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Translation API Documentation

Research-backed translation service for low-resource languages with RAG-enhanced accuracy and enterprise infrastructure.

Research Foundation

Our architecture is based on comprehensive research evaluating translation performance across 101 low-resource languages. The study demonstrated that word-based TF-IDF retrieval augmentation achieves a 26% improvement in semantic similarity scores, with gains observed in 95 out of 101 languages tested.

101
Languages Evaluated
26%
Average Improvement
95%
Languages Improved

Technical Architecture

Authentication Layer

Firebase Firestore-based API key management with configurable rate limiting (1000 req/day default)

Translation Engine

Groq Llama 4 Maverick (17B) for primary translation with Gemini 2.5 Pro for linguistic context enhancement

OCR Processing

Groq Llama 4 Maverick for document OCR - superior handling of messy, real-world data vs traditional OCR

RAG Enhancement

Word-based TF-IDF retrieval proven to boost low-resource language performance by 26% across 101 languages

Infrastructure

Django backend on AWS EC2 with Route 53 DNS management for enterprise-grade reliability

Context Processing

Gemini's extended context window (up to 1M tokens) for comprehensive parallel sentence analysis

Model Selection & Technology Decisions

Groq Llama 4 Maverick (17B Parameters)

  • • OCR Processing: Superior performance on messy, real-world document data compared to traditional OCR systems
  • • Translation: Optimized for low-resource language pairs with fast inference on Groq's hardware
  • • Reliability: Consistent output format and robust error handling for production environments

Google Gemini 2.5 Pro

  • • Extended Context: Up to 2M token context window for comprehensive parallel text analysis
  • • Linguistic Analysis: Deep morphological and syntactic pattern extraction from document corpora
  • • Context Enhancement: Generates rich linguistic metadata to improve translation accuracy

Word-Based TF-IDF Retrieval

  • • Research Validated: Outperformed BPE token-based approaches across 101 languages
  • • Morphology Preservation: Maintains word integrity crucial for complex morphological languages
  • • Stable Performance: Robust with limited training data unlike sub-word tokenization approaches

Infrastructure & Deployment

Backend Architecture

  • • Framework: Django with PostgreSQL database
  • • Hosting: AWS EC2 instances with auto-scaling
  • • DNS: Route 53 for global load balancing
  • • Storage: Firebase Firestore + local filesystem

Performance Features

  • • Caching: API key validation and rate limit caching
  • • Async Processing: Background document processing
  • • Streaming: Real-time translation response streaming
  • • Monitoring: Comprehensive logging and error tracking

API Reference

Endpoint

POST https://latest.gaia-ml.com/api/translate/

Request Parameters

ParameterTypeRequiredDescription
textstringYesSource text to translate
src_langstringYesSource language name
tgt_langstringYesTarget language name
model_idstringYesCustom model UUID with domain-specific context
streambooleanNoEnable streaming response (default: true)

Required Headers

Content-Type: application/json
X-API-Key: your_api_key_here

Implementation Examples

cURL

curl -X POST https://latest.gaia-ml.com/api/translate/ \
    -H "Content-Type: application/json" \
    -H "X-API-Key: your_api_key_here" \
    -d '{
        "text": "Hola a todos",
        "src_lang": "español", 
        "tgt_lang": "wampis",
        "model_id": "00b31e2e-a318-42ba-b4be-03fadfed855b"
    }'

JavaScript/React

const translateText = async (text, srcLang, tgtLang, modelId, apiKey) => {
  try {
    const response = await fetch('https://latest.gaia-ml.com/api/translate/', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'X-API-Key': apiKey
      },
      body: JSON.stringify({
        text,
        src_lang: srcLang,
        tgt_lang: tgtLang,
        model_id: modelId,
        stream: false
      })
    });

    if (!response.ok) {
      throw new Error(`HTTP error! status: ${response.status}`);
    }

    const result = await response.json();
    return result.data.translation;
  } catch (error) {
    console.error('Translation failed:', error);
    throw error;
  }
};

Response Format

{
  "success": true,
  "data": {
    "translation": "Atash pujaamu",
    "raw_translation": "Atash pujaamu"
  },
  "source_text": "Hola a todos",
  "response_time": 1.2456,
  "source_language": "español",
  "target_language": "wampis"
}

Performance Benchmarks

Research Validation

Baseline Performance:0.625 similarity
RAG-Enhanced:0.788 similarity
Best Improvement (Choctaw):+0.78 similarity
Languages Improved:95/101 tested

Production Metrics

Average Response Time:1-3 seconds
Max Text Length:2000 characters
Rate Limit (default):1000 req/day
Uptime SLA:99.9%

Technical Specifications

System Capabilities

  • • Multi-provider fallback system for high availability
  • • Document processing with OCR for parallel corpus creation
  • • Real-time streaming and batch translation modes
  • • Comprehensive error handling with detailed HTTP status codes

Security & Monitoring

  • • SHA256-hashed API keys with Firestore validation
  • • Configurable rate limiting per user account
  • • Complete request/response logging for audit trails
  • • Usage analytics with token counting and cost tracking