Yigtwxx/awesome-rag-production
📦 Open Source ProjectYigtwxx
A curated collection of production-ready tools and best practices for building scalable RAG systems.
Building RAG systems that work in production is significantly more challenging than creating a simple proof-of-concept. This repository addresses this gap by categorizing essential components of the RAG stack. It covers a wide range of topics including advanced retrieval techniques, indexing strategies, and data ingestion pipelines. The list features industry-standard frameworks like LangChain and LlamaIndex, alongside specialized tools for vector storage, embedding models, and observability.
Beyond just listing tools, the repository emphasizes 'battle-tested' methodologies, providing insights into how to handle data chunking, query transformation, and system evaluation. It is an invaluable resource for developers aiming to implement robust RAG architectures that can handle real-world data complexity, latency requirements, and accuracy constraints. Whether you are selecting a vector database or designing an evaluation pipeline, this curated list provides the necessary starting points to ensure your AI system is production-ready.
💡Highlights
- ├─Curated production-grade RAG tools
- ├─Covers vector DBs and evaluation
- └─Focuses on scalable architecture
🎯For
- ├─AI Engineers
- ├─MLOps Practitioners
- └─Software Architects