RAG Pipelines Made Simple: Ballerina for AI-Powered Search

Building RAG systems often involves complex steps like document loading, embedding, and LLM integration, leading to cumbersome code. Ballerina simplifies this with a unified, type-safe AI module that streamlines these processes. Traditional approaches require configuring multiple libraries and extensive manual coding for each step. Ballerina offers a more straightforward configuration-based approach, reducing boilerplate and emphasizing AI logic. The module handles chunking, embedding, and prompt engineering, removing the need for manual setup. It uses a configuration-based approach, eliminating the need to hardcode API keys or model names. Ballerina's DataLoader simplifies document ingestion by managing chunking and embedding automatically. Query expressions in Ballerina provide a type-safe way to transform data, making your code explicit and readable. The built-in `ai:augmentUserQuery()` function handles prompt engineering, enhancing answer quality. Exposing the RAG system as a REST API is simple, allowing for easy integration. The clear, concise code provided allows developers to focus on the AI capabilities rather than the underlying infrastructure.