Graph RAG into Production — Step-by-Step

This guide explores how to productionize Graph RAG using a Google Cloud-native, fully serverless implementation. It introduces Graphrag-lite for deploying an end-to-end Graph RAG pipeline, covering steps from graph extraction and storage to community detection and query processing. The article also discusses optimizing throughput latency in LLM applications via parallelized and serverless architectures. Graph2nosql, a lightweight Python interface, is highlighted for managing knowledge graphs in NoSQL databases like Firestore.