jsontranslator实现
This commit is contained in:
xunbu
@@ -21,13 +21,13 @@
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> QQ交流群：1047781902
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UI界面：
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**UI界面**：
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论文翻译：
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**论文翻译**：
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小说翻译：
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**小说翻译**：
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## 整合包
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34
docutranslate/agents/segments_agent.py
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34
docutranslate/agents/segments_agent.py
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@@ -0,0 +1,34 @@
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from dataclasses import dataclass
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from docutranslate.agents import AgentConfig, Agent
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@dataclass
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class JsonTranslateAgentConfig(AgentConfig):
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to_lang: str
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custom_prompt: str | None = None
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class JsonTranslateAgent(Agent):
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def __init__(self, config: JsonTranslateAgentConfig):
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super().__init__(config)
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self.system_prompt = f"""
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# 角色
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你是一个专业的机器翻译引擎
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# 工作
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翻译输入的json的值，保持键不改变
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目标语言{config.to_lang}
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# 要求
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翻译要求专业准确
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不输出任何解释和注释
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不能改变形如<ph-xxxxxx>的占位符
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# 输出
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翻译后的json纯文本
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# 示例
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## 输入
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{r'{"1":"hello","2":"apple","3":true,"4":"false"}'}
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## 输出
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{r'{"1":"你好","2":"苹果","3":true,"4":"错误"}'}
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"""
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if config.custom_prompt:
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self.system_prompt += "\n# 重要规则或背景【非常重要】\n" + config.custom_prompt + '\n'
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200
docutranslate/translator/ai_translator/json_translator.py
Normal file
200
docutranslate/translator/ai_translator/json_translator.py
Normal file
@@ -0,0 +1,200 @@
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import json
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from dataclasses import dataclass
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from typing import Self, Any
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from jsonpath_ng import parse
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from docutranslate.agents.segments_agent import JsonTranslateAgentConfig, JsonTranslateAgent
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from docutranslate.ir.document import Document
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from docutranslate.translator.ai_translator.base import AiTranslatorConfig
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from docutranslate.translator.base import Translator
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from docutranslate.utils.json_utils import flat_json_split
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@dataclass
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class JsonTranslatorConfig(AiTranslatorConfig):
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jsonpaths: list[str]
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class JsonTranslator(Translator):
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def __init__(self, config: JsonTranslatorConfig):
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super().__init__(config=config)
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self.chunk_size = config.chunk_size
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agent_config = JsonTranslateAgentConfig(custom_prompt=config.custom_prompt,
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to_lang=config.to_lang,
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baseurl=config.base_url,
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key=config.api_key,
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model_id=config.model_id,
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system_prompt=None,
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temperature=config.temperature,
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thinking=config.thinking,
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max_concurrent=config.concurrent,
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timeout=config.timeout,
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logger=self.logger)
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self.translate_agent = JsonTranslateAgent(agent_config)
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self.jsonpaths = config.jsonpaths
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def _extract_matches(self, content: dict) -> list[Any]:
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"""
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根据 self.jsonpaths 从 JSON 内容中提取所有匹配项。
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与原始代码不同，这里直接返回 Match 对象列表，它同时包含了值和路径信息。
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"""
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all_matches = []
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for path_str in self.jsonpaths:
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path_expr = parse(path_str)
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matches = path_expr.find(content)
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all_matches.extend(matches)
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return all_matches
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def _translate_texts_in_batches(self, texts: list[str]) -> list[str]:
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"""
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将文本列表打包、分块、发送翻译并返回翻译结果。
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此函数封装了与翻译代理交互的所有细节。
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"""
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# 1. 使用索引作为唯一ID，将文本列表转换为字典，便于API处理
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indexed_originals = {str(i): text for i, text in enumerate(texts)}
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# 2. 将大字典分割成小块，以满足API的限制
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chunks = flat_json_split(indexed_originals, self.chunk_size)
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# 3. 将每个块序列化为JSON字符串并发送翻译
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prompts = [json.dumps(chunk) for chunk in chunks]
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translated_chunks = self.translate_agent.send_prompts(prompts)
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# 4. 将翻译结果合并回一个字典
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# 我们从原始字典的副本开始，以确保即使翻译失败，我们也能保持结构
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indexed_translated = indexed_originals.copy()
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for chunk_str in translated_chunks:
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translated_part = json.loads(chunk_str)
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indexed_translated.update(translated_part)
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# 5. 按原始顺序返回翻译后的文本列表
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return list(indexed_translated.values())
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async def _translate_texts_in_batches_async(self, texts: list[str]) -> list[str]:
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"""
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将文本列表打包、分块、发送翻译并返回翻译结果。
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此函数封装了与翻译代理交互的所有细节。
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"""
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# 1. 使用索引作为唯一ID，将文本列表转换为字典，便于API处理
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indexed_originals = {str(i): text for i, text in enumerate(texts)}
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# 2. 将大字典分割成小块，以满足API的限制
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chunks = flat_json_split(indexed_originals, self.chunk_size)
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# 3. 将每个块序列化为JSON字符串并发送翻译
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prompts = [json.dumps(chunk) for chunk in chunks]
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translated_chunks = await self.translate_agent.send_prompts_async(prompts)
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# 4. 将翻译结果合并回一个字典
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# 我们从原始字典的副本开始，以确保即使翻译失败，我们也能保持结构
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indexed_translated = indexed_originals.copy()
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for chunk_str in translated_chunks:
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translated_part = json.loads(chunk_str)
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indexed_translated.update(translated_part)
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# 5. 按原始顺序返回翻译后的文本列表
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return list(indexed_translated.values())
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def _update_content_with_translations(self, content: dict, matches: list[Any], translated_texts: list[str]):
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"""
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使用翻译后的文本更新原始JSON内容。
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"""
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# 使用 zip 将每个匹配项与其对应的翻译文本配对
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for match, translated_text in zip(matches, translated_texts):
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# match.full_path 包含了更新原始 content 所需的精确位置信息
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match.full_path.update(content, translated_text)
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def translate(self, document: Document) -> Self:
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"""
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主方法：提取、翻译并更新JSON文档中的指定内容。
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流程:
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1. 解析输入的JSON文档。
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2. 提取所有符合jsonpath规则的匹配项 (Match对象)。
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3. 从匹配项中获取原始文本，并批量发送进行翻译。
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4. 将翻译回来的文本根据其原始位置，更新回JSON对象中。
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5. 将更新后的 content 写回 document
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"""
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content = json.loads(document.content.decode())
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# 步骤 1: 提取所有需要翻译的匹配项
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all_matches = self._extract_matches(content)
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if not all_matches:
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# 如果没有找到任何内容，则无需执行任何操作
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return self
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original_texts = [match.value for match in all_matches]
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# 步骤 2: 批量翻译提取出的文本
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translated_texts = self._translate_texts_in_batches(original_texts)
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# 健壮性检查：确保翻译回来的项目数量与发送的一致
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if len(original_texts) != len(translated_texts):
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raise ValueError("翻译服务返回的项目数量与发送的数量不匹配。")
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# 步骤 3: 将翻译结果写回原始JSON对象
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self._update_content_with_translations(content, all_matches, translated_texts)
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# 更新原始 document 对象的内容（可选，但良好实践）
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document.content = json.dumps(content, ensure_ascii=False).encode('utf-8')
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return self
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# def translate(self, document: Document) -> Self:
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# content = json.loads(document.content.decode())
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# path_vals: dict[str:list[str]] = {}
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# for jsonpath in self.jsonpaths:
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# jsonpath_expr = parse(jsonpath)
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# matches = [match.value for match in jsonpath_expr.find(content)]
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# path_vals[jsonpath] = matches
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# vals_all = []
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# for vals in path_vals.values():
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# vals_all.extend(vals)
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# vals_dict = {str(i): val for i, val in enumerate(vals_all)}#{"1":"val1","2":"val2"}
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# chunks=flat_json_split(vals_dict,self.chunk_size)
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# chunks_translated=self.translate_agent.send_prompts([json.dumps(chunk) for chunk in chunks])
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# for chunk in chunks_translated:
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# vals_dict.update(json.loads(chunk))
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#
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# #翻译后的{path1:[val1',val2']}
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# reconstructed_dict = {}
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# start_index = 0
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# for key in path_vals.keys():
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# length = len(path_vals[key])
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# sub_list = list(vals_dict.values())[start_index: start_index + length]
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# reconstructed_dict[key] = sub_list
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# start_index += length
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#
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# for path in reconstructed_dict:
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# path_expression = parse(path)
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# matches = path_expression.find(content)
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# for idx,match in enumerate(matches):
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# match.full_path.update(content, reconstructed_dict[path][idx])
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# return self
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async def translate_async(self, document: Document) -> Self:
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content = json.loads(document.content.decode())
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# 步骤 1: 提取所有需要翻译的匹配项
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all_matches = self._extract_matches(content)
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if not all_matches:
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# 如果没有找到任何内容，则无需执行任何操作
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return self
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original_texts = [match.value for match in all_matches]
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# 步骤 2: 批量翻译提取出的文本
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translated_texts = await self._translate_texts_in_batches_async(original_texts)
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# 健壮性检查：确保翻译回来的项目数量与发送的一致
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if len(original_texts) != len(translated_texts):
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raise ValueError("翻译服务返回的项目数量与发送的数量不匹配。")
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# 步骤 3: 将翻译结果写回原始JSON对象
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self._update_content_with_translations(content, all_matches, translated_texts)
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# 更新原始 document 对象的内容（可选，但良好实践）
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document.content = json.dumps(content, ensure_ascii=False).encode('utf-8')
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return self
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31
docutranslate/utils/json_utils.py
Normal file
31
docutranslate/utils/json_utils.py
Normal file
@@ -0,0 +1,31 @@
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import json
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def flat_json_split(js: dict, chunk_size_max: int) -> list[dict]:
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"""
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用给扁平的json（形如{key:val}）的分块，每个分块大小不超过chunksize字节
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"""
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chunks = []
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chunk = {}
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for key, val in js.items():
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t = chunk.copy()
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t[key] = val
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chunk_size = get_json_size(t)
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if chunk_size <= chunk_size_max:
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chunk[key] = val
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else:
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chunks.append(json.dumps(chunk,ensure_ascii=False))
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chunk = {key:val}
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chunks.append(chunk)
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return chunks
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def get_json_size(js: dict):
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return len(json.dumps(js,ensure_ascii=False).encode())
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if __name__ == '__main__':
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js={1:2,3:4,5:"哈哈"}
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ls=flat_json_split(js,30)
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print(ls)
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# for chunk in ls:
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# print(len(chunk.encode()))
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@@ -7,7 +7,7 @@ dependencies = [
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"httpx==0.27.2",
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"markdown2>=2.5.3",
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"fastapi[standard]>=0.115.12",
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"jsonpath-ng>=1.7.0",
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]
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dynamic = ["version"]
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557
uv.lock
generated
557
uv.lock
generated
@@ -1,23 +1,13 @@
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version = 1
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revision = 1
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requires-python = ">=3.10"
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requires-python = ">=3.11"
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resolution-markers = [
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"python_full_version >= '3.13' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
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"python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
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"python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
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"python_full_version == '3.12.*' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
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"python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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"(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
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"(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
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"python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
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"python_full_version == '3.11.*' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
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||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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||||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
|
||||
"python_full_version < '3.11' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
|
||||
"python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version < '3.12' and sys_platform == 'darwin'",
|
||||
"python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -43,7 +33,6 @@ name = "anyio"
|
||||
version = "4.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "exceptiongroup", marker = "python_full_version < '3.11'" },
|
||||
{ name = "idna" },
|
||||
{ name = "sniffio" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
@@ -90,19 +79,6 @@ version = "3.4.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e4/33/89c2ced2b67d1c2a61c19c6751aa8902d46ce3dacb23600a283619f5a12d/charset_normalizer-3.4.2.tar.gz", hash = "sha256:5baececa9ecba31eff645232d59845c07aa030f0c81ee70184a90d35099a0e63", size = 126367 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/95/28/9901804da60055b406e1a1c5ba7aac1276fb77f1dde635aabfc7fd84b8ab/charset_normalizer-3.4.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c48ed483eb946e6c04ccbe02c6b4d1d48e51944b6db70f697e089c193404941", size = 201818 },
|
||||
{ url = "https://files.pythonhosted.org/packages/d9/9b/892a8c8af9110935e5adcbb06d9c6fe741b6bb02608c6513983048ba1a18/charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2d318c11350e10662026ad0eb71bb51c7812fc8590825304ae0bdd4ac283acd", size = 144649 },
|
||||
{ url = "https://files.pythonhosted.org/packages/7b/a5/4179abd063ff6414223575e008593861d62abfc22455b5d1a44995b7c101/charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9cbfacf36cb0ec2897ce0ebc5d08ca44213af24265bd56eca54bee7923c48fd6", size = 155045 },
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Reference in New Issue
Block a user