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Welcome to Awesome RAG Production

Moving a Retrieval-Augmented Generation (RAG) system from a local Jupyter notebook to a scalable production environment is where most projects fail. This repository is dedicated to bridging that gap.

We curate high-quality resources, frameworks, and patterns specifically focused on the reliability, scalability, and observability of RAG pipelines.

Key Pillars of Production RAG

Data Engineering: Robust ingestion and cleaning at scale.

Retrieval Optimization: Beyond simple vector search (Hybrid search, Re-ranking).

Evaluation & Guardrails: Moving from "it looks right" to quantitative metrics.

Infrastructure: Serving models and vector databases with low latency.