Merge branch 'main' of https://github.com/Azure-Samples/azure-sql-db-vector-search

Author: Pookam90

5-Min-RAG-SQL-Accelerator/Readme.md

@@ -0,0 +1,42 @@
+# 5-Min RAG SQL Accelerator
+
+Welcome! 🎉 This repository contains two solution accelerators. These tools demonstrate the ease of deployment and the powerful vector search capabilities of Azure SQL.
+
+Credits: Developed by Kushagra Agarwal - Orginal [Repo](https://github.com/Kushagra-2000/ARM_SQL_OpenAI)
+---
+
+## 🚀 Step 1: One-Click Deployment Accelerator
+
+**Purpose**: Automatically deploy the entire Azure SQL + OpenAI setup for a Retrieval-Augmented Generation (RAG) application using an ARM template.
+
+**Highlights**:
+- One-click deployment via Azure Resource Manager (ARM)
+- Sets up Azure SQL Database, OpenAI integration, and supporting resources
+- Reduces manual configuration to just click and go
+- Completes setup in under 5 minutes
+
+**Setup Instructions**:
+1. Navigate to the folder [Step1-OneClick-Deployment](https://github.com/Azure-Samples/azure-sql-db-vector-search/tree/main/5-Min-RAG-SQL-Accelerator/Step1-OneClick-Deployment)
+2. Follow the instructions in the README to deploy using the ARM template.
+3. Once deployed, your backend RAG infrastructure is ready to use.
+
+---
+
+## 🌐 Step 2: Web-Based Demo for Vector Search
+
+**Purpose**: Launch a Streamlit-based web UI that showcases Azure SQL’s native vector search capabilities.
+
+**Highlights**:
+- Visual demo for semantic search scenarios (e.g., product search, resume matching)
+- No coding required to explore and validate vector search
+- Helps users understand and interact with vector search features in Azure SQL
+
+**Setup Instructions**:
+1. Navigate to the [Step2-Deploy-RAG-App](https://github.com/Azure-Samples/azure-sql-db-vector-search/tree/main/5-Min-RAG-SQL-Accelerator/Step2-Deploy-RAG-App)
+2. Follow the README to install dependencies and run the Streamlit app.
+3. Use the web UI to explore vector search on sample data.
+
+---
+
+
+

5-Min-RAG-SQL-Accelerator/Step2-Deploy-RAG-App/RAG-unstructured-docs/Readme.md

@@ -29,7 +29,7 @@ This Streamlit app demonstrates how to build a Retrieval-Augmented Generation (R
 
 ## Automated Deployments
 - **ARM Template Scripts:**
-    - ARM templates are provided separately to automate the deployment of required resources. Please refer to [this repository](https://github.com/Kushagra-2000/ARM_SQL_OpenAI) for scripts and detailed instructions. 
+    - ARM templates are provided separately to automate the deployment of required resources. Please refer to [this repository](https://github.com/Azure-Samples/azure-sql-db-vector-search/tree/main/5-Min-RAG-SQL-Accelerator/Step1-OneClick-Deployment) for scripts and detailed instructions. 
     - Follow the RAG for Unstructured Docs for this particular demo.
 - **SQL on Fabric:**
     - Create a workspace in Fabric, if not existed before.
@@ -52,7 +52,7 @@ This Streamlit app demonstrates how to build a Retrieval-Augmented Generation (R
 5. **Run the Streamlit App:**
    - Navigate to the cloned repository destination and then run the below command to start the app on `localhost:8501`
      ```
-     streamlit run streamlit_unstructured.py
+    streamlit run <filename.py> --server.maxUploadSize 500
      ```
 6. **Configure Credentials:**
    - Launch the app and enter your Azure endpoints, API keys, and SQL connection string in the sidebar.

5-Min-RAG-SQL-Accelerator/Step2-Deploy-RAG-App/README.md

@@ -25,7 +25,7 @@ These demo follow the Jupyter notebook provided in the [azure-sql-db-vector-sear
 ## Resources Deployment
 
 ### One Click Deployment
-Automated deployment scripts are available in a separate [GitHub repository](https://github.com/Kushagra-2000/ARM_SQL_OpenAI) that will help to deploy required component.
+Automated deployment scripts are available in a separate [GitHub repository](https://github.com/Azure-Samples/azure-sql-db-vector-search/tree/main/5-Min-RAG-SQL-Accelerator/Step1-OneClick-Deployment) that will help to deploy required component.
 
 This includes ARM templates to provision:
 - Azure SQL DB