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