Vector Query Tool with Database-Specific Configurations

The createVectorQueryTool function now supports database-specific configurations to handle unique properties and optimizations for different vector databases.

Database Configuration Types

Pinecone Configuration

typescript

import { createVectorQueryTool } from '@mastra/rag/tools';

const pineconeVectorTool = createVectorQueryTool({
  id: 'pinecone-search',
  indexName: 'my-index',
  vectorStoreName: 'pinecone',
  model: embedModel,
  databaseConfig: {
    pinecone: {
      namespace: 'my-namespace', // Pinecone namespace
      sparseVector: {
        // For hybrid search
        indices: [0, 1, 2],
        values: [0.1, 0.2, 0.3],
      },
    },
  },
});

pgVector Configuration

typescript

const pgVectorTool = createVectorQueryTool({
  id: 'pgvector-search',
  indexName: 'my-index',
  vectorStoreName: 'postgres',
  model: embedModel,
  databaseConfig: {
    pgvector: {
      minScore: 0.7, // Minimum similarity score
      ef: 200, // HNSW search parameter
      probes: 10, // IVFFlat probe parameter
    },
  },
});

Chroma Configuration

typescript

const chromaTool = createVectorQueryTool({
  id: 'chroma-search',
  indexName: 'my-index',
  vectorStoreName: 'chroma',
  model: embedModel,
  databaseConfig: {
    chroma: {
      where: {
        // Metadata filtering
        category: 'documents',
      },
      whereDocument: {
        // Document content filtering
        $contains: 'important',
      },
    },
  },
});

Runtime Configuration Override

You can also override database configurations at runtime using the runtime context:

typescript

import { RequestContext } from '@mastra/core/runtime-context';

const requestContext = new RequestContext();

// Override Pinecone namespace at runtime
requestContext.set('databaseConfig', {
  pinecone: {
    namespace: 'runtime-namespace',
  },
});

await vectorTool.execute({
  context: { queryText: 'search query' },
  mastra,
  requestContext,
});

Extensibility for New Databases

The system is designed to be extensible. For new vector databases, you can:

Add configuration types:

typescript

export interface NewDatabaseConfig {
  customParam1?: string;
  customParam2?: number;
}

export type DatabaseConfig = {
  pinecone?: PineconeConfig;
  pgvector?: PgVectorConfig;
  chroma?: ChromaConfig;
  newdatabase?: NewDatabaseConfig; // Add your config here
  [key: string]: any;
};

The configuration will be automatically passed through to the vector store's query method.

Type Safety

All database configurations are fully typed, providing IntelliSense and compile-time checking:

typescript

const config: DatabaseConfig = {
  pinecone: {
    namespace: 'valid-namespace',
    sparseVector: {
      indices: [1, 2, 3],
      values: [0.1, 0.2, 0.3],
    },
  },
  pgvector: {
    minScore: 0.8,
    ef: 100,
    probes: 5,
  },
};

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:

typescript

import { createVectorQueryTool, VectorStoreResolver } from '@mastra/rag/tools';
import { PgVector } from '@mastra/pg';

// Resolver function receives requestContext and mastra instance
const vectorStoreResolver: VectorStoreResolver = async ({ requestContext }) => {
  const tenantId = requestContext?.get('tenantId');

  return new PgVector({
    id: `pg-vector-${tenantId}`,
    connectionString: process.env.POSTGRES_CONNECTION_STRING!,
    schemaName: `tenant_${tenantId}`, // Each tenant has their own schema
  });
};

const vectorQueryTool = createVectorQueryTool({
  indexName: 'embeddings',
  model: embedModel,
  vectorStore: vectorStoreResolver, // Dynamic resolution!
});

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

const result = await vectorQueryTool.execute({ queryText: 'search query', topK: 5 }, { requestContext });

The same pattern works with createGraphRAGTool:

typescript

import { createGraphRAGTool } from '@mastra/rag/tools';

const graphTool = createGraphRAGTool({
  indexName: 'embeddings',
  model: embedModel,
  vectorStore: vectorStoreResolver,
});

Migration Guide

Existing code will continue to work without changes. To add database-specific configurations:

diff

const vectorTool = createVectorQueryTool({
  indexName: 'my-index',
  vectorStoreName: 'pinecone',
  model: embedModel,
+ databaseConfig: {
+   pinecone: {
+     namespace: 'my-namespace'
+   }
+ }
});