284 lines
13 KiB
Python
284 lines
13 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 ..utils.common import format_api_error, format_model_with_thinking, generate_request_id, log_prepare, log_error, TASK_EXPAND, SOURCE_NODE
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from ..services.thinking_control import build_thinking_suppression
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from .base import LLMNodeBase
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class PromptExpand(LLMNodeBase):
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"""
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提示词增强节点
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- 输入"source_text"，根据所选规则模板或自定义规则进行增强/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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@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（支持分类格式：类别/规则名称）
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prompt_template_options = []
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id_to_display_name = {}
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for key, value in expand_prompts.items():
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# 过滤掉不在后端显示的规则
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show_in = value.get('showIn', ["frontend", "node"])
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if 'node' not in show_in:
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continue
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name = value.get('name', key)
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category = value.get('category', '')
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# 如果有分类，显示为 "类别/规则名称"，否则直接显示规则名称
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display_name = f"{category}/{name}" if category else name
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id_to_display_name[key] = display_name
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prompt_template_options.append(display_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_display_name:
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default_template_name = id_to_display_name[active_expand_id]
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# ---动态获取LLM服务/模型列表---
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service_options = cls.get_llm_service_options()
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default_service = service_options[0] if service_options else "智谱"
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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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"rule": (prompt_template_options or ["扩写-自然语言"], {"default": default_template_name, "tooltip": "Choose a preset rule for prompt enhancement"}),
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# 临时规则开关
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"custom_rule": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable", "tooltip": "Enable to use custom rule content below instead of preset"}),
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# 临时规则内容输入框
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"custom_rule_content": ("STRING", {"multiline": True, "default": "", "placeholder": "在此输入临时规则，仅在启用'临时规则'时生效"}),
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# 用户提示词
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"user_prompt": ("STRING", {"multiline": True, "default": "", "placeholder": "填写的要优化的提示词原文，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
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"llm_service": (service_options, {"default": default_service, "tooltip": "Select LLM service and model"}),
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# Ollama自动释放显存
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"ollama_auto_unload": ("BOOLEAN", {"default": True, "label_on": "Enable", "label_off": "Disable", "tooltip": "Auto unload Ollama model after generation"}),
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},
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"optional": {
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# 原文输入端口
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"source_text": ("STRING", {"default": "", "multiline": True, "defaultInput": True, "placeholder": "Input text to enhance..."}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("enhanced_text",)
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FUNCTION = "enhance"
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CATEGORY = "✨Prompt Assistant"
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OUTPUT_NODE = False
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@classmethod
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def IS_CHANGED(cls, rule=None, custom_rule=None, custom_rule_content=None, user_prompt=None, llm_service=None, ollama_auto_unload=None, source_text=None, unique_id=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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# 检查是否包含强制刷新符号 [R]
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if cls._check_is_changed_bypass(rule, custom_rule_content, user_prompt, source_text):
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return float("nan")
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text_hash = hashlib.md5(((source_text or "")).encode('utf-8')).hexdigest()
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temp_rule_hash = hashlib.md5((custom_rule_content or "").encode('utf-8')).hexdigest()
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user_hint_hash = hashlib.md5((user_prompt or "").encode('utf-8')).hexdigest()
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input_hash = hash((
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rule,
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bool(custom_rule),
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temp_rule_hash,
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user_hint_hash,
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llm_service,
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bool(ollama_auto_unload),
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text_hash,
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))
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return input_hash
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def enhance(self, rule, custom_rule, custom_rule_content, user_prompt, llm_service, ollama_auto_unload, source_text=None, unique_id=None):
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"""
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增强/扩写文本函数
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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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source_text = (source_text or "").strip()
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user_prompt = (user_prompt or "").strip()
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if not source_text and not user_prompt:
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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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rule_name = "Custom Rule" if (custom_rule and custom_rule_content) else rule
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if custom_rule and custom_rule_content:
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# 使用临时规则
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system_message = {"role": "system", "content": custom_rule_content}
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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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# 显示名称格式：有分类时为 "类别/规则名称"，无分类时为 "规则名称"
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template_found = False
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for key, value in expand_prompts.items():
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name = value.get('name', key)
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category = value.get('category', '')
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# 构建与下拉列表一致的显示名称
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display_name = f"{category}/{name}" if category else name
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if display_name == rule:
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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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break
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if not template_found:
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# 允许用规则名称或键名直接匹配（兼容旧格式）
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for key, value in expand_prompts.items():
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if value.get('name') == rule or key == rule:
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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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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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rule_name = "Default Rule"
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# ---解析服务/模型字符串---
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service_id, model_name = self.parse_service_model(llm_service)
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if not service_id:
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raise ValueError(f"Invalid service selection: {llm_service}")
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# ---获取服务配置---
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from ..config_manager import config_manager
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service = config_manager.get_service(service_id)
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if not service:
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raise ValueError(f"Service config not found: {llm_service}")
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# ---构建provider_config---
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# 查找指定的模型或默认模型
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llm_models = service.get('llm_models', [])
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target_model = None
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if model_name:
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# 查找指定的模型
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target_model = next((m for m in llm_models if m.get('name') == model_name), None)
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if not target_model:
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# 使用默认模型或第一个模型
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target_model = next((m for m in llm_models if m.get('is_default')),
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llm_models[0] if llm_models else None)
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if not target_model:
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raise ValueError(f"Service {llm_service} has no available models")
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# 构建配置对象
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provider_config = {
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'provider': service_id,
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'model': target_model.get('name', ''),
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'base_url': service.get('base_url', ''),
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'api_key': service.get('api_key', ''),
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'temperature': target_model.get('temperature', 0.7),
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'max_tokens': target_model.get('max_tokens', 1000),
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'top_p': target_model.get('top_p', 0.9),
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}
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# Ollama特殊处理:添加auto_unload配置
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if service.get('type') == 'ollama':
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provider_config['auto_unload'] = ollama_auto_unload
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# 执行扩写（异步线程 + 可中断）
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request_id = generate_request_id("exp", None, unique_id)
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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(source_text)在前，节点输入框(user_prompt)在后
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combined_text = user_prompt if not source_text else (f"{source_text}\n\n{user_prompt}" if user_prompt else source_text)
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# 检查是否关闭思维链
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model_name = provider_config.get('model')
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disable_thinking_enabled = service.get('disable_thinking', True)
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thinking_extra = build_thinking_suppression(service_id, model_name) if disable_thinking_enabled else None
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model_display = format_model_with_thinking(model_name, bool(thinking_extra))
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# 获取服务显示名称
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service_display_name = service.get('name', service_id)
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# 准备阶段日志
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log_prepare(TASK_EXPAND, request_id, SOURCE_NODE, service_display_name, model_display, rule_name, {"长度": len(combined_text)})
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# 检查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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if not api_key or not model:
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raise ValueError(f"Please configure API key and model for {llm_service}")
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# 执行扩写（异步线程 + 可中断）
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result = self._run_llm_task(
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LLMService.expand_prompt,
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service_id,
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prompt=combined_text,
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request_id=request_id,
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stream_callback=None,
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custom_provider=service_id,
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custom_provider_config=provider_config,
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system_message_override=system_message,
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task_type=TASK_EXPAND,
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source=SOURCE_NODE
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)
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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 returned empty result'
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log_error(TASK_EXPAND, request_id, error_msg, source=SOURCE_NODE)
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raise RuntimeError(f"Enhancement failed: {error_msg}")
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# 结果阶段日志由服务层统一输出，节点层不再重复打印
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return (expanded_text,)
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else:
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error_msg = result.get('error', 'Unknown error') if result else 'No result returned'
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# 如果是中断错误,直接抛出InterruptProcessingException,不打印日志(由基类打印)
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if error_msg == "任务被中断":
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raise InterruptProcessingException()
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log_error(TASK_EXPAND, request_id, error_msg, source=SOURCE_NODE)
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raise RuntimeError(f"Enhancement failed: {error_msg}")
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except InterruptProcessingException:
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# 不打印日志,由基类统一打印
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raise
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except Exception as e:
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error_msg = format_api_error(e, llm_service)
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log_error(TASK_EXPAND, request_id, error_msg, source=SOURCE_NODE)
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raise RuntimeError(f"Enhancement error: {error_msg}")
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# _get_provider_config 方法已由基类 LLMNodeBase 提供
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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": "✨Prompt Enhance",
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}
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