graphrag-rs
A Rust implementation of GraphRAG (by automataIA, open-source): it builds a knowledge-graph from documents and answers natural-language queries over it. Where sift-kg stops at extracting the graph, graphrag-rs adds the half sift-kg lacks — the graph-based retrieval layer — so it spans the whole loop from documents to answers.
What it does (the full GraphRAG loop)
- Extract a knowledge graph — entities + typed relationships pulled from text (the same document→graph step as sift-kg).
- Community detection — runs the Leiden algorithm to cluster the graph into communities, then PageRank to rank within them. This is the move that defines GraphRAG and that the wiki’s other KG tools don’t make.
- Retrieve + generate — graph-based semantic search feeds an LLM that synthesizes the answer. So the extracted graph is turned back into answers, not just browsed.
Why it matters here — the global-query gap
The wiki’s retrieval-augmented-generation page tracks a taxonomy of what plain vector RAG misses: no-accumulation, exact-token, factual-connection, temporal-validity. graphrag-rs brings the GraphRAG answer to a fifth: global / corpus-sensemaking queries (“what are the main themes across the whole corpus?”). Community detection + community summaries let the system answer holistic questions over an entire document set that chunk-level vector retrieval structurally can’t — the case graphrag exists for. It complements the factual-connection lift the typed knowledge-graph already gave (gbrain‘s +31.4 P@5), at a coarser, whole-corpus grain.
The distinctive bit — Rust + local-first, everywhere
A trait-based portable core (graphrag-core) ships four ways: a CLI/TUI (graphrag-cli), a
REST server with Qdrant vectors (graphrag-server), and a browser-native WASM build with
WebGPU acceleration (graphrag-wasm). Async via Tokio; embeddings via ONNX Runtime (GPU);
local models via Ollama. So it’s a local-first / on-device take on GraphRAG — runnable in
the browser with no server — distinct from the cloud-LLM pipelines the rest of this cluster assumes.
Tier note (T3)
Project README / self-description of an open-source tool — concrete (named crates, real algorithms, deployment targets) but vendor-authored, not independently evaluated. Maturity is self-reported (“production-ready Phase 1; 214+ tests, 100% pass; Phase 2 WASM/UI ~60%”); extraction/retrieval quality is asserted, not benchmarked against alternatives.
Related
graphrag · knowledge-graph · retrieval-augmented-generation · sift-kg · gbrain · hybrid-retrieval-rag · tools-for-thought