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🚀 Vector Search vs Traditional Search | Semantic Search using FAISS & Sentence Transformers (Python) Still using keyword-based search? In this video, we build a Vector Search engine in Python and understand the difference between Traditional Keyword Search vs Semantic Search. You will learn how modern AI systems understand meaning using vector embeddings, instead of just matching words. We will build a complete semantic search engine in Python using: 1. Sentence Transformers 2. FAISS (Facebook AI Similarity Search) Links: Sentence-Transformer: https://sbert.net/ FAISS: https://faiss.ai/index.html Medium: https://medium.com/@pawanwork145/optimizing-vector-search-performance-tuning-hybrid-search-and-scaling-explained-38ac12c2d12b Github: https://github.com/Pawan-145/Vector-Search What is Vector Search? Traditional search systems rely on exact keyword matching. But Vector Search converts text into vector embeddings, allowing systems to perform similarity search in Python based on meaning. In this tutorial, you will understand: ✔ Vector embeddings explained ✔ Difference between keyword search and semantic search ✔ How sentence transformers generate embeddings ✔ How FAISS performs similarity search ✔ How KNN search works in vector databases 🔍 Why Traditional Search Fails? Traditional keyword search fails when wording changes. For example: “cheap car” vs “budget automobile” “AI engineer” vs “machine learning developer” Semantic search using FAISS understands context, not just words. This is the core idea behind modern AI search engines. Who Should Watch? - Beginners learning semantic search - Students building AI projects - Developers learning vector database concepts - Anyone curious about how similarity search works ⚠ Educational Disclaimer All platform interfaces shown (e.g., Spotify) are used strictly for educational and demonstration purposes only. All rights belong to their respective owners. #VectorSearch #SemanticSearch #FAISS #SentenceTransformers #pythonai #syntaxab #vector #meaningful #programming #search