From 2fc0d9f3a5e4ab08a7a3d476fa4312dcbc4389bb Mon Sep 17 00:00:00 2001
From: Laziz Turakulov <4857092+LazaUK@users.noreply.github.com>
Date: Tue, 3 Jun 2025 14:14:39 +0100
Subject: [PATCH] Extend tutorials to include cloud deployment options

The real power of BGE models can come from their deployment in the cloud to support live solutions. So, would suggest to include section # 7 on Deployments.

I've just completed development and proper testing of BGE Reranker in Azure cloud platform. Feel free to re-use the content from my GitHub repo / provided Jupyter notebook here: https://github.com/LazaUK/HuggingFace-BAAI--BGERerankerv2m3.
---
 Tutorials/README.md | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/Tutorials/README.md b/Tutorials/README.md
index 2d04748b..e657bfce 100644
--- a/Tutorials/README.md
+++ b/Tutorials/README.md
@@ -70,4 +70,11 @@ RAG is one of the most popular approach to enchance the capabilities of LLMs by
 - [x] RAG from scratch
 - [x] RAG with LangChain
 - [x] RAG with LlamaIndex
-- [ ] ...
\ No newline at end of file
+- [ ] ...
+
+## [Deployments](./7_Deployments/)
+
+Deploying BGE models to the cloud makes them accessible and scalable for real-world applications.
+
+- [x] Microsoft Azure
+- [ ] ...