                         "description": "不支持流式回复的平台分段输出",
                         "type": "bool",
-                        "hint": "启用后，若平台不支持流式回复，会分段输出。目前仅支持 aiocqhttp 和 gewechat 两个平台，不支持或无需使用流式分段输出的平台会静默忽略此选项",
+                        "hint": "启用后，若平台不支持流式回复，会分段输出。目前仅支持 aiocqhttp 两个平台，不支持或无需使用流式分段输出的平台会静默忽略此选项",
                     },
                 },
             },

astrbot/core/message/message_event_result.py

@@ -24,6 +24,8 @@ class MessageChain:
 
     chain: List[BaseMessageComponent] = field(default_factory=list)
     use_t2i_: Optional[bool] = None  # None 为跟随用户设置
+    type: Optional[str] = None
+    """消息链承载的消息的类型。可选，用于让消息平台区分不同业务场景的消息链。"""
 
     def message(self, message: str):
         """添加一条文本消息到消息链 `chain` 中。
@@ -98,6 +100,15 @@ class MessageChain:
         self.chain.append(Image.fromFileSystem(path))
         return self
 
+    def base64_image(self, base64_str: str):
+        """添加一条图片消息（base64 编码字符串）到消息链 `chain` 中。
+        Example:
+
+            CommandResult().base64_image("iVBORw0KGgoAAAANSUhEUgAAAAUA...")
+        """
+        self.chain.append(Image.fromBase64(base64_str))
+        return self
+
     def use_t2i(self, use_t2i: bool):
         """设置是否使用文本转图片服务。
 
@@ -157,7 +168,7 @@ class ResultContentType(enum.Enum):
     """普通的消息结果"""
     STREAMING_RESULT = enum.auto()
     """调用 LLM 产生的流式结果"""
-    STREAMING_FINISH= enum.auto()
+    STREAMING_FINISH = enum.auto()
     """流式输出完成"""
 
 

astrbot/core/pipeline/context.py

@@ -1,6 +1,14 @@
+import inspect
+import traceback
+import typing as T
 from dataclasses import dataclass
 from astrbot.core.config.astrbot_config import AstrBotConfig
+from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.star import PluginManager
+from astrbot.api import logger
+from astrbot.core.star.star_handler import star_handlers_registry, EventType
+from astrbot.core.star.star import star_map
+from astrbot.core.message.message_event_result import MessageEventResult, CommandResult
 
 
 @dataclass
@@ -9,3 +17,91 @@ class PipelineContext:
 
     astrbot_config: AstrBotConfig  # AstrBot 配置对象
     plugin_manager: PluginManager  # 插件管理器对象
+
+    async def call_event_hook(
+        self,
+        event: AstrMessageEvent,
+        hook_type: EventType,
+        *args,
+    ):
+        platform_id = event.get_platform_id()
+        handlers = star_handlers_registry.get_handlers_by_event_type(
+            hook_type, platform_id=platform_id
+        )
+        for handler in handlers:
+            try:
+                logger.debug(
+                    f"hook(on_llm_request) -> {star_map[handler.handler_module_path].name} - {handler.handler_name}"
+                )
+                await handler.handler(event, *args)
+            except BaseException:
+                logger.error(traceback.format_exc())
+
+            if event.is_stopped():
+                logger.info(
+                    f"{star_map[handler.handler_module_path].name} - {handler.handler_name} 终止了事件传播。"
+                )
+                return
+
+    async def call_handler(
+        self,
+        event: AstrMessageEvent,
+        handler: T.Awaitable,
+        *args,
+        **kwargs,
+    ) -> T.AsyncGenerator[None, None]:
+        """执行事件处理函数并处理其返回结果
+
+        该方法负责调用处理函数并处理不同类型的返回值。它支持两种类型的处理函数:
+        1. 异步生成器: 实现洋葱模型，每次 yield 都会将控制权交回上层
+        2. 协程: 执行一次并处理返回值
+
+        Args:
+            ctx (PipelineContext): 消息管道上下文对象
+            event (AstrMessageEvent): 事件对象
+            handler (Awaitable): 事件处理函数
+
+        Returns:
+            AsyncGenerator[None, None]: 异步生成器，用于在管道中传递控制流
+        """
+        ready_to_call = None  # 一个协程或者异步生成器
+
+        trace_ = None
+
+        try:
+            ready_to_call = handler(event, *args, **kwargs)
+        except TypeError as _:
+            # 向下兼容
+            trace_ = traceback.format_exc()
+            # 以前的 handler 会额外传入一个参数, 但是 context 对象实际上在插件实例中有一份
+            ready_to_call = handler(event, self.plugin_manager.context, *args, **kwargs)
+
+        if inspect.isasyncgen(ready_to_call):
+            _has_yielded = False
+            try:
+                async for ret in ready_to_call:
+                    # 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
+                    # 返回值只能是 MessageEventResult 或者 None（无返回值）
+                    _has_yielded = True
+                    if isinstance(ret, (MessageEventResult, CommandResult)):
+                        # 如果返回值是 MessageEventResult, 设置结果并继续
+                        event.set_result(ret)
+                        yield
+                    else:
+                        # 如果返回值是 None, 则不设置结果并继续
+                        # 继续执行后续阶段
+                        yield ret
+                if not _has_yielded:
+                    # 如果这个异步生成器没有执行到 yield 分支
+                    yield
+            except Exception as e:
+                logger.error(f"Previous Error: {trace_}")
+                raise e
+        elif inspect.iscoroutine(ready_to_call):
+            # 如果只是一个协程, 直接执行
+            ret = await ready_to_call
+            if isinstance(ret, (MessageEventResult, CommandResult)):
+                event.set_result(ret)
+                yield
+            else:
+                yield ret

astrbot/core/pipeline/process_stage/agent_runner/base.py

@@ -0,0 +1,57 @@
+import abc
+import typing as T
+from dataclasses import dataclass
+from astrbot.core.provider.entities import LLMResponse
+from ....message.message_event_result import MessageChain
+from enum import Enum, auto
+
+
+class AgentState(Enum):
+    """Agent 状态枚举"""
+    IDLE = auto()          # 初始状态
+    RUNNING = auto()       # 运行中
+    DONE = auto()          # 完成
+    ERROR = auto()         # 错误状态
+
+
+class AgentResponseData(T.TypedDict):
+    chain: MessageChain
+
+
+@dataclass
+class AgentResponse:
+    type: str
+    data: AgentResponseData
+
+
+class BaseAgentRunner:
+    @abc.abstractmethod
+    async def reset(self) -> None:
+        """
+        Reset the agent to its initial state.
+        This method should be called before starting a new run.
+        """
+        ...
+
+    @abc.abstractmethod
+    async def step(self) -> T.AsyncGenerator[AgentResponse, None]:
+        """
+        Process a single step of the agent.
+        """
+        ...
+
+    @abc.abstractmethod
+    def done(self) -> bool:
+        """
+        Check if the agent has completed its task.
+        Returns True if the agent is done, False otherwise.
+        """
+        ...
+
+    @abc.abstractmethod
+    def get_final_llm_resp(self) -> LLMResponse | None:
+        """
+        Get the final observation from the agent.
+        This method should be called after the agent is done.
+        """
+        ...

astrbot/core/pipeline/process_stage/agent_runner/tool_loop_agent.py

@@ -0,0 +1,300 @@
+import sys
+import traceback
+import typing as T
+from .base import BaseAgentRunner, AgentResponse, AgentResponseData, AgentState
+from ...context import PipelineContext
+from astrbot.core.provider.provider import Provider
+from astrbot.core.platform.astr_message_event import AstrMessageEvent
+from astrbot.core.message.message_event_result import (
+    MessageChain,
+)
+from astrbot.core.provider.entities import (
+    ProviderRequest,
+    LLMResponse,
+    ToolCallMessageSegment,
+    AssistantMessageSegment,
+    ToolCallsResult,
+)
+from mcp.types import (
+    TextContent,
+    ImageContent,
+    EmbeddedResource,
+    TextResourceContents,
+    BlobResourceContents,
+)
+from astrbot.core.star.star_handler import EventType
+from astrbot import logger
+
+if sys.version_info >= (3, 12):
+    from typing import override
+else:
+    from typing_extensions import override
+
+
+# TODO:
+# 1. 处理平台不兼容的处理器
+
+
+class ToolLoopAgent(BaseAgentRunner):
+    def __init__(
+        self, provider: Provider, event: AstrMessageEvent, pipeline_ctx: PipelineContext
+    ) -> None:
+        self.provider = provider
+        self.req = None
+        self.event = event
+        self.pipeline_ctx = pipeline_ctx
+        self._state = AgentState.IDLE
+        self.final_llm_resp = None
+        self.streaming = False
+
+    @override
+    async def reset(self, req: ProviderRequest, streaming: bool) -> None:
+        self.req = req
+        self.streaming = streaming
+        self.final_llm_resp = None
+        self._state = AgentState.IDLE
+
+    def _transition_state(self, new_state: AgentState) -> None:
+        """转换 Agent 状态"""
+        if self._state != new_state:
+            logger.debug(f"Agent state transition: {self._state} -> {new_state}")
+            self._state = new_state
+
+    async def _iter_llm_responses(self) -> T.AsyncGenerator[LLMResponse, None]:
+        """Yields chunks *and* a final LLMResponse."""
+        if self.streaming:
+            stream = self.provider.text_chat_stream(**self.req.__dict__)
+            async for resp in stream:  # type: ignore
+                yield resp
+        else:
+            yield await self.provider.text_chat(**self.req.__dict__)
+
+    @override
+    async def step(self):
+        """
+        Process a single step of the agent.
+        This method should return the result of the step.
+        """
+        if not self.req:
+            raise ValueError("Request is not set. Please call reset() first.")
+
+        # 开始处理，转换到运行状态
+        self._transition_state(AgentState.RUNNING)
+        llm_resp_result = None
+
+        async for llm_response in self._iter_llm_responses():
+            assert isinstance(llm_response, LLMResponse)
+            if llm_response.is_chunk:
+                if llm_response.result_chain:
+                    yield AgentResponse(
+                        type="streaming_delta",
+                        data=AgentResponseData(chain=llm_response.result_chain),
+                    )
+                else:
+                    yield AgentResponse(
+                        type="streaming_delta",
+                        data=AgentResponseData(
+                            chain=MessageChain().message(llm_response.completion_text)
+                        ),
+                    )
+                continue
+            llm_resp_result = llm_response
+            break  # got final response
+
+        if not llm_resp_result:
+            return
+
+        # 处理 LLM 响应
+        llm_resp = llm_resp_result
+        logger.debug(f"LLMResp: {llm_resp}")
+
+        if llm_resp.role == "err":
+            # 如果 LLM 响应错误，转换到错误状态
+            self.final_llm_resp = llm_resp
+            self._transition_state(AgentState.ERROR)
+            yield AgentResponse(
+                type="err",
+                data=AgentResponseData(
+                    chain=MessageChain().message(
+                        f"LLM 响应错误: {llm_resp.completion_text or '未知错误'}"
+                    )
+                ),
+            )
+
+        if not llm_resp.tools_call_name:
+            # 如果没有工具调用，转换到完成状态
+            self.final_llm_resp = llm_resp
+            self._transition_state(AgentState.DONE)
+
+            # 执行事件钩子
+            await self.pipeline_ctx.call_event_hook(
+                self.event, EventType.OnLLMResponseEvent, llm_resp
+            )
+
+        # 返回 LLM 结果
+        if llm_resp.result_chain:
+            yield AgentResponse(
+                type="llm_result",
+                data=AgentResponseData(chain=llm_resp.result_chain),
+            )
+        elif llm_resp.completion_text:
+            yield AgentResponse(
+                type="llm_result",
+                data=AgentResponseData(
+                    chain=MessageChain().message(llm_resp.completion_text)
+                ),
+            )
+
+        # 如果有工具调用，还需处理工具调用
+        if llm_resp.tools_call_name:
+            tool_call_result_blocks = []
+            for tool_call_name in llm_resp.tools_call_name:
+                yield AgentResponse(
+                    type="tool_call",
+                    data=AgentResponseData(
+                        chain=MessageChain().message(f"🔨 调用工具: {tool_call_name}")
+                    ),
+                )
+            async for result in self._handle_function_tools(self.req, llm_resp):
+                if isinstance(result, list):
+                    tool_call_result_blocks = result
+                elif isinstance(result, MessageChain):
+                    yield AgentResponse(
+                        type="tool_call_result",
+                        data=AgentResponseData(chain=result),
+                    )
+            # 将结果添加到上下文中
+            tool_calls_result = ToolCallsResult(
+                tool_calls_info=AssistantMessageSegment(
+                    role="assistant",
+                    tool_calls=llm_resp.to_openai_tool_calls(),
+                    content=llm_resp.completion_text,
+                ),
+                tool_calls_result=tool_call_result_blocks,
+            )
+            self.req.append_tool_calls_result(tool_calls_result)
+
+    async def _handle_function_tools(
+        self,
+        req: ProviderRequest,
+        llm_response: LLMResponse,
+    ) -> T.AsyncGenerator[MessageChain | list[ToolCallMessageSegment], None]:
+        """处理函数工具调用。"""
+        tool_call_result_blocks: list[ToolCallMessageSegment] = []
+        logger.info(f"Agent 使用工具: {llm_response.tools_call_name}")
+
+        # 执行函数调用
+        for func_tool_name, func_tool_args, func_tool_id in zip(
+            llm_response.tools_call_name,
+            llm_response.tools_call_args,
+            llm_response.tools_call_ids,
+        ):
+            try:
+                if not req.func_tool:
+                    return
+                func_tool = req.func_tool.get_func(func_tool_name)
+                if func_tool.origin == "mcp":
+                    logger.info(
+                        f"从 MCP 服务 {func_tool.mcp_server_name} 调用工具函数：{func_tool.name}，参数：{func_tool_args}"
+                    )
+                    client = req.func_tool.mcp_client_dict[func_tool.mcp_server_name]
+                    res = await client.session.call_tool(func_tool.name, func_tool_args)
+                    if not res:
+                        continue
+                    if isinstance(res.content[0], TextContent):
+                        tool_call_result_blocks.append(
+                            ToolCallMessageSegment(
+                                role="tool",
+                                tool_call_id=func_tool_id,
+                                content=res.content[0].text,
+                            )
+                        )
+                        yield MessageChain().message(res.content[0].text)
+                    elif isinstance(res.content[0], ImageContent):
+                        tool_call_result_blocks.append(
+                            ToolCallMessageSegment(
+                                role="tool",
+                                tool_call_id=func_tool_id,
+                                content="返回了图片(已直接发送给用户)",
+                            )
+                        )
+                        yield MessageChain().base64_image(res.content[0].data)
+                    elif isinstance(res.content[0], EmbeddedResource):
+                        resource = res.content[0].resource
+                        if isinstance(resource, TextResourceContents):
+                            tool_call_result_blocks.append(
+                                ToolCallMessageSegment(
+                                    role="tool",
+                                    tool_call_id=func_tool_id,
+                                    content=resource.text,
+                                )
+                            )
+                            yield MessageChain().message(resource.text)
+                        elif (
+                            isinstance(resource, BlobResourceContents)
+                            and resource.mimeType
+                            and resource.mimeType.startswith("image/")
+                        ):
+                            tool_call_result_blocks.append(
+                                ToolCallMessageSegment(
+                                    role="tool",
+                                    tool_call_id=func_tool_id,
+                                    content="返回了图片(已直接发送给用户)",
+                                )
+                            )
+                            yield MessageChain().base64_image(res.content[0].data)
+                        else:
+                            tool_call_result_blocks.append(
+                                ToolCallMessageSegment(
+                                    role="tool",
+                                    tool_call_id=func_tool_id,
+                                    content="返回的数据类型不受支持",
+                                )
+                            )
+                            yield MessageChain().message("返回的数据类型不受支持。")
+                else:
+                    logger.info(f"使用工具：{func_tool_name}，参数：{func_tool_args}")
+                    # 尝试调用工具函数
+                    wrapper = self.pipeline_ctx.call_handler(
+                        self.event, func_tool.handler, **func_tool_args
+                    )
+                    async for resp in wrapper:
+                        if resp is not None:
+                            # Tool 返回结果
+                            tool_call_result_blocks.append(
+                                ToolCallMessageSegment(
+                                    role="tool",
+                                    tool_call_id=func_tool_id,
+                                    content=resp,
+                                )
+                            )
+                            yield MessageChain().message(resp)
+                        else:
+                            # Tool 直接请求发送消息给用户
+                            # 这里我们将直接结束 Agent Loop。
+                            self._transition_state(AgentState.DONE)
+                            if res := self.event.get_result():
+                                if res.chain:
+                                    yield MessageChain(chain=res.chain)
+
+                self.event.clear_result()
+            except Exception as e:
+                logger.warning(traceback.format_exc())
+                tool_call_result_blocks.append(
+                    ToolCallMessageSegment(
+                        role="tool",
+                        tool_call_id=func_tool_id,
+                        content=f"error: {str(e)}",
+                    )
+                )
+
+        # 处理函数调用响应
+        if tool_call_result_blocks:
+            yield tool_call_result_blocks
+
+    def done(self) -> bool:
+        """检查 Agent 是否已完成工作"""
+        return self._state in (AgentState.DONE, AgentState.ERROR)
+
+    def get_final_llm_resp(self) -> LLMResponse | None:
+        return self.final_llm_resp

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

@@ -3,6 +3,7 @@
 """
 
 import traceback
+import copy
 import asyncio
 import json
 from typing import Union, AsyncGenerator
@@ -20,40 +21,27 @@ from astrbot.core.utils.metrics import Metric
 from astrbot.core.provider.entities import (
     ProviderRequest,
     LLMResponse,
-    ToolCallMessageSegment,
-    AssistantMessageSegment,
-    ToolCallsResult,
-)
-from astrbot.core.star.star_handler import star_handlers_registry, EventType
-from astrbot.core.star.star import star_map
-from astrbot.core.star.session_llm_manager import SessionServiceManager
-from mcp.types import (
-    TextContent,
-    ImageContent,
-    EmbeddedResource,
-    TextResourceContents,
-    BlobResourceContents,
 )
+from astrbot.core.star.star_handler import EventType
 from astrbot.core import web_chat_back_queue
+from ..agent_runner.tool_loop_agent import ToolLoopAgent
 
 
 class LLMRequestSubStage(Stage):
     async def initialize(self, ctx: PipelineContext) -> None:
         self.ctx = ctx
-        self.bot_wake_prefixs = ctx.astrbot_config["wake_prefix"]  # list
-        self.provider_wake_prefix = ctx.astrbot_config["provider_settings"][
-            "wake_prefix"
-        ]  # str
-        self.max_context_length = ctx.astrbot_config["provider_settings"][
-            "max_context_length"
-        ]  # int
-        self.dequeue_context_length = min(
-            max(1, ctx.astrbot_config["provider_settings"]["dequeue_context_length"]),
+        conf = ctx.astrbot_config
+        settings = conf["provider_settings"]
+        self.bot_wake_prefixs: list[str] = conf["wake_prefix"]  # list
+        self.provider_wake_prefix: str = settings["wake_prefix"]  # str
+        self.max_context_length = settings["max_context_length"]  # int
+        self.dequeue_context_length: int = min(
+            max(1, settings["dequeue_context_length"]),
             self.max_context_length - 1,
-        )  # int
-        self.streaming_response = ctx.astrbot_config["provider_settings"][
-            "streaming_response"
-        ]  # bool
+        )
+        self.streaming_response: bool = settings["streaming_response"]
+        self.max_step: int = settings.get("max_agent_step", 10)
+        self.show_tool_use: bool = settings.get("show_tool_use_status", True)
 
         for bwp in self.bot_wake_prefixs:
             if self.provider_wake_prefix.startswith(bwp):
@@ -90,10 +78,7 @@ class LLMRequestSubStage(Stage):
             )
 
             if req.conversation:
-                all_contexts = json.loads(req.conversation.history)
-                req.contexts = self._process_tool_message_pairs(
-                    all_contexts, remove_tags=True
-                )
+                req.contexts = json.loads(req.conversation.history)
 
         else:
             req = ProviderRequest(prompt="", image_urls=[])
@@ -135,26 +120,7 @@ class LLMRequestSubStage(Stage):
             return
 
         # 执行请求 LLM 前事件钩子。
-        # 装饰 system_prompt 等功能
-        # 获取当前平台ID
-        platform_id = event.get_platform_id()
-        handlers = star_handlers_registry.get_handlers_by_event_type(
-            EventType.OnLLMRequestEvent, platform_id=platform_id
-        )
-        for handler in handlers:
-            try:
-                logger.debug(
-                    f"hook(on_llm_request) -> {star_map[handler.handler_module_path].name} - {handler.handler_name}"
-                )
-                await handler.handler(event, req)
-            except BaseException:
-                logger.error(traceback.format_exc())
-
-            if event.is_stopped():
-                logger.info(
-                    f"{star_map[handler.handler_module_path].name} - {handler.handler_name} 终止了事件传播。"
-                )
-                return
+        await self.ctx.call_event_hook(event, EventType.OnLLMRequestEvent, req)
 
         if isinstance(req.contexts, str):
             req.contexts = json.loads(req.contexts)
@@ -184,83 +150,63 @@ class LLMRequestSubStage(Stage):
         if not req.session_id:
             req.session_id = event.unified_msg_origin
 
-        async def requesting(req: ProviderRequest):
-            try:
-                need_loop = True
-                while need_loop:
-                    need_loop = False
-                    
-                    # 在每次实际请求 LLM 前检查会话级别的启停状态，这可以防止插件或函数工具调用时绕过会话级别的限制
-                    if not SessionServiceManager.should_process_llm_request(event):
-                        logger.debug(f"会话 {event.unified_msg_origin} 禁用了 LLM，终止 LLM 请求。")
-                        return
-                    
-                    logger.debug(f"提供商请求 Payload: {req}")
 
-                    final_llm_response = None
+        # fix messages
+        req.contexts = self.fix_messages(req.contexts)
 
-                    if self.streaming_response:
-                        stream = provider.text_chat_stream(**req.__dict__)
-                        async for llm_response in stream:
-                            if llm_response.is_chunk:
-                                if llm_response.result_chain:
-                                    yield llm_response.result_chain  # MessageChain
-                                else:
-                                    yield MessageChain().message(
-                                        llm_response.completion_text
-                                    )
-                            else:
-                                final_llm_response = llm_response
-                    else:
-                        final_llm_response = await provider.text_chat(
-                            **req.__dict__
-                        )  # 请求 LLM
+        # Call Agent
+        tool_loop_agent = ToolLoopAgent(
+            provider=provider,
+            event=event,
+            pipeline_ctx=self.ctx,
+        )
+        await tool_loop_agent.reset(req=req, streaming=self.streaming_response)
 
-                    if not final_llm_response:
-                        raise Exception("LLM response is None.")
+        async def requesting():
+            step_idx = 0
+            while step_idx < self.max_step:
+                step_idx += 1
+                try:
+                    async for resp in tool_loop_agent.step():
+                        if resp.type == "tool_call_result":
+                            continue  # 跳过工具调用结果
+                        if resp.type == "tool_call":
+                            if self.streaming_response:
+                                # 用来标记流式响应需要分节
+                                yield MessageChain(chain=[], type="break")
+                            if self.show_tool_use or event.get_platform_name() == "webchat":
+                                resp.data["chain"].type = "tool_call"
+                                await event.send(resp.data["chain"])
+                            continue
 
-                    # 执行 LLM 响应后的事件钩子。
-                    handlers = star_handlers_registry.get_handlers_by_event_type(
-                        EventType.OnLLMResponseEvent
+                        if not self.streaming_response:
+                            content_typ = (
+                                ResultContentType.LLM_RESULT
+                                if resp.type == "llm_result"
+                                else ResultContentType.GENERAL_RESULT
+                            )
+                            event.set_result(
+                                MessageEventResult(
+                                    chain=resp.data["chain"].chain,
+                                    result_content_type=content_typ,
+                                )
+                            )
+                            yield
+                            event.clear_result()
+                        else:
+                            if resp.type == "streaming_delta":
+                                yield resp.data["chain"]  # MessageChain
+                    if tool_loop_agent.done():
+                        break
+
+                except Exception as e:
+                    logger.error(traceback.format_exc())
+                    event.set_result(
+                        MessageEventResult().message(
+                            f"AstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {str(e)}"
+                        )
                     )
-                    for handler in handlers:
-                        try:
-                            logger.debug(
-                                f"hook(on_llm_response) -> {star_map[handler.handler_module_path].name} - {handler.handler_name}"
-                            )
-                            await handler.handler(event, final_llm_response)
-                        except BaseException:
-                            logger.error(traceback.format_exc())
-
-                        if event.is_stopped():
-                            logger.info(
-                                f"{star_map[handler.handler_module_path].name} - {handler.handler_name} 终止了事件传播。"
-                            )
-                            return
-
-                    if self.streaming_response:
-                        # 流式输出的处理
-                        async for result in self._handle_llm_stream_response(
-                            event, req, final_llm_response
-                        ):
-                            if isinstance(result, ProviderRequest):
-                                # 有函数工具调用并且返回了结果，我们需要再次请求 LLM
-                                req = result
-                                need_loop = True
-                            else:
-                                yield
-                    else:
-                        # 非流式输出的处理
-                        async for result in self._handle_llm_response(
-                            event, req, final_llm_response
-                        ):
-                            if isinstance(result, ProviderRequest):
-                                # 有函数工具调用并且返回了结果，我们需要再次请求 LLM
-                                req = result
-                                need_loop = True
-                            else:
-                                yield
-
+                    return
                 asyncio.create_task(
                     Metric.upload(
                         llm_tick=1,
@@ -269,44 +215,38 @@ class LLMRequestSubStage(Stage):
                     )
                 )
 
-                # 保存到历史记录
-                await self._save_to_history(event, req, final_llm_response)
-
-            except BaseException as e:
-                logger.error(traceback.format_exc())
-                event.set_result(
-                    MessageEventResult().message(
-                        f"AstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {str(e)}"
-                    )
-                )
-
-        if not self.streaming_response:
-            event.set_extra("tool_call_result", None)
-            async for _ in requesting(req):
-                yield
-        else:
+        if self.streaming_response:
+            # 流式响应
             event.set_result(
                 MessageEventResult()
                 .set_result_content_type(ResultContentType.STREAMING_RESULT)
-                .set_async_stream(requesting(req))
+                .set_async_stream(requesting())
             )
-            # 这里使用yield来暂停当前阶段，等待流式输出完成后继续处理
             yield
-
-            if event.get_extra("tool_call_result"):
-                event.set_result(event.get_extra("tool_call_result"))
-                event.set_extra("tool_call_result", None)
+            if tool_loop_agent.done():
+                if final_llm_resp := tool_loop_agent.get_final_llm_resp():
+                    if final_llm_resp.completion_text:
+                        chain = (
+                            MessageChain().message(final_llm_resp.completion_text).chain
+                        )
+                    else:
+                        chain = final_llm_resp.result_chain.chain
+                    event.set_result(
+                        MessageEventResult(
+                            chain=chain,
+                            result_content_type=ResultContentType.STREAMING_FINISH,
+                        )
+                    )
+        else:
+            async for _ in requesting():
                 yield
 
-        # 暂时直接发出去
-        if img_b64 := event.get_extra("tool_call_img_respond"):
-            await event.send(MessageChain(chain=[Image.fromBase64(img_b64)]))
-            event.set_extra("tool_call_img_respond", None)
-
+        # 异步处理 WebChat 特殊情况
         if event.get_platform_name() == "webchat":
-            # 异步处理 WebChat 特殊情况
             asyncio.create_task(self._handle_webchat(event, req))
 
+        await self._save_to_history(event, req, tool_loop_agent.get_final_llm_resp())
+
     async def _handle_webchat(self, event: AstrMessageEvent, req: ProviderRequest):
         """处理 WebChat 平台的特殊情况，包括第一次 LLM 对话时总结对话内容生成 title"""
         # 检查会话级别的LLM启停状态，防止标题生成功能绕过会话级别限制
@@ -324,10 +264,6 @@ class LLMRequestSubStage(Stage):
                 return
             provider = self.ctx.plugin_manager.context.get_using_provider()
             cleaned_text = "User: " + latest_pair[0].get("content", "").strip()
-            # if len(latest_pair) > 1:
-            #     cleaned_text += (
-            #         "\nAssistant: " + latest_pair[1].get("content", "").strip()
-            #     )
             logger.debug(f"WebChat 对话标题生成请求，清理后的文本: {cleaned_text}")
             llm_resp = await provider.text_chat(
                 system_prompt="You are expert in summarizing user's query.",
@@ -368,322 +304,50 @@ class LLMRequestSubStage(Stage):
                         }
                     )
 
-    async def _handle_llm_response(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-        llm_response: LLMResponse,
-    ) -> AsyncGenerator[Union[None, ProviderRequest], None]:
-        """处理非流式 LLM 响应。
-
-        Returns:
-            AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
-
-        Yields:
-            Iterator[Union[None, ProviderRequest]]: 将 event 交付给下一个 stage 或者返回 ProviderRequest 表示需要再次调用 LLM
-        """
-        if llm_response.role == "assistant":
-            # text completion
-            if llm_response.result_chain:
-                event.set_result(
-                    MessageEventResult(
-                        chain=llm_response.result_chain.chain
-                    ).set_result_content_type(ResultContentType.LLM_RESULT)
-                )
-            else:
-                event.set_result(
-                    MessageEventResult()
-                    .message(llm_response.completion_text)
-                    .set_result_content_type(ResultContentType.LLM_RESULT)
-                )
-        elif llm_response.role == "err":
-            event.set_result(
-                MessageEventResult().message(
-                    f"AstrBot 请求失败。\n错误信息: {llm_response.completion_text}"
-                )
-            )
-        elif llm_response.role == "tool":
-            # 处理函数工具调用
-            async for result in self._handle_function_tools(event, req, llm_response):
-                yield result
-
-    async def _handle_llm_stream_response(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-        llm_response: LLMResponse,
-    ) -> AsyncGenerator[Union[None, ProviderRequest], None]:
-        """处理流式 LLM 响应。
-
-        专门用于处理流式输出完成后的响应，与非流式响应处理分离。
-
-        Returns:
-            AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
-
-        Yields:
-            Iterator[Union[None, ProviderRequest]]: 将 event 交付给下一个 stage 或者返回 ProviderRequest 表示需要再次调用 LLM
-        """
-        if llm_response.role == "assistant":
-            # text completion
-            if llm_response.result_chain:
-                event.set_result(
-                    MessageEventResult(
-                        chain=llm_response.result_chain.chain
-                    ).set_result_content_type(ResultContentType.STREAMING_FINISH)
-                )
-            else:
-                event.set_result(
-                    MessageEventResult()
-                    .message(llm_response.completion_text)
-                    .set_result_content_type(ResultContentType.STREAMING_FINISH)
-                )
-        elif llm_response.role == "err":
-            event.set_result(
-                MessageEventResult().message(
-                    f"AstrBot 请求失败。\n错误信息: {llm_response.completion_text}"
-                )
-            )
-        elif llm_response.role == "tool":
-            # 处理函数工具调用
-            async for result in self._handle_function_tools(event, req, llm_response):
-                yield result
-
-    async def _handle_function_tools(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-        llm_response: LLMResponse,
-    ) -> AsyncGenerator[Union[None, ProviderRequest], None]:
-        """处理函数工具调用。
-
-        Returns:
-            AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
-        """
-        # function calling
-        tool_call_result: list[ToolCallMessageSegment] = []
-        logger.info(
-            f"触发 {len(llm_response.tools_call_name)} 个函数调用: {llm_response.tools_call_name}"
-        )
-        for func_tool_name, func_tool_args, func_tool_id in zip(
-            llm_response.tools_call_name,
-            llm_response.tools_call_args,
-            llm_response.tools_call_ids,
-        ):
-            try:
-                func_tool = req.func_tool.get_func(func_tool_name)
-                if func_tool.origin == "mcp":
-                    logger.info(
-                        f"从 MCP 服务 {func_tool.mcp_server_name} 调用工具函数：{func_tool.name}，参数：{func_tool_args}"
-                    )
-                    client = req.func_tool.mcp_client_dict[func_tool.mcp_server_name]
-                    res = await client.session.call_tool(func_tool.name, func_tool_args)
-                    if res:
-                        # TODO 仅对ImageContent | EmbeddedResource进行了简单的Fallback
-                        if isinstance(res.content[0], TextContent):
-                            tool_call_result.append(
-                                ToolCallMessageSegment(
-                                    role="tool",
-                                    tool_call_id=func_tool_id,
-                                    content=res.content[0].text,
-                                )
-                            )
-                        elif isinstance(res.content[0], ImageContent):
-                            tool_call_result.append(
-                                ToolCallMessageSegment(
-                                    role="tool",
-                                    tool_call_id=func_tool_id,
-                                    content="返回了图片(已直接发送给用户)",
-                                )
-                            )
-                            event.set_extra(
-                                "tool_call_img_respond",
-                                res.content[0].data,
-                            )
-                        elif isinstance(res.content[0], EmbeddedResource):
-                            resource = res.content[0].resource
-                            if isinstance(resource, TextResourceContents):
-                                tool_call_result.append(
-                                    ToolCallMessageSegment(
-                                        role="tool",
-                                        tool_call_id=func_tool_id,
-                                        content=resource.text,
-                                    )
-                                )
-                            elif (
-                                isinstance(resource, BlobResourceContents)
-                                and resource.mimeType
-                                and resource.mimeType.startswith("image/")
-                            ):
-                                tool_call_result.append(
-                                    ToolCallMessageSegment(
-                                        role="tool",
-                                        tool_call_id=func_tool_id,
-                                        content="返回了图片(已直接发送给用户)",
-                                    )
-                                )
-                                event.set_extra(
-                                    "tool_call_img_respond",
-                                    res.content[0].data,
-                                )
-                            else:
-                                tool_call_result.append(
-                                    ToolCallMessageSegment(
-                                        role="tool",
-                                        tool_call_id=func_tool_id,
-                                        content="返回的数据类型不受支持",
-                                    )
-                                )
-                else:
-                    # 获取处理器，过滤掉平台不兼容的处理器
-                    platform_id = event.get_platform_id()
-                    star_md = star_map.get(func_tool.handler_module_path)
-                    if (
-                        star_md
-                        and platform_id in star_md.supported_platforms
-                        and not star_md.supported_platforms[platform_id]
-                    ):
-                        logger.debug(
-                            f"处理器 {func_tool_name}({star_md.name}) 在当前平台不兼容或者被禁用，跳过执行"
-                        )
-                        # 直接跳过，不添加任何消息到tool_call_result
-                        continue
-
-                    logger.info(
-                        f"调用工具函数：{func_tool_name}，参数：{func_tool_args}"
-                    )
-                    # 尝试调用工具函数
-                    wrapper = self._call_handler(
-                        self.ctx, event, func_tool.handler, **func_tool_args
-                    )
-                    async for resp in wrapper:
-                        if resp is not None:  # 有 return 返回
-                            tool_call_result.append(
-                                ToolCallMessageSegment(
-                                    role="tool",
-                                    tool_call_id=func_tool_id,
-                                    content=resp,
-                                )
-                            )
-                        else:
-                            res = event.get_result()
-                            if res and res.chain:
-                                event.set_extra("tool_call_result", res)
-                            yield  # 有生成器返回
-                event.clear_result()  # 清除上一个 handler 的结果
-            except BaseException as e:
-                logger.warning(traceback.format_exc())
-                tool_call_result.append(
-                    ToolCallMessageSegment(
-                        role="tool",
-                        tool_call_id=func_tool_id,
-                        content=f"error: {str(e)}",
-                    )
-                )
-        if tool_call_result:
-            # 函数调用结果
-            req.func_tool = None  # 暂时不支持递归工具调用
-            assistant_msg_seg = AssistantMessageSegment(
-                role="assistant", tool_calls=llm_response.to_openai_tool_calls()
-            )
-            # 在多轮 Tool 调用的情况下，这里始终保持最新的 Tool 调用结果，减少上下文长度。
-            req.tool_calls_result = ToolCallsResult(
-                tool_calls_info=assistant_msg_seg,
-                tool_calls_result=tool_call_result,
-            )
-            yield req  # 再次执行 LLM 请求
-        else:
-            if llm_response.completion_text:
-                event.set_result(
-                    MessageEventResult().message(llm_response.completion_text)
-                )
-
     async def _save_to_history(
-        self, event: AstrMessageEvent, req: ProviderRequest, llm_response: LLMResponse
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+        llm_response: LLMResponse | None,
     ):
-        if not req or not req.conversation or not llm_response:
+        if (
+            not req
+            or not req.conversation
+            or not llm_response
+            or llm_response.role != "assistant"
+        ):
             return
 
-        if llm_response.role == "assistant":
-            # 文本回复
-            contexts = req.contexts.copy()
-            contexts.append(await req.assemble_context())
+        # 历史上下文
+        messages = copy.deepcopy(req.contexts)
+        # 这一轮对话请求的用户输入
+        messages.append(await req.assemble_context())
+        # 这一轮对话的 LLM 响应
+        if req.tool_calls_result:
+            if not isinstance(req.tool_calls_result, list):
+                messages.extend(req.tool_calls_result.to_openai_messages())
+            elif isinstance(req.tool_calls_result, list):
+                for tcr in req.tool_calls_result:
+                    messages.extend(tcr.to_openai_messages())
+        messages.append({"role": "assistant", "content": llm_response.completion_text})
+        messages = list(filter(lambda item: "_no_save" not in item, messages))
+        await self.conv_manager.update_conversation(
+            event.unified_msg_origin, req.conversation.cid, history=messages
+        )
+        logger.debug(f"messages persisted: {messages}")
 
-            # 记录并标记函数调用结果
-            if req.tool_calls_result:
-                tool_calls_messages = req.tool_calls_result.to_openai_messages()
-
-                # 添加标记
-                for message in tool_calls_messages:
-                    message["_tool_call_history"] = True
-
-                processed_tool_messages = self._process_tool_message_pairs(
-                    tool_calls_messages, remove_tags=False
-                )
-
-                contexts.extend(processed_tool_messages)
-
-            contexts.append(
-                {"role": "assistant", "content": llm_response.completion_text}
-            )
-            contexts_to_save = list(
-                filter(lambda item: "_no_save" not in item, contexts)
-            )
-            await self.conv_manager.update_conversation(
-                event.unified_msg_origin, req.conversation.cid, history=contexts_to_save
-            )
-
-    def _process_tool_message_pairs(self, messages, remove_tags=True):
-        """处理工具调用消息，确保assistant和tool消息成对出现
-
-        Args:
-            messages (list): 消息列表
-            remove_tags (bool): 是否移除_tool_call_history标记
-
-        Returns:
-            list: 处理后的消息列表，保证了assistant和对应tool消息的成对出现
-        """
-        result = []
-        i = 0
-
-        while i < len(messages):
-            current_msg = messages[i]
-
-            # 普通消息直接添加
-            if "_tool_call_history" not in current_msg:
-                result.append(current_msg.copy() if remove_tags else current_msg)
-                i += 1
-                continue
-
-            # 工具调用消息成对处理
-            if current_msg.get("role") == "assistant" and "tool_calls" in current_msg:
-                assistant_msg = current_msg.copy()
-
-                if remove_tags and "_tool_call_history" in assistant_msg:
-                    del assistant_msg["_tool_call_history"]
-
-                related_tools = []
-                j = i + 1
-                while (
-                    j < len(messages)
-                    and messages[j].get("role") == "tool"
-                    and "_tool_call_history" in messages[j]
-                ):
-                    tool_msg = messages[j].copy()
-
-                    if remove_tags:
-                        del tool_msg["_tool_call_history"]
-
-                    related_tools.append(tool_msg)
-                    j += 1
-
-                # 成对的时候添加到结果
-                if related_tools:
-                    result.append(assistant_msg)
-                    result.extend(related_tools)
-
-                i = j  # 跳过已处理
+    def fix_messages(self, messages: list[dict]) -> list[dict]:
+        """验证并且修复上下文"""
+        fixed_messages = []
+        for message in messages:
+            if message.get("role") == "tool":
+                # tool block 前面必须要有 user 和 assistant block
+                if len(fixed_messages) < 2:
+                    # 这种情况可能是上下文被截断导致的
+                    # 我们直接将之前的上下文都清空
+                    fixed_messages = []
+                else:
+                    fixed_messages.append(message)
             else:
-                # 单独的tool消息
-                i += 1
-
-        return result
+                fixed_messages.append(message)
+        return fixed_messages

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

@@ -50,7 +50,7 @@ class StarRequestSubStage(Stage):
                 logger.debug(
                     f"plugin -> {star_map.get(handler.handler_module_path).name} - {handler.handler_name}"
                 )
-                wrapper = self._call_handler(self.ctx, event, handler.handler, **params)
+                wrapper = self.ctx.call_handler(event, handler.handler, **params)
                 async for ret in wrapper:
                     yield ret
                 event.clear_result()  # 清除上一个 handler 的结果

astrbot/core/pipeline/stage.py

@@ -1,12 +1,8 @@
 from __future__ import annotations
 import abc
-import inspect
-import traceback
-from astrbot.api import logger
-from typing import List, AsyncGenerator, Union, Awaitable
+from typing import List, AsyncGenerator, Union
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from .context import PipelineContext
-from astrbot.core.message.message_event_result import MessageEventResult, CommandResult
 
 registered_stages: List[Stage] = []  # 维护了所有已注册的 Stage 实现类
 
@@ -41,70 +37,3 @@ class Stage(abc.ABC):
             Union[None, AsyncGenerator[None, None]]: 处理结果，可能是 None 或者异步生成器, 如果为 None 则表示不需要继续处理, 如果为异步生成器则表示需要继续处理(进入下一个阶段)
         """
         raise NotImplementedError
-
-    async def _call_handler(
-        self,
-        ctx: PipelineContext,
-        event: AstrMessageEvent,
-        handler: Awaitable,
-        *args,
-        **kwargs,
-    ) -> AsyncGenerator[None, None]:
-        """执行事件处理函数并处理其返回结果
-
-        该方法负责调用处理函数并处理不同类型的返回值。它支持两种类型的处理函数:
-        1. 异步生成器: 实现洋葱模型，每次yield都会将控制权交回上层
-        2. 协程: 执行一次并处理返回值
-
-        Args:
-            ctx (PipelineContext): 消息管道上下文对象
-            event (AstrMessageEvent): 待处理的事件对象
-            handler (Awaitable): 事件处理函数
-            *args: 传递给handler的位置参数
-            **kwargs: 传递给handler的关键字参数
-
-        Returns:
-            AsyncGenerator[None, None]: 异步生成器，用于在管道中传递控制流
-        """
-        ready_to_call = None  # 一个协程或者异步生成器(async def)
-
-        trace_ = None
-
-        try:
-            ready_to_call = handler(event, *args, **kwargs)
-        except TypeError as _:
-            # 向下兼容
-            trace_ = traceback.format_exc()
-            # 以前的handler会额外传入一个参数, 但是context对象实际上在插件实例中有一份
-            ready_to_call = handler(event, ctx.plugin_manager.context, *args, **kwargs)
-
-        if isinstance(ready_to_call, AsyncGenerator):
-            # 如果是一个异步生成器, 进入洋葱模型
-            _has_yielded = False  # 是否返回过值
-            try:
-                async for ret in ready_to_call:
-                    # 这里逐步执行异步生成器, 对于每个yield返回的ret, 执行下面的代码
-                    # 返回值只能是 MessageEventResult 或者 None（无返回值）
-                    _has_yielded = True
-                    if isinstance(ret, (MessageEventResult, CommandResult)):
-                        # 如果返回值是 MessageEventResult, 设置结果并继续
-                        event.set_result(ret)
-                        yield  # 传递控制权给上一层的process函数
-                    else:
-                        # 如果返回值是 None, 则不设置结果并继续
-                        # 继续执行后续阶段
-                        yield ret  # 传递控制权给上一层的process函数
-                if not _has_yielded:
-                    # 如果这个异步生成器没有执行到yield分支
-                    yield
-            except Exception as e:
-                logger.error(f"Previous Error: {trace_}")
-                raise e
-        elif inspect.iscoroutine(ready_to_call):
-            # 如果只是一个协程, 直接执行
-            ret = await ready_to_call
-            if isinstance(ret, (MessageEventResult, CommandResult)):
-                event.set_result(ret)
-                yield  # 传递控制权给上一层的process函数
-            else:
-                yield ret  # 传递控制权给上一层的process函数

astrbot/core/platform/sources/telegram/tg_event.py

@@ -158,6 +158,12 @@ class TelegramPlatformEvent(AstrMessageEvent):
 
         async for chain in generator:
             if isinstance(chain, MessageChain):
+                if chain.type == "break":
+                    # 分割符
+                    message_id = None  # 重置消息 ID
+                    delta = ""  # 重置 delta
+                    continue
+
                 # 处理消息链中的每个组件
                 for i in chain.chain:
                     if isinstance(i, Plain):

astrbot/core/platform/sources/webchat/webchat_event.py

@@ -35,6 +35,7 @@ class WebChatMessageEvent(AstrMessageEvent):
                         "cid": cid,
                         "data": data,
                         "streaming": streaming,
+                        "chain_type": message.type,
                     }
                 )
             elif isinstance(comp, Image):
@@ -110,6 +111,18 @@ class WebChatMessageEvent(AstrMessageEvent):
     async def send_streaming(self, generator, use_fallback: bool = False):
         final_data = ""
         async for chain in generator:
+            if chain.type == "break" and final_data:
+                # 分割符
+                await web_chat_back_queue.put(
+                    {
+                        "type": "end",
+                        "data": final_data,
+                        "streaming": True,
+                        "cid": self.session_id.split("!")[-1],
+                    }
+                )
+                final_data = ""
+                continue
             final_data += await WebChatMessageEvent._send(
                 chain, session_id=self.session_id, streaming=True
             )

astrbot/core/provider/entities.py

@@ -58,7 +58,7 @@ class AssistantMessageSegment:
     """OpenAI 格式的上下文中 role 为 assistant 的消息段。参考: https://platform.openai.com/docs/guides/function-calling"""
 
     content: str = None
-    tool_calls: List[ChatCompletionMessageToolCall | Dict] = None
+    tool_calls: List[ChatCompletionMessageToolCall | Dict] = field(default_factory=list)
     role: str = "assistant"
 
     def to_dict(self):
@@ -67,7 +67,7 @@ class AssistantMessageSegment:
         }
         if self.content:
             ret["content"] = self.content
-        elif self.tool_calls:
+        if self.tool_calls:
             ret["tool_calls"] = self.tool_calls
         return ret
 
@@ -95,19 +95,19 @@ class ProviderRequest:
     """提示词"""
     session_id: str = ""
     """会话 ID"""
-    image_urls: List[str] = None
+    image_urls: list[str] = field(default_factory=list)
     """图片 URL 列表"""
-    func_tool: FuncCall = None
+    func_tool: FuncCall | None = None
     """可用的函数工具"""
-    contexts: List = None
+    contexts: list[dict] = field(default_factory=list)
     """上下文。格式与 openai 的上下文格式一致：
     参考 https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages
     """
     system_prompt: str = ""
     """系统提示词"""
-    conversation: Conversation = None
+    conversation: Conversation | None = None
 
-    tool_calls_result: ToolCallsResult = None
+    tool_calls_result: list[ToolCallsResult] | ToolCallsResult | None = None
     """附加的上次请求后工具调用的结果。参考: https://platform.openai.com/docs/guides/function-calling#handling-function-calls"""
 
     def __repr__(self):
@@ -116,6 +116,14 @@ class ProviderRequest:
     def __str__(self):
         return self.__repr__()
 
+    def append_tool_calls_result(self, tool_calls_result: ToolCallsResult):
+        """添加工具调用结果到请求中"""
+        if not self.tool_calls_result:
+            self.tool_calls_result = []
+        if isinstance(self.tool_calls_result, ToolCallsResult):
+            self.tool_calls_result = [self.tool_calls_result]
+        self.tool_calls_result.append(tool_calls_result)
+
     def _print_friendly_context(self):
         """打印友好的消息上下文。将 image_url 的值替换为 <Image>"""
         if not self.contexts:

astrbot/core/provider/manager.py

@@ -190,11 +190,6 @@ class ProviderManager:
                     from .sources.anthropic_source import (
                         ProviderAnthropic as ProviderAnthropic,
                     )
-                case "llm_tuner":
-                    logger.info("加载 LLM Tuner 工具 ...")
-                    from .sources.llmtuner_source import (
-                        LLMTunerModelLoader as LLMTunerModelLoader,
-                    )
                 case "dify":
                     from .sources.dify_source import ProviderDify as ProviderDify
                 case "dashscope":
@@ -330,8 +325,6 @@ class ProviderManager:
                 inst = provider_metadata.cls_type(
                     provider_config,
                     self.provider_settings,
-                    self.db_helper,
-                    self.provider_settings.get("persistant_history", True),
                     self.selected_default_persona,
                 )
 

astrbot/core/provider/provider.py

@@ -1,6 +1,5 @@
 import abc
 from typing import List
-from astrbot.core.db import BaseDatabase
 from typing import TypedDict, AsyncGenerator
 from astrbot.core.provider.func_tool_manager import FuncCall
 from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
@@ -53,15 +52,13 @@ class Provider(AbstractProvider):
         self,
         provider_config: dict,
         provider_settings: dict,
-        persistant_history: bool = True,
-        db_helper: BaseDatabase = None,
-        default_persona: Personality = None,
+        default_persona: Personality | None = None,
     ) -> None:
         super().__init__(provider_config)
 
         self.provider_settings = provider_settings
 
-        self.curr_personality: Personality = default_persona
+        self.curr_personality = default_persona
         """维护了当前的使用的 persona，即人格。可能为 None"""
 
     @abc.abstractmethod
@@ -86,11 +83,11 @@ class Provider(AbstractProvider):
         self,
         prompt: str,
         session_id: str = None,
-        image_urls: List[str] = None,
+        image_urls: list[str] = None,
         func_tool: FuncCall = None,
-        contexts: List = None,
+        contexts: list = None,
         system_prompt: str = None,
-        tool_calls_result: ToolCallsResult = None,
+        tool_calls_result: ToolCallsResult | list[ToolCallsResult] = None,
         **kwargs,
     ) -> LLMResponse:
         """获得 LLM 的文本对话结果。会使用当前的模型进行对话。
@@ -114,11 +111,11 @@ class Provider(AbstractProvider):
         self,
         prompt: str,
         session_id: str = None,
-        image_urls: List[str] = None,
+        image_urls: list[str] = None,
         func_tool: FuncCall = None,
-        contexts: List = None,
+        contexts: list = None,
         system_prompt: str = None,
-        tool_calls_result: ToolCallsResult = None,
+        tool_calls_result: ToolCallsResult | list[ToolCallsResult] = None,
         **kwargs,
     ) -> AsyncGenerator[LLMResponse, None]:
         """获得 LLM 的流式文本对话结果。会使用当前的模型进行对话。在生成的最后会返回一次完整的结果。

astrbot/core/provider/sources/anthropic_source.py

@@ -1,3 +1,6 @@
+import json
+import anthropic
+import base64
 from typing import List
 from mimetypes import guess_type
 
@@ -5,41 +8,33 @@ from anthropic import AsyncAnthropic
 from anthropic.types import Message
 
 from astrbot.core.utils.io import download_image_by_url
-from astrbot.core.db import BaseDatabase
-from astrbot.api.provider import Provider, Personality
+from astrbot.api.provider import Provider
 from astrbot import logger
 from astrbot.core.provider.func_tool_manager import FuncCall
 from ..register import register_provider_adapter
-from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
-from .openai_source import ProviderOpenAIOfficial
+from astrbot.core.provider.entities import LLMResponse
+from typing import AsyncGenerator
 
 
 @register_provider_adapter(
     "anthropic_chat_completion", "Anthropic Claude API 提供商适配器"
 )
-class ProviderAnthropic(ProviderOpenAIOfficial):
+class ProviderAnthropic(Provider):
     def __init__(
         self,
-        provider_config: dict,
-        provider_settings: dict,
-        db_helper: BaseDatabase,
-        persistant_history=True,
-        default_persona: Personality = None,
+        provider_config,
+        provider_settings,
+        default_persona=None,
     ) -> None:
-        # Skip OpenAI's __init__ and call Provider's __init__ directly
-        Provider.__init__(
-            self,
+        super().__init__(
             provider_config,
             provider_settings,
-            persistant_history,
-            db_helper,
             default_persona,
         )
 
-        self.chosen_api_key = None
+        self.chosen_api_key: str = ""
         self.api_keys: List = provider_config.get("key", [])
-        self.chosen_api_key = self.api_keys[0] if len(self.api_keys) > 0 else None
+        self.chosen_api_key = self.api_keys[0] if len(self.api_keys) > 0 else ""
         self.base_url = provider_config.get("api_base", "https://api.anthropic.com")
         self.timeout = provider_config.get("timeout", 120)
         if isinstance(self.timeout, str):
@@ -51,10 +46,63 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
 
         self.set_model(provider_config["model_config"]["model"])
 
+    def _prepare_payload(self, messages: list[dict]):
+        """准备 Anthropic API 的请求 payload
+
+        Args:
+            messages: OpenAI 格式的消息列表，包含用户输入和系统提示等信息
+        Returns:
+            system_prompt: 系统提示内容
+            new_messages: 处理后的消息列表，去除系统提示
+        """
+        system_prompt = ""
+        new_messages = []
+        for message in messages:
+            if message["role"] == "system":
+                system_prompt = message["content"]
+            elif message["role"] == "assistant":
+                blocks = []
+                if isinstance(message["content"], str):
+                    blocks.append({"type": "text", "text": message["content"]})
+                if "tool_calls" in message:
+                    for tool_call in message["tool_calls"]:
+                        blocks.append(  # noqa: PERF401
+                            {
+                                "type": "tool_use",
+                                "name": tool_call["function"]["name"],
+                                "input": json.loads(tool_call["function"]["arguments"])
+                                if isinstance(tool_call["function"]["arguments"], str)
+                                else tool_call["function"]["arguments"],
+                                "id": tool_call["id"],
+                            }
+                        )
+                new_messages.append(
+                    {
+                        "role": "assistant",
+                        "content": blocks,
+                    }
+                )
+            elif message["role"] == "tool":
+                new_messages.append(
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "tool_result",
+                                "tool_use_id": message["tool_call_id"],
+                                "content": message["content"],
+                            }
+                        ],
+                    }
+                )
+            else:
+                new_messages.append(message)
+
+        return system_prompt, new_messages
+
     async def _query(self, payloads: dict, tools: FuncCall) -> LLMResponse:
         if tools:
-            tool_list = tools.get_func_desc_anthropic_style()
-            if tool_list:
+            if tool_list := tools.get_func_desc_anthropic_style():
                 payloads["tools"] = tool_list
 
         completion = await self.client.messages.create(**payloads, stream=False)
@@ -64,70 +112,157 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
 
         if len(completion.content) == 0:
             raise Exception("API 返回的 completion 为空。")
-        # TODO: 如果进行函数调用，思维链被截断，用户可能需要思维链的内容
-        # 选最后一条消息，如果要进行函数调用，anthropic会先返回文本消息的思维链，然后再返回函数调用请求
-        content = completion.content[-1]
 
-        llm_response = LLMResponse("assistant")
+        llm_response = LLMResponse(role="assistant")
 
-        if content.type == "text":
-            # text completion
-            completion_text = str(content.text).strip()
-            # llm_response.completion_text = completion_text
-            llm_response.result_chain = MessageChain().message(completion_text)
-
-        # Anthropic每次只返回一个函数调用
-        if completion.stop_reason == "tool_use":
-            # tools call (function calling)
-            args_ls = []
-            func_name_ls = []
-            tool_use_ids = []
-            func_name_ls.append(content.name)
-            args_ls.append(content.input)
-            tool_use_ids.append(content.id)
-            llm_response.role = "tool"
-            llm_response.tools_call_args = args_ls
-            llm_response.tools_call_name = func_name_ls
-            llm_response.tools_call_ids = tool_use_ids
+        for content_block in completion.content:
+            if content_block.type == "text":
+                completion_text = str(content_block.text).strip()
+                llm_response.completion_text = completion_text
 
+            if content_block.type == "tool_use":
+                llm_response.tools_call_args.append(content_block.input)
+                llm_response.tools_call_name.append(content_block.name)
+                llm_response.tools_call_ids.append(content_block.id)
+        # TODO(Soulter): 处理 end_turn 情况
         if not llm_response.completion_text and not llm_response.tools_call_args:
-            logger.error(f"API 返回的 completion 无法解析：{completion}。")
-            raise Exception(f"API 返回的 completion 无法解析：{completion}。")
-
-        llm_response.raw_completion = completion
+            raise Exception(f"Anthropic API 返回的 completion 无法解析：{completion}。")
 
         return llm_response
 
+    async def _query_stream(
+        self, payloads: dict, tools: FuncCall
+    ) -> AsyncGenerator[LLMResponse, None]:
+        if tools:
+            if tool_list := tools.get_func_desc_anthropic_style():
+                payloads["tools"] = tool_list
+
+        # 用于累积工具调用信息
+        tool_use_buffer = {}
+        # 用于累积最终结果
+        final_text = ""
+        final_tool_calls = []
+
+        async with self.client.messages.stream(**payloads) as stream:
+            assert isinstance(stream, anthropic.AsyncMessageStream)
+            async for event in stream:
+                if event.type == "content_block_start":
+                    if event.content_block.type == "text":
+                        # 文本块开始
+                        yield LLMResponse(
+                            role="assistant", completion_text="", is_chunk=True
+                        )
+                    elif event.content_block.type == "tool_use":
+                        # 工具使用块开始，初始化缓冲区
+                        tool_use_buffer[event.index] = {
+                            "id": event.content_block.id,
+                            "name": event.content_block.name,
+                            "input": {},
+                        }
+
+                elif event.type == "content_block_delta":
+                    if event.delta.type == "text_delta":
+                        # 文本增量
+                        final_text += event.delta.text
+                        yield LLMResponse(
+                            role="assistant",
+                            completion_text=event.delta.text,
+                            is_chunk=True,
+                        )
+                    elif event.delta.type == "input_json_delta":
+                        # 工具调用参数增量
+                        if event.index in tool_use_buffer:
+                            # 累积 JSON 输入
+                            if "input_json" not in tool_use_buffer[event.index]:
+                                tool_use_buffer[event.index]["input_json"] = ""
+                            tool_use_buffer[event.index]["input_json"] += (
+                                event.delta.partial_json
+                            )
+
+                elif event.type == "content_block_stop":
+                    # 内容块结束
+                    if event.index in tool_use_buffer:
+                        # 解析完整的工具调用
+                        tool_info = tool_use_buffer[event.index]
+                        try:
+                            if "input_json" in tool_info:
+                                tool_info["input"] = json.loads(tool_info["input_json"])
+
+                            # 添加到最终结果
+                            final_tool_calls.append(
+                                {
+                                    "id": tool_info["id"],
+                                    "name": tool_info["name"],
+                                    "input": tool_info["input"],
+                                }
+                            )
+
+                            yield LLMResponse(
+                                role="tool",
+                                completion_text="",
+                                tools_call_args=[tool_info["input"]],
+                                tools_call_name=[tool_info["name"]],
+                                tools_call_ids=[tool_info["id"]],
+                                is_chunk=True,
+                            )
+                        except json.JSONDecodeError:
+                            # JSON 解析失败，跳过这个工具调用
+                            logger.warning(f"工具调用参数 JSON 解析失败: {tool_info}")
+
+                        # 清理缓冲区
+                        del tool_use_buffer[event.index]
+
+        # 返回最终的完整结果
+        final_response = LLMResponse(
+            role="assistant", completion_text=final_text, is_chunk=False
+        )
+
+        if final_tool_calls:
+            final_response.tools_call_args = [
+                call["input"] for call in final_tool_calls
+            ]
+            final_response.tools_call_name = [call["name"] for call in final_tool_calls]
+            final_response.tools_call_ids = [call["id"] for call in final_tool_calls]
+
+        yield final_response
+
     async def text_chat(
         self,
-        prompt: str,
-        session_id: str = None,
-        image_urls: List[str] = [],
-        func_tool: FuncCall = None,
+        prompt,
+        session_id=None,
+        image_urls=None,
+        func_tool=None,
         contexts=None,
         system_prompt=None,
-        tool_calls_result: ToolCallsResult = None,
+        tool_calls_result=None,
         **kwargs,
     ) -> LLMResponse:
         if contexts is None:
             contexts = []
-        if not prompt:
-            prompt = "<image>"
-
         new_record = await self.assemble_context(prompt, image_urls)
         context_query = [*contexts, new_record]
+        if system_prompt:
+            context_query.insert(0, {"role": "system", "content": system_prompt})
 
         for part in context_query:
             if "_no_save" in part:
                 del part["_no_save"]
 
+        # tool calls result
         if tool_calls_result:
-            # 暂时这样写。
-            prompt += f"Here are the related results via using tools: {str(tool_calls_result.tool_calls_result)}"
+            if not isinstance(tool_calls_result, list):
+                context_query.extend(tool_calls_result.to_openai_messages())
+            else:
+                for tcr in tool_calls_result:
+                    context_query.extend(tcr.to_openai_messages())
+
+        system_prompt, new_messages = self._prepare_payload(context_query)
 
         model_config = self.provider_config.get("model_config", {})
+        model_config["model"] = self.get_model()
+
+        payloads = {"messages": new_messages, **model_config}
 
-        payloads = {"messages": context_query, **model_config}
         # Anthropic has a different way of handling system prompts
         if system_prompt:
             payloads["system"] = system_prompt
@@ -135,32 +270,9 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
         llm_response = None
         try:
             llm_response = await self._query(payloads, func_tool)
-
         except Exception as e:
-            if "maximum context length" in str(e):
-                retry_cnt = 20
-                while retry_cnt > 0:
-                    logger.warning(
-                        f"上下文长度超过限制。尝试弹出最早的记录然后重试。当前记录条数: {len(context_query)}"
-                    )
-                    try:
-                        await self.pop_record(context_query)
-                        response = await self.client.messages.create(
-                            messages=context_query, **model_config
-                        )
-                        llm_response = LLMResponse("assistant")
-                        llm_response.result_chain = MessageChain().message(response.content[0].text)
-                        llm_response.raw_completion = response
-                        return llm_response
-                    except Exception as e:
-                        if "maximum context length" in str(e):
-                            retry_cnt -= 1
-                        else:
-                            raise e
-                return LLMResponse("err", "err: 请尝试 /reset 清除会话记录。")
-            else:
-                logger.error(f"发生了错误。Provider 配置如下: {model_config}")
-                raise e
+            logger.error(f"发生了错误。Provider 配置如下: {model_config}")
+            raise e
 
         return llm_response
 
@@ -175,21 +287,34 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
         tool_calls_result=None,
         **kwargs,
     ):
-        # raise NotImplementedError("This method is not implemented yet.")
-        # 调用 text_chat 模拟流式
-        llm_response = await self.text_chat(
-            prompt=prompt,
-            session_id=session_id,
-            image_urls=image_urls,
-            func_tool=func_tool,
-            contexts=contexts,
-            system_prompt=system_prompt,
-            tool_calls_result=tool_calls_result,
-        )
-        llm_response.is_chunk = True
-        yield llm_response
-        llm_response.is_chunk = False
-        yield llm_response
+        if contexts is None:
+            contexts = []
+        new_record = await self.assemble_context(prompt, image_urls)
+        context_query = [*contexts, new_record]
+        if system_prompt:
+            context_query.insert(0, {"role": "system", "content": system_prompt})
+
+        for part in context_query:
+            if "_no_save" in part:
+                del part["_no_save"]
+
+        # tool calls result
+        if tool_calls_result:
+            context_query.extend(tool_calls_result.to_openai_messages())
+
+        system_prompt, new_messages = self._prepare_payload(context_query)
+
+        model_config = self.provider_config.get("model_config", {})
+        model_config["model"] = self.get_model()
+
+        payloads = {"messages": new_messages, **model_config}
+
+        # Anthropic has a different way of handling system prompts
+        if system_prompt:
+            payloads["system"] = system_prompt
+
+        async for llm_response in self._query_stream(payloads, func_tool):
+            yield llm_response
 
     async def assemble_context(self, text: str, image_urls: List[str] = None):
         """组装上下文，支持文本和图片"""
@@ -232,3 +357,28 @@ class ProviderAnthropic(ProviderOpenAIOfficial):
             )
 
         return {"role": "user", "content": content}
+
+    async def encode_image_bs64(self, image_url: str) -> str:
+        """
+        将图片转换为 base64
+        """
+        if image_url.startswith("base64://"):
+            return image_url.replace("base64://", "data:image/jpeg;base64,")
+        with open(image_url, "rb") as f:
+            image_bs64 = base64.b64encode(f.read()).decode("utf-8")
+            return "data:image/jpeg;base64," + image_bs64
+        return ""
+
+    def get_current_key(self) -> str:
+        return self.chosen_api_key
+
+    async def get_models(self) -> List[str]:
+        models_str = []
+        models = await self.client.models.list()
+        models = sorted(models.data, key=lambda x: x.id)
+        for model in models:
+            models_str.append(model.id)
+        return models_str
+
+    def set_key(self, key: str):
+        self.chosen_api_key = key

astrbot/core/provider/sources/dashscope_source.py

@@ -5,7 +5,6 @@ from typing import List
 from .. import Provider, Personality
 from ..entities import LLMResponse
 from ..func_tool_manager import FuncCall
-from astrbot.core.db import BaseDatabase
 from ..register import register_provider_adapter
 from astrbot.core.message.message_event_result import MessageChain
 from .openai_source import ProviderOpenAIOfficial
@@ -19,16 +18,12 @@ class ProviderDashscope(ProviderOpenAIOfficial):
         self,
         provider_config: dict,
         provider_settings: dict,
-        db_helper: BaseDatabase,
-        persistant_history=False,
-        default_persona: Personality = None,
+        default_persona: Personality | None = None,
     ) -> None:
         Provider.__init__(
             self,
             provider_config,
             provider_settings,
-            persistant_history,
-            db_helper,
             default_persona,
         )
         self.api_key = provider_config.get("dashscope_api_key", "")

astrbot/core/provider/sources/dify_source.py

@@ -1,10 +1,9 @@
 import astrbot.core.message.components as Comp
 import os
 from typing import List
-from .. import Provider, Personality
+from .. import Provider
 from ..entities import LLMResponse
 from ..func_tool_manager import FuncCall
-from astrbot.core.db import BaseDatabase
 from ..register import register_provider_adapter
 from astrbot.core.utils.dify_api_client import DifyAPIClient
 from astrbot.core.utils.io import download_image_by_url, download_file
@@ -17,17 +16,13 @@ from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 class ProviderDify(Provider):
     def __init__(
         self,
-        provider_config: dict,
-        provider_settings: dict,
-        db_helper: BaseDatabase,
-        persistant_history=False,
-        default_persona: Personality = None,
+        provider_config,
+        provider_settings,
+        default_persona = None,
     ) -> None:
         super().__init__(
             provider_config,
             provider_settings,
-            persistant_history,
-            db_helper,
             default_persona,
         )
         self.api_key = provider_config.get("dify_api_key", "")

astrbot/core/provider/sources/gemini_source.py

@@ -12,8 +12,7 @@ from google.genai.errors import APIError
 
 import astrbot.core.message.components as Comp
 from astrbot import logger
-from astrbot.api.provider import Personality, Provider
-from astrbot.core.db import BaseDatabase
+from astrbot.api.provider import Provider
 from astrbot.core.message.message_event_result import MessageChain
 from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
 from astrbot.core.provider.func_tool_manager import FuncCall
@@ -52,17 +51,13 @@ class ProviderGoogleGenAI(Provider):
 
     def __init__(
         self,
-        provider_config: dict,
-        provider_settings: dict,
-        db_helper: BaseDatabase,
-        persistant_history=True,
-        default_persona: Personality = None,
+        provider_config,
+        provider_settings,
+        default_persona=None,
     ) -> None:
         super().__init__(
             provider_config,
             provider_settings,
-            persistant_history,
-            db_helper,
             default_persona,
         )
         self.api_keys: list = provider_config.get("key", [])
@@ -264,12 +259,10 @@ class ProviderGoogleGenAI(Provider):
                 contents.append(content_cls(parts=part))
 
         gemini_contents: list[types.Content] = []
-        native_tool_enabled = any(
-            [
-                self.provider_config.get("gm_native_coderunner", False),
-                self.provider_config.get("gm_native_search", False),
-            ]
-        )
+        native_tool_enabled = any([
+            self.provider_config.get("gm_native_coderunner", False),
+            self.provider_config.get("gm_native_search", False),
+        ])
         for message in payloads["messages"]:
             role, content = message["role"], message.get("content")
 
@@ -506,12 +499,12 @@ class ProviderGoogleGenAI(Provider):
     async def text_chat(
         self,
         prompt: str,
-        session_id: str = None,
-        image_urls: list[str] = None,
-        func_tool: FuncCall = None,
-        contexts: list = None,
-        system_prompt: str = None,
-        tool_calls_result: ToolCallsResult = None,
+        session_id=None,
+        image_urls=None,
+        func_tool=None,
+        contexts=None,
+        system_prompt=None,
+        tool_calls_result=None,
         **kwargs,
     ) -> LLMResponse:
         if contexts is None:
@@ -527,7 +520,11 @@ class ProviderGoogleGenAI(Provider):
 
         # tool calls result
         if tool_calls_result:
-            context_query.extend(tool_calls_result.to_openai_messages())
+            if not isinstance(tool_calls_result, list):
+                context_query.extend(tool_calls_result.to_openai_messages())
+            else:
+                for tcr in tool_calls_result:
+                    context_query.extend(tcr.to_openai_messages())
 
         model_config = self.provider_config.get("model_config", {})
         model_config["model"] = self.get_model()
@@ -631,9 +628,10 @@ class ProviderGoogleGenAI(Provider):
                 if not image_data:
                     logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
                     continue
-                user_content["content"].append(
-                    {"type": "image_url", "image_url": {"url": image_data}}
-                )
+                user_content["content"].append({
+                    "type": "image_url",
+                    "image_url": {"url": image_data},
+                })
             return user_content
         else:
             return {"role": "user", "content": text}

astrbot/core/provider/sources/llmtuner_source.py

@@ -1,134 +0,0 @@
-import os
-from llmtuner.chat import ChatModel
-from typing import List
-from .. import Provider
-from ..entities import LLMResponse
-from ..func_tool_manager import FuncCall
-from astrbot.core.db import BaseDatabase
-from ..register import register_provider_adapter
-
-
-@register_provider_adapter(
-    "llm_tuner", "LLMTuner 适配器, 用于装载使用 LlamaFactory 微调后的模型"
-)
-class LLMTunerModelLoader(Provider):
-    def __init__(
-        self,
-        provider_config: dict,
-        provider_settings: dict,
-        db_helper: BaseDatabase,
-        persistant_history=True,
-        default_persona=None,
-    ) -> None:
-        super().__init__(
-            provider_config,
-            provider_settings,
-            persistant_history,
-            db_helper,
-            default_persona,
-        )
-        if not os.path.exists(provider_config["base_model_path"]) or not os.path.exists(
-            provider_config["adapter_model_path"]
-        ):
-            raise FileNotFoundError("模型文件路径不存在。")
-        self.base_model_path = provider_config["base_model_path"]
-        self.adapter_model_path = provider_config["adapter_model_path"]
-        self.model = ChatModel(
-            {
-                "model_name_or_path": self.base_model_path,
-                "adapter_name_or_path": self.adapter_model_path,
-                "template": provider_config["llmtuner_template"],
-                "finetuning_type": provider_config["finetuning_type"],
-                "quantization_bit": provider_config["quantization_bit"],
-            }
-        )
-        self.set_model(
-            os.path.basename(self.base_model_path)
-            + "_"
-            + os.path.basename(self.adapter_model_path)
-        )
-
-    async def assemble_context(self, text: str, image_urls: List[str] = None):
-        """
-        组装上下文。
-        """
-        return {"role": "user", "content": text}
-
-    async def text_chat(
-        self,
-        prompt: str,
-        session_id: str = None,
-        image_urls: List[str] = None,
-        func_tool: FuncCall = None,
-        contexts: List = None,
-        system_prompt: str = None,
-        **kwargs,
-    ) -> LLMResponse:
-        if contexts is None:
-            contexts = []
-        system_prompt = ""
-        new_record = {"role": "user", "content": prompt}
-        query_context = [*contexts, new_record]
-
-        # 提取出系统提示
-        system_idxs = []
-        for idx, context in enumerate(query_context):
-            if context["role"] == "system":
-                system_idxs.append(idx)
-
-            if "_no_save" in context:
-                del context["_no_save"]
-
-        for idx in reversed(system_idxs):
-            system_prompt += " " + query_context.pop(idx)["content"]
-
-        conf = {
-            "messages": query_context,
-            "system": system_prompt,
-        }
-        if func_tool:
-            tool_list = func_tool.get_func_desc_openai_style()
-            if tool_list:
-                conf["tools"] = tool_list
-
-        responses = await self.model.achat(**conf)
-
-        llm_response = LLMResponse("assistant", responses[-1].response_text)
-
-        return llm_response
-
-    async def text_chat_stream(
-        self,
-        prompt,
-        session_id=None,
-        image_urls=...,
-        func_tool=None,
-        contexts=...,
-        system_prompt=None,
-        tool_calls_result=None,
-        **kwargs,
-    ):
-        # raise NotImplementedError("This method is not implemented yet.")
-        # 调用 text_chat 模拟流式
-        llm_response = await self.text_chat(
-            prompt=prompt,
-            session_id=session_id,
-            image_urls=image_urls,
-            func_tool=func_tool,
-            contexts=contexts,
-            system_prompt=system_prompt,
-            tool_calls_result=tool_calls_result,
-        )
-        llm_response.is_chunk = True
-        yield llm_response
-        llm_response.is_chunk = False
-        yield llm_response
-
-    async def get_current_key(self):
-        return "none"
-
-    async def set_key(self, key):
-        pass
-
-    async def get_models(self):
-        return [self.get_model()]

astrbot/core/provider/sources/openai_source.py

@@ -9,14 +9,12 @@ import astrbot.core.message.components as Comp
 from openai import AsyncOpenAI, AsyncAzureOpenAI
 from openai.types.chat.chat_completion import ChatCompletion
 
-# from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
 from openai._exceptions import NotFoundError, UnprocessableEntityError
 from openai.lib.streaming.chat._completions import ChatCompletionStreamState
 from astrbot.core.utils.io import download_image_by_url
 from astrbot.core.message.message_event_result import MessageChain
 
-from astrbot.core.db import BaseDatabase
-from astrbot.api.provider import Provider, Personality
+from astrbot.api.provider import Provider
 from astrbot import logger
 from astrbot.core.provider.func_tool_manager import FuncCall
 from typing import List, AsyncGenerator
@@ -30,17 +28,13 @@ from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
 class ProviderOpenAIOfficial(Provider):
     def __init__(
         self,
-        provider_config: dict,
-        provider_settings: dict,
-        db_helper: BaseDatabase,
-        persistant_history=True,
-        default_persona: Personality = None,
+        provider_config,
+        provider_settings,
+        default_persona = None,
     ) -> None:
         super().__init__(
             provider_config,
             provider_settings,
-            persistant_history,
-            db_helper,
             default_persona,
         )
         self.chosen_api_key = None
@@ -224,12 +218,10 @@ class ProviderOpenAIOfficial(Provider):
     async def _prepare_chat_payload(
         self,
         prompt: str,
-        session_id: str = None,
-        image_urls: list[str] = None,
-        func_tool: FuncCall = None,
-        contexts: list = None,
-        system_prompt: str = None,
-        tool_calls_result: ToolCallsResult = None,
+        image_urls: list[str] | None = None,
+        contexts: list | None = None,
+        system_prompt: str | None = None,
+        tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
         **kwargs,
     ) -> tuple:
         """准备聊天所需的有效载荷和上下文"""
@@ -246,14 +238,18 @@ class ProviderOpenAIOfficial(Provider):
 
         # tool calls result
         if tool_calls_result:
-            context_query.extend(tool_calls_result.to_openai_messages())
+            if isinstance(tool_calls_result, ToolCallsResult):
+                context_query.extend(tool_calls_result.to_openai_messages())
+            else:
+                for tcr in tool_calls_result:
+                    context_query.extend(tcr.to_openai_messages())
 
         model_config = self.provider_config.get("model_config", {})
         model_config["model"] = self.get_model()
 
         payloads = {"messages": context_query, **model_config}
 
-        return payloads, context_query, func_tool
+        return payloads, context_query
 
     async def _handle_api_error(
         self,
@@ -352,11 +348,9 @@ class ProviderOpenAIOfficial(Provider):
         tool_calls_result=None,
         **kwargs,
     ) -> LLMResponse:
-        payloads, context_query, func_tool = await self._prepare_chat_payload(
+        payloads, context_query = await self._prepare_chat_payload(
             prompt,
-            session_id,
             image_urls,
-            func_tool,
             contexts,
             system_prompt,
             tool_calls_result,
@@ -422,11 +416,9 @@ class ProviderOpenAIOfficial(Provider):
         **kwargs,
     ) -> AsyncGenerator[LLMResponse, None]:
         """流式对话，与服务商交互并逐步返回结果"""
-        payloads, context_query, func_tool = await self._prepare_chat_payload(
+        payloads, context_query = await self._prepare_chat_payload(
             prompt,
-            session_id,
             image_urls,
-            func_tool,
             contexts,
             system_prompt,
             tool_calls_result,

astrbot/core/provider/sources/zhipu_source.py

@@ -1,4 +1,3 @@
-from astrbot.core.db import BaseDatabase
 from astrbot import logger
 from astrbot.core.provider.func_tool_manager import FuncCall
 from typing import List
@@ -13,15 +12,11 @@ class ProviderZhipu(ProviderOpenAIOfficial):
         self,
         provider_config: dict,
         provider_settings: dict,
-        db_helper: BaseDatabase,
-        persistant_history=True,
         default_persona=None,
     ) -> None:
         super().__init__(
             provider_config,
             provider_settings,
-            db_helper,
-            persistant_history,
             default_persona,
         )
 

changelogs/v3.5.18.md

@@ -0,0 +1,18 @@
+# What's Changed
+
+> 重构了大模型请求部分，如果发现此部分使用时有问题请提交 issue
+
+1. 修复: 安装插件按钮被删除、无法自定义安装插件
+2. 修复: 环境变量中的代理地址无法生效
+1. 修复: randomize jwt secret
+2. 修复: 在 Node 消息段发送简单文本信息的问题
+1. 修复: QQ 官方机器人适配器使用 SessionController(会话控制)功能时机器人回复消息无法发送到聊天平台
+4. 修复: Discord 适配器无法优雅重载
+1. 修复: Telegram 适配器无法主动回复
+1. 修复: 仪表盘的『插件配置』中不显示代码编辑器
+3. 新增: Gemini TTS API
+1. 新增: 允许 html_render 方法传入 Playwright.screenshot 配置参数
+1. 优化: 修复 CommandFilter 支持对布尔类型进行解析
+4. 新增: WechatPadPro 发送 TTS 时 添加对 MP3 格式音频支持
+1. 重构: 将大模型请求部分抽象成 AgentRunner，提高可读性和可扩展性，工具调用结果支持持久化保存到数据库，完善 Agent 的多轮工具调用能力。
+1. 移除: LLMTuner 模型提供商适配器。请使用 Ollama 来加载微调模型

dashboard/src/layouts/full/vertical-header/VerticalHeader.vue

@@ -29,7 +29,7 @@ let dashboardCurrentVersion = ref('');
 let version = ref('');
 let releases = ref([]);
 let devCommits = ref([]);
-
+let updatingDashboardLoading = ref(false);
 let installLoading = ref(false);
 
 let tab = ref(0);
@@ -217,6 +217,7 @@ function switchVersion(version: string) {
 }
 
 function updateDashboard() {
+  updatingDashboardLoading.value = true;
   updateStatus.value = t('core.header.updateDialog.status.updating');
   axios.post('/api/update/dashboard')
       .then((res) => {
@@ -230,7 +231,9 @@ function updateDashboard() {
       .catch((err) => {
         console.log(err);
         updateStatus.value = err
-      });
+      }).finally(() => {
+    updatingDashboardLoading.value = false;
+  });
 }
 
 function toggleDarkMode() {
@@ -416,7 +419,7 @@ commonStore.getStartTime();
               </div>
 
               <v-btn color="primary" style="border-radius: 10px;" @click="updateDashboard()"
-                     :disabled="!dashboardHasNewVersion">
+                     :disabled="!dashboardHasNewVersion" :loading="updatingDashboardLoading">
                 {{ t('core.header.updateDialog.dashboardUpdate.downloadAndUpdate') }}
               </v-btn>
             </div>

dashboard/src/theme/DarkTheme.ts

@@ -39,7 +39,8 @@ const PurpleThemeDark: ThemeTypes = {
     background: '#111111',
     overlay: '#111111aa',
     codeBg: '#282833',
-    code: '#ffffffdd'
+    code: '#ffffffdd',
+    chatMessageBubble: '#2d2e30',
   }
 };
 

dashboard/src/theme/LightTheme.ts

@@ -20,7 +20,7 @@ const PurpleTheme: ThemeTypes = {
     lightsuccess: '#b9f6ca',
     lighterror: '#f9d8d8',
     lightwarning: '#fff8e1',
-    primaryText: '#000000dd',
+    primaryText: '#1b1c1d',
     secondaryText: '#000000aa',
     darkprimary: '#1565c0',
     darksecondary: '#4527a0',
@@ -39,7 +39,8 @@ const PurpleTheme: ThemeTypes = {
     background: '#f9fafcf4',
     overlay: '#ffffffaa',
     codeBg: '#f5f0ff',
-    code: '#673ab7'
+    code: '#673ab7',
+    chatMessageBubble: '#e7ebf4',
   }
 };
 

dashboard/src/types/themeTypes/ThemeType.ts

@@ -35,5 +35,6 @@ export type ThemeTypes = {
     secondary200?: string;
     codeBg?: string;
     code?: string;
+    chatMessageBubble?: string;
   };
 };

dashboard/src/views/ChatPage.vue

@@ -3,13 +3,16 @@
         <v-card-text class="chat-page-container">
             <div class="chat-layout">
                 <div class="sidebar-panel" :class="{ 'sidebar-collapsed': sidebarCollapsed }"
+                    :style="{ 'background-color': isDark ? sidebarCollapsed ? '#1e1e1e' : '#2d2d2d' : sidebarCollapsed ? '#ffffff' : '#f5f5f5' }"
                     @mouseenter="handleSidebarMouseEnter" @mouseleave="handleSidebarMouseLeave">
 
-                    <div style="display: flex; align-items: center; justify-content: center; padding: 16px; padding-bottom: 0px;" v-if="chatboxMode">
+                    <div style="display: flex; align-items: center; justify-content: center; padding: 16px; padding-bottom: 0px;"
+                        v-if="chatboxMode">
                         <img width="50" src="@/assets/images/astrbot_logo_mini.webp" alt="AstrBot Logo">
-                        <span v-if="!sidebarCollapsed" style="font-weight: 1000; font-size: 26px; margin-left: 8px;" class="text-secondary">AstrBot</span>
+                        <span v-if="!sidebarCollapsed" style="font-weight: 1000; font-size: 26px; margin-left: 8px;"
+                            class="text-secondary">AstrBot</span>
                     </div>
-                    
+
 
                     <div class="sidebar-collapse-btn-container">
                         <v-btn icon class="sidebar-collapse-btn" @click="toggleSidebar" variant="text"
@@ -21,7 +24,9 @@
 
                     <div style="padding: 16px; padding-top: 8px;">
                         <v-btn block variant="text" class="new-chat-btn" @click="newC" :disabled="!currCid"
-                            v-if="!sidebarCollapsed" prepend-icon="mdi-plus" style="box-shadow: 0 1px 2px rgba(0,0,0,0.1); background-color: transparent !important; border-radius: 4px;">{{ tm('actions.newChat') }}</v-btn>
+                            v-if="!sidebarCollapsed" prepend-icon="mdi-plus"
+                            style="background-color: transparent !important; border-radius: 4px;">{{
+                                tm('actions.newChat') }}</v-btn>
                         <v-btn icon="mdi-plus" rounded="lg" @click="newC" :disabled="!currCid" v-if="sidebarCollapsed"
                             elevation="0"></v-btn>
                     </div>
@@ -29,11 +34,12 @@
                         <v-divider class="mx-2"></v-divider>
                     </div>
 
-                    <div style="overflow-y: auto; flex-grow: 1;" class="sidebar-panel" :class="{ 'fade-in': sidebarHoverExpanded }"
+
+                    <div style="overflow-y: auto; flex-grow: 1;" :class="{ 'fade-in': sidebarHoverExpanded }"
                         v-if="!sidebarCollapsed">
-                        <v-card class="conversation-list-card" v-if="conversations.length > 0" flat>
+                        <v-card v-if="conversations.length > 0" flat style="background-color: transparent;">
                             <v-list density="compact" nav class="conversation-list"
-                                @update:selected="getConversationMessages">
+                                style="background-color: transparent;" @update:selected="getConversationMessages">
                                 <v-list-item v-for="(item, i) in conversations" :key="item.cid" :value="item.cid"
                                     rounded="lg" class="conversation-item" active-color="secondary">
                                     <v-list-item-title v-if="!sidebarCollapsed" class="conversation-title">{{ item.title
@@ -62,42 +68,13 @@
                     <div v-if="!sidebarCollapsed">
                         <v-divider class="mx-2"></v-divider>
                     </div>
-                    <div style="padding: 16px;" :class="{ 'fade-in': sidebarHoverExpanded }"
-                        v-if="!sidebarCollapsed">
-                        <div class="sidebar-section-title">
-                            {{ tm('conversation.systemStatus') }}
-                        </div>
-                        <div class="status-chips">
-                            <v-chip class="status-chip" :color="status?.llm_enabled ? 'primary' : 'grey-lighten-2'"
-                                variant="outlined" size="small" rounded="sm">
-                                <template v-slot:prepend>
-                                    <v-icon :icon="status?.llm_enabled ? 'mdi-check-circle' : 'mdi-alert-circle'"
-                                        size="x-small"></v-icon>
-                                </template>
-                                <span>{{ tm('conversation.llmService') }}</span>
-                            </v-chip>
-
-                            <v-chip class="status-chip" :color="status?.stt_enabled ? 'success' : 'grey-lighten-2'"
-                                variant="outlined" size="small" rounded="sm">
-                                <template v-slot:prepend>
-                                    <v-icon :icon="status?.stt_enabled ? 'mdi-check-circle' : 'mdi-alert-circle'"
-                                        size="x-small"></v-icon>
-                                </template>
-                                <span>{{ tm('conversation.speechToText') }}</span>
-                            </v-chip>
-                        </div>
-
-                        <transition
-                            name="expand"
-                            @before-enter="beforeEnter"
-                            @enter="enter"
-                            @after-enter="afterEnter"
-                            @before-leave="beforeLeave"
-                            @leave="leave"
-                        >
+                    <div style="padding: 16px;" :class="{ 'fade-in': sidebarHoverExpanded }" v-if="!sidebarCollapsed">
+                        <transition name="expand" @before-enter="beforeEnter" @enter="enter" @after-enter="afterEnter"
+                            @before-leave="beforeLeave" @leave="leave">
                             <div v-if="currCid" class="delete-btn-container">
                                 <v-btn variant="outlined" rounded="sm" class="delete-chat-btn"
-                                    @click="deleteConversation(currCid)" color="error" density="comfortable" size="small">
+                                    @click="deleteConversation(currCid)" color="error" density="comfortable"
+                                    size="small">
                                     <v-icon start size="small">mdi-delete</v-icon>
                                     {{ tm('actions.deleteChat') }}
                                 </v-btn>
@@ -111,14 +88,18 @@
 
                     <div class="conversation-header fade-in">
                         <div class="conversation-header-content" v-if="currCid && getCurrentConversation">
-                            <h2 class="conversation-header-title">{{ getCurrentConversation.title || tm('conversation.newConversation') }}</h2>
-                            <div class="conversation-header-time">{{ formatDate(getCurrentConversation.updated_at) }}</div>
+                            <h2 class="conversation-header-title">{{ getCurrentConversation.title ||
+                                tm('conversation.newConversation')
+                            }}</h2>
+                            <div class="conversation-header-time">{{ formatDate(getCurrentConversation.updated_at) }}
+                            </div>
                         </div>
                         <div class="conversation-header-actions">
                             <!-- router 推送到 /chatbox -->
                             <v-tooltip :text="tm('actions.fullscreen')" v-if="!chatboxMode">
                                 <template v-slot:activator="{ props }">
-                                    <v-icon v-bind="props" @click="router.push(currCid ? `/chatbox/${currCid}` : '/chatbox')"
+                                    <v-icon v-bind="props"
+                                        @click="router.push(currCid ? `/chatbox/${currCid}` : '/chatbox')"
                                         class="fullscreen-icon">mdi-fullscreen</v-icon>
                                 </template>
                             </v-tooltip>
@@ -131,7 +112,8 @@
                             <!-- 主题切换按钮 -->
                             <v-tooltip :text="isDark ? tm('modes.lightMode') : tm('modes.darkMode')" v-if="chatboxMode">
                                 <template v-slot:activator="{ props }">
-                                    <v-btn v-bind="props" icon @click="toggleTheme" class="theme-toggle-icon" variant="text">
+                                    <v-btn v-bind="props" icon @click="toggleTheme" class="theme-toggle-icon"
+                                        variant="text">
                                         <v-icon>{{ isDark ? 'mdi-weather-night' : 'mdi-white-balance-sunny' }}</v-icon>
                                     </v-btn>
                                 </template>
@@ -176,7 +158,8 @@
                             <div class="message-item fade-in" v-for="(msg, index) in messages" :key="index">
                                 <!-- 用户消息 -->
                                 <div v-if="msg.type == 'user'" class="user-message">
-                                    <div class="message-bubble user-bubble">
+                                    <div class="message-bubble user-bubble"
+                                        :style="{ backgroundColor: isDark ? '#2d2e30' : '#e7ebf4' }">
                                         <span>{{ msg.message }}</span>
 
                                         <!-- 图片附件 -->
@@ -195,15 +178,12 @@
                                             </audio>
                                         </div>
                                     </div>
-                                    <v-avatar class="user-avatar" color="deep-purple-lighten-3" size="36">
-                                        <v-icon icon="mdi-account" />
-                                    </v-avatar>
                                 </div>
 
                                 <!-- 机器人消息 -->
                                 <div v-else class="bot-message">
-                                    <v-avatar class="bot-avatar" color="deep-purple" size="36">
-                                        <span class="text-h6">✨</span>
+                                    <v-avatar class="bot-avatar" size="36">
+                                        <span class="text-h2">✨</span>
                                     </v-avatar>
                                     <div class="message-bubble bot-bubble">
                                         <div v-html="marked(msg.message)" class="markdown-content"></div>
@@ -215,34 +195,21 @@
 
                     <!-- 输入区域 -->
                     <div class="input-area fade-in">
-                        <v-text-field autocomplete="off" id="input-field" variant="outlined" v-model="prompt"
-                            :label="inputFieldLabel" :placeholder="tm('input.placeholder')" :loading="loadingChat"
-                            clear-icon="mdi-close-circle" clearable @click:clear="clearMessage" class="message-input"
-                            @keydown="handleInputKeyDown" hide-details>
-                            <template v-slot:loader>
-                                <v-progress-linear :active="loadingChat" height="3" color="deep-purple"
-                                    indeterminate></v-progress-linear>
-                            </template>
-
-                            <template v-slot:append>
-                                <v-tooltip :text="tm('input.send')">
-                                    <template v-slot:activator="{ props }">
-                                        <v-btn v-bind="props" @click="sendMessage" class="send-btn" icon="mdi-send"
-                                            variant="text" color="deep-purple"
-                                            :disabled="!prompt && stagedImagesName.length === 0 && !stagedAudioUrl" />
-                                    </template>
-                                </v-tooltip>
-
-                                <v-tooltip :text="tm('input.voice')">
-                                    <template v-slot:activator="{ props }">
-                                        <v-btn v-bind="props" @click="isRecording ? stopRecording() : startRecording()"
-                                            class="record-btn"
-                                            :icon="isRecording ? 'mdi-stop-circle' : 'mdi-microphone'" variant="text"
-                                            :color="isRecording ? 'error' : 'deep-purple'" />
-                                    </template>
-                                </v-tooltip>
-                            </template>
-                        </v-text-field>
+                        <div
+                            style="width: 85%; max-width: 900px; margin: 0 auto; border: 1px solid #e0e0e0; border-radius: 24px; padding: 4px;">
+                            <textarea id="input-field" v-model="prompt" @keydown="handleInputKeyDown"
+                                @click:clear="clearMessage" placeholder="Ask AstrBot..."
+                                style="width: 100%; resize: none; outline: none; border: 1px solid var(--v-theme-border); border-radius: 12px; padding: 12px 16px; min-height: 40px; font-family: inherit; font-size: 16px; background-color: var(--v-theme-surface);"
+                                :disabled="loadingChat"></textarea>
+                            <div style="display: flex; justify-content: flex-end; margin-top: 8px;">
+                                <v-btn @click="sendMessage" icon="mdi-send" variant="text" color="deep-purple"
+                                    :disabled="!prompt && stagedImagesName.length === 0 && !stagedAudioUrl"
+                                    class="send-btn" size="small" />
+                                <v-btn @click="isRecording ? stopRecording() : startRecording()"
+                                    :icon="isRecording ? 'mdi-stop-circle' : 'mdi-microphone'" variant="text"
+                                    :color="isRecording ? 'error' : 'deep-purple'" class="record-btn" size="small" />
+                            </div>
+                        </div>
 
                         <!-- 附件预览区 -->
                         <div class="attachments-preview" v-if="stagedImagesUrl.length > 0 || stagedAudioUrl">
@@ -263,16 +230,17 @@
                         </div>
                     </div>
                 </div>
+
             </div>
         </v-card-text>
-    </v-card>    
+    </v-card>
     <!-- 编辑对话标题对话框 -->
     <v-dialog v-model="editTitleDialog" max-width="400">
         <v-card>
             <v-card-title class="dialog-title">{{ tm('actions.editTitle') }}</v-card-title>
             <v-card-text>
-                                    <v-text-field v-model="editingTitle" :label="tm('conversation.newConversation')" variant="outlined" hide-details class="mt-2"
-                    @keyup.enter="saveTitle" autofocus />
+                <v-text-field v-model="editingTitle" :label="tm('conversation.newConversation')" variant="outlined"
+                    hide-details class="mt-2" @keyup.enter="saveTitle" autofocus />
             </v-card-text>
             <v-card-actions>
                 <v-spacer></v-spacer>
@@ -280,9 +248,9 @@
                 <v-btn text @click="saveTitle" color="primary">{{ t('core.common.save') }}</v-btn>
             </v-card-actions>
         </v-card>
-    </v-dialog>    <!-- 连接冲突提示对话框 -->
+    </v-dialog> <!-- 连接冲突提示对话框 -->
     <v-dialog v-model="connectionConflictDialog" max-width="600" persistent>
-        <v-card class="rounded-lg">            
+        <v-card class="rounded-lg">
             <v-toolbar color="primary" density="comfortable" flat>
                 <v-icon color="white" class="ml-4 mr-2">mdi-information-outline</v-icon>
                 <v-toolbar-title class="text-white">{{ tm('connection.title') }}</v-toolbar-title>
@@ -296,13 +264,8 @@
                 <div class="text-body-1 mb-4">
                     {{ tm('connection.message') }}
                 </div>
-                
-                <v-alert 
-                    type="info" 
-                    variant="tonal" 
-                    class="mb-4"
-                    icon="mdi-lightbulb-outline"
-                >
+
+                <v-alert type="info" variant="tonal" class="mb-4" icon="mdi-lightbulb-outline">
                     <div class="text-body-2 mb-2">
                         <strong>{{ tm('connection.reasons') }}</strong>
                     </div>
@@ -313,12 +276,7 @@
                     </ul>
                 </v-alert>
 
-                <v-alert 
-                    type="warning" 
-                    variant="tonal" 
-                    icon="mdi-alert-circle-outline"
-                    class="mb-0"
-                >
+                <v-alert type="warning" variant="tonal" icon="mdi-alert-circle-outline" class="mb-0">
                     <div class="text-body-2">
                         {{ tm('connection.notice') }}
                     </div>
@@ -327,12 +285,7 @@
 
             <v-card-actions class="px-6 pb-4">
                 <v-spacer></v-spacer>
-                <v-btn 
-                    color="primary" 
-                    variant="elevated"
-                    @click="connectionConflictDialog = false"
-                    class="px-6"
-                >
+                <v-btn color="primary" variant="elevated" @click="connectionConflictDialog = false" class="px-6">
                     {{ tm('connection.understand') }}
                 </v-btn>
             </v-card-actions>
@@ -340,15 +293,10 @@
     </v-dialog>
 
     <!-- 连接状态消息提示 -->
-    <v-snackbar 
-        v-model="connectionStatusSnackbar" 
-        :color="connectionStatusColor" 
-        :timeout="4000"
-        location="top"
-    >
+    <v-snackbar v-model="connectionStatusSnackbar" :color="connectionStatusColor" :timeout="4000" location="top">
         <v-icon class="mr-2">
-            {{ connectionStatusColor === 'success' ? 'mdi-check-circle' : 
-               connectionStatusColor === 'warning' ? 'mdi-alert-circle' : 'mdi-information' }}
+            {{ connectionStatusColor === 'success' ? 'mdi-check-circle' :
+                connectionStatusColor === 'warning' ? 'mdi-alert-circle' : 'mdi-information' }}
         </v-icon>
         {{ connectionStatusMessage }}
     </v-snackbar>
@@ -377,10 +325,10 @@ export default {
             type: Boolean,
             default: false
         }
-    },    setup() {
+    }, setup() {
         const { t } = useI18n();
         const { tm } = useModuleI18n('features/chat');
-        
+
         return {
             t,
             tm,
@@ -413,7 +361,7 @@ export default {
             eventSourceReader: null,
             sseReconnecting: false, // 添加重连状态标志
 
-            
+
             // // Ctrl键长按相关变量
             ctrlKeyDown: false,
             ctrlKeyTimer: null,
@@ -440,7 +388,7 @@ export default {
             connectionStatusColor: 'info',
         }
     },
-    
+
     computed: {
         isDark() {
             return useCustomizerStore().uiTheme === 'PurpleThemeDark';
@@ -458,13 +406,13 @@ export default {
             immediate: true,
             handler(to, from) {
                 console.log('Route changed:', to.path, 'from:', from?.path);                // 如果是从不同的路由模式切换（chat <-> chatbox），重新建立SSE连接
-                if (from && 
+                if (from &&
                     ((from.path.startsWith('/chat') && to.path.startsWith('/chatbox')) ||
-                     (from.path.startsWith('/chatbox') && to.path.startsWith('/chat')))) {
+                        (from.path.startsWith('/chatbox') && to.path.startsWith('/chat')))) {
                     console.log('Route mode changed, reconnecting SSE...');
                     this.reconnectSSE();
                 }
-                
+
                 // Check if the route matches /chat/<cid> or /chatbox/<cid> pattern
                 if (to.path.startsWith('/chat/') || to.path.startsWith('/chatbox/')) {
                     const pathCid = to.path.split('/')[2];
@@ -484,7 +432,7 @@ export default {
                 }
             }
         },
-        
+
         // Watch for conversations loaded to handle pending cid
         conversations: {
             handler(newConversations) {
@@ -530,7 +478,7 @@ export default {
 
     beforeUnmount() {
         this.disconnectSSE();
-        
+
         // 移除keyup事件监听
         document.removeEventListener('keyup', this.handleInputKeyUp);
 
@@ -541,7 +489,7 @@ export default {
 
         // Cleanup blob URLs
         this.cleanupMediaCache();
-    },    
+    },
     methods: {
         // 显示连接冲突对话框
         showConnectionConflictDialog() {
@@ -661,7 +609,7 @@ export default {
                 }
                 this.eventSourceReader = null;
             }
-            
+
             if (this.eventSource) {
                 try {
                     this.eventSource.cancel();
@@ -679,33 +627,33 @@ export default {
                 console.log('SSE reconnection already in progress');
                 return;
             }
-            
+
             this.sseReconnecting = true;
             console.log('Reconnecting SSE...');
             this.disconnectSSE();
-            
+
             // 等待更长时间确保后端连接完全清理
             await new Promise(resolve => setTimeout(resolve, 1000));
-            
+
             this.startListeningEvent();
         },
 
         async startListeningEvent() {
             // 确保之前的连接已断开
             this.disconnectSSE();
-            
+
             // 如果正在重连过程中，等待一下
             if (this.sseReconnecting) {
                 await new Promise(resolve => setTimeout(resolve, 500));
             }
-            
+
             let retryCount = 0;
             const maxRetries = 3;
-            
+
             while (retryCount < maxRetries) {
                 try {
                     console.log(`尝试建立SSE连接 (${retryCount + 1}/${maxRetries})`);
-                    
+
                     const response = await fetch('/api/chat/listen', {
                         method: 'GET',
                         headers: {
@@ -719,13 +667,13 @@ export default {
                     }
 
                     const reader = response.body.getReader();
-                    const decoder = new TextDecoder();                    
+                    const decoder = new TextDecoder();
                     this.eventSource = reader;
                     this.eventSourceReader = reader;
                     this.sseReconnecting = false;
 
                     let in_streaming = false;
-                    let message_obj = null;                    
+                    let message_obj = null;
                     console.log('SSE连接已建立');
                     // 显示连接成功状态
                     if (retryCount > 0) {
@@ -859,28 +807,28 @@ export default {
                             break;
                         }
                     }
-                    
+
                     // 如果成功连接并正常结束，跳出重试循环
                     break;
-                    
+
                 } catch (error) {
                     console.error(`SSE连接错误 (尝试 ${retryCount + 1}):`, error);
-                    
-                    retryCount++;                    
+
+                    retryCount++;
                     if (error.message === 'CONNECTION_CONFLICT' && retryCount < maxRetries) {
                         console.log(`连接冲突，等待 ${2000 * retryCount}ms 后重试...`);
                         this.showConnectionStatus(`${this.tm('connection.status.reconnecting')} (${retryCount}/${maxRetries})`, 'warning');
                         await new Promise(resolve => setTimeout(resolve, 2000 * retryCount));
                         continue;
                     }
-                    
+
                     if (retryCount >= maxRetries) {
                         console.error('SSE连接重试次数已达上限');
                         this.showConnectionStatus(this.tm('connection.status.failed'), 'error');
                         this.sseReconnecting = false;
                         break;
                     }
-                    
+
                     // 等待一段时间后重试
                     await new Promise(resolve => setTimeout(resolve, 1000 * retryCount));
                 } finally {
@@ -888,7 +836,7 @@ export default {
                     this.eventSourceReader = null;
                 }
             }
-            
+
             this.sseReconnecting = false;
         },
 
@@ -984,7 +932,7 @@ export default {
         getConversations() {
             axios.get('/api/chat/conversations').then(response => {
                 this.conversations = response.data.data;
-                
+
                 // If there's a pending conversation ID from the route
                 if (this.pendingCid) {
                     const conversation = this.conversations.find(c => c.cid === this.pendingCid);
@@ -1003,7 +951,7 @@ export default {
         getConversationMessages(cid) {
             if (!cid[0])
                 return;
-                
+
             // Update the URL to reflect the selected conversation
             if (this.$route.path !== `/chat/${cid[0]}` && this.$route.path !== `/chatbox/${cid[0]}`) {
                 if (this.$route.path.startsWith('/chatbox')) {
@@ -1013,7 +961,7 @@ export default {
                 }
             }
 
-                
+
             axios.get('/api/chat/get_conversation?conversation_id=' + cid[0]).then(async response => {
                 this.currCid = cid[0];
                 let message = JSON.parse(response.data.data.history);
@@ -1101,9 +1049,9 @@ export default {
 
         // 检查是否可以发送消息
         canSendMessage() {
-            return (this.prompt && this.prompt.trim()) || 
-                   this.stagedImagesName.length > 0 || 
-                   this.stagedAudioUrl;
+            return (this.prompt && this.prompt.trim()) ||
+                this.stagedImagesName.length > 0 ||
+                this.stagedAudioUrl;
         },
 
         async sendMessage() {
@@ -1183,11 +1131,11 @@ export default {
                 const container = this.$refs.messageContainer;
                 container.scrollTop = container.scrollHeight;
             });
-        },        
+        },
         handleInputKeyDown(e) {
             if (e.ctrlKey && e.keyCode === 66) { // Ctrl+B组合键
                 e.preventDefault(); // 防止默认行为
-                
+
                 // 防止重复触发
                 if (this.ctrlKeyDown) return;
 
@@ -1200,7 +1148,7 @@ export default {
                     }
                 }, this.ctrlKeyLongPressThreshold);
             }
-        },        
+        },
         handleInputKeyUp(e) {
             if (e.keyCode === 66) { // B键释放
                 this.ctrlKeyDown = false;
@@ -1308,26 +1256,27 @@ export default {
     opacity: 0;
 }
 
-
-/* 聊天页面布局 */
 .chat-page-card {
-    margin-bottom: 16px;
     width: 100%;
-    height: 100%;
-    border-radius: 16px;
+    height: calc(100vh - 84px);
+    max-height: 100%;
     box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05) !important;
+    overflow: hidden;
 }
 
 .chat-page-container {
     width: 100%;
     height: 100%;
-    max-height: calc(100vh - 120px);
+    max-height: 100%;
     padding: 0;
+    overflow: hidden;
 }
 
 .chat-layout {
     height: 100%;
+    max-height: 100%;
     display: flex;
+    overflow: hidden;
 }
 
 .sidebar-panel {
@@ -1337,29 +1286,11 @@ export default {
     flex-direction: column;
     padding: 0;
     border-right: 1px solid rgba(0, 0, 0, 0.05);
-    background-color: var(--v-theme-containerBg);
     height: 100%;
+    max-height: 100%;
     position: relative;
     transition: all 0.3s ease;
     overflow: hidden;
-    /* 防止内容溢出 */
-}
-
-.sidebar-panel ::-webkit-scrollbar {
-    width: 6px;
-}
-
-.sidebar-panel ::-webkit-scrollbar-track {
-    background: transparent;
-}
-
-.sidebar-panel ::-webkit-scrollbar-thumb {
-    background-color: rgba(0, 0, 0, 0.2);
-    border-radius: 3px;
-}
-
-.sidebar-panel ::-webkit-scrollbar-thumb:hover {
-    background-color: rgba(0, 0, 0, 0.3);
 }
 
 /* 侧边栏折叠状态 */
@@ -1442,7 +1373,7 @@ export default {
 .status-chips .v-chip {
     flex: 1 1 0;
     justify-content: center;
-    opacity: 0.7; /* Make border and text slightly transparent */
+    opacity: 0.7;
 }
 
 .status-chip {
@@ -1470,7 +1401,8 @@ export default {
 }
 
 .delete-btn-container {
-    /* margin-top: -8px; */ /* Removed for better layout practices */
+    /* margin-top: -8px; */
+    /* Removed for better layout practices */
 }
 
 .expand-enter-active,
@@ -1495,20 +1427,24 @@ export default {
     transition: opacity 0.25s ease;
 }
 
-/* 聊天内容区域 */
 .chat-content-panel {
     height: 100%;
+    max-height: 100%;
     width: 100%;
     display: flex;
     flex-direction: column;
+    overflow: hidden;
 }
 
 .messages-container {
-    height: calc(100% - 80px);
+    height: 100%;
+    max-height: 100%;
     overflow-y: auto;
     padding: 16px;
     display: flex;
     flex-direction: column;
+    flex: 1;
+    min-height: 0;
 }
 
 /* 欢迎页样式 */
@@ -1574,28 +1510,26 @@ export default {
 }
 
 .message-bubble {
-    padding: 12px 16px;
-    border-radius: 18px;
+    padding: 8px 16px;
+    border-radius: 12px;
     max-width: 80%;
-    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.1);
 }
 
 .user-bubble {
-    background-color: var(--v-theme-background);
     color: var(--v-theme-primaryText);
-    border-top-right-radius: 4px;
+    padding: 12px 16px;
+    font-size: 16px;
 }
 
 .bot-bubble {
-    background-color: var(--v-theme-surface);
     border: 1px solid var(--v-theme-border);
     color: var(--v-theme-primaryText);
-    border-top-left-radius: 4px;
 }
 
 .user-avatar,
 .bot-avatar {
-    align-self: flex-end;
+    align-self: flex-start;
+    margin-top: 12px;
 }
 
 /* 附件样式 */
@@ -1639,17 +1573,8 @@ export default {
     background-color: var(--v-theme-surface);
     position: relative;
     border-top: 1px solid var(--v-theme-border);
-}
-
-.message-input {
-    border-radius: 24px;
-    max-width: 900px;
-    margin: 0 auto;
-}
-
-.send-btn,
-.record-btn {
-    margin-left: 4px;
+    flex-shrink: 0;
+    /* 防止输入区域被压缩 */
 }
 
 /* 附件预览区 */
@@ -1803,38 +1728,7 @@ export default {
     border-bottom: 1px solid var(--v-theme-border);
     width: 100%;
     padding-right: 32px;
-}
-
-.conversation-header-content {
-    display: flex;
-    flex-direction: column;
-}
-
-.conversation-header-title {
-    font-size: 18px;
-    font-weight: 600;
-    margin: 0;
-    color: var(--v-theme-primaryText);
-}
-
-.conversation-header-time {
-    font-size: 12px;
-    color: var(--v-theme-secondaryText);
-    margin-top: 4px;
-}
-
-.conversation-header-actions {
-    display: flex;
-    align-items: center;
-}
-
-.fullscreen-icon {
-    opacity: 0.7;
-    transition: opacity 0.2s;
-    cursor: pointer;
-}
-
-.fullscreen-icon:hover {
-    opacity: 1;
+    flex-shrink: 0;
+    /* 防止header被压缩 */
 }
 </style>

dashboard/src/views/ProviderPage.vue

@@ -414,7 +414,6 @@ export default {
         "anthropic_chat_completion": "chat_completion",
         "googlegenai_chat_completion": "chat_completion",
         "zhipu_chat_completion": "chat_completion",
-        "llm_tuner": "chat_completion",
         "dify": "chat_completion",
         "dashscope": "chat_completion",
         "openai_whisper_api": "speech_to_text",

packages/thinking_filter/main.py

@@ -0,0 +1,71 @@
+import re
+from astrbot.api.event import filter, AstrMessageEvent
+from astrbot.api.star import Context, Star, register
+from astrbot.api.provider import LLMResponse
+from openai.types.chat.chat_completion import ChatCompletion
+
+
+@register(
+    "thinking_filter",
+    "Soulter",
+    "可选择是否过滤推理模型的思考内容",
+    "1.0.0",
+    "https://astrbot.app",
+)
+class R1Filter(Star):
+    def __init__(self, context: Context):
+        super().__init__(context)
+        self.display_reasoning_text = (
+            self.context.get_config()
+            .get("provider_settings", {})
+            .get("display_reasoning_text", False)
+        )
+
+    @filter.on_llm_response()
+    async def resp(self, event: AstrMessageEvent, response: LLMResponse):
+        if self.display_reasoning_text:
+            # 显示推理内容的处理逻辑
+            if (
+                response
+                and response.raw_completion
+                and isinstance(response.raw_completion, ChatCompletion)
+                and len(response.raw_completion.choices) > 0
+                and response.raw_completion.choices[0].message
+            ):
+                message = response.raw_completion.choices[0].message
+                reasoning_content = ""  # 初始化 reasoning_content
+
+                # 检查 Groq deepseek-r1-distill-llama-70b 模型的 'reasoning' 属性
+                if hasattr(message, "reasoning") and message.reasoning:
+                    reasoning_content = message.reasoning
+                # 检查 DeepSeek deepseek-reasoner 模型的 'reasoning_content'
+                elif (
+                    hasattr(message, "reasoning_content") and message.reasoning_content
+                ):
+                    reasoning_content = message.reasoning_content
+
+                if reasoning_content:
+                    response.completion_text = (
+                        f"🤔思考：{reasoning_content}\n\n{message.content}"
+                    )
+                else:
+                    response.completion_text = message.content
+        else:
+            # 过滤推理标签的处理逻辑
+            completion_text = response.completion_text
+
+            # 检查并移除 <think> 标签
+            if r"<think>" in completion_text or r"</think>" in completion_text:
+                # 移除配对的标签及其内容
+                completion_text = re.sub(
+                    r"<think>.*?</think>", "", completion_text, flags=re.DOTALL
+                ).strip()
+
+                # 移除可能残留的单个标签
+                completion_text = (
+                    completion_text.replace(r"<think>", "")
+                    .replace(r"</think>", "")
+                    .strip()
+                )
+
+            response.completion_text = completion_text

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "AstrBot"
-version = "3.5.17"
+version = "3.5.18"
 description = "易上手的多平台 LLM 聊天机器人及开发框架"
 readme = "README.md"
 requires-python = ">=3.10"