NEOPOLIS AKADEMY
Introduction to Embeddings with the OpenAI API
Hands-on introduction to embeddings with the OpenAI API: vector representations, semantic search and recommendation pipelines. Covers creating embeddings, indexing, vector querying and practical scaling strategies.
View this course on Akademy ↗Enrolment and practical details are available on Neopolis Akademy.
What you will explore
Explains embeddings concepts and how they enable textual similarity and semantic search systems.
Demonstrates generating embeddings with the API, structuring vector indexes and performing similarity queries.
Covers embedding-based recommendation workflows and matching algorithms used in practice.
Discusses operational best practices: vector management, dimensionality reduction, and trade-offs in cost and latency.
STEP BY STEP
Course programme
01Introduction to Embeddings with the OpenAI API
Technical exploration of embeddings with the OpenAI API: definitions, creation, enrichment and practical applications. Covers semantic search, similarity sorting, vector enrichment and vector databases including ChromaDB.
What an embedding is and how embeddings represent text for downstream tasks.
How to create embeddings and enrich them to improve semantic search and similarity measures.
Methods for similarity sorting and indexing for search and recommendation scenarios.
Overview of vector databases: selecting a solution, handling metadata and building vector stores with ChromaDB.
Explore this module on Akademy ↗Programme source: Neopolis Akademy. Original course page