🚀WikiSearch — a Semantic Search Engine powered by Endee Vector Database! 🔎 The Problem with Today's Search: Traditional keyword search is broken. When you search "how stars collapse", it misses articles about "Neutron Stars" or "Black Holes" just because the EXACT words don't match. You get irrelevant results and miss what you actually need. 💡 The Solution — Semantic Search: WikiSearch understands the MEANING behind your query, not just the words. It uses AI embeddings to find conceptually related results even when words don't match. Search "thinking machines" → finds Artificial Intelligence Search "editing human genes" → finds CRISPR and DNA Search "warming of the planet" → finds Climate Change No exact word match needed. Just ask naturally! 🤯 🗄️ Why Endee Vector Database? Normal databases store text. Endee stores MEANING. Every article is converted into 384 numbers that represent its concept mathematically. When you search, Endee finds the articles whose numbers are closest to your query — like GPS finding the nearest location, but for IDEAS instead of places. ✅ Handles 1 billion vectors on a single machine ✅ Open source and free ✅ Lightning fast query response (~10ms) ✅ Simple Python SDK ✅ Perfect for AI and semantic search applications ⚙️ How it works: 1️⃣ Wikipedia articles are converted into 384-dimensional vectors 2️⃣ Your query is embedded in the same AI vector space 3️⃣ Endee Vector DB finds the most semantically similar articles 4️⃣ Results ranked by meaning, not just keywords 🛠️ Tech Stack: - Endee Vector Database - Sentence Transformers (AI embeddings) - Flask + Python - 384-dimensional vector space - Cosine Similarity scoring 🔗 GitHub: https://lnkd.in/gQ3vCNnC #Python #AI #MachineLearning #SemanticSearch #NLP #VectorDatabase #OpenSource #BuildInPublic
