The real-time LLM translation project pipeline includes the Kafka Streams Pipe process and FastAPI implementation. (Complete and final version down below)

Real-Time LLM Translation Project: Complete Pipeline Overview

1. Data Ingestion (Kafka Producer):
   • Input Source: Fetch Spanish news articles or other data sources.
   • Output Topic: processed_news_spanish
   • Function: Send raw or preprocessed Spanish articles to the Kafka topic.
2. Message Bridge (Kafka Streams Pipe Process):
   • Input Topic: processed_news_spanish
   • Output Topic: translated_news_spanish_to_english
   • Function: Bridge messages between topics or optionally enrich/filter the data (e.g., adding metadata).
3. Real-Time Translation (FastAPI Service):
   • Input Topic: translated_news_spanish_to_english
   • Output Topic: final_translations (Optional)
   • Function:
     • Consume articles from the input topic.
     • Translate articles from Spanish to English using a Hugging Face model.
     • Write the translated articles to another Kafka topic (final_translations) or store them in a database.
4. Downstream Processing: (Future Works)
   • Input Topic: final_translations
   • Function:
     • Analyze translations.
     • Store translations in a database or forward them to other systems for further use.
5. Monitoring and Logging:
   • Use tools like Prometheus and Grafana to monitor:
     • Topic activity (processed_news_spanish, translated_news_spanish_to_english, final_translations).
     • Kafka Streams and FastAPI service performance.
     • Translation latency and throughput.

Translation Pipeline

How It Fits into the Translation Pipeline

Step-by-Step Pipeline