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https://github.com/FuQuan233/nonebot-plugin-llmchat.git
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✨ 发送图片可参考
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slexce
parent
25a9cd0d76
commit
7e87981167
3 changed files with 86 additions and 16 deletions
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@ -418,6 +418,9 @@ async def process_messages(context_id: int, is_group: bool = True):
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content: list[ChatCompletionContentPartParam] = []
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# 收集用户消息中的图片（用于传递给子模型作为参考）
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user_message_images: list[str] = []
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# 将机器人错过的消息推送给LLM
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past_events_snapshot = list(state.past_events)
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state.past_events.clear()
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@ -426,11 +429,19 @@ async def process_messages(context_id: int, is_group: bool = True):
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content.append({"type": "text", "text": text_content})
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# 将消息中的图片转成 base64
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base64_images = await process_images(ev)
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# 收集图片用于子模型调用
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user_message_images.extend(base64_images)
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# 如果主模型支持图片输入，也传递给主模型
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if preset.support_image:
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base64_images = await process_images(ev)
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for base64_image in base64_images:
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content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}})
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if user_message_images:
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logger.info(f"用户消息中包含 {len(user_message_images)} 张图片，将用于子模型调用")
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new_messages: list[ChatCompletionMessageParam] = [
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{"role": "user", "content": content}
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]
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@ -484,6 +495,9 @@ async def process_messages(context_id: int, is_group: bool = True):
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# 发送工具调用提示
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await handler.send(Message(f"正在使用{mcp_client.get_friendly_name(tool_name)}"))
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# 对于子模型调用，传递用户消息中的图片作为参考
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images_for_submodel = user_message_images if tool_name.startswith("submodel__") else None
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if is_group:
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result = await mcp_client.call_tool(
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tool_name,
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@ -492,7 +506,8 @@ async def process_messages(context_id: int, is_group: bool = True):
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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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current_preset=preset
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current_preset=preset,
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user_images=images_for_submodel
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)
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else:
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result = await mcp_client.call_tool(
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@ -501,7 +516,8 @@ async def process_messages(context_id: int, is_group: bool = True):
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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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current_preset=preset
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current_preset=preset,
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user_images=images_for_submodel
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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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@ -195,7 +195,8 @@ class MCPClient:
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bot_id: str | None = None,
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user_id: int | None = None,
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is_group: bool = True,
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current_preset: PresetConfig | None = None
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current_preset: PresetConfig | None = None,
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user_images: list[str] | None = None
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):
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"""按需连接调用工具，调用后立即断开
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@ -207,6 +208,7 @@ class MCPClient:
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user_id: 用户ID
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is_group: 是否群聊
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current_preset: 当前使用的预设配置（子模型调用时必需）
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user_images: 用户消息中的图片列表（base64 编码），用于子模型参考
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"""
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# 检查是否是OneBot内置工具
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if tool_name.startswith("ob__"):
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@ -231,8 +233,10 @@ class MCPClient:
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return "子模型调用器未初始化"
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if not current_preset:
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return "子模型调用需要提供 current_preset 参数"
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logger.info(f"调用子模型工具[{tool_name}]")
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result = await self.submodel_caller.call_tool(tool_name, tool_args, current_preset)
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logger.info(f"调用子模型工具[{tool_name}]，参考图片数: {len(user_images) if user_images else 0}")
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result = await self.submodel_caller.call_tool(
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tool_name, tool_args, current_preset, reference_images=user_images
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)
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# 返回结构化结果，让上层处理
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return result
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@ -99,6 +99,7 @@ class SubModelCaller:
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使用说明：
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- 当用户要求生成图片时使用此工具
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- prompt 应该是详细的图片描述，用英文效果更好
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- 如果用户消息中包含图片（发送或引用），系统会自动将这些图片作为参考传递给子模型，无需在 prompt 中描述
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- 系统会自动选择最优的模型，如果失败会自动切换备选模型
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- 返回结果包含 base64 编码的图片数据""",
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"parameters": {
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@ -106,7 +107,7 @@ class SubModelCaller:
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"properties": {
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"prompt": {
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"type": "string",
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"description": "图片生成提示词，描述要生成的图片内容，建议使用英文"
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"description": "图片生成提示词，描述要生成的图片内容或对参考图片的修改要求"
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},
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"preferred_model": {
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"type": "string",
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@ -307,7 +308,8 @@ class SubModelCaller:
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self,
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current_preset: PresetConfig,
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prompt: str,
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preferred_model: str | None = None
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preferred_model: str | None = None,
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reference_images: list[str] | None = None
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) -> dict[str, Any]:
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"""生成图片
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@ -315,6 +317,7 @@ class SubModelCaller:
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current_preset: 当前主模型预设
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prompt: 图片生成提示词
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preferred_model: 可选的指定模型名称
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reference_images: 可选的参考图片列表（base64 编码）
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Returns:
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包含生成结果的字典：
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@ -351,15 +354,37 @@ class SubModelCaller:
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except Exception as e:
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logger.debug(f"获取 MCP 工具失败: {e}")
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# 构建用户消息内容
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user_content: list[dict[str, Any]] = []
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# 添加文本提示
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user_content.append({"type": "text", "text": prompt})
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# 如果有参考图片，添加到消息中
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if reference_images:
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logger.info(f"子模型调用包含 {len(reference_images)} 张参考图片")
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for img_base64 in reference_images:
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# 确保格式正确
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if not img_base64.startswith("data:"):
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img_base64 = f"data:image/jpeg;base64,{img_base64}"
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user_content.append({
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"type": "image_url",
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"image_url": {"url": img_base64}
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})
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# 构建消息
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system_prompt = "你是一个图片生成助手。请根据用户的描述生成图片。直接生成图片，不需要额外解释。"
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if reference_images:
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system_prompt += "\n用户提供了参考图片，请根据参考图片和用户的描述来生成或修改图片。"
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messages = [
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{
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"role": "system",
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"content": "你是一个图片生成助手。请根据用户的描述生成图片。直接生成图片，不需要额外解释。"
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"content": system_prompt
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},
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{
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"role": "user",
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"content": prompt
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"content": user_content if reference_images else prompt
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}
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]
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@ -493,7 +518,8 @@ class SubModelCaller:
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self,
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current_preset: PresetConfig,
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prompt: str,
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preferred_model: str | None = None
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preferred_model: str | None = None,
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reference_images: list[str] | None = None
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) -> dict[str, Any]:
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"""生成视频
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@ -501,6 +527,7 @@ class SubModelCaller:
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current_preset: 当前主模型预设
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prompt: 视频生成提示词
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preferred_model: 可选的指定模型名称
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reference_images: 可选的参考图片列表（base64 编码）
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Returns:
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包含生成结果的字典
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@ -521,14 +548,33 @@ class SubModelCaller:
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key=lambda p: 0 if p.name == preferred_model else 1
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)
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# 构建用户消息内容
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user_content: list[dict[str, Any]] = []
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user_content.append({"type": "text", "text": prompt})
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# 如果有参考图片，添加到消息中
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if reference_images:
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logger.info(f"视频生成包含 {len(reference_images)} 张参考图片")
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for img_base64 in reference_images:
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if not img_base64.startswith("data:"):
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img_base64 = f"data:image/jpeg;base64,{img_base64}"
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user_content.append({
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"type": "image_url",
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"image_url": {"url": img_base64}
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})
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system_prompt = "你是一个视频生成助手。请根据用户的描述生成视频。"
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if reference_images:
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system_prompt += "\n用户提供了参考图片，请根据参考图片和用户的描述来生成视频。"
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messages = [
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{
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"role": "system",
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"content": "你是一个视频生成助手。请根据用户的描述生成视频。"
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"content": system_prompt
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},
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{
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"role": "user",
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"content": prompt
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"content": user_content if reference_images else prompt
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}
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]
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@ -566,7 +612,8 @@ class SubModelCaller:
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self,
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tool_name: str,
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tool_args: dict[str, Any],
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current_preset: PresetConfig
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current_preset: PresetConfig,
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reference_images: list[str] | None = None
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) -> dict[str, Any]:
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"""工具调用入口
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@ -574,6 +621,7 @@ class SubModelCaller:
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tool_name: 工具名称
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tool_args: 工具参数
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current_preset: 当前主模型预设
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reference_images: 可选的参考图片列表（base64 编码），来自用户消息
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Returns:
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工具调用结果
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@ -582,7 +630,8 @@ class SubModelCaller:
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return await self.generate_image(
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current_preset=current_preset,
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prompt=tool_args.get("prompt", ""),
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preferred_model=tool_args.get("preferred_model")
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preferred_model=tool_args.get("preferred_model"),
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reference_images=reference_images
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)
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elif tool_name == "submodel__generate_voice":
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return await self.generate_voice(
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@ -594,7 +643,8 @@ class SubModelCaller:
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return await self.generate_video(
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current_preset=current_preset,
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prompt=tool_args.get("prompt", ""),
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preferred_model=tool_args.get("preferred_model")
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preferred_model=tool_args.get("preferred_model"),
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reference_images=reference_images
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)
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else:
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return {
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