EdgeQuake converts documents into "smart" knowledge graphs for better search and generation.
🟢 How it Actually works?
Classic RAG systems search for relevant text pieces mainly by vector similarity. This works fine for simple queries, but starts to falter with multi-hop reasoning (how is X related to Y via Z?), thematic questions (what are the main topics?), and connection queries. The problem is that vectors capture semantics well, but lose the structure of relationships between concepts.
EdgeQuake solves this by implementing the LightRAG algorithm in Rust: documents are not just chunked and embedded, but decomposed into a knowledge graph of entities and relationships. At the query stage, the system traverses both the vector space and the graph structure, combining the speed of vector search with the "logic" of graph traversal.
Features:
☞ Knowledge Graphs: extracting entities and building relationships with LLMs provides a structural understanding of documents, not just keyword matching
☞ 6 query modes: from fast naive vector search to hybrid queries with graph traversal, for different types of questions
☞ Rust performance: async-first architecture on Tokio and zero-copy operations, handling thousands of concurrent requests
☞ Advanced PDF processing (planned, coming soon) ⚠️: table detection, multi-column layout, OCR with quality feedback mode
☞ Production ready: OpenAPI 3.0 REST API, SSE streaming, health checks, multi-tenant workspace isolation
☞ Modern frontend: React 19 + interactive graph visualizations on Sigma.js
GitHub
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