K2Cr2O1
d6623e2cc8
新增向量数据库管理器模块，支持文本的存储、检索和相似度查询 添加知识库插件和AI聊天插件，利用向量数据库实现记忆功能 优化跨平台翻译模块，集成向量数据库存储历史翻译记录 改进消息处理逻辑，优先使用用户显示名称
224 lines
8.7 KiB
Python
224 lines
8.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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跨平台消息互通插件翻译模块
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"""
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import time
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import uuid
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from typing import Dict, List
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from core.utils.logger import ModuleLogger
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from core.managers.vectordb_manager import vectordb_manager
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from .config import config
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# 创建模块专用日志记录器
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logger = ModuleLogger("CrossPlatformTranslator")
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# 翻译上下文缓存（每个通道15条消息）
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TRANSLATION_CONTEXT_CACHE: Dict[str, List[Dict[str, str]]] = {}
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MAX_CONTEXT_MESSAGES = 15
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def get_translation_context(channel_id: int, direction: str) -> List[Dict[str, str]]:
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"""获取翻译上下文缓存"""
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cache_key = f"{channel_id}_{direction}"
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return TRANSLATION_CONTEXT_CACHE.get(cache_key, [])
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def add_translation_context(channel_id: int, direction: str, original: str, translated: str):
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"""添加翻译到上下文缓存和向量数据库"""
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cache_key = f"{channel_id}_{direction}"
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if cache_key not in TRANSLATION_CONTEXT_CACHE:
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TRANSLATION_CONTEXT_CACHE[cache_key] = []
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TRANSLATION_CONTEXT_CACHE[cache_key].append({
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"original": original,
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"translated": translated
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})
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if len(TRANSLATION_CONTEXT_CACHE[cache_key]) > MAX_CONTEXT_MESSAGES:
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TRANSLATION_CONTEXT_CACHE[cache_key] = TRANSLATION_CONTEXT_CACHE[cache_key][-MAX_CONTEXT_MESSAGES:]
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# 将翻译记录保存到向量数据库
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try:
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collection_name = f"translation_memory_{channel_id}"
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doc_id = str(uuid.uuid4())
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# 将原文和译文组合作为向量化文本
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text_to_embed = f"原文: {original}\n译文: {translated}"
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metadata = {
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"channel_id": channel_id,
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"direction": direction,
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"original": original,
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"translated": translated,
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"timestamp": int(time.time())
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}
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vectordb_manager.add_texts(
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collection_name=collection_name,
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texts=[text_to_embed],
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metadatas=[metadata],
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ids=[doc_id]
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)
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logger.debug(f"[CrossPlatform] 翻译记录已保存到向量数据库: {collection_name}")
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except Exception as e:
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logger.error(f"[CrossPlatform] 保存翻译记录到向量数据库失败: {e}")
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def get_similar_translations(channel_id: int, text: str, direction: str, limit: int = 3) -> str:
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"""从向量数据库检索相似的翻译记录"""
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try:
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collection_name = f"translation_memory_{channel_id}"
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# 检索相似文本
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results = vectordb_manager.query_texts(
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collection_name=collection_name,
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query_texts=[text],
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n_results=limit,
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where={"direction": direction}
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)
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if not results or not results.get("documents") or not results["documents"][0]:
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return ""
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context_ref = "\n\n参考历史相似翻译（向量检索）：\n"
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for i, metadata in enumerate(results["metadatas"][0], 1):
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original = metadata.get("original", "")
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translated = metadata.get("translated", "")
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context_ref += f"{i}. 原文: {original[:100]}\n 译文: {translated[:100]}\n"
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return context_ref
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except Exception as e:
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logger.error(f"[CrossPlatform] 从向量数据库检索翻译记录失败: {e}")
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return ""
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async def translate_with_deepseek(
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text: str,
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target_lang: str = "zh-CN",
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channel_id: int = 0,
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direction: str = "en2zh"
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) -> str:
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"""使用 DeepSeek API 翻译文本"""
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if not config.ENABLE_TRANSLATION or not text.strip():
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return text
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if config.DEEPSEEK_API_KEY == "your-deepseek-api-key-here":
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logger.warning("[CrossPlatform] DeepSeek API 密钥未配置，跳过翻译")
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return text
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lang_name = "中文" if target_lang == "zh-CN" else "英文"
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messages = []
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context_ref = ""
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if channel_id > 0:
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# 1. 获取最近的上下文缓存
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context = get_translation_context(channel_id, direction)
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if context:
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context_ref = "\n\n参考最近的翻译：\n"
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for i, ctx in enumerate(context[-5:], 1):
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context_ref += f"{i}. 原文: {ctx['original'][:100]}\n 译文: {ctx['translated'][:100]}\n"
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# 2. 从向量数据库检索相似的历史翻译
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similar_context = get_similar_translations(channel_id, text, direction)
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if similar_context:
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context_ref += similar_context
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system_prompt = f"""你是一个专业的翻译助手。请将以下文本翻译成{lang_name}。
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只返回翻译后的文本，不要添加任何解释、注释或其他内容。避免翻译出仇视言论以及违反中国大陆相关法律法规的内容。如果有，请在翻译后有敏感的词语中把文本替换成井号（#）
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保持原文的语气和格式。如果文本已经是目标语言，直接返回原文。{context_ref}"""
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messages.append({"role": "user", "content": text})
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try:
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from openai import AsyncOpenAI
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client = AsyncOpenAI(
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api_key=config.DEEPSEEK_API_KEY,
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base_url=config.DEEPSEEK_API_URL.replace("/chat/completions", "")
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)
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response = await client.chat.completions.create(
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model=config.DEEPSEEK_MODEL,
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messages=[{"role": "system", "content": system_prompt}] + messages,
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temperature=0.3,
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max_tokens=4000
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)
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translated_text = response.choices[0].message.content
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if translated_text:
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translated_text = translated_text.strip()
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logger.info(f"[CrossPlatform] 翻译成功: {text[:50]}... -> {translated_text[:50]}...")
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if channel_id > 0:
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add_translation_context(channel_id, direction, text, translated_text)
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return translated_text
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else:
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logger.warning("[CrossPlatform] DeepSeek 返回空翻译结果")
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return text
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except ImportError:
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logger.warning("[CrossPlatform] openai 库未安装，尝试使用同步请求")
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return await translate_with_deepseek_sync(text, target_lang, channel_id, direction)
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except Exception as e:
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logger.error(f"[CrossPlatform] 翻译失败: {e}")
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return text
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async def translate_with_deepseek_sync(
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text: str,
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target_lang: str = "zh-CN",
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channel_id: int = 0,
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direction: str = "en2zh"
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) -> str:
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"""使用同步请求的 DeepSeek 翻译（备用方案）"""
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if not config.ENABLE_TRANSLATION or not text.strip():
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return text
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if config.DEEPSEEK_API_KEY == "your-deepseek-api-key-here":
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return text
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lang_name = "中文" if target_lang == "zh-CN" else "英文"
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context_ref = ""
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if channel_id > 0:
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# 1. 获取最近的上下文缓存
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context = get_translation_context(channel_id, direction)
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if context:
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context_ref = "\n\n参考最近的翻译：\n"
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for i, ctx in enumerate(context[-5:], 1):
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context_ref += f"{i}. 原文: {ctx['original'][:100]}\n 译文: {ctx['translated'][:100]}\n"
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# 2. 从向量数据库检索相似的历史翻译
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similar_context = get_similar_translations(channel_id, text, direction)
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if similar_context:
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context_ref += similar_context
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system_prompt = f"""你是一个专业的翻译助手。请将以下文本翻译成{lang_name}。
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只返回翻译后的文本，不要添加任何解释、注释或其他内容。避免翻译出仇视言论以及违反中国大陆相关法律法规的内容。如果有，请在翻译后有敏感的词语中把文本替换成井号（#）
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保持原文的语气和格式。如果文本已经是目标语言，直接返回原文。{context_ref}"""
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messages = [{"role": "user", "content": text}]
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try:
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from openai import OpenAI
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client = OpenAI(
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api_key=config.DEEPSEEK_API_KEY,
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base_url=config.DEEPSEEK_API_URL.replace("/chat/completions", "")
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)
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response = client.chat.completions.create(
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model=config.DEEPSEEK_MODEL,
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messages=[{"role": "system", "content": system_prompt}] + messages,
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temperature=0.3,
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max_tokens=4000
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)
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translated_text = response.choices[0].message.content
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if translated_text:
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translated_text = translated_text.strip()
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if channel_id > 0:
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add_translation_context(channel_id, direction, text, translated_text)
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return translated_text
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return text
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except Exception as e:
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logger.error(f"[CrossPlatform] 同步翻译失败: {e}")
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return text
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