2 posts tagged with "Embeddings"

This is a step-by-step guide to building your first AI search engine using Cloudflare's Vectorize and Workers AI. It covers everything from setting up the environment to querying the vector database, with clear explanations and runnable code examples.

You'll see both paths:

1. Manual embeddings (Euclidean, 32‑dim vectors) for learning and quick demos.
2. AI embeddings (BGE Base, 768‑dim, cosine) using Workers AI, for real-world semantic search.

By the end, you’ll be able to seed your own index, query it via API, and understand exactly what’s going on.

In this blog, we will explore the Azure OpenAI Service, how it compares to the OpenAI public API, and walk through a complete tutorial showing how to implement semantic search with embeddings using real legislative data.

If you have used ChatGPT and wondered, Why should I care about Azure OpenAI? — this blog will help you understand the key differences, enterprise benefits, and how to get started. This blog is based on a real spoken walkthrough that demonstrates:

• What embeddings are
• How to set up Azure OpenAI
• How to prepare and search data semantically

The walkthrough focuses on practical application using PowerShell and .NET DataTables, with references to the official Azure OpenAI documentation.