edgequake

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

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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.

Service	Docker quickstart	make dev
Web UI	http://localhost:3000	http://localhost:3010
REST API / Swagger	http://localhost:8080 · /swagger-ui	see make status
Health	http://localhost:8080/health	see make status

Pin a version: EDGEQUAKE_VERSION=0.40.7 sh quickstart.sh. More options: Docker deployment options · Enable login.

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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.

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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.

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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

So the pitch is not "more accurate than LightRAG". It is the same retrieval quality, productionised: a single Rust binary, Postgres as the only datastore, multi-tenant isolation, auth, a web UI, MCP, SDKs, and explicit, tested upgrades. Methodology and caveats: EQ vs LightRAG · vs GraphRAG · vs traditional RAG.

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Built for production

• One datastore for the graph. PostgreSQL 16/17/18 with pgvector. No separate vector DB or graph DB to run. PDF bytes, uploads, page images, and extracted markdown can move to a private bucket. Text and chunks stay in Postgres. The default is still the database. See originals.
• Safe upgrades. The API never migrates your database. edgequake migrate is explicit, progressive, and replay-tested from every published release (SPEC-150, upgrading).
• Secure defaults. Quickstart binds to loopback; secrets are generated; connection keys are AES-256-GCM encrypted and write-only (SECURITY.md, SPEC-163).
• Multi-tenant. Fail-closed workspace isolation for query, delete, and recovery. Built-in auth, SSO (Keycloak/OIDC), and audit logging.
• PDF vision pipeline. Text mode by default (pdfium embedded); vision mode for tables, scans, and complex layouts, with automatic fallback.
• Honest health. /health probes real providers; edgequake doctor checks DB, schema, roles, and security posture.
• Multi-arch images. linux/amd64 + linux/arm64 on GHCR, every release.

Integrations

SDKs	Python · TypeScript · Rust · Go, Java, Kotlin, C#, PHP, Ruby, Swift
AI agents	MCP server
Apps	OpenWebUI · LangChain
API	OpenAPI 3.0, SSE streaming, batch ingestion · REST reference

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Documentation

I want to…	Read
Click through the app	Visual tour — every screen, in plain English, with pictures from the public demo
Get running	Getting started · First RAG app
Choose / configure a model	Providers · Env reference
Deploy	Deployment · Docker options · Configuration
Upgrade	Upgrading · What's new · CHANGELOG
Operate	Monitoring · Runtime auth · Originals · Troubleshooting
Understand	Architecture · Deep dives · FAQ

Contributing

Contributions are welcome, from typo fixes to new providers. Start with CONTRIBUTING.md; for a local build run make install && make dev (see AGENTS.md and the pre-delivery checklist).

• Found a bug or want a feature? Open an issue.
• Questions or show-and-tell? GitHub Discussions.
• Like it? A ⭐ helps others find the project.

Acknowledgments

EdgeQuake implements the LightRAG algorithm by Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. Also inspired by Microsoft's GraphRAG.

License

Apache License 2.0, see LICENSE. Copyright 2024-2026 Raphaël MANSUY

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