284 lines
14 KiB
Python
284 lines
14 KiB
Python
import asyncio
|
|
import random
|
|
import time
|
|
import threading
|
|
import hashlib
|
|
import re
|
|
|
|
from comfy.model_management import InterruptProcessingException
|
|
|
|
from ..services.llm import LLMService
|
|
from ..utils.common import format_api_error, format_model_with_thinking, generate_request_id, log_prepare, log_error, TASK_EXPAND, SOURCE_NODE
|
|
from ..services.thinking_control import build_thinking_suppression
|
|
from .base import LLMNodeBase
|
|
|
|
|
|
class PromptExpand(LLMNodeBase):
|
|
"""
|
|
提示词增强节点
|
|
- 输入"source_text"，根据所选规则模板或自定义规则进行增强/扩写
|
|
- 仅包含一个字符串输入和一个字符串输出
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
# 从config_manager获取系统提示词配置
|
|
from ..config_manager import config_manager
|
|
system_prompts = config_manager.get_system_prompts()
|
|
|
|
# 获取所有expand_prompts作为下拉选项
|
|
expand_prompts = {}
|
|
active_expand_id = None
|
|
if system_prompts:
|
|
expand_prompts = system_prompts.get('expand_prompts', {}) or {}
|
|
active_expand_id = system_prompts.get('active_prompts', {}).get('expand')
|
|
|
|
# 构建提示词模板选项（支持分类格式：类别/规则名称）
|
|
prompt_template_options = []
|
|
id_to_display_name = {}
|
|
for key, value in expand_prompts.items():
|
|
# 过滤掉不在后端显示的规则
|
|
show_in = value.get('showIn', ["frontend", "node"])
|
|
if 'node' not in show_in:
|
|
continue
|
|
|
|
name = value.get('name', key)
|
|
category = value.get('category', '')
|
|
# 如果有分类，显示为 "类别/规则名称"，否则直接显示规则名称
|
|
display_name = f"{category}/{name}" if category else name
|
|
id_to_display_name[key] = display_name
|
|
prompt_template_options.append(display_name)
|
|
|
|
# 默认选项回退
|
|
default_template_name = prompt_template_options[0] if prompt_template_options else "扩写-自然语言"
|
|
if active_expand_id and active_expand_id in id_to_display_name:
|
|
default_template_name = id_to_display_name[active_expand_id]
|
|
|
|
# ---动态获取LLM服务/模型列表---
|
|
service_options = cls.get_llm_service_options()
|
|
default_service = service_options[0] if service_options else "智谱"
|
|
|
|
return {
|
|
"required": {
|
|
# 规则模板：来自系统配置的所有扩写规则
|
|
"rule": (prompt_template_options or ["扩写-自然语言"], {"default": default_template_name, "tooltip": "Choose a preset rule for prompt enhancement"}),
|
|
# 临时规则开关
|
|
"custom_rule": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable", "tooltip": "Enable to use custom rule content below instead of preset"}),
|
|
# 临时规则内容输入框
|
|
"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", "tooltip": "在此输入您的自定义规则内容; 💡输入触发词[R],可以让节点每次都被执行"}),
|
|
# 用户提示词
|
|
"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", "tooltip": "想要增强的原始提示词; 💡输入触发词[R],可以让节点每次都被执行"}),
|
|
# 扩写服务
|
|
"llm_service": (service_options, {"default": default_service, "tooltip": "Select LLM service and model"}),
|
|
# Ollama自动释放显存
|
|
"ollama_auto_unload": ("BOOLEAN", {"default": True, "label_on": "Enable", "label_off": "Disable", "tooltip": "Auto unload Ollama model after generation"}),
|
|
},
|
|
"optional": {
|
|
# 原文输入端口
|
|
"source_text": ("STRING", {"default": "", "multiline": True, "defaultInput": True, "placeholder": "Input text to enhance...", "tooltip": "可选的输入文本; 💡输入触发词[R],可以让节点每次都被执行"}),
|
|
},
|
|
"hidden": {
|
|
"unique_id": "UNIQUE_ID",
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("enhanced_text",)
|
|
FUNCTION = "enhance"
|
|
CATEGORY = "✨Prompt Assistant"
|
|
OUTPUT_NODE = False
|
|
|
|
@classmethod
|
|
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):
|
|
"""
|
|
只在输入内容真正变化时才触发重新执行
|
|
使用输入参数的哈希值作为判断依据
|
|
"""
|
|
# 检查是否包含强制刷新符号 [R]
|
|
if cls._check_is_changed_bypass(rule, custom_rule_content, user_prompt, source_text):
|
|
return float("nan")
|
|
|
|
text_hash = hashlib.md5(((source_text or "")).encode('utf-8')).hexdigest()
|
|
temp_rule_hash = hashlib.md5((custom_rule_content or "").encode('utf-8')).hexdigest()
|
|
user_hint_hash = hashlib.md5((user_prompt or "").encode('utf-8')).hexdigest()
|
|
|
|
input_hash = hash((
|
|
rule,
|
|
bool(custom_rule),
|
|
temp_rule_hash,
|
|
user_hint_hash,
|
|
llm_service,
|
|
bool(ollama_auto_unload),
|
|
text_hash,
|
|
))
|
|
return input_hash
|
|
|
|
def enhance(self, rule, custom_rule, custom_rule_content, user_prompt, llm_service, ollama_auto_unload, source_text=None, unique_id=None):
|
|
"""
|
|
增强/扩写文本函数
|
|
"""
|
|
try:
|
|
# 允许原文为空，但原文与用户提示词至少有一项非空
|
|
source_text = (source_text or "").strip()
|
|
user_prompt = (user_prompt or "").strip()
|
|
if not source_text and not user_prompt:
|
|
return ("",)
|
|
|
|
# 准备系统提示词（规则）
|
|
system_message = None
|
|
rule_name = "Custom Rule" if (custom_rule and custom_rule_content) else rule
|
|
|
|
if custom_rule and custom_rule_content:
|
|
# 使用临时规则
|
|
system_message = {"role": "system", "content": custom_rule_content}
|
|
else:
|
|
# 使用模板：从config_manager获取系统提示词配置
|
|
from ..config_manager import config_manager
|
|
system_prompts = config_manager.get_system_prompts()
|
|
expand_prompts = system_prompts.get('expand_prompts', {}) if system_prompts else {}
|
|
|
|
# 查找选定的提示词模板（按显示名称匹配）
|
|
# 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"
|
|
template_found = False
|
|
for key, value in expand_prompts.items():
|
|
name = value.get('name', key)
|
|
category = value.get('category', '')
|
|
# 构建与下拉列表一致的显示名称
|
|
display_name = f"{category}/{name}" if category else name
|
|
if display_name == rule:
|
|
system_message = {"role": value.get('role', 'system'), "content": value.get('content', '')}
|
|
template_found = True
|
|
break
|
|
if not template_found:
|
|
# 允许用规则名称或键名直接匹配（兼容旧格式）
|
|
for key, value in expand_prompts.items():
|
|
if value.get('name') == rule or key == rule:
|
|
system_message = {"role": value.get('role', 'system'), "content": value.get('content', '')}
|
|
template_found = True
|
|
break
|
|
if not template_found or not system_message or not system_message.get('content'):
|
|
# 回退到默认
|
|
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。"}
|
|
rule_name = "Default Rule"
|
|
|
|
# ---解析服务/模型字符串---
|
|
service_id, model_name = self.parse_service_model(llm_service)
|
|
if not service_id:
|
|
raise ValueError(f"Invalid service selection: {llm_service}")
|
|
|
|
# ---获取服务配置---
|
|
from ..config_manager import config_manager
|
|
service = config_manager.get_service(service_id)
|
|
if not service:
|
|
raise ValueError(f"Service config not found: {llm_service}")
|
|
|
|
# ---构建provider_config---
|
|
# 查找指定的模型或默认模型
|
|
llm_models = service.get('llm_models', [])
|
|
target_model = None
|
|
|
|
if model_name:
|
|
# 查找指定的模型
|
|
target_model = next((m for m in llm_models if m.get('name') == model_name), None)
|
|
|
|
if not target_model:
|
|
# 使用默认模型或第一个模型
|
|
target_model = next((m for m in llm_models if m.get('is_default')),
|
|
llm_models[0] if llm_models else None)
|
|
|
|
if not target_model:
|
|
raise ValueError(f"Service {llm_service} has no available models")
|
|
|
|
# 构建配置对象
|
|
provider_config = {
|
|
'provider': service_id,
|
|
'model': target_model.get('name', ''),
|
|
'base_url': service.get('base_url', ''),
|
|
'api_key': service.get('api_key', ''),
|
|
'temperature': target_model.get('temperature', 0.7),
|
|
'max_tokens': target_model.get('max_tokens', 1000),
|
|
'top_p': target_model.get('top_p', 0.9),
|
|
}
|
|
|
|
# Ollama特殊处理:添加auto_unload配置
|
|
if service.get('type') == 'ollama':
|
|
provider_config['auto_unload'] = ollama_auto_unload
|
|
|
|
# 执行扩写（异步线程 + 可中断）
|
|
request_id = generate_request_id("exp", None, unique_id)
|
|
|
|
# 合并原文与用户提示词
|
|
# 合并顺序：输入端口(source_text)在前，节点输入框(user_prompt)在后
|
|
combined_text = user_prompt if not source_text else (f"{source_text}\n\n{user_prompt}" if user_prompt else source_text)
|
|
|
|
# 检查是否关闭思维链
|
|
model_name = provider_config.get('model')
|
|
disable_thinking_enabled = service.get('disable_thinking', True)
|
|
thinking_extra = build_thinking_suppression(service_id, model_name) if disable_thinking_enabled else None
|
|
model_display = format_model_with_thinking(model_name, bool(thinking_extra))
|
|
|
|
# 获取服务显示名称
|
|
service_display_name = service.get('name', service_id)
|
|
|
|
# 准备阶段日志
|
|
log_prepare(TASK_EXPAND, request_id, SOURCE_NODE, service_display_name, model_display, rule_name, {"长度": len(combined_text)})
|
|
|
|
# 检查API密钥和模型
|
|
api_key = provider_config.get('api_key', '')
|
|
model = provider_config.get('model', '')
|
|
|
|
if not api_key or not model:
|
|
raise ValueError(f"Please configure API key and model for {llm_service}")
|
|
|
|
# 执行扩写（异步线程 + 可中断）
|
|
result = self._run_llm_task(
|
|
LLMService.expand_prompt,
|
|
service_id,
|
|
prompt=combined_text,
|
|
request_id=request_id,
|
|
stream_callback=None,
|
|
custom_provider=service_id,
|
|
custom_provider_config=provider_config,
|
|
system_message_override=system_message,
|
|
task_type=TASK_EXPAND,
|
|
source=SOURCE_NODE
|
|
)
|
|
|
|
if result and result.get('success'):
|
|
expanded_text = result.get('data', {}).get('expanded', '').strip()
|
|
if not expanded_text:
|
|
error_msg = 'API returned empty result'
|
|
log_error(TASK_EXPAND, request_id, error_msg, source=SOURCE_NODE)
|
|
raise RuntimeError(f"Enhancement failed: {error_msg}")
|
|
# 结果阶段日志由服务层统一输出，节点层不再重复打印
|
|
return (expanded_text,)
|
|
else:
|
|
error_msg = result.get('error', 'Unknown error') if result else 'No result returned'
|
|
# 如果是中断错误,直接抛出InterruptProcessingException,不打印日志(由基类打印)
|
|
if error_msg == "任务被中断":
|
|
raise InterruptProcessingException()
|
|
log_error(TASK_EXPAND, request_id, error_msg, source=SOURCE_NODE)
|
|
raise RuntimeError(f"Enhancement failed: {error_msg}")
|
|
|
|
except InterruptProcessingException:
|
|
# 不打印日志,由基类统一打印
|
|
raise
|
|
except Exception as e:
|
|
error_msg = format_api_error(e, llm_service)
|
|
log_error(TASK_EXPAND, request_id, error_msg, source=SOURCE_NODE)
|
|
raise RuntimeError(f"Enhancement error: {error_msg}")
|
|
|
|
# _get_provider_config 方法已由基类 LLMNodeBase 提供
|
|
|
|
|
|
|
|
# 节点映射，用于向ComfyUI注册节点
|
|
NODE_CLASS_MAPPINGS = {
|
|
"PromptExpand": PromptExpand,
|
|
}
|
|
|
|
# 节点显示名称映射
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"PromptExpand": "✨Prompt Enhance",
|
|
}
|