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Module 3 - Embeddings Deep Dive

Overview​

Embeddings are the heart of semantic search in RAG. This module explores what embeddings are, how they work, and how to choose the right embedding model for your use case.

Learning Objectives​

By the end of this module, you will be able to:

  • Understand what embeddings are and how they represent meaning
  • Compare different embedding models (OpenAI, Cohere, Open Source)
  • Use MTEB benchmarks to evaluate models
  • Choose the right model based on cost, performance, and requirements

Module Content​

1. What are Embeddings?​

  • Vector representations of text
  • Semantic meaning in vector space
  • Similarity metrics (cosine, euclidean)
  • Embedding dimensions

2. Embedding Models​

  • OpenAI (text-embedding-3-small/large)
  • Cohere embed-v3
  • Sentence Transformers
  • BGE models

3. Choosing the Right Model​

  • MTEB benchmarks
  • Language support
  • Cost analysis
  • Latency considerations
  1. What are Embeddings? - Understanding vectors
  2. Embedding Models - Available options
  3. Choosing Models - Decision framework
  4. Quiz - Module 3 - 30 questions

Master the foundation of semantic search!