Dadeumi

What is Dadeumi?

Dadeumi is a sophisticated AI-powered translation tool designed specifically for literary and creative translations. The name "Dadeumi" is inspired by the traditional Korean textile refinement process, which involves multiple passes of increasingly fine work to create an exceptional final product.

Unlike conventional translation tools that focus on literal, one-pass translations, Dadeumi follows a multi-stage workflow that includes analysis, cultural adaptation, and iterative refinement to produce translations that capture not just the words, but the essence, tone, and cultural nuances of the original text.

Features

• Multi-stage Translation Workflow - Employs a 10-step iterative refinement process similar to the traditional Korean textile method
• Cultural Adaptation - Carefully adapts cultural references and idioms for the target language
• Self-critique and Refinement - Built-in reflection and improvement cycles
• External Review Option - Optional second-opinion from a different AI model
• CLI and Library Support - Use as a command-line tool or integrate into your applications
• Multi-model Support - Works with both OpenAI (GPT-4o, GPT-4, etc.) and Anthropic (Claude) models
• Detailed Metrics - Provides comprehensive statistics on the translation process
• Resumable Sessions - Can resume interrupted translation workflows
• Real-time Progress Tracking - Shows elapsed time for each translation step
• Detailed Documentation - Generated at each step to explain the translation choices
• Robust Continuation System - Handles truncated responses and seamlessly continues long translations
• Interruption Recovery - Safely resumes from the same step after Ctrl+C interruption
• Smart Context Management - Optimizes token usage for each model's context window size

Installation

Install Dadeumi globally to use the CLI tool:

npm install -g dadeumi

Or locally to use as a library:

npm install dadeumi

Usage

CLI

# Basic usage
dadeumi -i input.txt -t Japanese -o output-directory

# With source language specified
dadeumi -i input.txt -s English -t Spanish -o output-directory

# Use a specific model
dadeumi -i input.txt -t German -m gpt-4o-mini -o output-directory

# Use Claude 3.7 Sonnet for translation (requires ANTHROPIC_API_KEY)
dadeumi -i input.txt -t Korean -m claude-3-7-sonnet-latest -o output-directory

# Skip external review
dadeumi -i input.txt -t French --skip-external-review -o output-directory

# Add custom translation instructions
dadeumi -i input.txt -t Korean --instructions "Translate with a formal tone suitable for academic audiences" -o output-directory

# Running without arguments shows help and pricing information
dadeumi

See the examples directory for more CLI usage examples.

Programmatic Usage

// ESM
import { translate } from "dadeumi";

// CommonJS
const { translate } = require("dadeumi");

async function runTranslation() {
  const translatedText = await translate("Your source text here", "Spanish", {
    sourceLanguage: "English", // Optional, will be auto-detected
    modelName: "gpt-4o", // Default model
    verbose: true, // Display detailed logs
    outputDir: "./translations", // Where to save intermediates
  });

  console.log(translatedText);
}

For more detailed examples, see the JavaScript example and TypeScript example in the examples directory.

The Dadeumi Workflow

Dadeumi's translation process is modeled after the Korean "Dadeumi" textile refinement method, which involves multiple steps of increasing refinement:

1. Initial Analysis - Analyzes the source text for tone, style, and cultural elements
2. Expression Exploration - Explores how to express key concepts in the target language
3. Cultural Adaptation - Discusses how to adapt cultural references appropriately
4. Title & Inspiration - Considers title translation options and literary inspirations
5. First Translation - Creates an initial translation draft
6. Self-critique & Refinement - Critically reviews and improves the translation
7. Second Refinement - Further polishes the translation with fresh perspective
8. Final Translation - Creates a comprehensive review and final translation
9. External Review (optional) - Gets feedback from a different AI model
10. Final Refinement - Applies the external feedback for the polished result

Advanced Features

Smart Continuation System

Dadeumi features a sophisticated continuation handling system that:

• Automatically detects truncated responses from the AI model
• Efficiently resumes translation from the exact point where it left off
• Combines partial translations seamlessly for a coherent result
• Handles edge cases like XML tag truncation and "to be continued" markers
• Prevents infinite loops by detecting minimal progress situations
• Properly backs up intermediate results for resumability

Interruption Recovery

If your translation is interrupted (by Ctrl+C or other means), Dadeumi:

• Saves the current state and all intermediate files
• On restart, picks up exactly where it left off in the step sequence
• Doesn't skip or repeat steps in the workflow
• Preserves all previously generated content and translation metrics

Context Window Optimization

Dadeumi intelligently manages token usage to maximize the capabilities of different AI models:

• Automatically detects the correct context window size for each model (e.g., 128K for GPT-4o, 200K for Claude)
• Only trims conversation history when necessary (at 90% of window capacity)
• Provides warnings at 70% usage to help anticipate potential issues
• Preserves essential context even when trimming is required
• Uses context efficiently for handling very long texts

API

translate

async function translate(
  text: string,
  targetLanguage: string,
  options?: {
    sourceLanguage?: string;
    modelName?: string;
    outputDir?: string;
    verbose?: boolean;
    skipExternalReview?: boolean;
    customInstructions?: string;
  }
): Promise<string>;

TranslationWorkflow

For more advanced usage, you can use the TranslationWorkflow class directly:

import { TranslationWorkflow, TranslationConfig } from "dadeumi";

const config: TranslationConfig = {
  sourceText: "Your text here",
  targetLanguage: "Japanese",
  // ... other options
};

const workflow = new TranslationWorkflow(config);
await workflow.execute();

Environment Variables

Dadeumi requires API keys to work:

• OPENAI_API_KEY - Required for using OpenAI models (default)
• ANTHROPIC_API_KEY - Optional, for using Claude models or for external review

You can set these in a .env file in your project root.

FAQ

Q: What makes Dadeumi different from other translation tools?

A: Dadeumi employs a multi-stage, iterative refinement process inspired by traditional Korean textile techniques. Unlike other tools that produce single-pass translations, Dadeumi analyzes, explores, and refines translations through multiple stages for higher quality results.

Q: What kinds of texts work best with Dadeumi?

A: Dadeumi excels with creative, literary, and nuanced texts where preserving tone, style, and cultural elements is important. It's ideal for literature, marketing copy, poetry, and culturally-rich content.

Q: Which models work best?

A: For high-quality translations, we recommend using GPT-4o or Claude 3.7 Sonnet. For faster or more economical translations, GPT-4o-mini works well.

Q: Can I customize the translation style?

A: Yes, use the --instructions parameter or customInstructions option to specify tone, audience, formality level, or other stylistic preferences.

Q: How does Dadeumi handle very long texts?

A: Dadeumi includes a robust continuation system that detects when a translation is truncated and seamlessly continues it. The system intelligently manages conversation context to make optimal use of each model's token limits, allowing for translation of very long documents.

Q: What happens if my translation is interrupted?

A: Dadeumi saves intermediate results at each step. If interrupted, simply run the same command again and it will resume from where it left off, without skipping or repeating steps.

License

MIT

Acknowledgments

• Inspired by the traditional Korean textile refinement process
• Built with OpenAI and Anthropic AI models
• Uses a modular architecture for extensibility