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Developer Builds Self-Hosted RAG Stack Combining Vector, Graph, and Relational Storage

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A software developer has published a detailed breakdown of myRAG, a fully self-hosted retrieval-augmented generation (RAG) application built with a FastAPI backend, React frontend, and three storage engines: Qdrant, PostgreSQL, and Neo4j. The system processes documents through Docling for clean Markdown conversion, then chunks and indexes them using both dense semantic vectors and sparse BM25 vectors for hybrid search. At query time, results from both search methods are fused using Reciprocal Rank Fusion (RRF) and further refined with a Cohere cross-encoder reranker to improve relevance. The stack also incorporates a Neo4j knowledge graph populated via LLM-extracted entity triples, adding structured relational context alongside vector retrieval. All six components — app, frontend, document parser, vector store, relational database, and graph database — are orchestrated via Docker Compose, making the entire pipeline self-contained and reproducible.

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