Knowledge retrieval: Check in quickstart sample

quickstart-knowledge-retrieval/README.md

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+# Azure AI Search - Knowledge Retrieval Quickstart
+
+This sample demonstrates how to use Azure AI Search's Knowledge Retrieval capabilities to build a RAG (Retrieval Augmented Generation) solution using TypeScript. It shows how to create a search index, configure it as a knowledge source, and integrate with Azure OpenAI for intelligent question answering.
+
+## Features
+
+- Creates a search index with vector embeddings
+- Configures semantic search capabilities
+- Sets up a knowledge source from the search index
+- Integrates with Azure OpenAI for answer synthesis
+- Demonstrates knowledge retrieval with chat interface
+
+## Prerequisites
+
+- Node.js 18 or later
+- Azure subscription
+- Azure AI Search service
+- Azure OpenAI service with:
+  - An embedding model deployment (e.g., `text-embedding-3-large`)
+  - A GPT model deployment (e.g., `gpt-4.1-mini`)
+- Azure CLI (for authentication)
+
+## Setup
+
+1. Install dependencies:
+   ```bash
+   npm install
+   ```
+
+2. Copy `sample.env` to `.env` and update with your values:
+   ```bash
+   cp sample.env .env
+   ```
+
+3. Configure the following environment variables:
+   - `AZURE_SEARCH_ENDPOINT` - Your Azure AI Search endpoint
+   - `AZURE_SEARCH_INDEX_NAME` - Name for the search index
+   - `AOAI_ENDPOINT` - Your Azure OpenAI endpoint
+   - `AOAI_EMBEDDING_MODEL` - OpenAI embedding model name
+   - `AOAI_EMBEDDING_DEPLOYMENT` - Deployment name for embeddings
+   - `AOAI_GPT_MODEL` - GPT model name
+   - `AOAI_GPT_DEPLOYMENT` - Deployment name for GPT
+
+4. Sign in to Azure CLI:
+   ```bash
+   az login
+   ```
+
+## Run the Sample
+
+Build and run the complete workflow:
+```bash
+npm start
+```
+
+This will:
+1. Create a search index with hotel data
+2. Upload sample documents with vector embeddings
+3. Configure a knowledge source pointing to the index
+4. Create a knowledge base with Azure OpenAI integration
+5. Perform a sample knowledge retrieval query
+
+## Project Structure
+
+- `src/createIndex.ts` - Defines the search index schema with vector fields and semantic configuration
+- `src/uploadDocuments.ts` - Contains sample hotel documents with pre-computed vector embeddings
+- `src/main.ts` - Main orchestration script that demonstrates the complete workflow
+
+## Learn More
+
+- [Azure AI Search Documentation](https://learn.microsoft.com/azure/search/)
+- [Knowledge Retrieval in Azure AI Search](https://learn.microsoft.com/azure/search/knowledge-retrieval-overview)
+- [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/)