# createVectorQueryTool()

The `createVectorQueryTool()` function creates a tool for semantic search over vector stores. It supports filtering, reranking, database-specific configurations, and integrates with various vector store backends.

## Basic usage

```typescript
import { createVectorQueryTool } from '@mastra/rag'
import { ModelRouterEmbeddingModel } from '@mastra/core/llm'

const queryTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
})
```

## Parameters

> **Note:** **Parameter Requirements:** Most fields can be set at creation as defaults. Some fields can be overridden at runtime via the request context or input. If a required field is missing from both creation and runtime, an error will be thrown. Note that `model`, `id`, and `description` can only be set at creation time.

**id** (`string`): Custom ID for the tool. By default: 'VectorQuery {vectorStoreName} {indexName} Tool'. (Set at creation only.)

**description** (`string`): Custom description for the tool. By default: 'Access the knowledge base to find information needed to answer user questions' (Set at creation only.)

**model** (`EmbeddingModel`): Embedding model to use for vector search. (Set at creation only.)

**vectorStoreName** (`string`): Name of the vector store to query. (Can be set at creation or overridden at runtime.)

**indexName** (`string`): Name of the index within the vector store. (Can be set at creation or overridden at runtime.)

**enableFilter** (`boolean`): Enable filtering of results based on metadata. (Set at creation only, but will be automatically enabled if a filter is provided in the request context.) (Default: `false`)

**includeVectors** (`boolean`): Include the embedding vectors in the results. (Can be set at creation or overridden at runtime.) (Default: `false`)

**includeSources** (`boolean`): Include the full retrieval objects in the results. (Can be set at creation or overridden at runtime.) (Default: `true`)

**reranker** (`RerankConfig`): Options for reranking results. (Can be set at creation or overridden at runtime.)

**reranker.model** (`MastraLanguageModel`): Language model to use for reranking

**reranker.options** (`RerankerOptions`): Options for the reranking process

**reranker.options.weights** (`WeightConfig`): Weights for scoring components (semantic: 0.4, vector: 0.4, position: 0.2)

**reranker.options.topK** (`number`): Number of top results to return

**databaseConfig** (`DatabaseConfig`): Database-specific configuration options for optimizing queries. (Can be set at creation or overridden at runtime.)

**databaseConfig.pinecone** (`PineconeConfig`): Configuration specific to Pinecone vector store

**databaseConfig.pinecone.namespace** (`string`): Pinecone namespace for organizing vectors

**databaseConfig.pinecone.sparseVector** (`{ indices: number[]; values: number[]; }`): Sparse vector for hybrid search

**databaseConfig.pgvector** (`PgVectorConfig`): Configuration specific to PostgreSQL with pgvector extension

**databaseConfig.pgvector.minScore** (`number`): Minimum similarity score threshold for results

**databaseConfig.pgvector.ef** (`number`): HNSW search parameter - controls accuracy vs speed tradeoff

**databaseConfig.pgvector.probes** (`number`): IVFFlat probe parameter - number of cells to visit during search

**databaseConfig.chroma** (`ChromaConfig`): Configuration specific to Chroma vector store

**databaseConfig.chroma.where** (`Record<string, any>`): Metadata filtering conditions

**databaseConfig.chroma.whereDocument** (`Record<string, any>`): Document content filtering conditions

**providerOptions** (`Record<string, Record<string, any>>`): Provider-specific options for the embedding model (e.g., outputDimensionality). \*\*Important\*\*: Only works with AI SDK EmbeddingModelV2 models. For V1 models, configure options when creating the model itself.

**vectorStore** (`MastraVector | VectorStoreResolver`): Direct vector store instance or a resolver function for dynamic selection. Use a function for multi-tenant applications where the vector store is selected based on request context. When provided, \`vectorStoreName\` becomes optional.

## Returns

The tool returns an object with:

**relevantContext** (`string`): Combined text from the most relevant document chunks

**sources** (`QueryResult[]`): Array of full retrieval result objects. Each object contains all information needed to reference the original document, chunk, and similarity score.

### `QueryResult` object structure

```typescript
{
  id: string;         // Unique chunk/document identifier
  metadata: any;      // All metadata fields (document ID, etc.)
  vector: number[];   // Embedding vector (if available)
  score: number;      // Similarity score for this retrieval
  document: string;   // Full chunk/document text (if available)
}
```

## Default tool description

The default description focuses on:

- Finding relevant information in stored knowledge
- Answering user questions
- Retrieving factual content

## Result handling

The tool determines the number of results to return based on the user's query, with a default of 10 results. This can be adjusted based on the query requirements.

## Example with filters

```typescript
const queryTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  enableFilter: true,
})
```

With filtering enabled, the tool processes queries to construct metadata filters that combine with semantic search. The process works as follows:

1. A user makes a query with specific filter requirements like "Find content where the 'version' field is greater than 2.0"
2. The agent analyzes the query and constructs the appropriate filters:
   ```typescript
   {
      "version": { "$gt": 2.0 }
   }
   ```

This agent-driven approach:

- Processes natural language queries into filter specifications
- Implements vector store-specific filter syntax
- Translates query terms to filter operators

For detailed filter syntax and store-specific capabilities, see the [Metadata Filters](https://mastra.ai/reference/rag/metadata-filters) documentation.

For an example of how agent-driven filtering works, see the [Agent-Driven Metadata Filtering](https://github.com/mastra-ai/mastra/tree/main/examples/basics/rag/filter-rag) example.

## Example with reranking

```typescript
const queryTool = createVectorQueryTool({
  vectorStoreName: 'milvus',
  indexName: 'documentation',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  reranker: {
    model: 'openai/gpt-5.5',
    options: {
      weights: {
        semantic: 0.5, // Semantic relevance weight
        vector: 0.3, // Vector similarity weight
        position: 0.2, // Original position weight
      },
      topK: 5,
    },
  },
})
```

Reranking improves result quality by combining:

- Semantic relevance: Using LLM-based scoring of text similarity
- Vector similarity: Original vector distance scores
- Position bias: Consideration of original result ordering
- Query analysis: Adjustments based on query characteristics

The reranker processes the initial vector search results and returns a reordered list optimized for relevance.

## Example with custom description

```typescript
const queryTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  description:
    'Search through document archives to find relevant information for answering questions about company policies and procedures',
})
```

This example shows how to customize the tool description for a specific use case while maintaining its core purpose of information retrieval.

## Database-specific configuration examples

The `databaseConfig` parameter allows you to leverage unique features and optimizations specific to each vector database. These configurations are automatically applied during query execution.

**Pinecone**:

### Pinecone Configuration

```typescript
const pineconeQueryTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  databaseConfig: {
    pinecone: {
      namespace: 'production', // Organize vectors by environment
      sparseVector: {
        // Enable hybrid search
        indices: [0, 1, 2, 3],
        values: [0.1, 0.2, 0.15, 0.05],
      },
    },
  },
})
```

**Pinecone Features:**

- **Namespace**: Isolate different data sets within the same index
- **Sparse Vector**: Combine dense and sparse embeddings for improved search quality
- **Use Cases**: Multi-tenant applications, hybrid semantic search

**pgVector**:

### pgVector Configuration

```typescript
const pgVectorQueryTool = createVectorQueryTool({
  vectorStoreName: 'postgres',
  indexName: 'embeddings',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  databaseConfig: {
    pgvector: {
      minScore: 0.7, // Only return results above 70% similarity
      ef: 200, // Higher value = better accuracy, slower search
      probes: 10, // For IVFFlat: more probes = better recall
    },
  },
})
```

**pgVector Features:**

- **minScore**: Filter out low-quality matches
- **ef (HNSW)**: Control accuracy vs speed for HNSW indexes
- **probes (IVFFlat)**: Control recall vs speed for IVFFlat indexes
- **Use Cases**: Performance tuning, quality filtering

**Chroma**:

### Chroma Configuration

```typescript
const chromaQueryTool = createVectorQueryTool({
  vectorStoreName: 'chroma',
  indexName: 'documents',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  databaseConfig: {
    chroma: {
      where: {
        // Metadata filtering
        category: 'technical',
        status: 'published',
      },
      whereDocument: {
        // Document content filtering
        $contains: 'API',
      },
    },
  },
})
```

**Chroma Features:**

- **where**: Filter by metadata fields
- **whereDocument**: Filter by document content
- **Use Cases**: Advanced filtering, content-based search

**Multiple Configs**:

### Multiple Database Configurations

```typescript
// Configure for multiple databases (useful for dynamic stores)
const multiDbQueryTool = createVectorQueryTool({
  vectorStoreName: 'dynamic-store', // Will be set at runtime
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  databaseConfig: {
    pinecone: {
      namespace: 'default',
    },
    pgvector: {
      minScore: 0.8,
      ef: 150,
    },
    chroma: {
      where: { type: 'documentation' },
    },
  },
})
```

**Multi-Config Benefits:**

- Support multiple vector stores with one tool
- Database-specific optimizations are automatically applied
- Flexible deployment scenarios

### Runtime Configuration Override

You can override database configurations at runtime to adapt to different scenarios:

```typescript
import { RequestContext } from '@mastra/core/request-context'

const queryTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  databaseConfig: {
    pinecone: {
      namespace: 'development',
    },
  },
})

// Override at runtime
const requestContext = new RequestContext()
requestContext.set('databaseConfig', {
  pinecone: {
    namespace: 'production', // Switch to production namespace
  },
})

const response = await agent.generate('Find information about deployment', {
  requestContext,
})
```

This approach allows you to:

- Switch between environments (dev/staging/prod)
- Adjust performance parameters based on load
- Apply different filtering strategies per request

## Example: Using request context

```typescript
const queryTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'docs',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
})
```

When using request context, provide required parameters at execution time via the request context:

```typescript
const requestContext = new RequestContext<{
  vectorStoreName: string
  indexName: string
  topK: number
  filter: VectorFilter
  databaseConfig: DatabaseConfig
}>()
requestContext.set('vectorStoreName', 'my-store')
requestContext.set('indexName', 'my-index')
requestContext.set('topK', 5)
requestContext.set('filter', { category: 'docs' })
requestContext.set('databaseConfig', {
  pinecone: { namespace: 'runtime-namespace' },
})
requestContext.set('model', 'openai/text-embedding-3-small')

const response = await agent.generate('Find documentation from the knowledge base.', {
  requestContext,
})
```

For more information on request context, please see:

- [Agent Request Context](https://mastra.ai/docs/server/request-context)
- [Request Context](https://mastra.ai/docs/server/request-context)

## Usage without a Mastra server

The tool can be used by itself to retrieve documents matching a query:

```typescript
import { RequestContext } from '@mastra/core/request-context'
import { createVectorQueryTool } from '@mastra/rag'
import { PgVector } from '@mastra/pg'

const pgVector = new PgVector({
  id: 'pg-vector',
  connectionString: process.env.POSTGRES_CONNECTION_STRING!,
})

const vectorQueryTool = createVectorQueryTool({
  vectorStoreName: 'pgVector', // optional since we're passing in a store
  vectorStore: pgVector,
  indexName: 'embeddings',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
})

const requestContext = new RequestContext()
const queryResult = await vectorQueryTool.execute({ queryText: 'foo', topK: 1 }, { requestContext })

console.log(queryResult.sources)
```

## Dynamic vector store for multi-tenant applications

For multi-tenant applications where each tenant has isolated data (e.g., separate PostgreSQL schemas), you can pass a resolver function instead of a static vector store instance. The function receives the request context and can return the appropriate vector store for the current tenant:

```typescript
import { createVectorQueryTool, VectorStoreResolver } from '@mastra/rag'
import { PgVector } from '@mastra/pg'

// Cache for tenant-specific vector stores
const vectorStoreCache = new Map<string, PgVector>()

// Resolver function that returns the correct vector store based on tenant
const vectorStoreResolver: VectorStoreResolver = async ({ requestContext }) => {
  const tenantId = requestContext?.get('tenantId')

  if (!tenantId) {
    throw new Error('tenantId is required in request context')
  }

  // Return cached instance or create new one
  if (!vectorStoreCache.has(tenantId)) {
    vectorStoreCache.set(
      tenantId,
      new PgVector({
        id: `pg-vector-${tenantId}`,
        connectionString: process.env.POSTGRES_CONNECTION_STRING!,
        schemaName: `tenant_${tenantId}`, // Each tenant has their own schema
      }),
    )
  }

  return vectorStoreCache.get(tenantId)!
}

const vectorQueryTool = createVectorQueryTool({
  indexName: 'embeddings',
  model: new ModelRouterEmbeddingModel('openai/text-embedding-3-small'),
  vectorStore: vectorStoreResolver, // Dynamic resolution!
})

// Usage with tenant context
const requestContext = new RequestContext()
requestContext.set('tenantId', 'acme-corp')

const result = await vectorQueryTool.execute(
  { queryText: 'company policies', topK: 5 },
  { requestContext },
)
```

This pattern is similar to how `Agent.memory` supports dynamic configuration and enables:

- **Schema isolation**: Each tenant's data in separate PostgreSQL schemas
- **Database isolation**: Route to different database instances per tenant
- **Dynamic configuration**: Adjust vector store settings based on request context

## Tool details

The tool is created with:

- **ID**: `VectorQuery {vectorStoreName} {indexName} Tool`
- **Input Schema**: Requires queryText and filter objects
- **Output Schema**: Returns relevantContext string

## Related

- [rerank()](https://mastra.ai/reference/rag/rerank)
- [createGraphRAGTool](https://mastra.ai/reference/tools/graph-rag-tool)