构建一个翻译助手Agent：提升翻译效率的实践

在上一篇文章中,我们讨论了如何构建一个测试助手Agent。今天,我想分享另一个实际项目:如何构建一个翻译助手Agent。这个项目源于我们一个出海团队的真实需求 - 提升翻译效率,保障翻译质量。

从翻译痛点说起

记得和产品团队讨论时的场景：

小王：我们要把产品文档翻译成多种语言,人工翻译太慢了
小李：是啊,而且专业术语的翻译要保持一致性
我：主要是哪些翻译场景？
小王：产品文档、技术文档、营销文案这些
我：这些场景很适合用AI Agent来协助

经过需求分析,我们确定了几个核心功能：

智能翻译
术语管理
质量检查
多语言协同

技术方案设计

首先是整体架构：

from typing import List, Dict, Any, Optional
from enum import Enum
from pydantic import BaseModel
import asyncioclass TranslateTask(Enum):TRANSLATE = "translate"TERM = "term"CHECK = "check"SYNC = "sync"class TranslateContext(BaseModel):task_type: TranslateTasksource_text: strsource_lang: strtarget_lang: strdomain: Optional[str]glossary: Optional[Dict[str, Dict[str, str]]]class TranslateAssistant:def __init__(self,config: Dict[str, Any]):# 1. 初始化翻译模型self.translate_model = TranslateLLM(model="gpt-4",temperature=0.3,context_length=8000)# 2. 初始化工具集self.tools = {"translator": Translator(),"terminator": TermManager(),"checker": QualityChecker(),"syncer": LanguageSyncer()}# 3. 初始化知识库self.knowledge_base = VectorStore(embeddings=TranslateEmbeddings(),collection="translate_knowledge")async def process_task(self,context: TranslateContext) -> Dict[str, Any]:# 1. 分析任务task_info = await self._analyze_task(context)# 2. 准备资源resources = await self._prepare_resources(context,task_info)# 3. 生成方案plan = await self._generate_plan(task_info,resources)# 4. 执行任务result = await self._execute_task(plan,context)return resultasync def _analyze_task(self,context: TranslateContext) -> Dict[str, Any]:# 1. 识别文本类型text_type = await self._identify_text_type(context.source_text)# 2. 评估难度difficulty = await self._evaluate_difficulty(context)# 3. 确定策略strategy = await self._determine_strategy(text_type,difficulty)return {"type": text_type,"difficulty": difficulty,"strategy": strategy}

智能翻译功能

首先实现智能翻译功能：

class Translator:def __init__(self,model: TranslateLLM):self.model = modelasync def translate_text(self,context: TranslateContext) -> Dict[str, Any]:# 1. 预处理文本processed = await self._preprocess_text(context)# 2. 执行翻译translated = await self._perform_translation(processed,context)# 3. 后处理优化optimized = await self._postprocess_translation(translated,context)return optimizedasync def _preprocess_text(self,context: TranslateContext) -> Dict[str, Any]:# 1. 分段处理segments = await self._segment_text(context.source_text)# 2. 提取术语terms = await self._extract_terms(segments,context.glossary)# 3. 标记格式formatted = await self._mark_formats(segments)return {"segments": segments,"terms": terms,"formats": formatted}async def _perform_translation(self,processed: Dict[str, Any],context: TranslateContext) -> List[Dict[str, Any]]:translations = []for segment in processed["segments"]:# 1. 准备上下文translation_context = await self._prepare_context(segment,processed,context)# 2. 生成翻译translation = await self.model.translate(segment,translation_context)# 3. 应用术语translation = await self._apply_terms(translation,processed["terms"])translations.append({"source": segment,"target": translation,"terms": processed["terms"]})return translations

术语管理功能

接下来是术语管理功能：

class TermManager:def __init__(self,model: TranslateLLM):self.model = modelasync def manage_terms(self,context: TranslateContext) -> Dict[str, Any]:# 1. 提取术语terms = await self._extract_terms(context)# 2. 管理术语库glossary = await self._manage_glossary(terms,context)# 3. 应用术语applied = await self._apply_glossary(context,glossary)return appliedasync def _extract_terms(self,context: TranslateContext) -> Dict[str, Any]:# 1. 识别专业术语professional = await self._identify_professional_terms(context.source_text)# 2. 识别产品术语product = await self._identify_product_terms(context.source_text)# 3. 合并去重merged = await self._merge_terms(professional,product)return mergedasync def _manage_glossary(self,terms: Dict[str, Any],context: TranslateContext) -> Dict[str, Dict[str, str]]:# 1. 加载现有术语库existing = context.glossary or {}# 2. 更新术语updated = await self._update_terms(existing,terms)# 3. 验证一致性validated = await self._validate_consistency(updated)return validated

质量检查功能

再来实现质量检查功能：

class QualityChecker:def __init__(self,model: TranslateLLM):self.model = modelasync def check_quality(self,context: TranslateContext,translation: Dict[str, Any]) -> Dict[str, Any]:# 1. 检查准确性accuracy = await self._check_accuracy(translation,context)# 2. 检查流畅性fluency = await self._check_fluency(translation)# 3. 生成报告report = await self._generate_report(accuracy,fluency)return reportasync def _check_accuracy(self,translation: Dict[str, Any],context: TranslateContext) -> Dict[str, Any]:# 1. 语义对比semantic = await self._compare_semantics(translation["source"],translation["target"])# 2. 术语检查terminology = await self._check_terminology(translation,context.glossary)# 3. 格式检查formatting = await self._check_formatting(translation)return {"semantic": semantic,"terminology": terminology,"formatting": formatting}async def _generate_report(self,accuracy: Dict[str, Any],fluency: Dict[str, Any]) -> Dict[str, Any]:# 1. 汇总问题issues = await self._collect_issues(accuracy,fluency)# 2. 评分打分scores = await self._calculate_scores(accuracy,fluency)# 3. 生成建议suggestions = await self._generate_suggestions(issues)return {"issues": issues,"scores": scores,"suggestions": suggestions}

多语言协同功能

最后是多语言协同功能：

class LanguageSyncer:def __init__(self,model: TranslateLLM):self.model = modelasync def sync_languages(self,context: TranslateContext,translations: Dict[str, Dict[str, Any]]) -> Dict[str, Any]:# 1. 分析差异diffs = await self._analyze_differences(translations)# 2. 同步更新synced = await self._sync_updates(translations,diffs)# 3. 验证一致性validated = await self._validate_sync(synced)return validatedasync def _analyze_differences(self,translations: Dict[str, Dict[str, Any]]) -> Dict[str, Any]:# 1. 内容对比content_diffs = await self._compare_content(translations)# 2. 术语对比term_diffs = await self._compare_terms(translations)# 3. 格式对比format_diffs = await self._compare_formats(translations)return {"content": content_diffs,"terms": term_diffs,"formats": format_diffs}async def _sync_updates(self,translations: Dict[str, Dict[str, Any]],diffs: Dict[str, Any]) -> Dict[str, Dict[str, Any]]:# 1. 更新内容content_updated = await self._update_content(translations,diffs["content"])# 2. 更新术语terms_updated = await self._update_terms(content_updated,diffs["terms"])# 3. 更新格式format_updated = await self._update_formats(terms_updated,diffs["formats"])return format_updated