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：
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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：
python
from typing import List, Dict, Any, Optional
from enum import Enum
from pydantic import BaseModel
import asyncio
class TranslateTask(Enum):
TRANSLATE = "translate"
TERM = "term"
CHECK = "check"
SYNC = "sync"
class TranslateContext(BaseModel):
task_type: TranslateTask
source_text: str
source_lang: str
target_lang: str
domain: 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 result
async 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：
python
class Translator:
def __init__(
self,
model: TranslateLLM
):
self.model = model
async 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 optimized
async 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：
python
class TermManager:
def __init__(
self,
model: TranslateLLM
):
self.model = model
async 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 applied
async 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 merged
async 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：
python
class QualityChecker:
def __init__(
self,
model: TranslateLLM
):
self.model = model
async 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 report
async 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：
python
class LanguageSyncer:
def __init__(
self,
model: TranslateLLM
):
self.model = model
async 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 validated
async 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实际效果
经过三个月的使用,这个翻译助手Agent带来了显著的改善：
-
效率提升
- 翻译速度提升80%
- 术语一致性提高60%
- 多语言同步更快
-
质量改善
- 翻译更准确
- 表达更地道
- 风格更统一
-
成本优化
- 人工成本降低
- 返工率减少
- 维护更便捷
实践心得
在开发这个翻译助手Agent的过程中,我总结了几点经验：
-
场景理解
- 理解专业领域
- 把握语言特点
- 注重文化差异
-
质量保障
- 术语要统一
- 风格要一致
- 表达要地道
-
持续优化
- 收集反馈
- 更新知识库
- 优化算法
写在最后
一个好的翻译助手Agent不仅要能准确翻译文本,更要理解专业领域和文化差异,帮助团队提供高质量的多语言内容。它就像一个经验丰富的翻译专家,在合适的时候给出恰当的建议。
在下一篇文章中,我会讲解如何开发一个写作助手Agent。如果你对翻译助手Agent的开发有什么想法,欢迎在评论区交流。