652 lines
30 KiB
Python
652 lines
30 KiB
Python
"""
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LLM服务 - 重构版本
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提供大语言模型的扩写和翻译功能
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继承OpenAICompatibleService以复用通用逻辑
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"""
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import json
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import time
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import asyncio
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from typing import Optional, Dict, Any, List, Callable
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import httpx
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from .openai_base import OpenAICompatibleService, filter_thinking_content
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from ..utils.common import (
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format_api_error, ProgressBar, log_complete, log_error,
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PREFIX, PROCESS_PREFIX, WARN_PREFIX, ERROR_PREFIX, format_elapsed_time,
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TASK_EXPAND, TASK_TRANSLATE
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)
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from .thinking_control import build_thinking_suppression
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class LLMService(OpenAICompatibleService):
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"""
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大语言模型服务
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支持提示词扩写和文本翻译
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"""
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@staticmethod
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def _get_config() -> Dict[str, Any]:
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"""获取LLM配置"""
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from ..config_manager import config_manager
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config = config_manager.get_llm_config()
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current_provider = config.get('provider')
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if 'providers' in config and current_provider in config['providers']:
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provider_config = config['providers'][current_provider]
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return {
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'provider': current_provider,
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'model': provider_config.get('model', ''),
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'base_url': provider_config.get('base_url', ''),
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'api_key': provider_config.get('api_key', ''),
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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', 2000),
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'auto_unload': provider_config.get('auto_unload', True)
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}
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else:
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return config
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@staticmethod
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def _is_chinese(text: str) -> bool:
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"""判断文本是否包含中文"""
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return any('\u4e00' <= char <= '\u9fff' for char in text)
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@staticmethod
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async def _call_ollama_native(
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model: str,
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messages: List[Dict[str, str]],
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temperature: float,
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top_p: float,
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max_tokens: int,
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base_url: str,
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stream_callback: Optional[Callable[[str], None]] = None,
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request_id: Optional[str] = None,
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provider_display_name: str = "Ollama",
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auto_unload: bool = True,
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enable_advanced_params: bool = False,
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thinking_extra: Optional[Dict[str, Any]] = None,
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cancel_event: Optional[Any] = None,
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task_type: str = None,
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source: str = None
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) -> Dict[str, Any]:
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"""
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调用Ollama原生API（支持流式输出）
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用于支持智能上下文窗口和思维链控制
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参数:
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enable_advanced_params: 是否发送高级参数(temperature/top_p/num_predict)
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thinking_extra: 思维链控制参数
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"""
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# ---初始化请求参数---
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try:
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start_time = time.perf_counter()
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_thinking_extra = thinking_extra # 使用传入的参数
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_thinking_tag = "（已关闭思维链）" if _thinking_extra else ""
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# 计算基准 URL (确保移除 /v1 和末尾斜杠)
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native_base = base_url.rstrip('/') if base_url else 'http://localhost:11434'
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if native_base.endswith('/v1'):
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native_base = native_base[:-3].rstrip('/')
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# 智能动态上下文窗口计算 (Token估算策略: 中文0.7/char, 英文0.3/char -> 保守取 0.6/char)
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# 安全地计算输入长度 (包含 System Prompt 和 User Prompt，前提是都在 messages 中)
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input_char_len = 0
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for msg in messages:
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input_char_len += len(msg.get('content', '') or '')
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estimated_input_tokens = int(input_char_len * 0.6)
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# --- 智能预留策略 ---
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# 关键点：思考过程 (Thinking Process) 也占用 Output Token 额度
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# 1. 如果成功禁用了思维链 (_thinking_extra 非空) -> 安全
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# 2. 如果模型名明确包含 instruct/chat (通常无思考过程) -> 安全
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# 3. 其他未知模型 -> 假定可能存在思考过程，预留更多空间
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is_safe_standard_model = False
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if model:
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m = model.lower()
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if "instruct" in m or "chat" in m:
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is_safe_standard_model = True
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if _thinking_extra or is_safe_standard_model:
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# 已关闭思维链 OR 标准指令模型 -> 极致节省模式
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min_output = 512
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min_ctx = 1024
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else:
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# 未知/潜在思考模型 -> 安全能够模式 (为思考过程预留空间)
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min_output = 1024
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min_ctx = 2048
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# 任务类型预留
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output_reserve = max(min_output, int(estimated_input_tokens * 1.5))
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# 384为系统开销Buffer (System Prompt通常已在estimated_input_tokens中，这里是额外的安全余量)
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required_ctx = estimated_input_tokens + output_reserve + 384
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# 对齐到 1024 倍数
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# 限制在 [min_ctx, 32768] 范围内
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num_ctx = max(min_ctx, min(32768, required_ctx))
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num_ctx = ((num_ctx + 1023) // 1024) * 1024
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# 合并多条 System Message（Ollama 对多条 system 消息处理不佳）
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merged_messages = LLMService._merge_system_prompts(messages)
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# 构建请求（安全地构建messages列表）
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ollama_messages = []
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for msg in merged_messages:
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ollama_messages.append({
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"role": msg.get('role', 'user'),
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"content": msg.get('content', '')
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})
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# 构建基础请求体
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payload = {
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"model": model,
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"messages": ollama_messages,
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"stream": True
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}
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# ---构建 options---
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# 基础参数：num_ctx（动态上下文窗口大小）
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options = {
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"num_ctx": num_ctx
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}
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# 高级参数：仅在用户启用时发送
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# 参数说明（基于 Ollama 官方文档）：
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# - temperature: 控制随机性，默认0.8，值越低输出越稳定
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# - top_p: 核采样，默认0.9，限制候选词概率范围
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# - num_predict: 最大生成Token数，默认-1（无限）
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if enable_advanced_params:
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options["temperature"] = temperature
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options["top_p"] = top_p
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options["num_predict"] = max_tokens
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payload["options"] = options
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# 添加思维链控制参数（如 think: true 或 think: false）
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if _thinking_extra:
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payload.update(_thinking_extra)
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from ..server import is_streaming_progress_enabled
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# 动态超时计算: 基础30s + 每1000个Token预估增加5秒
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estimated_timeout = 30.0 + (num_ctx / 1000) * 5.0
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final_timeout = min(600.0, max(60.0, estimated_timeout)) # 限制在 60s - 600s 之间
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# 创建统一进度条（自动处理等待→生成→完成的完整生命周期）
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extra_info = f"Context:{num_ctx} | Timeout:{int(final_timeout)}s"
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pbar = ProgressBar(
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request_id=request_id,
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service_name=provider_display_name,
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extra_info=extra_info,
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streaming=is_streaming_progress_enabled(),
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task_type=task_type,
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source=source
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)
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start_time = time.perf_counter()
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async with httpx.AsyncClient(
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timeout=httpx.Timeout(final_timeout, connect=10.0, read=final_timeout),
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limits=httpx.Limits(max_keepalive_connections=5, max_connections=10)
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) as client:
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full_content = ""
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# 定义请求核心逻辑
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async def _request_core():
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async with client.stream('POST', f"{native_base}/api/chat", json=payload, follow_redirects=True) as resp:
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if resp.status_code != 200:
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error_text = await resp.aread()
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try:
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error_data = json.loads(error_text)
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return {"success": False, "error": error_data.get('error', f'HTTP {resp.status_code}')}
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except:
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return {"success": False, "error": f'HTTP {resp.status_code}'}
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nonlocal full_content
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async for line in resp.aiter_lines():
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if not line: continue
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try:
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chunk_data = json.loads(line)
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message = chunk_data.get('message')
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if message and isinstance(message, dict):
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content = message.get('content', '')
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if content and content.strip():
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full_content += content
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pbar.set_generating(len(full_content))
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pbar.update(len(full_content))
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if stream_callback: stream_callback(content)
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if chunk_data.get('done', False):
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pbar.done(char_count=len(full_content), elapsed_ms=int((time.perf_counter() - start_time) * 1000))
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break
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except:
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continue
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return {"success": True, "content": full_content.strip()}
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# 定义监视器逻辑
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async def _monitor_interrupts(target_task):
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while not target_task.done():
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is_interrupted = False
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if cancel_event is not None and cancel_event.is_set():
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is_interrupted = True
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else:
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try:
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from server import PromptServer
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if hasattr(PromptServer.instance, 'execution_interrupted') and PromptServer.instance.execution_interrupted:
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is_interrupted = True
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except: pass
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if is_interrupted:
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target_task.cancel()
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return True
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await asyncio.sleep(0.1)
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return False
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# 并发执行
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req_task = asyncio.create_task(_request_core())
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monitor_task = asyncio.create_task(_monitor_interrupts(req_task))
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try:
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result = await req_task
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# 关键修复：检查返回的结果，如果失败则停止进度条
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if not result.get("success"):
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pbar.error(result.get("error", "未知错误"))
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return result
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except Exception as req_err:
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if 'pbar' in locals() and pbar:
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pbar.error(f"Ollama 请求异常: {req_err}")
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return {"success": False, "error": f"Ollama 请求异常: {req_err}"}
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except asyncio.CancelledError:
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pbar.cancel(f"{WARN_PREFIX} 任务被中断 | 服务:Ollama")
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return {"success": False, "error": "任务被中断", "interrupted": True}
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finally:
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if not monitor_task.done(): monitor_task.cancel()
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# 强力显存释放保证：不仅是成功，中断也要释放
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if auto_unload:
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try:
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await cls._unload_ollama_model(model, {"base_url": native_base, "auto_unload": True})
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except: pass
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# 关键修复：单独捕获外层 CancelledError，确保 pbar 被正确停止
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except asyncio.CancelledError:
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if 'pbar' in locals() and pbar:
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pbar.cancel(f"{WARN_PREFIX} 任务被外部取消 | 服务:Ollama")
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return {"success": False, "error": "任务被取消", "interrupted": True}
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except Exception as e:
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# 关键修复：确保 pbar 在异常时也被停止
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if 'pbar' in locals() and pbar:
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pbar.error(format_api_error(e, provider_display_name))
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return {"success": False, "error": format_api_error(e, provider_display_name)}
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@staticmethod
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async def expand_prompt(
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prompt: str,
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request_id: Optional[str] = None,
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stream_callback: Optional[Callable[[str], None]] = None,
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custom_provider: Optional[str] = None,
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custom_provider_config: Optional[Dict[str, Any]] = None,
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system_message_override: Optional[Dict[str, str]] = None,
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cancel_event: Optional[Any] = None,
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task_type: str = None,
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source: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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使用大语言模型扩写提示词
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参数:
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prompt: 要扩写的提示词
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request_id: 请求ID
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stream_callback: 流式输出的回调函数
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custom_provider: 自定义服务商
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custom_provider_config: 自定义配置
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system_message_override: 覆盖系统提示词
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返回:
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Dict: {"success": bool, "data": {"original": str, "expanded": str}, "error": str}
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"""
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try:
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# 获取配置
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if custom_provider and custom_provider_config:
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provider = custom_provider
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api_key = custom_provider_config.get('api_key')
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model = custom_provider_config.get('model')
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temperature = custom_provider_config.get('temperature', 0.7)
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top_p = custom_provider_config.get('top_p', 0.9)
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max_tokens = custom_provider_config.get('max_tokens', 2000)
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base_url = custom_provider_config.get('base_url', '')
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else:
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config = LLMService._get_config()
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provider = config.get('provider', 'unknown')
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api_key = config.get('api_key')
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model = config.get('model')
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temperature = config.get('temperature', 0.7)
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top_p = config.get('top_p', 0.9)
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max_tokens = config.get('max_tokens', 2000)
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base_url = config.get('base_url', '')
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# 注：允许空API Key，支持无认证服务商（如deepinfra公开端点）
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if not model:
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return {"success": False, "error": "未配置模型名称"}
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provider_display_name = LLMService.get_provider_display_name(provider)
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from ..utils.common import REQUEST_PREFIX, PREFIX, format_model_with_thinking
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# 获取系统提示词
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if system_message_override and system_message_override.get('content'):
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system_message = system_message_override
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prompt_name = system_message.get('name', '节点自定义规则')
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else:
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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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if not system_prompts or 'expand_prompts' not in system_prompts:
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return {"success": False, "error": "提示词优化系统提示词加载失败"}
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active_prompt_id = system_prompts.get('active_prompts', {}).get('expand', 'expand_default')
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if active_prompt_id not in system_prompts['expand_prompts']:
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if len(system_prompts['expand_prompts']) > 0:
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active_prompt_id = list(system_prompts['expand_prompts'].keys())[0]
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else:
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return {"success": False, "error": "未找到可用的提示词优化系统提示词"}
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system_message = system_prompts['expand_prompts'][active_prompt_id]
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prompt_name = system_message.get('name', active_prompt_id)
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# 检查服务配置的disable_thinking参数,只有开启时才关闭思维链
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from ..config_manager import config_manager
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service = config_manager.get_service(provider)
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disable_thinking_enabled = service.get('disable_thinking', True) if service else True
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# 始终调用 build_thinking_suppression，传递 disable_thinking 参数
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_thinking_extra = build_thinking_suppression(provider, model, disable_thinking=disable_thinking_enabled)
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thinking_disabled = _thinking_extra is not None and disable_thinking_enabled
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model_display = format_model_with_thinking(model, thinking_disabled)
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# 构建消息
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lang_message = {
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"role": "system",
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"content": "请用中文回答" if LLMService._is_chinese(prompt) else "Please answer in English."
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}
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messages = [lang_message, system_message, {"role": "user", "content": prompt}]
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# Ollama走原生API (通过服务类型判断)
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if service and service.get('type') == 'ollama':
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# 读取 Ollama 服务的配置
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enable_advanced_params = service.get('enable_advanced_params', False)
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filter_thinking_output = service.get('filter_thinking_output', True)
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# 统一计算 native_base (确保移除 /v1 和末尾斜杠)
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native_base = base_url.rstrip('/')
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if native_base.endswith('/v1'):
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native_base = native_base[:-3].rstrip('/')
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# 再次兜底
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if not native_base:
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native_base = 'http://localhost:11434'
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# 提前计算auto_unload配置
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_cfg = {
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'auto_unload': custom_provider_config.get('auto_unload', True) if custom_provider_config else config.get('auto_unload', True),
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'base_url': native_base
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}
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auto_unload = _cfg['auto_unload']
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result = await LLMService._call_ollama_native(
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model=model,
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messages=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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base_url=base_url,
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stream_callback=stream_callback,
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request_id=request_id,
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provider_display_name=provider_display_name,
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auto_unload=auto_unload,
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enable_advanced_params=enable_advanced_params,
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thinking_extra=_thinking_extra,
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cancel_event=cancel_event,
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task_type=task_type or TASK_EXPAND,
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source=source
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)
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if result["success"]:
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# 自动卸载 (原生调用路径需要手动触发，但使用补全后的基类逻辑)
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await LLMService._unload_ollama_model(model, _cfg)
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# 应用思维链输出过滤
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content = result["content"]
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if filter_thinking_output:
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content = filter_thinking_content(content)
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return {
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"success": True,
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"data": {"original": prompt, "expanded": content}
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}
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else:
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return result
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# 其他服务走HTTP直连
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if not base_url:
|
||
base_url = LLMService.get_provider_base_url(provider, custom_provider_config if custom_provider else None)
|
||
|
||
# 检查disable_thinking、enable_advanced_params和filter_thinking_output配置
|
||
from ..config_manager import config_manager
|
||
service = config_manager.get_service(provider)
|
||
disable_thinking_enabled = service.get('disable_thinking', True) if service else True
|
||
enable_advanced_params = service.get('enable_advanced_params', False) if service else False
|
||
filter_thinking_output = service.get('filter_thinking_output', True) if service else True
|
||
thinking_extra = build_thinking_suppression(provider, model) if disable_thinking_enabled else None
|
||
|
||
result = await LLMService._http_request_chat_completions(
|
||
base_url=base_url,
|
||
api_key=api_key,
|
||
model=model,
|
||
messages=messages,
|
||
temperature=temperature,
|
||
top_p=top_p,
|
||
max_tokens=max_tokens,
|
||
thinking_extra=thinking_extra,
|
||
enable_advanced_params=enable_advanced_params,
|
||
stream_callback=stream_callback,
|
||
request_id=request_id,
|
||
provider_display_name=provider_display_name,
|
||
cancel_event=cancel_event,
|
||
task_type=task_type or TASK_EXPAND,
|
||
source=source
|
||
)
|
||
|
||
if result["success"]:
|
||
# 根据配置决定是否应用思维链输出过滤
|
||
content = result["content"]
|
||
if filter_thinking_output:
|
||
content = filter_thinking_content(content)
|
||
return {
|
||
"success": True,
|
||
"data": {"original": prompt, "expanded": content}
|
||
}
|
||
else:
|
||
return result
|
||
|
||
except Exception as e:
|
||
return {"success": False, "error": format_api_error(e, "LLM服务")}
|
||
|
||
@staticmethod
|
||
async def translate(
|
||
text: str,
|
||
from_lang: str = 'auto',
|
||
to_lang: str = 'zh',
|
||
request_id: Optional[str] = None,
|
||
stream_callback: Optional[Callable[[str], None]] = None,
|
||
custom_provider: Optional[str] = None,
|
||
custom_provider_config: Optional[Dict[str, Any]] = None,
|
||
cancel_event: Optional[Any] = None,
|
||
task_type: str = None,
|
||
source: Optional[str] = None
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
使用大语言模型翻译文本
|
||
|
||
参数:
|
||
text: 要翻译的文本
|
||
from_lang: 源语言
|
||
to_lang: 目标语言
|
||
request_id: 请求ID
|
||
stream_callback: 流式输出回调
|
||
custom_provider: 自定义服务商
|
||
custom_provider_config: 自定义配置
|
||
|
||
返回:
|
||
Dict: {"success": bool, "data": {"original": str, "translated": str}, "error": str}
|
||
"""
|
||
try:
|
||
# 获取配置（翻译使用专门的翻译服务配置）
|
||
if custom_provider and custom_provider_config:
|
||
provider = custom_provider
|
||
api_key = custom_provider_config.get('api_key')
|
||
model = custom_provider_config.get('model')
|
||
temperature = custom_provider_config.get('temperature', 0.7)
|
||
top_p = custom_provider_config.get('top_p', 0.9)
|
||
max_tokens = custom_provider_config.get('max_tokens', 2000)
|
||
base_url = custom_provider_config.get('base_url', '')
|
||
else:
|
||
# 使用翻译服务配置（而非LLM配置）
|
||
from ..config_manager import config_manager
|
||
config = config_manager.get_translate_config()
|
||
provider = config.get('provider', 'unknown')
|
||
api_key = config.get('api_key')
|
||
model = config.get('model')
|
||
temperature = config.get('temperature', 0.7)
|
||
top_p = config.get('top_p', 0.9)
|
||
max_tokens = config.get('max_tokens', 2000)
|
||
base_url = config.get('base_url', '')
|
||
|
||
# 注：允许空API Key，支持无认证服务商
|
||
if not model:
|
||
return {"success": False, "error": "未配置模型名称"}
|
||
|
||
provider_display_name = LLMService.get_provider_display_name(provider)
|
||
|
||
from ..config_manager import config_manager
|
||
service = config_manager.get_service(provider)
|
||
|
||
from ..utils.common import REQUEST_PREFIX, PREFIX, format_model_with_thinking
|
||
|
||
# 检测是否关闭思维链
|
||
disable_thinking_enabled = service.get('disable_thinking', True) if service else True
|
||
_thinking_extra = build_thinking_suppression(provider, model) if disable_thinking_enabled else None
|
||
thinking_disabled = _thinking_extra is not None and disable_thinking_enabled
|
||
model_display = format_model_with_thinking(model, thinking_disabled)
|
||
|
||
# 翻译提示词
|
||
translate_instruction = f"请将以下文本从{from_lang}翻译成{to_lang}，只输出翻译结果，不要添加任何解释或额外内容："
|
||
|
||
messages = [
|
||
{"role": "system", "content": translate_instruction},
|
||
{"role": "user", "content": text}
|
||
]
|
||
|
||
# Ollama走原生API (通过服务类型判断)
|
||
if service and service.get('type') == 'ollama':
|
||
# 读取 Ollama 服务的配置
|
||
disable_thinking_enabled = service.get('disable_thinking', True)
|
||
enable_advanced_params = service.get('enable_advanced_params', False)
|
||
filter_thinking_output = service.get('filter_thinking_output', True)
|
||
_ollama_thinking_extra = build_thinking_suppression(provider, model) if disable_thinking_enabled else None
|
||
|
||
# 统一计算 native_base (确保移除 /v1 和末尾斜杠)
|
||
native_base = base_url.rstrip('/')
|
||
if native_base.endswith('/v1'):
|
||
native_base = native_base[:-3].rstrip('/')
|
||
|
||
# 再次兜底
|
||
if not native_base:
|
||
native_base = 'http://localhost:11434'
|
||
|
||
_cfg = {
|
||
'auto_unload': custom_provider_config.get('auto_unload', True) if custom_provider_config else config.get('auto_unload', True),
|
||
'base_url': native_base
|
||
}
|
||
auto_unload = _cfg['auto_unload']
|
||
|
||
result = await LLMService._call_ollama_native(
|
||
model=model,
|
||
messages=[{"role": "system", "content": translate_instruction}, {"role": "user", "content": text}],
|
||
temperature=temperature,
|
||
top_p=top_p,
|
||
max_tokens=max_tokens,
|
||
base_url=base_url,
|
||
stream_callback=stream_callback,
|
||
request_id=request_id,
|
||
provider_display_name=provider_display_name,
|
||
auto_unload=auto_unload,
|
||
enable_advanced_params=enable_advanced_params,
|
||
thinking_extra=_ollama_thinking_extra,
|
||
cancel_event=cancel_event,
|
||
task_type=task_type or TASK_TRANSLATE,
|
||
source=source
|
||
)
|
||
|
||
if result["success"]:
|
||
await LLMService._unload_ollama_model(model, _cfg)
|
||
|
||
# 应用思维链输出过滤
|
||
content = result["content"]
|
||
if filter_thinking_output:
|
||
content = filter_thinking_content(content)
|
||
|
||
return {
|
||
"success": True,
|
||
"data": {"original": text, "translated": content}
|
||
}
|
||
else:
|
||
return result
|
||
|
||
# 其他服务走HTTP直连
|
||
if not base_url:
|
||
base_url = LLMService.get_provider_base_url(provider, custom_provider_config if custom_provider else None)
|
||
|
||
# 检查enable_advanced_params和filter_thinking_output配置
|
||
enable_advanced_params = service.get('enable_advanced_params', False) if service else False
|
||
filter_thinking_output = service.get('filter_thinking_output', True) if service else True
|
||
thinking_extra = _thinking_extra # 复用前面计算好的 suppression
|
||
|
||
result = await LLMService._http_request_chat_completions(
|
||
base_url=base_url,
|
||
api_key=api_key,
|
||
model=model,
|
||
messages=messages,
|
||
temperature=temperature,
|
||
top_p=top_p,
|
||
max_tokens=max_tokens,
|
||
thinking_extra=thinking_extra,
|
||
enable_advanced_params=enable_advanced_params,
|
||
stream_callback=stream_callback,
|
||
request_id=request_id,
|
||
provider_display_name=provider_display_name,
|
||
cancel_event=cancel_event,
|
||
task_type=task_type or TASK_TRANSLATE,
|
||
source=source
|
||
)
|
||
|
||
if result["success"]:
|
||
# 根据配置决定是否应用思维链输出过滤
|
||
content = result["content"]
|
||
if filter_thinking_output:
|
||
content = filter_thinking_content(content)
|
||
return {
|
||
"success": True,
|
||
"data": {"original": text, "translated": content}
|
||
}
|
||
else:
|
||
return result
|
||
|
||
except Exception as e:
|
||
return {"success": False, "error": format_api_error(e, "LLM服务")} |