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Vector Search & Similarity Mathematics

Learn Cosine Similarity, Dot Product, Euclidean distance, and Nearest Neighbors indexing used in RAG systems.

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Subtopics
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Exercises
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(1)

🎓 Learning objectives

  • •Calculate Dot Product between two vectors
  • •Explain Cosine Similarity and why it ignores vector magnitude
  • •Understand Nearest Neighbors search logic

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📂 Subtopics

1

The Search Problem: Why Keyword Search Fails for Semantic Queries

2

Similarity Metrics: Cosine Similarity, Dot Product and Euclidean Distance

3

Approximate Nearest Neighbor (ANN) Search and HNSW Intuition

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Vector Databases: FAISS vs Pinecone vs Weaviate vs ChromaDB

Related concepts

embeddings-basics

Next to learn

rag-workflow →rag-architectures →
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