Document RAG and GraphRAG APIs with HazelJS

Real-world document ingestion, semantic search, RAG Q&A, and knowledge-graph retrieval—all in one production-ready REST API Introduction Retrieval-augmented generation (RAG) has become the standard way to ground LLMs in your own data. But production RAG is more than “chunk and embed”: you need flexible ingestion (files, web, GitHub, YouTube), semantic search, and—for complex, multi-hop questions—knowledge-graph–backed retrieval (GraphRAG). The HazelJS RAG Documents Starter demonstrates exactly that. Built on HazelJS and @hazeljs/rag , it ships with every document loader in the package, a full ingest API, semantic vector search, RAG Q&A, and a complete GraphRAG pipeline (entity extraction, community detection, local/global/hybrid search). In this post we walk through what’s in the starter and how to use it. What’s in the box Feature Component Source type Local text TextFileLoader .txt Markdown (heading splits) MarkdownFileLoader .md , .mdx JSON JSONFileLoader .json CSV CSVFileLoader .cs