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@step def load_rag_documents dict # Load and chunk documents for RAG pipeline "https://www.zenml.io/" return "chunks" @step(experiment_tracker="mlflow") experiment_tracker="mlflow" "mlflow" def generate_embeddings data: dict dict None # Generate embeddings for RAG pipeline 'chunks' return "embeddings" @step( settings={"resources": ResourceSettings(memory="2Gi") }, settings={"resources": ResourceSettings(memory="2Gi") }, "resources" memory="2Gi" "2Gi" "my_model" def index_generator embeddings: dict, dict str # Generate index for RAG pipeline "vector_store_credentials" return id @pipeline( active_stack="my_stack", active_stack="my_stack", "my_stack" def rag_pipeline str return Essential Essential Essential Building a retrieval-augmented generation system involves many moving parts – from ingesting documents and creating embeddings to making the right data available to your deployed app. ZenML handles that complexity for you by tracking all your pipelines, and artifacts in a single plane.This allows you to stay on top, and build a flywheel to continuously improve your RAG performance ZenML makes fine-tuning large language models reproducible and hassle-free. Define your training pipeline once – ZenML handles data versioning, experiment tracking, and pushing the new model to production.LLM drifting or there's new data? Simply re-run the pipeline and automatically log comparisons to previous runs, MCP server