EdgeQuake Documentation
EdgeQuake Documentation
Section titled “EdgeQuake Documentation”High-Performance Graph-Enhanced RAG in Rust
Welcome to EdgeQuake — an advanced Retrieval-Augmented Generation (RAG) framework that combines knowledge graphs with vector search for superior context retrieval.
┌────────────────────────────────────────────────────────────────────┐│ EdgeQuake ││ ││ Document ──▶ [Pipeline] ──▶ Knowledge Graph ──▶ Query Engine ││ │ │ │ ││ ▼ ▼ ▼ ││ ┌─────────┐ ┌─────────┐ ┌─────────┐ ││ │ Chunks │ │ Entities│ │ Hybrid │ ││ │ + Embed │ │ + Rels │ │ Results │ ││ └─────────┘ └─────────┘ └─────────┘ ││ ││ [REST API] [Next.js WebUI] [Rust SDK] [PostgreSQL/Memory] │└────────────────────────────────────────────────────────────────────┘📚 Documentation Index
Section titled “📚 Documentation Index”🚀 Getting Started
Section titled “🚀 Getting Started”| Guide | Description | Time |
|---|---|---|
| Installation | Prerequisites and setup | 5 min |
| Quick Start | First ingestion and query | 10 min |
| First Ingestion | Understanding the pipeline | 15 min |
🏗️ Architecture
Section titled “🏗️ Architecture”| Document | Description |
|---|---|
| Overview | System design and components |
| Data Flow | How documents flow through the system |
| Crate Reference | 11 Rust crates explained |
💡 Core Concepts
Section titled “💡 Core Concepts”| Concept | Description |
|---|---|
| Graph-RAG | Why knowledge graphs enhance RAG |
| Entity Extraction | LLM-based entity recognition |
| Knowledge Graph | Nodes, edges, and communities |
| Hybrid Retrieval | Combining vector and graph search |
🔬 Deep Dives
Section titled “🔬 Deep Dives”| Article | Description |
|---|---|
| LightRAG Algorithm | Core algorithm: extraction, graph, retrieval |
| Query Modes | 6 modes with trade-offs explained |
| Entity Normalization | Deduplication and description merging |
| Gleaning | Multi-pass extraction for completeness |
| Entity Extraction | LLM-based extraction pipeline |
| Community Detection | Louvain clustering for global queries |
| Chunking Strategies | Token-based segmentation with overlap |
| Embedding Models | Model selection and dimension trade-offs |
| Graph Storage | Apache AGE property graph backend |
| Vector Storage | pgvector HNSW indexing and search |
| PDF Processing | Text/Vision/Hybrid extraction pipeline |
| Cost Tracking | LLM cost monitoring per operation |
| Pipeline Progress | Real-time progress tracking |
📊 Comparisons
Section titled “📊 Comparisons”| Comparison | Key Insights |
|---|---|
| vs LightRAG (Python) | Performance and design differences |
| vs GraphRAG | Microsoft’s approach comparison |
| vs Traditional RAG | Why graphs matter |
📖 Tutorials
Section titled “📖 Tutorials”| Tutorial | Description |
|---|---|
| Building Your First RAG App | End-to-end tutorial |
| PDF Ingestion | PDF upload and configuration |
| Multi-Tenant Setup | Workspace isolation |
| Document Ingestion | Upload and processing workflows |
| Migration from LightRAG | Python to Rust migration guide |
🔌 Integrations
Section titled “🔌 Integrations”| Integration | Description |
|---|---|
| OpenWebUI | Chat interface with Ollama emulation |
| LangChain | Retriever and agent integration |
| Custom Clients | Python, TypeScript, Rust, Go clients |
📖 API Reference
Section titled “📖 API Reference”| API | Description |
|---|---|
| REST API | HTTP endpoints |
| Extended API | Advanced API features |
📓 Reference
Section titled “📓 Reference”| Resource | Description |
|---|---|
| Cookbook | Practical recipes for common tasks |
| FAQ | Frequently asked questions |
🛠️ Operations
Section titled “🛠️ Operations”| Guide | Description |
|---|---|
| Deployment | Production deployment |
| Configuration | All config options |
| Monitoring | Observability setup |
| Performance Tuning | Optimization guide |
🔒 Security & Troubleshooting
Section titled “🔒 Security & Troubleshooting”| Guide | Description |
|---|---|
| Security Best Practices | Security guidelines |
| Common Issues | Debugging guide |
⚡ Quick Links
Section titled “⚡ Quick Links”I want to…
| Goal | Go To |
|---|---|
| Get running in 5 minutes | Quick Start |
| Understand the architecture | Overview |
| Learn how the algorithm works | LightRAG Algorithm |
| See API endpoints | REST API |
| Deploy to production | Deployment |
🔧 Technology Stack
Section titled “🔧 Technology Stack”┌─────────────────────────────────────────────────────────────┐│ Backend (Rust) ││ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ ││ │ Tokio │ │ Axum │ │ SQLx │ │ async- │ ││ │ (async) │ │ (HTTP) │ │ (database)│ │ openai │ ││ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │├─────────────────────────────────────────────────────────────┤│ Frontend (TypeScript) ││ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ ││ │ Next.js │ │ React 19 │ │ Sigma.js │ │ Zustand │ ││ │ 16.1.0 │ │ 19.2.3 │ │ (graph) │ │ (state) │ ││ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │├─────────────────────────────────────────────────────────────┤│ Storage ││ ┌───────────────────────┐ ┌───────────────────────────┐ ││ │ PostgreSQL 15+ │ │ In-Memory (dev) │ ││ │ + pgvector + Apache AGE│ │ Fast prototyping │ ││ └───────────────────────┘ └───────────────────────────┘ │└─────────────────────────────────────────────────────────────┘📈 Key Metrics
Section titled “📈 Key Metrics”| Metric | Value | Notes |
|---|---|---|
| Lines of Rust | ~130,000 | Across 11 crates |
| Query Modes | 6 | naive, local, global, hybrid, mix, bypass |
| Entity Types | 7 default | PERSON, ORGANIZATION, LOCATION, CONCEPT, EVENT, TECHNOLOGY, PRODUCT |
| Embedding Dims | Configurable | 1536 (OpenAI), 768 (Ollama/LM Studio) |
🏃 One-Liner Start
Section titled “🏃 One-Liner Start”# Clone and run with Ollama (free, local LLM)git clone https://github.com/raphaelmansuy/edgequake.git && cd edgequake && make dev📄 License
Section titled “📄 License”Apache-2.0
🔗 Links
Section titled “🔗 Links”- GitHub: github.com/raphaelmansuy/edgequake
- LightRAG Paper: arxiv.org/abs/2410.05779