{"repo":"justrach/emergentDB","free":true,"listed":false,"github":"https://github.com/justrach/emergentDB","clone":"git clone https://github.com/justrach/emergentDB.git","description":"🧬 Self-optimizing vector database using MAP-Elites. 51x faster than ChromaDB, 82x faster than LanceDB, 100% recall. Auto-evolves optimal index configs via Quality Diversity.","language":"Rust","stars":21,"topics":["gemini","quality-diversity","quality-diversity-algorithms","semantic-search","vector-database"],"license":"AGPL-3.0","category":"databases-storage","readme_excerpt":"EmergentDB Proof of Concept - A self-optimizing vector database that uses MAP-Elites evolutionary algorithm to automatically discover the optimal index configuration for your workload. 51-82x faster than competitors. 100% recall on real embeddings. What are Embeddings? Embeddings are numerical representations of data (text, images, audio) that capture semantic meaning. Similar concepts have similar embeddings, enabling semantic search - finding results by meaning rather than exact keyword matches. New to embeddings? Watch this excellent explainer: What are Embeddings? EmergentDB stores these vectors and finds the most similar ones at blazing speed. The Problem As embedding dimensions grow (768-3072), traditional vector databases struggle: - Manual Tuning Hell : HNSW M=16? M=32? ef construction=100? Most teams guess and hope. - Workload Mismatch : Optimal config for 1K vectors ≠ optimal for 100K. Databases don't adapt. - Recall vs Speed : Fast search often means lower recall. You shouldn't have to choose. The Solution EmergentDB uses a Dual Quality-Diversity System : 1. IndexQD - 3D behavior space (Recall × Latency × Memory) evolves optimal index type and hyperparameters 2. InsertQD - 2D behavior space (Throughput × Efficiency) discovers fastest SIMD insertion strategy The system automatically selects between HNSW, Flat, and IVF indices with evolved hyperparameters, achieving maximum search speed while enforcing a 99% recall floor . Benchmark Results 768-dimensional Gemini emb","default_branch":null,"files":null,"tree":[],"storefront":"/r/justrach","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/justrach/emergentDB/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}