kttc 0.3.0

Knowledge Translation Transmutation Core - Transforming translations into gold-standard quality

English · Русский · 中文

KTTC - Knowledge Translation Transmutation Core

Autonomous AI-powered translation quality assurance

KTTC uses specialized multi-agent systems to automatically detect, analyze, and fix translation quality issues following the industry-standard MQM (Multidimensional Quality Metrics) framework. Get production-ready translation quality in seconds, not hours.

---

Key Features

• Multi-Agent QA System - 5 specialized agents analyze accuracy, fluency, terminology, hallucinations, and context
• MQM Scoring - Industry-standard quality metrics used in WMT benchmarks
• Smart Routing - Automatically selects optimal models based on text complexity (60% cost savings)
• Auto-Correction - LLM-powered error fixing with iterative refinement (TEaR loop)
• Language-Specific Agents - Native-level checks for English, Chinese, and Russian
• Translation Memory - Semantic search with quality tracking and reuse
• Glossary Management - Custom terminology validation and consistency
• Batch Processing - Process thousands of translations in parallel
• CI/CD Ready - GitHub Actions integration, exit codes, multiple output formats
• Multi-LLM Support - OpenAI, Anthropic, GigaChat, YandexGPT

Performance: 90% cost reduction vs manual review • 100-1000x faster • 95+ MQM quality target

---

Quick Start

1. Install

pip install kttc

Optional language enhancements:

pip install kttc[english]        # English: LanguageTool (5,000+ grammar rules)
pip install kttc[chinese]        # Chinese: HanLP (measure words, particles)
pip install kttc[all-languages]  # All language helpers

2. Set API Key

export KTTC_OPENAI_API_KEY="sk-..."
# or
export KTTC_ANTHROPIC_API_KEY="sk-ant-..."

3. Check Translation Quality

kttc check source.txt translation.txt --source-lang en --target-lang es

Output:

✅ MQM Score: 96.5 (PASS - Excellent Quality)
📊 5 agents analyzed translation
⚠️  Found 2 minor issues, 0 major, 0 critical
✓ Quality threshold met (≥95.0)

That's it! KTTC works out of the box with smart defaults:

• ✅ Smart routing (auto-selects cheaper models for simple texts)
• ✅ Auto-glossary (uses 'base' glossary if exists)
• ✅ Auto-format (detects output format from file extension)

---

Commands

KTTC provides a unified CLI with smart auto-detection:

kttc check source.txt translation.txt          # Single quality check
kttc check source.txt t1.txt t2.txt t3.txt     # Auto-compares multiple translations
kttc check translations.csv                     # Auto-detects batch mode (CSV/JSON)
kttc check source_dir/ trans_dir/              # Auto-detects directory batch mode

kttc batch --file translations.csv              # Explicit batch processing
kttc compare --source src.txt -t t1 -t t2      # Compare translations side-by-side
kttc translate --text "Hello" --source-lang en --target-lang es  # Translate with QA
kttc benchmark --source text.txt --providers openai,anthropic    # Benchmark LLMs

# Glossary management (project + user global storage)
kttc glossary list                              # List all glossaries
kttc glossary create tech --from-csv terms.csv  # Create project glossary
kttc glossary create personal --from-csv my.csv --user  # Create user glossary

See full command reference: docs/en/reference/cli-commands.md

---

Python API

import asyncio
from kttc.agents import AgentOrchestrator
from kttc.llm import OpenAIProvider
from kttc.core import TranslationTask

async def check_quality():
    llm = OpenAIProvider(api_key="your-key")
    orchestrator = AgentOrchestrator(llm)

    task = TranslationTask(
        source_text="Hello, world!",
        translation="¡Hola, mundo!",
        source_lang="en",
        target_lang="es",
    )

    report = await orchestrator.evaluate(task)
    print(f"MQM Score: {report.mqm_score}")
    print(f"Status: {report.status}")

asyncio.run(check_quality())

See full API reference: docs/en/reference/api-reference.md

---

Documentation

📚 Complete documentation is available in docs/

Quick Links

• Quickstart Guide - Get started in 5 minutes
• Installation Guide - Detailed setup instructions
• CLI Reference - All commands and options
• Architecture - How KTTC works
• Language Features - English/Chinese/Russian specialization

Documentation Structure

Following the Diátaxis framework:

• 📚 Tutorials - Learn by doing (step-by-step guides)
• 📖 Guides - Solve specific problems (how-to guides)
• 📋 Reference - Look up technical details (API, CLI)
• 💡 Explanation - Understand concepts (architecture, design)

Translations

Languages: 🇺🇸 English (primary) · 🇷🇺 Русский · 🇨🇳 中文

Full documentation available in:

• 🇺🇸 English - Complete (primary source)
• 🇷🇺 Русский - In progress
• 🇨🇳 中文 - In progress

---

Development

Setup

git clone https://github.com/kttc-ai/kttc.git
cd kttc
python3.11 -m venv venv
source venv/bin/activate
pip install -e ".[dev]"
pre-commit install

Quality Standards

• Type Checking: mypy --strict
• Formatting: black (line length: 100)
• Linting: ruff
• Testing: pytest with asyncio support

# Run all checks
pre-commit run --all-files
pytest --cov=kttc

---

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick start:

1. Fork the repository
2. Create a feature branch
3. Make changes and add tests
4. Run quality checks: pre-commit run --all-files && pytest
5. Submit a pull request

Please read our Code of Conduct before contributing.

---

Security

For security vulnerabilities, see SECURITY.md. Do not open public issues for security concerns.

---

License

Licensed under the Apache License 2.0. See LICENSE for details.

Copyright 2025 KTTC AI (https://github.com/kttc-ai)

---

Citation

If you use KTTC in your research:

@software{kttc2025,
  title = {KTTC: Knowledge Translation Transmutation Core},
  author = {KTTC AI},
  year = {2025},
  url = {https://github.com/kttc-ai/kttc},
  version = {0.1.0}
}

---

Links

• 📦 PyPI Package
• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions
• 🔒 Security Policy