Master vector databases and embeddings in Python: map text to high-dimensional vectors, spin up a local ChromaDB, run end-to-end CRUD on collections and points, search by meaning, and persist your database to disk. We cover Sentence Transformers (local auto-embedding), OpenAI embeddings, and best practices for semantic search and RAG pipelines. Notebook link below. --- 🔗 *Links* - 📓 Colab Notebook (Code): - 🚀 Complete AI Engineer Bootcamp: [ ]( ) - ❤️ Support me: [Buy me a coffee or beer]( ) - 💬 Join the Discord Help Server: [Click here]( ) - ✉️ AI Engineering Newsletter: --- 🤓 *Topics Covered* - Embeddings & Semantic Search: high-dimensional vectors, similarity, powering RAG and search - Vector Databases: storing embeddings, distance metrics, in-memory vs persistent setups - ChromaDB Basics: install, client setup, collections management, pagination - CRUD & Queries: add/update/upsert points, top-k similarity search, metadata for RAG - Best Practices: persisting to disk, remote/cloud connections, isolating data per user --- ⏰ *Timestamps* 0:00:00 - Intro & goals 0:00:56 - What are vector databases and embeddings? 0:04:36 - ChromaDB quick start (install + client) 0:09:33 - CRUD on points (add, get, update, upsert) 0:14:07 - CRUD on collections (create, list, modify, delete) 0:17:29 - Persistence to disk with 0:19:53 - Final tips & best practices 0:20:50 - Outro