Let Flink Cook: Mastering Real-Time Retrieval-Augmented Generation (RAG) with Flink

This post details how to build a real-time Retrieval-Augmented Generation (RAG) pipeline using Apache Kafka and Apache Flink in Confluent Cloud. It introduces new Flink SQL functions like `ml_predict()` and `federated_search()` to integrate LLMs and vector stores directly into data processing pipelines. The post outlines two key patterns: data preparation (chunking, vector embedding creation using `ml_predict()` with OpenAI embeddings) and inference (retrieving context and augmenting prompts). It highlights the benefits of using Flink for real-time data orchestration, state management for LLMs, and post-processing for reliability checks. The contribution is the practical application of Flink SQL for RAG, enabling real-time data grounding for LLMs.