GraphRAG is a retrieval-augmented generation (RAG) technique introduced by Microsoft Research in 2024 that augments vector search with an explicit knowledge graph. Instead of retrieving only the top-k chunks semantically similar to a query, GraphRAG extracts entities, relations, and claims from each chunk, builds a graph, runs Louvain community detection on it, and then serves queries against either local (entity neighbourhood) or global (community summary) views.
The key claim is that graph-derived structure recovers global context that pure vector search cannot: "who are the main actors in this corpus", "what are the dominant themes", questions that require a view of the whole rather than a handful of passages.
docsiq is a Go implementation of this technique, shipping as a single binary with an embedded React UI. It supports Azure OpenAI, OpenAI, and Ollama as LLM providers, storing everything in SQLite with FTS5 and the sqlite-vec extension for ANN vector search.