Skip to content

Latest commit

 

History

History
16 lines (12 loc) · 735 Bytes

README.md

File metadata and controls

16 lines (12 loc) · 735 Bytes

embeddings_analyzer

Edit in StackBlitz next generation editor ⚡️

Overview

This repository illustrates how segmenting lengthy documents and averaging their embedding vectors can affect similarity scores and search outcomes when using Azure OpenAI models.

Usage

  • npm install
  • npm run dev
  • Provide your Azure OpenAI credentials (including the deployment name for a v3 large embedding model).
  • Enter a lengthy text snippet (under 8k tokens).
  • Enter a sample search query.
  • Click “Go.”

The strategy dropdown is for visualization only—when you select “Go,” all strategies are evaluated and displayed side by side in the results table.