Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Appearance settings

Commit c6c4a30

Browse filesBrowse files
committed
2 parents ebe769f + 44d3b45 commit c6c4a30
Copy full SHA for c6c4a30

File tree

1 file changed

+13
-0
lines changed
Filter options

1 file changed

+13
-0
lines changed

‎README.md

Copy file name to clipboardExpand all lines: README.md
+13Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,16 @@
1+
---
2+
page_type: sample
3+
languages:
4+
- sql
5+
products:
6+
- azure-openai
7+
- azure-sql-database
8+
urlFragment: azure-sql-db-openai
9+
name: Vector similarity search with Azure SQL & Azure OpenAI
10+
description: |
11+
Use Azure OpenAI from Azure SQL database to get the vector embeddings of any choosen text, and then calculate the cosine similarity to find related topics
12+
---
13+
114
# Vector similarity search with Azure SQL & Azure OpenAI
215

316
This example shows how to use Azure OpenAI from Azure SQL database to get the vector embeddings of any choosen text, and then calculate the [cosine similarity](https://learn.microsoft.com/en-us/azure/storage/common/storage-sas-overview) against the Wikipedia articles (for which vector embeddings have been already calculated,) to find the articles that covers topics that are close - or similar - to the provided text.

0 commit comments

Comments
0 (0)
Morty Proxy This is a proxified and sanitized view of the page, visit original site.