Embeddings & Vector Databases

Vector databases store embeddings and find the nearest neighbours fast. These lessons cover embedding models, similarity search, ANN indexes such as HNSW, and scaling vector search.

6 lessons across 3 courses

Embeddings & Vector Databases lessons by course

Practical AI System Architecture: Building Intelligent Systems with LLMs, RAG, and Agent Frameworks

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  1. Day 13 : Text Splitting Strategies: Preparing Data for Embeddings.
  2. Day 14 : Embeddings: The Language of Semantic Search.
  3. Day 15 Vector Databases: Storing and Retrieving Semantic Context.

AI Models Deep Learning Intermediate Edition

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  1. Lesson 13 — Word2Vec Explorer: Word Embeddings From First Principles

Advanced Architectures for Vertical AI Agents

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  1. Lesson 6: Vector Databases & Retrieval

Related topics

AI Agents (27) LLM Serving & Inference (16) MLOps (9) RAG: Retrieval-Augmented Generation (4) All topics →