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谭凯
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"""
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LLM Client Module - Generic OpenAI Compatible
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Features:
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1. Fully configurable via config.json (base_url, headers).
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2. Mode-aware prompt building (bilingual vs chinese).
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3. Format repair capability for chinese mode.
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"""
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import asyncio
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import json
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import re
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from openai import AsyncOpenAI
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from typing import List, Dict, Optional, Any
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from loguru import logger
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import time
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from tenacity import retry, stop_after_attempt, wait_exponential
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from .manifest_manager import ManifestItem
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class RateLimiter:
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"""Rate limiter for concurrency and RPM."""
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def __init__(self, requests_per_minute: int, concurrent_requests: int):
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self.semaphore = asyncio.Semaphore(concurrent_requests)
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self.min_interval = 60.0 / requests_per_minute if requests_per_minute > 0 else 0
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self.last_request_time = 0
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self._lock = asyncio.Lock()
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async def acquire(self):
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await self.semaphore.acquire()
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async with self._lock:
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current_time = time.time()
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wait_time = self.min_interval - (current_time - self.last_request_time)
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if wait_time > 0:
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await asyncio.sleep(wait_time)
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self.last_request_time = time.time()
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def release(self):
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self.semaphore.release()
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class LLMClient:
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"""Generic OpenAI-compatible API Client."""
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def __init__(self, config: Dict):
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self.config = config
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llm_config = config["llm"]
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api_key = llm_config.get("api_key")
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base_url = llm_config.get("base_url")
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extra_headers = llm_config.get("extra_headers", {})
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if not api_key:
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raise ValueError("API Key is missing in config")
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self.client = AsyncOpenAI(
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base_url=base_url,
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api_key=api_key,
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default_headers=extra_headers
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)
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self.models = llm_config.get("models", {"fast": "gpt-3.5-turbo", "smart": "gpt-4"})
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self.rate_limiter = RateLimiter(
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llm_config["rate_limits"]["requests_per_minute"],
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llm_config["rate_limits"]["concurrent_requests"]
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)
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self.prompts = self._load_prompts()
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def _load_prompts(self) -> Dict:
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try:
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with open("config/prompts.json", "r", encoding="utf-8") as f:
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return json.load(f)
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except:
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return {}
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async def translate_chunk(self, items: List[ManifestItem], glossary: Dict = None,
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instruction: str = None, model_type: str = "fast",
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mode: str = "bilingual") -> Dict[str, str]:
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"""
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Translate a chunk of items.
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Args:
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items: List of ManifestItem to translate
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glossary: Term dictionary
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instruction: Style guide
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model_type: "fast" or "smart"
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mode: "bilingual" or "chinese"
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"""
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if not items: return {}
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model = self.models.get(model_type, self.models.get("fast"))
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prompt = self._build_prompt(items, mode)
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try:
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# Build System Prompt
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base_sys_prompt = self.prompts.get("translation", {}).get("system", "You are a professional translator.")
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# 中文模式：添加占位符保护指令
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if mode == "chinese":
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base_sys_prompt += """
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Placeholder Instructions (CRITICAL):
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1. Text contains PAIRED placeholders: φNφ (start) and φ/Nφ (end), like HTML tags.
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2. Example: "φ1φTable Talkφ/1φ" means italic text, translate as "φ1φ桌谈φ/1φ"
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3. Single placeholders φNφ without φ/Nφ are inline elements (footnotes, formulas) - keep them in place.
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4. RULES:
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- DO NOT create new placeholder numbers that don't exist in the original
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- DO NOT remove or modify existing placeholders
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- Keep placeholders in the SAME relative position in your translation
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- If word order changes, keep placeholders with their associated text
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5. Each line starts with paragraph ID (p_xxxxx). Preserve them.
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"""
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if instruction:
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base_sys_prompt += f"\n\nBook Style Guide:\n{instruction}"
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if glossary:
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glossary_text = "\n".join([f"{k} -> {v}" for k, v in glossary.items()])
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base_sys_prompt += f"\n\nTerminology:\n{glossary_text}"
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# Strict formatting instructions
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base_sys_prompt += "\n\nRequirements:\n1. Each line MUST start with ID (p_xxxxx).\n2. DO NOT modify IDs.\n3. Return only translations."
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raw_response = await self._make_request(model, base_sys_prompt, prompt)
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if not raw_response:
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return {item.global_id: f"[Error - Empty Response]" for item in items}
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return self._simple_parse(raw_response, items, mode)
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except Exception as e:
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logger.error(f"Translation failed ({model}): {e}")
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return {item.global_id: f"[Error - {str(e)}]" for item in items}
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async def repair_format(self, original_text: str, broken_translation: str) -> str:
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"""
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修复翻译格式：将占位符正确插入到译文中。
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"""
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model = self.models.get("fast")
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system_prompt = "You are a format repair assistant. Your ONLY job is to insert placeholders into the translation."
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user_prompt = f"""
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Original Text (with placeholders):
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{original_text}
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Translation (placeholders missing/incorrect):
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{broken_translation}
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Task:
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Please rewrite the Translation to include ALL placeholders (φcXXXXXφ) from the Original Text.
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1. DO NOT translate again. Keep the meaning of the Translation.
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2. Place φcXXXXXφ tags exactly where they correspond to the original format (bold, italic, links).
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3. Output ONLY the fixed translation.
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"""
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try:
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return await self._make_request(model, system_prompt, user_prompt)
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except Exception as e:
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logger.error(f"Format repair failed: {e}")
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return broken_translation
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async def raw_chat_completion(self, system_prompt: str, user_prompt: str, model_type: str = "smart") -> str:
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"""Generic chat completion (for Profiler)."""
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model = self.models.get(model_type, self.models.get("smart"))
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return await self._make_request(model, system_prompt, user_prompt)
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def _build_prompt(self, items: List[ManifestItem], mode: str = "bilingual") -> str:
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"""构建翻译提示词"""
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lines = []
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for item in items:
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if mode == "chinese":
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# 中文模式：使用带占位符的文本和段落类型
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text = item.text_with_placeholders if item.text_with_placeholders else item.clean_text
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p_type = getattr(item, 'paragraph_type', 'body').upper()
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lines.append(f"{item.global_id} [{p_type}] {text}")
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else:
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# 双语模式：使用纯文本
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lines.append(f"{item.global_id} {item.clean_text}")
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return "\n".join(lines)
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def _simple_parse(self, response: str, items: List[ManifestItem], mode: str = "bilingual") -> Dict[str, str]:
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"""解析 LLM 响应"""
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results = {}
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for i, item in enumerate(items):
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current_id = item.global_id
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start_idx = response.find(current_id)
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if start_idx == -1: continue
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end_idx = len(response)
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if i + 1 < len(items):
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next_id = items[i+1].global_id
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next_found = response.find(next_id, start_idx + len(current_id))
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if next_found != -1:
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end_idx = next_found
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content = response[start_idx:end_idx].strip()
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clean_content = content[len(current_id):].strip()
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clean_content = clean_content.lstrip(":： \t")
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# 移除类型标记 (如 [BODY])
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if mode == "chinese":
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clean_content = re.sub(r'^\[[A-Z]+\]\s*', '', clean_content)
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if clean_content:
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results[current_id] = clean_content
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# Fallback: 逐行解析
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if len(results) < len(items):
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for line in response.split("\n"):
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line = line.strip()
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for item in items:
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if item.global_id not in results and line.startswith(item.global_id):
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res = line[len(item.global_id):].strip().lstrip(":： ")
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if mode == "chinese":
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res = re.sub(r'^\[[A-Z]+\]\s*', '', res)
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if res: results[item.global_id] = res
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return results
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@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10))
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async def _make_request(self, model: str, system_prompt: str, user_prompt: str) -> str:
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await self.rate_limiter.acquire()
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try:
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resp = await self.client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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],
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temperature=self.config['translation'].get('temperature', 0.2),
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max_tokens=8000
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)
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return resp.choices[0].message.content.strip()
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finally:
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self.rate_limiter.release()
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async def close(self):
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await self.client.close()
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# Alias for backward compatibility
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OpenRouterClient = LLMClient
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