### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-vector

Change the current directory to the vector search quickstart folder within the cloned repository. This is where you'll find the application settings and code examples.

```bash
cd azure-search-dotnet-samples/quickstart-vector-search
```

--------------------------------

### Navigate to Quickstart Folder and Open in VS Code

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-vector

Navigate to the vector search quickstart folder and open it in Visual Studio Code for further setup.

```bash
cd azure-search-python-samples/Quickstart-Vector-Search
code .
```

--------------------------------

### Full-Text Search Quickstart

Source: https://learn.microsoft.com/en-us/azure/search/samples-java

Guides through creating, loading, and querying a search index using sample data for full-text search capabilities.

```Java
quickstart-keyword-search
```

--------------------------------

### Agentic Retrieval Quickstart Application Output

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-agentic-retrieval

This is an example of the expected output when running the Agentic Retrieval Quickstart application. It shows the creation of resources, document upload, query execution, and response details.

```text
Index 'earth-at-night' created or updated successfully.
Documents uploaded to index 'earth-at-night' successfully.
Knowledge source 'earth-knowledge-source' created or updated successfully.
Knowledge base 'earth-knowledge-base' created or updated successfully.
Running the query...Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
Response:
December percent brightening is larger in suburban belts because many houses add seasonal residential/holiday lighting on yards and roofs, so a relatively dark suburban baseline can increase by 20-50% when those lights turn on, while dense urban cores already have high continuous lighting so the same added lights make a smaller percentage change [ref_id:2][ref_id:5][ref_id:8]. Phoenix's street grid appears sharply from space because the metropolitan layout is a regular, continuous north-south/east-west street and block grid with a major diagonal artery (Grand Avenue) and concentrated, continuous arterial and commercial lighting along intersections and corridors [ref_id:3][ref_id:0][ref_id:1]. ...
Activity:
Activity Type: KnowledgeBaseModelQueryPlanningActivityRecord
{
  "id" : 0,
  "elapsedMs" : 5229,
  "type" : "modelQueryPlanning",
  "inputTokens" : 1489,
  "outputTokens" : 383
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "id" : 1,
  "elapsedMs" : 2670,
  "knowledgeSourceName" : "earth-knowledge-source",
  "queryTime" : "2026-02-24T15:28:36.776Z",
  "count" : 3,
  "type" : "searchIndex",
  "searchIndexArguments" : {
    "search" : "December brightening suburban belts vs urban cores light pollution causes seasonal variation reasons \"December brightening\"",
    "sourceDataFields" : [ {
      "name" : "page_chunk"
    }, {
      "name" : "id"
    }, {
      "name" : "page_number"
    } ],
    "searchFields" : [ ],
    "semanticConfigurationName" : "semantic_config"
  }
}
... // Trimmed for brevity
References:
Reference Type: KnowledgeBaseSearchIndexReference
{
  "id" : "0",
  "activitySource" : 2,
  "rerankerScore" : 2.7486389,
  "type" : "searchIndex",
  "docKey" : "earth_at_night_508_page_105_verbalized"
}
... // Trimmed for brevity
Continue the conversation with this query: How do I find lava at night?
Response:
... // Trimmed for brevity
Activity:
... // Trimmed for brevity
References:
... // Trimmed for brevity
Knowledge base 'earth-knowledge-base' deleted successfully.
Knowledge source 'earth-knowledge-source' deleted successfully.
Index 'earth-at-night' deleted successfully.

```

--------------------------------

### Restore Project Dependencies

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Install the necessary project dependencies for the Azure AI Search quickstart. Ensure this command completes without errors before proceeding.

```bash
dotnet restore
```

--------------------------------

### Install Java Dependencies with Maven

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Clean the project and download all necessary dependencies for the Azure AI Search Java quickstart using Maven.

```bash
mvn clean dependency:copy-dependencies
```

--------------------------------

### Download Sample Code

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-powershell

Download the source code from GitHub to get started quickly.

```text
Download the source code on GitHub.
```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic

Change the current directory to the semantic ranking quickstart folder.

```bash
cd azure-search-javascript-samples/quickstart-semantic-ranking-js
```

--------------------------------

### Clone Azure AI Search Java Samples Repository

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Clone the sample repository to access quickstart code and examples for Azure AI Search with Java.

```bash
git clone https://github.com/Azure-Samples/azure-search-java-samples
```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic

Change the current directory to the semantic ranking quickstart folder within the cloned repository.

```bash
cd azure-search-dotnet-samples/quickstart-semantic-ranking
```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-agentic-retrieval

Navigate to the agentic retrieval quickstart folder and open it in Visual Studio Code.

```bash
cd azure-search-rest-samples/Quickstart-agentic-retrieval
code .

```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Change the current directory to the keyword search quickstart folder within the cloned repository.

```bash
cd azure-search-javascript-samples/quickstart-keyword-search
```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-agentic-retrieval

Change the current directory to the agentic retrieval quickstart folder.

```Bash
cd azure-search-dotnet-samples/quickstart-agentic-retrieval

```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-vector

Change the current directory to the vector search quickstart folder within the cloned repository.

```bash
cd azure-search-javascript-samples/quickstart-vector-js
```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Change the current directory to the quickstart folder within the cloned Azure AI Search PowerShell samples repository. This is where the quickstart script is located.

```powershell
cd azure-search-powershell-samples/Quickstart

```

--------------------------------

### Navigate to Semantic Ranking Quickstart Directory

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic

Change the current directory to the TypeScript quickstart for semantic ranking.

```bash
cd azure-search-javascript-samples/quickstart-semantic-ranking-ts
```

--------------------------------

### Navigate to Quickstart Folder and Open in VS Code

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Navigate to the specific quickstart directory within the cloned repository and open it in Visual Studio Code.

```bash
cd azure-search-python-samples/Quickstart-Keyword-Search
code .
```

--------------------------------

### Get SharePoint Online Indexer Status

Source: https://learn.microsoft.com/en-us/azure/search/search-how-to-index-sharepoint-online

Use this GET request to retrieve the status of a SharePoint Online indexer. This is crucial for obtaining the device login code during the initial setup.

```HTTP
GET https://[service name].search.windows.net/indexers/sharepoint-indexer/status?api-version=2026-05-01-preview
Content-Type: application/json
api-key: [admin key]
```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Change the current directory to the quickstart-keyword-search folder within the cloned repository.

```bash
cd azure-search-java-samples/quickstart-keyword-search
```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Change the directory to the specific quickstart folder within the cloned repository. This prepares your environment for the next steps.

```bash
cd azure-search-dotnet-samples/quickstart-keyword-search/AzureSearchQuickstart
```

--------------------------------

### Example Shared Private Link Status Response

Source: https://learn.microsoft.com/en-us/azure/search/search-indexer-howto-access-private

This JSON shows an example response from the Shared Private Link Resources - Get API. Verify that 'properties.status' is 'Approved' and 'properties.provisioningState' is 'Succeeded' for a functional connection.

```json
{
      "name": "blob-pe",
      "properties": {
        "privateLinkResourceId": "/subscriptions/aaaa0a0a-bb1b-cc2c-dd3d-eeeeee4e4e4e/resourceGroups/contoso/providers/Microsoft.Storage/storageAccounts/contoso-storage",
        "groupId": "blob",
        "requestMessage": "please approve",
        "status": "Approved",
        "resourceRegion": null,
        "provisioningState": "Succeeded"
      }
}

```

--------------------------------

### Get agent definition (2025-05-01-preview)

Source: https://learn.microsoft.com/en-us/azure/search/agentic-retrieval-how-to-migrate

This example shows how to retrieve the definition of a knowledge agent using the Knowledge Agents - Get REST API with the 2025-05-01-preview API version. This is useful for migrating from agents to knowledge bases.

```APIDOC
## GET /agents/{{agent-name}}

### Description
Retrieves the definition of a knowledge agent.

### Method
GET

### Endpoint
https://{{search-url}}/agents/{{agent-name}}?api-version=2025-05-01-preview

### Headers
- **api-key**: {{api-key}}

### Response
#### Success Response (200 OK)
Returns the agent's definition, including `indexName`, `defaultRerankerThreshold`, and `defaultIncludeReferenceSourceData` which are needed for migration.

### Response Example
```json
{
  "@odata.etag": "0x1234568AE7E58A1",
  "name": "my-knowledge-agent",
  "description": "My description of the agent",
  "targetIndexes": [
    {
      "indexName": "my-index",
      "defaultRerankerThreshold": 2.5,
      "defaultIncludeReferenceSourceData": true,
      "defaultMaxDocsForReranker": 100
    }
  ]
}
```
```

--------------------------------

### Get Distinct Value Counts for Facetable Fields

Source: https://learn.microsoft.com/en-us/azure/search/search-faceted-navigation-examples

Formulate a query to get distinct value counts for facetable fields. Set 'top' to zero to retrieve only counts without search results. This example targets 'Category' and 'Address/StateProvince' fields.

```HTTP
POST https://{{service_name}}.search.windows.net/indexes/hotels-sample/docs/search?api-version={{api_version}}
{
    "search": "*",
    "count": true,
    "top": 0,
    "facets": [
        "Category", "Address/StateProvince""
    ]
}

```

--------------------------------

### Navigate to Quickstart Folder and Open in VS Code

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-vector

Navigate to the quickstart-vectors folder within the cloned repository and open it in Visual Studio Code.

```bash
cd azure-search-rest-samples/Quickstart-vectors
code .

```

--------------------------------

### Example Output: Get Index Settings

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic

This output shows the index name, number of fields, field details, and existing semantic configurations retrieved by getIndexSettings.js.

```text
Getting semantic ranking index settings...
Index name: hotels-sample
Number of fields: 23
Field: HotelId, Type: Edm.String, Searchable: true
Field: HotelName, Type: Edm.String, Searchable: true
Field: Description, Type: Edm.String, Searchable: true
Field: Description_fr, Type: Edm.String, Searchable: true
Field: Category, Type: Edm.String, Searchable: true
Field: Tags, Type: Collection(Edm.String), Searchable: true
// Trimmed for brevity
Semantic ranking configurations: 1
Configuration name: hotels-sample-semantic-configuration
Title field: undefined


```

--------------------------------

### Navigate to Sample Directory

Source: https://learn.microsoft.com/en-us/azure/search/tutorial-csharp-overview

Change the current directory to the cloned azure-search-static-web-app repository.

```Bash
cd azure-search-static-web-app
```

--------------------------------

### Clone Azure Search .NET Samples Repository

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-text

Clone the sample repository to access the quickstart code. This is the first step in setting up your development environment.

```bash
git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
```

--------------------------------

### Indexes - Get (Data Plane Preview)

Source: https://learn.microsoft.com/en-us/azure/search/search-api-preview

Retrieves information about an index using the data plane REST API. This example uses the 2026-05-01-preview API version.

```APIDOC
## GET /indexes/("{indexName}")?api-version=2026-05-01-preview

### Description
Retrieves information about a specific index.

### Method
GET

### Endpoint
`{endpoint}/indexes('{indexName}')`

### Parameters
#### Query Parameters
- **api-version** (string) - Required - Specifies the API version to use for the request. Use `2026-05-01-preview` for this preview version.
```

--------------------------------

### Get Signed-in User ID with Azure CLI

Source: https://learn.microsoft.com/en-us/azure/search/keyless-connections

Retrieve your personal user object ID (GUID) using the Azure CLI. This ID is required for role assignments.

```bash
az ad signed-in-user show \
    --query id -o tsv

```

--------------------------------

### Navigate to Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-agentic-retrieval

Change the current directory to the TypeScript quickstart folder within the cloned repository. This is where you will configure your environment.

```bash
cd azure-search-javascript-samples/quickstart-agentic-retrieval-ts

```

--------------------------------

### Get Current User Object ID with Azure PowerShell

Source: https://learn.microsoft.com/en-us/azure/search/keyless-connections

Retrieve your personal user object ID (GUID) using Azure PowerShell. This ID is required for role assignments.

```powershell
(Get-AzContext).Account.ExtendedProperties.HomeAccountId.Split('.')[0]

```

--------------------------------

### Service Runtime Statistics JSON Example

Source: https://learn.microsoft.com/en-us/azure/search/search-indexer-high-density-serverless-overview

This JSON shows the indexersRuntime section of the response from the Get Service Statistics API, indicating a service whose six-hour daily quota hasn't been used.

```JSON
"indexersRuntime": {
    "usedSeconds": 0,
    "remainingSeconds": 21600,
    "beginningTime": "2026-05-16T00:00:00.000Z",
    "endingTime": "2026-05-17T00:00:00.000Z"
}

```

--------------------------------

### Navigate to Semantic Ranking Quickstart

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic

Change the current directory to the semantic ranking quickstart folder and open it in Visual Studio Code.

```bash
cd azure-search-python-samples/Quickstart-Semantic-Ranking
code .
```

--------------------------------

### Indexer Runtime Statistics JSON Example

Source: https://learn.microsoft.com/en-us/azure/search/search-indexer-high-density-serverless-overview

This JSON shows the runtime section of the response from the Get Indexer Status API, indicating an indexer on a service whose six-hour daily quota hasn't been used.

```JSON
"runtime": {
    "usedSeconds": 0,
    "remainingSeconds": 21600,
    "beginningTime": "2026-05-16T00:00:00.000Z",
    "endingTime": "2026-05-17T00:00:00.000Z"
}

```

--------------------------------

### Compile and Run Agentic Retrieval Quickstart (Windows)

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-agentic-retrieval

Use these commands to compile and run the Java application on Windows. Ensure you have the necessary dependencies in the target/dependency directory.

```bash
javac AgenticRetrievalQuickstart.java -cp ".;target\dependency\*"
java -cp ".;target\dependency\*" AgenticRetrievalQuickstart

```

--------------------------------

### Create File Knowledge Source with Python

Source: https://learn.microsoft.com/en-us/azure/search/agentic-knowledge-source-how-to-file

Utilize the Azure SDK for Python to programmatically create or update a file knowledge source. This example configures Azure OpenAI for generating embeddings. Ensure the necessary packages are installed.

```python
from azure.core.credentials import AzureKeyCredential
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    AzureOpenAIVectorizerParameters,
    FileKnowledgeSource,
    FileKnowledgeSourceParameters,
)
from azure.search.documents.knowledgebases.models import (
    KnowledgeSourceAzureOpenAIVectorizer,
    KnowledgeSourceIngestionParameters,
)

index_client = SearchIndexClient(endpoint="search_url", credential=AzureKeyCredential("api_key"))

embedding_params = AzureOpenAIVectorizerParameters(
    resource_url="aoai_endpoint",
    deployment_name="aoai_embedding_deployment",
    model_name="aoai_embedding_model",
)

ingestion_params = KnowledgeSourceIngestionParameters(
    content_extraction_mode="minimal",
    embedding_model=KnowledgeSourceAzureOpenAIVectorizer(
        azure_open_ai_parameters=embedding_params
    ),
)

knowledge_source = FileKnowledgeSource(
    name="my-file-ks",
    description="This knowledge source uses directly uploaded product manuals.",
    file_parameters=FileKnowledgeSourceParameters(ingestion_parameters=ingestion_params),
)

index_client.create_or_update_knowledge_source(knowledge_source=knowledge_source)
print(f"Knowledge source '{knowledge_source.name}' created or updated successfully.")

```

--------------------------------

### List Existing Indexes in Azure AI Search

Source: https://learn.microsoft.com/en-us/azure/search/search-how-to-integrated-vectorization

Verify your Azure AI Search service connection by sending a GET request to list existing indexes. Ensure the HTTP status code is 200 OK to confirm successful setup.

```http
### List existing indexes by name
GET {{baseUrl}}/indexes?api-version=2026-04-01  HTTP/1.1
  Content-Type: application/json
  Authorization: Bearer {{token}}

```

--------------------------------

### Initial Paged Query with Count

Source: https://learn.microsoft.com/en-us/azure/search/search-pagination-page-layout

This HTTP POST request demonstrates how to retrieve the first set of 15 search results for 'room with a view' and also get the total count of matching documents. The `skip` parameter is set to 0 to start from the beginning.

```http
POST https://contoso-search-eastus.search.windows.net/indexes/hotels-sample/docs/search?api-version=2026-04-01

{
    "search": "room with a view",
    "count": true,
    "top": 15,
    "skip": 0
}
```

--------------------------------

### Compile and Run Agentic Retrieval Quickstart (macOS/Linux)

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-agentic-retrieval

Use these commands to compile and run the Java application on macOS or Linux. Ensure you have the necessary dependencies in the target/dependency directory.

```bash
javac AgenticRetrievalQuickstart.java -cp ".:target/dependency/*"
java -cp ".:target/dependency/*" AgenticRetrievalQuickstart

```

--------------------------------

### Full-Text Search Query using REST API (POST)

Source: https://learn.microsoft.com/en-us/azure/search/search-query-create

This example demonstrates a full-text search query using the REST API with an HTTP POST request. It's suitable for queries with potentially large filter expressions that exceed GET request URL length limits.

```HTTP
POST https://[service name].search.windows.net/indexes/hotels-sample/docs/search?api-version=2026-04-01
{
    "search": "NY +view",
    "queryType": "simple",
    "searchMode": "all",
    "searchFields": "HotelName, Description, Address/City, Address/StateProvince, Tags",
    "select": "HotelName, Description, Address/City, Address/StateProvince, Tags",
    "count": true
}
```

--------------------------------

### Navigate to Vector Search Quickstart Folder

Source: https://learn.microsoft.com/en-us/azure/search/search-get-started-vector

Change directory into the vector search quickstart folder within the cloned repository.

```bash
cd azure-search-java-samples/quickstart-vector-search
```

--------------------------------

### Get Azure AI Search Access Token using Azure CLI

Source: https://learn.microsoft.com/en-us/azure/search/search-how-to-integrated-vectorization

Use this Azure CLI command to retrieve an access token for your Azure AI Search service. This token is required for authentication in REST requests. Ensure you have completed the 'Connect without keys' quickstart.

```bash
az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
```