Lecture 29: Embeddings Explained - Convert Text to Vectors for RAG & Semantic Search

Description

deep dive into embeddings! Learn how to transform text into high-dimensional vectors and unlock semantic understanding for intelligent RAG systems. 🎯 What You'll Learn: • What are embeddings and why they matter • Semantic meaning capture in vector space • Understanding embedding dimensions (1536, 3072, 4096) • Popular embedding models (OpenAI, Hugging Face) • Cosine similarity for semantic matching • OpenAI Text Embedding API • Batch processing embeddings • Practical implementation with Python 💡 Key Topics Covered: ✅ Column vectors and high-dimensional spaces ✅ Semantic similarity vs. token-based matching ✅ Embedding model comparison and benchmarks (MTEB) ✅ OpenAI's text-embedding-3-small and 3-large ✅ Hugging Face open-source alternatives ✅ Cosine similarity calculation and interpretation ✅ Vector database fundamentals ✅ Real-world similarity scoring (0-1 range) 📚 Practical Examples: • Calculating semantic similarity between sentences • Finding most relevant chunks with embeddings • Implementing cosine similarity search • Batch processing documents for embeddings • Building semantic search functionality • Complete RAG pipeline: Load → Chunk → Embed → Search 🛠️ Tools & Libraries: Python • OpenAI API • Hugging Face • LangChain • NumPy • Scikit-learn Text Embedding Models • Vector Operations Perfect for building production-grade semantic search and RAG systems! #GenAI #Embeddings #VectorDatabase #SemanticSearch #RAG #Python #NLP #MachineLearning #AI #Lecture29 #Embeddings #VectorGeneration #SemanticSearch #RAGSystem #LargeLanguageModels #OpenAI #HuggingFace #PythonForAI #GenAITutorial #CosineSimilarity #NLPTutorial #VectorDatabase