Vector Databases from Scratch: Foundations, Embeddings, and a Hands-On Chroma Project

A beginner-friendly, seven-part course covering vector databases from the ground up, starting with vectors and similarity measures and building up to indexing, storage, and semantic search. Along the way, you create real embeddings in a live demo and finish with a working project.

■ Vector Database Fundamentals
- What a vector database is, how the ingestion and retrieval pipeline works step by step
- Why keyword search falls short for unstructured data, plus use cases such as recommendation systems and NLP search

■ Vectors and Tokenization
- Vectors, dimensions, and the two main similarity measures: cosine similarity and Euclidean distance
- Tokenization types: character-level, word-level, and subword tokenizers used by modern language models

■ Embedding Models and Live Demo
- Word, text, and multimodal embedding models, including Gemini embedding 001 and Gemini Embedding 2
- Notebook walkthrough: generating 768-dimension embeddings with Gemini, building a cosine similarity matrix, and visualizing sentences with PCA

■ Internals and Practical Project
- Architecture layers: API gateway, indexing (IVF and HNSW), storage, and query processing
- Storing embeddings in Chroma DB and running semantic search queries

Aimed at software, AI, and machine learning engineers with no prior knowledge required, the course leaves you able to explain how vector databases store, index, and retrieve high-dimensional embeddings and to apply semantic search yourself. Start with the fundamentals lectures, follow along with the notebook demos, and build the Chroma project to practice the full workflow.