A comprehensive tutorial demonstrating how to build a vector search engine from scratch using Python. Covers the three core steps of vector search: converting text to numerical vectors, calculating similarity using cosine similarity, and retrieving the most relevant results. Includes practical code examples with NumPy and Matplotlib, visualization of vector spaces, and explains the connection to RAG systems. The implementation uses simplified word embeddings and averaging techniques to make concepts accessible while maintaining the fundamental principles of semantic search.
Table of contents
How Does Vector Search Work?Step 1: Setting Up the EnvironmentStep 2: Creating a Toy Dataset and Word EmbeddingsStep 3: Converting Sentences to VectorsStep 4: Implementing Cosine SimilarityStep 5: Building the Vector Search FunctionStep 6: Visualizing the VectorsWhy This Matters for RAGConclusionSort: