327 lines
14 KiB
Python
327 lines
14 KiB
Python
import asyncio
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import random
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import time
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import threading
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import hashlib
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import re
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from comfy.model_management import InterruptProcessingException
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from ..services.llm import LLMService
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from ..services.error_util import format_api_error
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# 定义ANSI颜色代码常量
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GREEN = "\033[92m"
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RESET = "\033[0m"
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class PromptExpand:
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"""
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文本扩写节点
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- 输入“原文”，根据所选扩写规则模板或临时规则进行扩写
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- 仅包含一个字符串输入和一个字符串输出
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"""
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# 定义日志前缀（带绿色）
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LOG_PREFIX = f"{GREEN}[PromptAssistant]{RESET}"
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@classmethod
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def INPUT_TYPES(cls):
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# 从config_manager获取系统提示词配置
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from ..config_manager import config_manager
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system_prompts = config_manager.get_system_prompts()
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# 获取所有expand_prompts作为下拉选项
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expand_prompts = {}
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active_expand_id = None
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if system_prompts:
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expand_prompts = system_prompts.get('expand_prompts', {}) or {}
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active_expand_id = system_prompts.get('active_prompts', {}).get('expand')
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# 构建提示词模板选项（显示name）
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prompt_template_options = []
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id_to_name = {}
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for key, value in expand_prompts.items():
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name = value.get('name', key)
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id_to_name[key] = name
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prompt_template_options.append(name)
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# 默认选项回退
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default_template_name = prompt_template_options[0] if prompt_template_options else "扩写-自然语言"
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if active_expand_id and active_expand_id in id_to_name:
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default_template_name = id_to_name[active_expand_id]
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return {
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"required": {
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# 规则模板：来自系统配置的所有扩写规则
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"规则模板": (prompt_template_options or ["扩写-自然语言"], {"default": default_template_name}),
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# 临时规则开关：BOOLEAN，并添加中文开关标签
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"临时规则": ("BOOLEAN", {"default": False, "label_on": "启用", "label_off": "禁用"}),
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# 临时规则内容输入框：仅在启用临时规则时生效
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"临时规则内容": ("STRING", {"multiline": True, "default": "", "placeholder": "请输入临时规则内容，仅在启用'临时规则'时生效"}),
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# 用户提示词：与原文合并后提交
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"用户提示词": ("STRING", {"multiline": True, "default": "", "placeholder": "填写的要扩写的提示词原文，若存在原文端口输入和内容输入，将合并提交"}),
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# 扩写服务（使用LLM提供商）
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"扩写服务": (["智谱", "硅基流动", "302.AI", "Ollama", "自定义"], {"default": "智谱"}),
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},
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"optional": {
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# 原文输入端口：非必选
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"原文": ("STRING", {"default": "", "multiline": True, "defaultInput": True, "placeholder": "输入需要扩写的文本..."}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("扩写结果",)
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FUNCTION = "expand"
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CATEGORY = "✨提示词小助手"
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OUTPUT_NODE = False
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@classmethod
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def IS_CHANGED(cls, 规则模板, 临时规则, 临时规则内容, 用户提示词, 扩写服务, 原文=None):
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"""
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只在输入内容真正变化时才触发重新执行
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使用输入参数的哈希值作为判断依据
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"""
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text_hash = hashlib.md5(((原文 or "")).encode('utf-8')).hexdigest()
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temp_rule_hash = hashlib.md5((临时规则内容 or "").encode('utf-8')).hexdigest()
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user_hint_hash = hashlib.md5((用户提示词 or "").encode('utf-8')).hexdigest()
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input_hash = hash((
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规则模板,
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bool(临时规则),
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temp_rule_hash,
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user_hint_hash,
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扩写服务,
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text_hash,
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))
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return input_hash
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def expand(self, 规则模板, 临时规则, 临时规则内容, 用户提示词, 扩写服务, 原文=None):
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"""
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扩写文本函数
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Args:
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原文: 输入的文本（可为空）
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规则模板: 选择的扩写规则模板名称（来自系统配置）
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临时规则: 是否启用临时规则（BOOLEAN）
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临时规则内容: 临时规则的内容（启用时生效）
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用户提示词: 附加的用户提示信息，将与原文合并提交
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扩写服务: LLM提供商（智谱/硅基流动/自定义）
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Returns:
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tuple: 扩写结果
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"""
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try:
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# 允许原文为空，但原文与用户提示词至少有一项非空
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原文 = (原文 or "").strip()
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用户提示词 = (用户提示词 or "").strip()
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if not 原文 and not 用户提示词:
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return ("",)
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# 准备系统提示词（规则）
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system_message = None
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if 临时规则 and 临时规则内容:
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# 使用临时规则
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system_message = {"role": "system", "content": 临时规则内容}
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print(f"{self.LOG_PREFIX} 扩写: 使用临时规则")
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else:
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# 使用模板：从config_manager获取系统提示词配置
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from ..config_manager import config_manager
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system_prompts = config_manager.get_system_prompts()
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expand_prompts = system_prompts.get('expand_prompts', {}) if system_prompts else {}
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# 查找选定的提示词模板（按显示名称匹配）
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template_found = False
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for key, value in expand_prompts.items():
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if value.get('name') == 规则模板:
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system_message = {"role": value.get('role', 'system'), "content": value.get('content', '')}
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template_found = True
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print(f"{self.LOG_PREFIX} 扩写: 使用模板 '{规则模板}'")
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break
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if not template_found:
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# 允许用键名直接匹配
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for key, value in expand_prompts.items():
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if key == 规则模板:
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system_message = {"role": value.get('role', 'system'), "content": value.get('content', '')}
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template_found = True
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print(f"{self.LOG_PREFIX} 扩写: 使用模板 '{规则模板}'(按键名)")
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break
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if not template_found or not system_message or not system_message.get('content'):
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# 回退到默认
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system_message = {"role": "system", "content": "你是一名提示词扩写专家，请将用户给定文本扩写为更完整、更具可读性和可执行性的提示词。"}
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print(f"{self.LOG_PREFIX} 扩写: 未找到模板 '{规则模板}'，使用默认扩写提示词")
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# 选择服务提供商
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provider_map = {
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"智谱": "zhipu",
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"硅基流动": "siliconflow",
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"302.AI": "302ai",
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"Ollama": "ollama",
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"自定义": "custom",
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}
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selected_provider = provider_map.get(扩写服务)
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if not selected_provider:
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raise ValueError(f"不支持的扩写服务: {扩写服务}")
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# 获取对应provider的配置
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from ..config_manager import config_manager
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provider_config = self._get_provider_config(config_manager, selected_provider)
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if not provider_config:
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provider_display = {"zhipu": "智谱", "siliconflow": "硅基流动", "302ai": "302.AI", "ollama": "Ollama", "custom": "自定义"}.get(selected_provider, selected_provider)
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raise ValueError(f"未找到{provider_display}的配置，请先完成API配置")
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print(f"{self.LOG_PREFIX} 扩写: 使用{扩写服务}服务, 模型: {provider_config.get('model')}")
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# 执行扩写（异步线程 + 可中断）
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request_id = f"expand_{int(time.time())}_{random.randint(1000, 9999)}"
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result_container = {}
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# 合并原文与用户提示词
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# 合并顺序：输入端口(原文)在前，节点输入框(用户提示词)在后
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combined_text = 用户提示词 if not 原文 else (f"{原文}\n\n{用户提示词}" if 用户提示词 else 原文)
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thread = threading.Thread(
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target=self._run_llm_expand,
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args=(combined_text, system_message, request_id, result_container, selected_provider, provider_config)
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)
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thread.start()
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# 等待扩写完成，同时检查中断
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while thread.is_alive():
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try:
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import nodes
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nodes.before_node_execution()
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except:
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print(f"{self.LOG_PREFIX} 检测到中断信号，正在终止扩写任务...")
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result_container['interrupted'] = True
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thread.join(timeout=1.0)
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if thread.is_alive():
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print(f"{self.LOG_PREFIX} 扩写线程未能及时响应中断")
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raise InterruptProcessingException()
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time.sleep(0.1)
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result = result_container.get('result')
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if result and result.get('success'):
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expanded_text = result.get('data', {}).get('expanded', '').strip()
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if not expanded_text:
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error_msg = 'API返回结果为空，请检查API密钥、模型配置或网络连接'
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print(f"{self.LOG_PREFIX} 扩写失败: {error_msg}")
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raise RuntimeError(f"扩写失败: {error_msg}")
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print(f"{self.LOG_PREFIX} 扩写完成，结果长度: {len(expanded_text)}")
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return (expanded_text,)
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else:
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error_msg = result.get('error', '扩写失败，未知错误') if result else '扩写服务未返回结果'
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print(f"{self.LOG_PREFIX} 扩写失败: {error_msg}")
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raise RuntimeError(f"扩写失败: {error_msg}")
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except InterruptProcessingException:
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print(f"{self.LOG_PREFIX} 扩写任务被用户取消")
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raise
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except Exception as e:
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error_msg = format_api_error(e, 扩写服务)
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print(f"{self.LOG_PREFIX} 扩写异常: {error_msg}")
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raise RuntimeError(f"扩写异常: {error_msg}")
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def _get_provider_config(self, config_manager, provider):
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"""获取指定provider的配置（LLM）"""
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llm_config = config_manager.get_llm_config()
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if 'providers' in llm_config and provider in llm_config['providers']:
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return llm_config['providers'][provider]
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return None
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def _run_llm_expand(self, text, system_message, request_id, result_container, provider, provider_config):
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"""在独立线程中运行LLM扩写"""
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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if result_container.get('interrupted'):
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result_container['result'] = {"success": False, "error": "任务被中断"}
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return
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# 获取API密钥和模型
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api_key = provider_config.get('api_key', '')
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model = provider_config.get('model', '')
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temperature = provider_config.get('temperature', 0.7)
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top_p = provider_config.get('top_p', 0.9)
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max_tokens = provider_config.get('max_tokens', 1500)
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if not api_key or not model:
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result_container['result'] = {
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"success": False,
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"error": f"请先配置{provider}的API密钥和模型"
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}
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return
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# 创建客户端
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client = LLMService.get_openai_client(api_key, provider)
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# 判断输入语言，设置输出语言
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def is_chinese(text):
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return any('\u4e00' <= ch <= '\u9fff' for ch in text)
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lang_message = {"role": "system", "content": "请用中文回答"} if is_chinese(text) else {"role": "system", "content": "Please answer in English."}
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messages = [
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lang_message,
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system_message,
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{"role": "user", "content": text},
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]
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print(f"{self.LOG_PREFIX} 调用{provider}扩写API | 模型:{model}")
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stream = loop.run_until_complete(client.chat.completions.create(
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model=model,
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messages=[{"role": m["role"], "content": m["content"]} for m in messages],
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temperature=temperature,
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top_p=top_p,
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max_tokens=max_tokens,
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stream=True,
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response_format={"type": "text"}
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))
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full_content = ""
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async def process_stream():
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nonlocal full_content
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async for chunk in stream:
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if result_container.get('interrupted'):
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break
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if chunk.choices[0].delta.content:
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full_content += chunk.choices[0].delta.content
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loop.run_until_complete(process_stream())
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if result_container.get('interrupted'):
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result_container['result'] = {"success": False, "error": "任务被中断"}
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return
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result_container['result'] = {
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"success": True,
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"data": {
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"original": text,
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"expanded": full_content,
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},
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}
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print(f"{self.LOG_PREFIX} 扩写完成 | 服务:{provider} | 结果字符数:{len(full_content)}")
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except Exception as e:
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result_container['result'] = {
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"success": False,
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"error": format_api_error(e, provider)
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}
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print(f"{self.LOG_PREFIX} 扩写失败 | 服务:{provider} | 错误:{result_container['result']['error']}")
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finally:
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loop.close()
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# 节点映射，用于向ComfyUI注册节点
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NODE_CLASS_MAPPINGS = {
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"PromptExpand": PromptExpand,
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}
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# 节点显示名称映射
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NODE_DISPLAY_NAME_MAPPINGS = {
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"PromptExpand": "✨提示词扩写",
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}
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