Semantic Search & Vector Embeddings Explained with Python | FAISS, Chroma & Sentence Transformers

Description

Welcome back to SummarizedAI 👋 In this video, we dive deep into Semantic Search and Vector Embeddings — the core concepts powering modern AI search engines, chatbots, and RAG (Retrieval-Augmented Generation) systems. 🔍 What You’ll Learn: What Semantic Search is and how it differs from keyword search How AI understands context and meaning using vector embeddings Real-world example: understanding “Apple” — the fruit vs. the company 🍎💻 How embeddings convert text, images, and audio into numerical vectors How to build your own vector search system using Python, FAISS, and Sentence Transformers Step-by-step breakdown of vector embedding creation, storage, and querying Popular vector databases: FAISS, Chroma, Pinecone Real-world use cases — Chatbots, Semantic Search Engines, Recommendation Systems, and Image Matching 🧠 Tech Stack: Python | Sentence Transformers | FAISS | Chroma | Vector Databases 💡 Whether you’re a beginner exploring AI search or a developer building a RAG pipeline, this tutorial will help you understand how semantic relationships and vector spaces work in practice. #SemanticSearch #VectorEmbeddings #AIwithPython #FAISS #Chroma #RAG #MachineLearning #ArtificialIntelligence #SentenceTransformers #SummarizedAI