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# Azure AI Search vector store

The `AzureAISearchVector` class provides vector search using [Azure AI Search](https://learn.microsoft.com/azure/search/vector-search-overview), Microsoft's cloud search service with native vector search support. It offers metadata filtering, hybrid (vector + text) search, and semantic ranking on top of an existing Azure AI Search resource.

## Constructor options

**id** (`string`): Unique identifier for this vector store instance.

**endpoint** (`string`): The endpoint URL of your Azure AI Search service, e.g. 'https\://your-service.search.windows.net'.

**credential** (`string | AzureKeyCredential | TokenCredential`): An admin API key string, an AzureKeyCredential, or an Azure Identity TokenCredential (e.g. DefaultAzureCredential) for Azure AD authentication.

**apiVersion** (`string`): Azure AI Search REST API version to use. Defaults to the SDK default.

**clientOptions** (`SearchClientOptions`): Additional options passed to the underlying SearchClient/SearchIndexClient, such as additionalPolicies for proxies, custom headers, or retry behavior.

**autoIndexMetadata** (`boolean`): Add a filterable field to the index the first time a top-level string, number, or boolean metadata key is seen in upsert() or updateVector(). Azure AI Search can only filter on declared fields, so this is required for Memory semantic recall (which filters by thread\_id and resource\_id) unless you declare those keys via metadataIndexes. Set to false to manage the schema yourself. (Default: `true`)

```typescript
import { AzureAISearchVector } from '@mastra/azure-ai-search'

const vectorStore = new AzureAISearchVector({
  id: 'azure-search-vectors',
  endpoint: process.env.AZURE_AI_SEARCH_ENDPOINT!,
  credential: process.env.AZURE_AI_SEARCH_CREDENTIAL!,
})
```

## Methods

### `createIndex()`

**indexName** (`string`): Name of the index to create

**dimension** (`number`): Vector dimension (must match your embedding model)

**metric** (`'cosine' | 'euclidean' | 'dotproduct'`): Distance metric for similarity search (Default: `cosine`)

**metadataIndexes** (`Array<string | { name: string; type: 'string' | 'number' | 'boolean' }>`): Metadata keys to declare as explicit filterable fields up front. Optional when autoIndexMetadata is enabled (the default), since fields are then added on first write. Values whose JavaScript type does not match the declared field type are kept in the JSON metadata blob only.

For Azure AI Search-specific index features (custom vector field name, additional schema fields, HNSW parameters, semantic configuration), use `createAdvancedIndex()` with `AzureAISearchCreateIndexParams`.

### `upsert()`

**indexName** (`string`): Name of the index to upsert into

**vectors** (`number[][]`): Array of embedding vectors

**metadata** (`Record<string, any>[]`): Metadata for each vector

**ids** (`string[]`): Optional vector IDs (auto-generated if not provided). IDs containing characters other than letters, digits, \_, - and = are stored base64url-encoded and decoded back on read, so any string is accepted.

**deleteFilter** (`AzureAISearchVectorFilter`): Azure AI Search-specific: delete documents matching this filter before upserting.

### `query()`

**indexName** (`string`): Name of the index to query

**queryVector** (`number[]`): Query vector to find similar vectors

**topK** (`number`): Number of results to return (Default: `10`)

**filter** (`AzureAISearchVectorFilter`): Metadata filter for the query, using Mastra operators ($eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $exists, $and, $or, $not). Translated to an Azure OData $filter; raw OData strings are not accepted.

**includeVector** (`boolean`): Whether to include vectors in the results (Default: `false`)

Unsupported filter operators (for example `$regex`, `$size`, or `$all`, none of which map to Azure AI Search's OData filter syntax) throw an error rather than being silently dropped from the query.

### `listIndexes()`

Returns an array of index names as strings.

### `describeIndex()`

**indexName** (`string`): Name of the index to describe

Returns:

```typescript
interface IndexStats {
  dimension: number
  count: number
  metric: 'cosine' | 'euclidean' | 'dotproduct'
}
```

### `deleteIndex()`

**indexName** (`string`): Name of the index to delete

### `updateVector()`

Update a single vector by ID or by metadata filter. Either `id` or `filter` must be provided, but not both.

**indexName** (`string`): Name of the index containing the vector to update

**id** (`string`): ID of the vector to update (mutually exclusive with filter)

**filter** (`AzureAISearchVectorFilter`): Metadata filter to identify vector(s) to update (mutually exclusive with id)

**update** (`object`): Update parameters: { vector?: number\[]; metadata?: Record\<string, any> }

### `deleteVector()`

**indexName** (`string`): Name of the index containing the vector to delete

**id** (`string`): ID of the vector to delete

### `deleteVectors()`

Delete multiple vectors by IDs or by metadata filter. Either `ids` or `filter` must be provided, but not both.

**indexName** (`string`): Name of the index containing the vectors to delete

**ids** (`string[]`): Array of vector IDs to delete (mutually exclusive with filter)

**filter** (`AzureAISearchVectorFilter`): Metadata filter to identify vectors to delete (mutually exclusive with ids)

## Azure-specific query methods

Beyond the standard `query()` method, `AzureAISearchVector` exposes Azure AI Search-specific capabilities:

- **`advancedQuery()`**: exposes Azure AI Search vector query parameters directly, including exhaustive search, query weighting, oversampling, additional vector queries for multi-vector search, pre/post filtering mode, and text-based query types (`semantic`, `hybrid`).
- **`semanticQuery()`**: convenience wrapper around `advancedQuery()` for semantic ranking with a configured semantic configuration.
- **`hybridQuery()`**: convenience wrapper combining vector search with a full-text query.
- **`multiVectorQuery()`**: convenience wrapper for querying against multiple weighted vectors at once.
- **`exactQuery()`**: convenience wrapper for `advancedQuery()` with exhaustive (non-approximate) search enabled.

These methods are additive: `query()` remains the Memory-compatible entry point used by Mastra's semantic recall.

## Response types

Query results are returned in this format:

```typescript
interface QueryResult {
  id: string
  score: number
  metadata: Record<string, any>
  vector?: number[] // Only included if includeVector is true
}
```

## Related

- [Metadata Filters](https://mastra.ai/reference/rag/metadata-filters)