Loading video player...
Welcome back to SummarizedAI ๐ In this video, we dive deep into Semantic Search and Vector Embeddings โ the core concepts powering modern AI search engines, chatbots, and RAG (Retrieval-Augmented Generation) systems. ๐ What Youโll 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 โAppleโ โ the fruit vs. the company ๐๐ป 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 โ Chatbots, Semantic Search Engines, Recommendation Systems, and Image Matching ๐ง Tech Stack: Python | Sentence Transformers | FAISS | Chroma | Vector Databases ๐ก Whether youโre 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