Hi all, As Large Language Models (LLMs) continue to transform the ML landscape, there's a growing need for robust, scalable RAG pipelines. Apache Beam already provides several components that can support RAG implementations, including IO transforms for data ingestion, MLTransform for embeddings, and Enrichment for data retrieval. However, these components aren't yet integrated into a cohesive RAG solution.
I have created a design proposal that outlines how we can make it easier for users to create RAG pipelines with minimal custom code: https://docs.google.com/document/d/1j-kujrxHw4R3-oT4pVAwEIqejoCXhFedqZnBUF8AKBQ/edit?usp=sharing Key highlights of the proposal: - Standardized chunking transforms with LangChain integration - Improved embedding interfaces - Vector database abstractions for BigQuery and Vertex AI - Enrichment handlers optimized for vector search - Clear patterns for extending RAG capabilities I encourage anyone who is interested to review the proposal and share their thoughts. Thanks, Claude
