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https://github.com/FuQuan233/nonebot-plugin-llmchat.git
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✨ 添加模型调用子模型的能力
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slexce
parent
bd16c6de8c
commit
604116ba7f
5 changed files with 773 additions and 14 deletions
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@ -360,7 +360,7 @@ async def process_messages(context_id: int, is_group: bool = True):
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logger.debug(f"从队列获取消息 用户：{context_id} 消息ID：{event.message_id}")
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group_id = None
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past_events_snapshot = []
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mcp_client = MCPClient.get_instance(plugin_config.mcp_servers)
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mcp_client = MCPClient.get_instance(plugin_config.mcp_servers, plugin_config)
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try:
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# 构建系统提示，分成多行以满足行长限制
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chat_type = "群聊" if is_group else "私聊"
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@ -447,9 +447,14 @@ async def process_messages(context_id: int, is_group: bool = True):
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}
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if preset.support_mcp:
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available_tools = await mcp_client.get_available_tools(is_group)
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available_tools = await mcp_client.get_available_tools(is_group, preset)
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client_config["tools"] = available_tools
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# 用于存储子模型生成的多媒体内容
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submodel_images: list[str] = []
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submodel_voices: list[str] = []
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submodel_videos: list[str] = []
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response = await client.chat.completions.create(
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**client_config,
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messages=messages + new_messages,
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@ -486,7 +491,8 @@ async def process_messages(context_id: int, is_group: bool = True):
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group_id=event.group_id,
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bot_id=str(event.self_id),
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user_id=event.user_id,
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is_group=True
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is_group=True,
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current_preset=preset
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)
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else:
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result = await mcp_client.call_tool(
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@ -494,9 +500,37 @@ async def process_messages(context_id: int, is_group: bool = True):
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tool_args,
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bot_id=str(event.self_id),
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user_id=event.user_id,
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is_group=False
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is_group=False,
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current_preset=preset
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)
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# 处理子模型返回的结构化结果
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if isinstance(result, dict) and tool_name.startswith("submodel__"):
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if result.get("success"):
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# 收集多媒体内容
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if result.get("images"):
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submodel_images.extend(result["images"])
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logger.info(f"子模型生成了 {len(result['images'])} 张图片")
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if result.get("audio"):
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submodel_voices.append(result["audio"])
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logger.info("子模型生成了语音")
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if result.get("video"):
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submodel_videos.append(result["video"])
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logger.info("子模型生成了视频")
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# 构建给主模型的结果消息
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result_msg = f"成功使用模型 {result.get('model_used', '未知')} 生成内容。"
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if result.get("content"):
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result_msg += f"\n子模型回复：{result['content']}"
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if result.get("images"):
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result_msg += f"\n已生成 {len(result['images'])} 张图片，将在你回复后发送给用户。"
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if result.get("audio"):
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result_msg += "\n已生成语音，将在你回复后发送给用户。"
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if result.get("video"):
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result_msg += "\n已生成视频，将在你回复后发送给用户。"
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result = result_msg
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else:
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result = f"生成失败：{result.get('error', '未知错误')}"
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new_messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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@ -557,6 +591,7 @@ async def process_messages(context_id: int, is_group: bool = True):
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assert reply is not None
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await send_split_messages(handler, reply)
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# 发送主模型直接生成的图片
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if reply_images:
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logger.debug(f"API响应 图片数：{len(reply_images)}")
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for i, image in enumerate(reply_images, start=1):
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@ -565,6 +600,50 @@ async def process_messages(context_id: int, is_group: bool = True):
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image_msg = MessageSegment.image(base64.b64decode(image_base64))
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await handler.send(image_msg)
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# 发送子模型生成的图片
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if submodel_images:
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logger.info(f"发送子模型生成的 {len(submodel_images)} 张图片")
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for i, img_base64 in enumerate(submodel_images, start=1):
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try:
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logger.debug(f"正在发送子模型图片 {i}/{len(submodel_images)}")
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# 处理可能的 data URL 前缀
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if img_base64.startswith("data:"):
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img_base64 = img_base64.split(",", 1)[-1] if "," in img_base64 else img_base64
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image_msg = MessageSegment.image(base64.b64decode(img_base64))
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await handler.send(image_msg)
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except Exception as e:
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logger.error(f"发送子模型图片失败: {e}")
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# 发送子模型生成的语音
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if submodel_voices:
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logger.info(f"发送子模型生成的 {len(submodel_voices)} 条语音")
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for i, voice_data in enumerate(submodel_voices, start=1):
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try:
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logger.debug(f"正在发送子模型语音 {i}/{len(submodel_voices)}")
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if voice_data.startswith("data:"):
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voice_data = voice_data.split(",", 1)[-1] if "," in voice_data else voice_data
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voice_msg = MessageSegment.record(base64.b64decode(voice_data))
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await handler.send(voice_msg)
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except Exception as e:
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logger.error(f"发送子模型语音失败: {e}")
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# 发送子模型生成的视频
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if submodel_videos:
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logger.info(f"发送子模型生成的 {len(submodel_videos)} 个视频")
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for i, video_data in enumerate(submodel_videos, start=1):
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try:
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logger.debug(f"正在发送子模型视频 {i}/{len(submodel_videos)}")
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# 视频可能是 URL 或 base64
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if video_data.startswith("http"):
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video_msg = MessageSegment.video(video_data)
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else:
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if video_data.startswith("data:"):
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video_data = video_data.split(",", 1)[-1] if "," in video_data else video_data
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video_msg = MessageSegment.video(base64.b64decode(video_data))
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await handler.send(video_msg)
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except Exception as e:
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logger.error(f"发送子模型视频失败: {e}")
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except Exception as e:
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logger.opt(exception=e).error(f"API请求失败 {'群号' if is_group else '用户'}：{context_id}")
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# 如果在处理过程中出现异常，恢复未处理的消息到state中
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