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DatabaseConfig

The DatabaseConfig type allows you to specify database-specific configurations when using vector query tools. These configurations enable you to leverage unique features and optimizations offered by different vector stores.

Type DefinitionDirect link to Type Definition

export type DatabaseConfig = {
  pinecone?: PineconeConfig;
  pgvector?: PgVectorConfig;
  chroma?: ChromaConfig;
  [key: string]: any; // Extensible for future databases
};

Database-Specific TypesDirect link to Database-Specific Types

PineconeConfigDirect link to PineconeConfig

Configuration options specific to Pinecone vector store.

namespace?:

string
Pinecone namespace for organizing and isolating vectors within the same index. Useful for multi-tenancy or environment separation.

sparseVector?:

{ indices: number[]; values: number[]; }
Sparse vector for hybrid search combining dense and sparse embeddings. Enables better search quality for keyword-based queries. The indices and values arrays must be the same length.
object

indices:

number[]
Array of indices for sparse vector components

values:

number[]
Array of values corresponding to the indices

Use Cases:

  • Multi-tenant applications (separate namespaces per tenant)
  • Environment isolation (dev/staging/prod namespaces)
  • Hybrid search combining semantic and keyword matching

PgVectorConfigDirect link to PgVectorConfig

Configuration options specific to PostgreSQL with pgvector extension.

minScore?:

number
Minimum similarity score threshold for results. Only vectors with similarity scores above this value will be returned.

ef?:

number
HNSW search parameter that controls the size of the dynamic candidate list during search. Higher values improve accuracy at the cost of speed. Typically set between topK and 200.

probes?:

number
IVFFlat probe parameter that specifies the number of index cells to visit during search. Higher values improve recall at the cost of speed.

Performance Guidelines:

  • ef: Start with 2-4x your topK value, increase for better accuracy
  • probes: Start with 1-10, increase for better recall
  • minScore: Use values between 0.5-0.9 depending on your quality requirements

Use Cases:

  • Performance optimization for high-load scenarios
  • Quality filtering to remove irrelevant results
  • Fine-tuning search accuracy vs speed tradeoffs

ChromaConfigDirect link to ChromaConfig

Configuration options specific to Chroma vector store.

where?:

Record<string, any>
Metadata filtering conditions using MongoDB-style query syntax. Filters results based on metadata fields.

whereDocument?:

Record<string, any>
Document content filtering conditions. Allows filtering based on the actual document text content.

Filter Syntax Examples:

// Simple equality
where: { "category": "technical" }

// Operators
where: { "price": { "$gt": 100 } }

// Multiple conditions
where: {
  "category": "electronics",
  "inStock": true
}

// Document content filtering
whereDocument: { "$contains": "API documentation" }

Use Cases:

  • Advanced metadata filtering
  • Content-based document filtering
  • Complex query combinations

Usage ExamplesDirect link to Usage Examples

Basic Database ConfigurationDirect link to Basic Database Configuration

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

const vectorTool = createVectorQueryTool({
  vectorStoreName: 'pinecone',
  indexName: 'documents',
  model: embedModel,
  databaseConfig: {
    pinecone: {
      namespace: 'production'
    }
  }
});

ExtensibilityDirect link to Extensibility

The DatabaseConfig type is designed to be extensible. To add support for a new vector database:

// 1. Define the configuration interface
export interface NewDatabaseConfig {
  customParam1?: string;
  customParam2?: number;
}

// 2. Extend DatabaseConfig type
export type DatabaseConfig = {
  pinecone?: PineconeConfig;
  pgvector?: PgVectorConfig;
  chroma?: ChromaConfig;
  newdatabase?: NewDatabaseConfig;
  [key: string]: any;
};

// 3. Use in vector query tool
const vectorTool = createVectorQueryTool({
  vectorStoreName: "newdatabase",
  indexName: "documents",
  model: embedModel,
  databaseConfig: {
    newdatabase: {
      customParam1: "value",
      customParam2: 42,
    },
  },
});

Best PracticesDirect link to Best Practices

  1. Environment Configuration: Use different namespaces or configurations for different environments
  2. Performance Tuning: Start with default values and adjust based on your specific needs
  3. Quality Filtering: Use minScore to filter out low-quality results
  4. Runtime Flexibility: Override configurations at runtime for dynamic scenarios
  5. Documentation: Document your specific configuration choices for team members

Migration GuideDirect link to Migration Guide

Existing vector query tools continue to work without changes. To add database configurations:

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