EdgeQuake
Graph-RAG in Rust: one binary, one Postgres, any LLM — cloud or fully local.
Turn documents into a knowledge graph and get answers with sources, not just similar chunks.
Quick start · Pick your LLM · How it works · Honest numbers · Docs
Quick start
Needs Docker. Nothing else to install: no Rust, no Node.js, no build.
curl -fsSL https://raw.githubusercontent.com/raphaelmansuy/edgequake/edgequake-main/quickstart.sh | sh
Then open http://localhost:3000. The wizard detects local servers (Ollama, LM Studio, oMLX, MLX-LM, llama.cpp) or takes an OpenAI / Anthropic key. Ports bind to 127.0.0.1 only.
# Headless / CI
quickstart.sh --yes --provider ollama
quickstart.sh --yes --provider omlx --base-url http://host.docker.internal:9050
Ask your first question (after dropping a PDF into the UI, or via the API):
curl -X POST http://localhost:8080/api/v1/documents/upload -F "[email protected]"
curl -X POST http://localhost:8080/api/v1/query \
-H "Content-Type: application/json" \
-d '{"query": "What are the main concepts?", "mode": "hybrid"}'
Heads-up: the Ollama path pulls chat and embedding models (multiple GB the first time). Use a cloud key if you want the fastest first answer.
Pin a version: EDGEQUAKE_VERSION=0.40.7 sh quickstart.sh. More options: Docker deployment options · Enable login.
Pick your LLM
EdgeQuake is not tied to one vendor. Configure a Connection (URL + key, encrypted at rest) in Settings → Connections, or by environment variable.
| You want | Use | Guide |
|---|
| Best quality, no hardware | OpenAI, Anthropic, Gemini, Mistral | providers |
| Private, runs on your Mac/PC | Ollama, LM Studio, oMLX, MLX-LM, llama.cpp, vLLM-MLX | Ollama · oMLX |
| Cloud LLM + local embeddings | Hybrid (EDGEQUAKE_EMBEDDING_PROVIDER) | model roles |
| Your own gateway | Any OpenAI- or Anthropic-shaped server | generic |
Each workspace can assign a different model to every role (extract, query, keyword, summary, embedding, vision). POST /api/v1/providers/test and edgequake doctor tell you what is actually reachable instead of guessing.
Connections, doctor, and encrypted keys land with v0.33.0 (schema 169, already on main). See upgrade-to-0.33.0.
Why Graph-RAG
Vector search finds similar text. It struggles when the answer lives in the relationships between things: "who reports to whom", "which contract affects which product", "what themes run across these 200 papers".
EdgeQuake implements the LightRAG algorithm in Rust. Documents become entities and relationships; at query time it combines vector search with graph traversal.
How it works
Document ─► chunks ─► LLM entity + relation extraction ─► knowledge graph
│ │
pgvector (embeddings) + Apache AGE (graph)
│ │
Question ─► keywords ─► vector search + graph walk ─► LLM ─► answer with sources
| Mode | Best for |
|---|
naive | Keyword-like lookups |
local | Specific entities and their neighbours |
global | Themes and high-level questions |
hybrid (default) | Balanced, comprehensive answers |
mix | Weighted vector + graph blend |
bypass | Direct LLM, no retrieval |
Details: feature tour · LightRAG deep dive · architecture.
Honest numbers
We publish what we measured, including where we tie. Benchmark: GraphRAG-Bench medical-mid, EdgeQuake vs LightRAG, same corpus and models (make bench, n=200).
| Metric | EdgeQuake | LightRAG (Python) | Verdict |
|---|
| Answer accuracy (Acc) | 0.792 | 0.786 | Statistical tie (95% CI includes 0) |
| Cold query p50 | 4447 ms | 4359 ms | ≈ tied (1.02×) |
| Warm query p50 (LLM + embed cache) | 82 ms | 993 ms | EdgeQuake faster when cached |
| Evidence recall | 0.932 | 0.949 | LightRAG ahead |
This is the beginning of the README. Read the rest at the original. Read the full README ↗
