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<p align="center">
<img src="./DocuTranslate.png" alt="Project Logo" style="width: 150px">
</p>
# DocuTranslate
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[**简体中文**](/README_ZH.md) / [**English**](/README.md) / [**日本語**](/README_JP.md)
**DocuTranslate** is a file translation tool that combines advanced document analysis engines (such as [docling](https://github.com/docling-project/docling) and [minerU](https://mineru.net/)) with large language models (LLMs). It can accurately translate documents in a wide variety of formats.
The new version's architecture adopts **Workflow** as its core, providing a highly configurable and extensible solution for various types of translation tasks.
- ✅ **Supports Multiple Formats**: Capable of translating various files such as `pdf`, `docx`, `xlsx`, `md`, `txt`, `json`, `epub`, `srt`, and more.
- ✅ **Table, Formula, and Code Recognition**: Utilizes `docling` and `mineru` to recognize and translate tables, formulas, and code frequently found in academic papers.
- ✅ **Automatic Glossary Generation**: Supports automatic glossary creation to ensure terminology consistency.
- ✅ **JSON Translation**: Allows specifying values to be translated in JSON using JSONPath (`jsonpath-ng` syntax) specifications.
- ✅ **High-Fidelity Word/Excel Translation**: Supports translation of `docx` and `xlsx` files (currently does not support `doc` or `xls` files) while preserving the original formatting.
- ✅ **Multi-AI Platform Support**: Compatible with most AI platforms, enabling high-performance concurrent AI translation with customizable prompts.
- ✅ **Asynchronous Support**: Designed for high-performance scenarios, offering full asynchronous support and service interfaces for parallel task execution.
- ✅ **Interactive Web Interface**: Provides an out-of-the-box Web UI and RESTful API for easy integration and use.
> When translating `pdf` files, they are first converted to markdown, so the original typesetting will be **lost**. Users with typesetting requirements should note this.
> QQ Discussion Group: 1047781902
**UI Interface**:
![翻译效果](/images/UI界面.png)
**Paper Translation**:
![翻译效果](/images/论文翻译.png)
**Novel Translation**:
![翻译效果](/images/小说翻译.png)
## Integrated Packages
For users who want to get started quickly, we provide integrated packages on [GitHub Releases](https://github.com/xunbu/docutranslate/releases). Simply download, unzip, and enter your AI platform's API key to start using.
- **DocuTranslate**: The standard version, which uses the online `minerU` engine to parse documents. Recommended for most users.
- **DocuTranslate_full**: The full version, which includes the `docling` local parsing engine. Suitable for offline scenarios or those with higher data privacy requirements.
## Installation
### Using pip
```bash
# Basic installation
pip install docutranslate
# If using the docling local parsing engine
pip install docutranslate[docling]
```
### Using uv
```bash
# Initialize the environment
uv init
# Basic installation
uv add docutranslate
# Install docling extension
uv add docutranslate[docling]
```
### Using git
```bash
# Initialize the environment
git clone https://github.com/xunbu/docutranslate.git
cd docutranslate
uv sync
```
## Core Concept: Workflow
The core of the new version of DocuTranslate is the **Workflow**. Each workflow is a complete end-to-end translation pipeline designed for a specific file type. Instead of interacting with large classes as before, you will select and configure the appropriate workflow according to the file type.
**The basic usage steps are as follows:**
1. **Select a Workflow**: Choose a workflow based on the input file type (e.g., PDF/Word or TXT). For example, `MarkdownBasedWorkflow` or `TXTWorkflow`.
2. **Build Configuration**: Create a configuration object corresponding to the selected workflow (such as `MarkdownBasedWorkflowConfig`). This configuration object contains all the necessary sub-configurations, such as:
* **Converter Config**: Defines how to convert the original file (e.g., PDF) to Markdown.
* **Translator Config**: Defines the LLM to use, API-Key, target language, etc.
* **Exporter Config**: Defines specific options for the output format (e.g., HTML).
3. **Instantiate the Workflow**: Create an instance of the workflow using the configuration object.
4. **Execute Translation**: Call the workflow's `.read_*()` method and `.translate()` / `.translate_async()` method.
5. **Export/Save Results**: Call the `.export_to_*()` method or `.save_as_*()` method to retrieve or save the translation results.
## Available Workflows
| Workflow | Application Scenario | Input Format | Output Format | Core Configuration Class |
|:---------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------|:-----------------------|:----------------------------------|
| **`MarkdownBasedWorkflow`** | Process rich text documents such as PDF, Word, and images. Flow: `File -> Markdown -> Translation -> Export`. | `.pdf`, `.docx`, `.md`, `.png`, `.jpg`, etc. | `.md`, `.zip`, `.html` | `MarkdownBasedWorkflowConfig` |
| **`TXTWorkflow`** | Process plain text documents. Flow: `txt -> Translation -> Export`. | `.txt` and other plain text formats | `.txt`, `.html` | `TXTWorkflowConfig` |
| **`JsonWorkflow`** | Process json files. Flow: `json -> Translation -> Export`. | `.json` | `.json`, `.html` | `JsonWorkflowConfig` |
| **`DocxWorkflow`** | Process docx files. Flow: `docx -> Translation -> Export`. | `.docx` | `.docx`, `.html` | `docxWorkflowConfig` |
| **`XlsxWorkflow`** | Process xlsx files. Flow: `xlsx -> Translation -> Export`. | `.xlsx` | `.xlsx`, `.html` | `XlsxWorkflowConfig` |
| **`SrtWorkflow`** | Process srt files. Flow: `srt -> Translation -> Export`. | `.srt` | `.srt`, `.html` | `SrtWorkflowConfig` |
| **`EpubWorkflow`** | Process epub files. Flow: `epub -> Translation -> Export`. | `.epub` | `.epub`, `.html` | `EpubWorkflowConfig` |
| **`HtmlWorkflow`** | Process html files. Flow: `html -> Translation -> Export`. | `.html`, `.htm` | `.html` | `HtmlWorkflowConfig` |
> The interactive interface allows export in pdf format.
## Starting the Web UI and API Service
For ease of use, DocuTranslate provides a feature-rich web interface and RESTful API.
**Starting the Service:**
```bash
# Start the service, which monitors port 8010 by default
docutranslate -i
# Start with a specified port
docutranslate -i -p 8011
# You can also specify the port using an environment variable
export DOCUTRANSLATE_PORT=8011
docutranslate -i
```
- **Interactive Interface**: After starting the service, access `http://127.0.0.1:8010` (or the specified port) in your browser.
- **API Documentation**: The complete API documentation (Swagger UI) is available at `http://127.0.0.1:8010/docs`.
## Usage
### Example 1: Translating a PDF File (Using `MarkdownBasedWorkflow`)
This is the most common use case. Convert the PDF to Markdown using the `minerU` engine and translate it with an LLM. Here, we use the asynchronous method as an example.
```python
import asyncio
from docutranslate.workflow.md_based_workflow import MarkdownBasedWorkflow, MarkdownBasedWorkflowConfig
from docutranslate.converter.x2md.converter_mineru import ConverterMineruConfig
from docutranslate.translator.ai_translator.md_translator import MDTranslatorConfig
from docutranslate.exporter.md.md2html_exporter import MD2HTMLExporterConfig
async def main():
# 1. Build translator configuration
translator_config = MDTranslatorConfig(
base_url="https://open.bigmodel.cn/api/paas/v4", # Base URL of the AI platform
api_key="YOUR_ZHIPU_API_KEY", # API Key of the AI platform
model_id="glm-4-air", # Model ID
to_lang="English", # Target language
chunk_size=3000, # Text chunk size
concurrent=10 # Number of concurrent executions
)
# 2. Build converter configuration (using minerU)
converter_config = ConverterMineruConfig(
mineru_token="YOUR_MINERU_TOKEN", # Your minerU Token
formula_ocr=True # Enable formula recognition
)
# 3. Build main workflow configuration
workflow_config = MarkdownBasedWorkflowConfig(
convert_engine="mineru", # Specify the parsing engine
converter_config=converter_config, # Pass the converter configuration
translator_config=translator_config, # Pass the translator configuration
html_exporter_config=MD2HTMLExporterConfig(cdn=True) # HTML export configuration
)
# 4. Instantiate the workflow
workflow = MarkdownBasedWorkflow(config=workflow_config)
# 5. Load the file and execute translation
print("Starting file loading and translation...")
workflow.read_path("path/to/your/document.pdf")
await workflow.translate_async()
# Or use the synchronous method
# workflow.translate()
print("Translation completed!")
# 6. Save the results
workflow.save_as_html(name="translated_document.html")
workflow.save_as_markdown_zip(name="translated_document.zip")
workflow.save_as_markdown(name="translated_document.md") # Markdown with embedded images
print("Files saved to the ./output folder.")
# Or directly get the content string
html_content = workflow.export_to_html()
html_content = workflow.export_to_markdown()
# print(html_content)
if __name__ == "__main__":
asyncio.run(main())
```
### Example 2: Translating TXT Files (Using `TXTWorkflow`)
For pure text files, the process is simpler as there is no need for document parsing (conversion). Here is an example using the asynchronous method.
```python
import asyncio
from docutranslate.workflow.txt_workflow import TXTWorkflow, TXTWorkflowConfig
from docutranslate.translator.ai_translator.txt_translator import TXTTranslatorConfig
from docutranslate.exporter.txt.txt2html_exporter import TXT2HTMLExporterConfig
async def main():
# 1. Build the translator configuration
translator_config = TXTTranslatorConfig(
base_url="https://api.openai.com/v1/",
api_key="YOUR_OPENAI_API_KEY",
model_id="gpt-4o",
to_lang="中文",
)
# 2. Build the main workflow configuration
workflow_config = TXTWorkflowConfig(
translator_config=translator_config,
html_exporter_config=TXT2HTMLExporterConfig(cdn=True)
)
# 3. Instantiate the workflow
workflow = TXTWorkflow(config=workflow_config)
# 4. Read the file and execute translation
workflow.read_path("path/to/your/notes.txt")
await workflow.translate_async()
# Or use the synchronous method
# workflow.translate()
# 5. Save the result
workflow.save_as_txt(name="translated_notes.txt")
print("TXT file saved.")
# You can also export the translated plain text
text = workflow.export_to_txt()
if __name__ == "__main__":
asyncio.run(main())
```
### Example 3: Translating a JSON file (using `JsonWorkflow`)
Here, we show an example using the asynchronous method. In the `json_paths` item of `JsonTranslatorConfig`, you need to specify the JSON paths to be translated (following the jsonpath-ng syntax rules).
Only the values matching the JSON paths will be translated.
```python
import asyncio
from docutranslate.exporter.js.json2html_exporter import Json2HTMLExporterConfig
from docutranslate.translator.ai_translator.json_translator import JsonTranslatorConfig
from docutranslate.workflow.json_workflow import JsonWorkflowConfig, JsonWorkflow
async def main():
# 1. Build the translator configuration
translator_config = JsonTranslatorConfig(
base_url="https://api.openai.com/v1/",
api_key="YOUR_OPENAI_API_KEY",
model_id="gpt-4o",
to_lang="Chinese",
json_paths=["$.*", "$.name"] # Compliant with the jsonpath-ng path syntax; all values matching the path will be translated
)
# 2. Build the main workflow configuration
workflow_config = JsonWorkflowConfig(
translator_config=translator_config,
html_exporter_config=Json2HTMLExporterConfig(cdn=True)
)
# 3. Instantiate the workflow
workflow = JsonWorkflow(config=workflow_config)
# 4. Read the file and execute translation
workflow.read_path("path/to/your/notes.json")
await workflow.translate_async()
# Or use the synchronous method
# workflow.translate()
# 5. Save the results
workflow.save_as_json(name="translated_notes.json")
print("The JSON file has been saved.")
# You can also export the translated json text
text = workflow.export_to_json()
if __name__ == "__main__":
asyncio.run(main())
```
### Example 4: Translating a docx File (Using `DocxWorkflow`)
Here, the asynchronous method is shown as an example.
```python
import asyncio
from docutranslate.exporter.docx.docx2html_exporter import Docx2HTMLExporterConfig
from docutranslate.translator.ai_translator.docx_translator import DocxTranslatorConfig
from docutranslate.workflow.docx_workflow import DocxWorkflowConfig, DocxWorkflow
async def main():
# 1. Build the translator configuration
translator_config = DocxTranslatorConfig(
base_url="https://api.openai.com/v1/",
api_key="YOUR_OPENAI_API_KEY",
model_id="gpt-4o",
to_lang="日本語",
insert_mode="replace", # Optional: "replace", "append", "prepend"
separator="\n", # Separator used in "append" and "prepend" modes
)
# 2. Build the main workflow configuration
workflow_config = DocxWorkflowConfig(
translator_config=translator_config,
html_exporter_config=Docx2HTMLExporterConfig(cdn=True)
)
# 3. Instantiate the workflow
workflow = DocxWorkflow(config=workflow_config)
# 4. Load the file and execute translation
workflow.read_path("path/to/your/notes.docx")
await workflow.translate_async()
# Or use the synchronous method
# workflow.translate()
# 5. Save the result
workflow.save_as_docx(name="translated_notes.docx")
print("The docx file has been saved.")
# You can also export the translated docx as binary
text_bytes = workflow.export_to_docx()
if __name__ == "__main__":
asyncio.run(main())
```
### Example 5: Translating an xlsx file (using `XlsxWorkflow`)
Here, we will use the asynchronous method as an example.
```python
import asyncio
from docutranslate.exporter.xlsx.xlsx2html_exporter import Xlsx2HTMLExporterConfig
from docutranslate.translator.ai_translator.xlsx_translator import XlsxTranslatorConfig
from docutranslate.workflow.xlsx_workflow import XlsxWorkflowConfig, XlsxWorkflow
async def main():
# 1. Build the translator configuration
translator_config = XlsxTranslatorConfig(
base_url="https://api.openai.com/v1/",
api_key="YOUR_OPENAI_API_KEY",
model_id="gpt-4o",
to_lang="日本語",
insert_mode="replace", # Optional: "replace", "append", "prepend"
separator="\n", # Separator used in "append" and "prepend" modes
)
# 2. Build the main workflow configuration
workflow_config = XlsxWorkflowConfig(
translator_config=translator_config,
html_exporter_config=Xlsx2HTMLExporterConfig(cdn=True)
)
# 3. Instantiate the workflow
workflow = XlsxWorkflow(config=workflow_config)
# 4. Load the file and execute translation
workflow.read_path("path/to/your/notes.xlsx")
await workflow.translate_async()
# Or use the synchronous method
# workflow.translate()
# 5. Save the result
workflow.save_as_xlsx(name="translated_notes.xlsx")
print("The XLSX file has been saved.")
# You can also export the binary data of the translated XLSX
text_bytes = workflow.export_to_xlsx()
if __name__ == "__main__":
asyncio.run(main())
```
## Detailed Explanation of Prerequisites and Settings
### 1. Obtaining a Large Language Model API Key
The translation function relies on a large language model, and you need to obtain the `base_url`, `api_key`, and `model_id` from the corresponding AI platform.
> Recommended models: Volcano Engine's `doubao-seed-1-6-250615`, `doubao-seed-1-6-flash-250715`, Zhipu's `glm-4-flash`, Alibaba Cloud's `qwen-plus`, `qwen-turbo`, DeepSeek's `deepseek-chat`, etc.
| Platform Name | Method to Obtain API Key | baseurl |
|------------|-----------------------------------------------------------------------------------|----------------------------------------------------------|
| ollama | | http://127.0.0.1:11434/v1 |
| lm studio | | http://127.0.0.1:1234/v1 |
| openrouter | [Click to Obtain](https://openrouter.ai/settings/keys) | https://openrouter.ai/api/v1 |
| openai | [Click to Obtain](https://platform.openai.com/api-keys) | https://api.openai.com/v1/ |
| gemini | [Click to Obtain](https://aistudio.google.com/u/0/apikey) | https://generativelanguage.googleapis.com/v1beta/openai/ |
| deepseek | [Click to Obtain](https://platform.deepseek.com/api_keys) | https://api.deepseek.com/v1 |
| 智譜ai | [Click to Obtain](https://open.bigmodel.cn/usercenter/apikeys) | https://open.bigmodel.cn/api/paas/v4 |
| 騰訊混元 | [Click to Obtain](https://console.cloud.tencent.com/hunyuan/api-key) | https://api.hunyuan.cloud.tencent.com/v1 |
| 阿里云百煉 | [Click to Obtain](https://bailian.console.aliyun.com/?tab=model#/api-key) | https://dashscope.aliyuncs.com/compatible-mode/v1 |
| 火山引擎 | [Click to Obtain](https://console.volcengine.com/ark/region:ark+cn-beijing/apiKey?apikey=%7B%7D) | https://ark.cn-beijing.volces.com/api/v3 |
| 硅基流動 | [Click to Obtain](https://cloud.siliconflow.cn/account/ak) | https://api.siliconflow.cn/v1 |
| DMXAPI | [Click to Obtain](https://www.dmxapi.cn/token) | https://www.dmxapi.cn/v1 |
### 2. Obtaining minerU Token (Online Parsing)
If you select `mineru` as the document parsing engine (`convert_engine="mineru"`), you need to apply for a free Token.
1. Visit the [minerU official website](https://mineru.net/apiManage/docs), register, and apply for the API.
2. Create a new API Token on the [API Token management page](https://mineru.net/apiManage/token).
> **Note**: The minerU Token is valid for 14 days. If it expires, please recreate it.
### 3. Configuring the docling Engine (Local Parsing)
If you select `docling` as the document parsing engine (`convert_engine="docling"`), the required models will be downloaded from Hugging Face during the first use.
**Solutions for Network Issues:**
1. **Setting up a Hugging Face Mirror (Recommended)**:
* **Method A (Environment Variable)**: Set the system environment variable `HF_ENDPOINT` and restart your IDE or terminal.
```
HF_ENDPOINT=https://hf-mirror.com
```
* **Method B (Setting in Code)**: Add the following code at the beginning of your Python script.
```python
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
```
2. **Offline Use (Download Model Packages in Advance)**:
* Download `docling_artifact.zip` from [GitHub Releases](https://github.com/xunbu/docutranslate/releases).
* Extract it to your project directory.
* Specify the model path in the configuration:
```python
from docutranslate.converter.x2md.converter_docling import ConverterDoclingConfig
converter_config = ConverterDoclingConfig(
artifact="./docling_artifact", # Specify the extracted folder
code_ocr=True,
formula_ocr=True
)
```
## FAQ
**Q: What should I do if port 8010 is occupied?**
A: Specify a new port using the `-p` parameter or set the `DOCUTRANSLATE_PORT` environment variable.
**Q: Is translation of scanned documents supported?**
A: Yes, it is supported. Please use the `mineru` parsing engine, which is equipped with powerful OCR capabilities.
**Q: Why is it slow on first use?**
A: When using the `docling` engine, the model needs to be downloaded from Hugging Face during the first run. To speed up this process, refer to the "Solutions for Network Issues" section above.
**Q: How can it be used in an intranet (offline) environment?**
A: It is completely possible. The following two conditions need to be met:
1. **Local Parsing Engine**: Use the `docling` engine and download the model package in advance according to the "Offline Use" guide above.
2. **Local LLM**: Deploy a language model locally using tools such as [Ollama](https://ollama.com/) or [LM Studio](https://lmstudio.ai/), and enter the `base_url` of the local model in `TranslatorConfig`.
**Q: How does the caching mechanism work?**
A: `MarkdownBasedWorkflow` automatically caches the results of document parsing (conversion from files to Markdown) to avoid wasting time and resources on repeated parsing. The cache is stored in memory by default and records the most recent 10 parsing operations. The number of cached items can be changed via the `DOCUTRANSLATE_CACHE_NUM` environment variable.
**Q: How can I use the software via a proxy?**
A: The software does not use a proxy by default. Set the `DOCUTRANSLATE_PROXY_ENABLED` environment variable to `true` to enable communication via a proxy.
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