SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG – in Real-Time!

In Lesson 4 of the "LLM Twin: Building Your Production-Ready AI Replica" course, the focus is on developing a feature pipeline within a three-pipeline architecture, which includes feature, training, and inference pipelines. This lesson emphasizes the use of Bytewax as a streaming engine to process data in real-time, enabling the ingestion of diverse data types such as posts, articles, and code from a RabbitMQ queue. The data is then cleaned, chunked, and embedded for fine-tuning and retrieval-augmented generation (RAG), before being stored in a Qdrant vector database, which serves as the feature store. The design ensures that the feature pipeline is isolated from training and inference processes, promoting real-time synchronization and reducing I/O bottlenecks through an efficient streaming setup. The lesson also introduces the use of Pydantic models for data validation and a dispatcher layer employing creational and behavioral design patterns to handle multiple data types, all aimed at creating a scalable and modular system suitable for fine-tuning language models.

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