Convex vector store

The ConvexVector class provides vector storage and similarity search using Convex. It stores embeddings inside Convex and performs cosine similarity search.

Installation

npm install @mastra/convex@latest

Convex setup

Before using ConvexVector, you need to set up the Convex schema and storage handler. See Convex Storage Setup for setup instructions.

Constructor options

<PropertiesTable content={[ { name: 'deploymentUrl', type: 'string', description: 'Convex deployment URL (e.g., https://your-project.convex.cloud)', isOptional: false, }, { name: 'adminAuthToken', type: 'string', description: 'Convex admin authentication token', isOptional: false, }, { name: 'storageFunction', type: 'string', description: 'Path to the storage mutation function', isOptional: true, defaultValue: 'mastra/storage:handle', }, ]} />

Constructor examples

Basic Configuration

import { ConvexVector } from '@mastra/convex'

const vectorStore = new ConvexVector({
  id: 'convex-vectors',
  deploymentUrl: 'https://your-project.convex.cloud',
  adminAuthToken: 'your-admin-token',
})

Custom Storage Function

const vectorStore = new ConvexVector({
  id: 'convex-vectors',
  deploymentUrl: 'https://your-project.convex.cloud',
  adminAuthToken: 'your-admin-token',
  storageFunction: 'custom/path:handler',
})

Methods

createIndex()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to create', }, { name: 'dimension', type: 'number', description: 'Vector dimension (must match your embedding model)', }, { name: 'metric', type: "'cosine' | 'euclidean' | 'dotproduct'", isOptional: true, defaultValue: 'cosine', description: 'Distance metric for similarity search (only cosine is currently supported)', }, ]} />

await vectorStore.createIndex({
  indexName: 'my_vectors',
  dimension: 1536,
})

upsert()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to upsert vectors into', }, { name: 'vectors', type: 'number[][]', description: 'Array of embedding vectors', }, { name: 'metadata', type: 'Record<string, any>[]', isOptional: true, description: 'Metadata for each vector', }, { name: 'ids', type: 'string[]', isOptional: true, description: 'Optional vector IDs (auto-generated if not provided)', }, ]} />

await vectorStore.upsert({
  indexName: "my_vectors",
  vectors: [[0.1, 0.2, 0.3, ...]],
  metadata: [{ label: "example" }],
  ids: ["vec-1"],
});

query()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to query', }, { name: 'queryVector', type: 'number[]', description: 'Query vector', }, { name: 'topK', type: 'number', isOptional: true, defaultValue: '10', description: 'Number of results to return', }, { name: 'filter', type: 'Record<string, any>', isOptional: true, description: 'Metadata filters', }, { name: 'includeVector', type: 'boolean', isOptional: true, defaultValue: 'false', description: 'Whether to include the vector in the result', }, ]} />

const results = await vectorStore.query({
  indexName: "my_vectors",
  queryVector: [0.1, 0.2, 0.3, ...],
  topK: 5,
  filter: { category: "documents" },
});

listIndexes()

Returns an array of index names as strings.

const indexes = await vectorStore.listIndexes()
// ["my_vectors", "embeddings", ...]

describeIndex()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to describe', }, ]} />

Returns:

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

deleteIndex()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to delete', }, ]} />

Deletes the index and all its vectors.

await vectorStore.deleteIndex({ indexName: 'my_vectors' })

updateVector()

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

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the vector', }, { name: 'id', type: 'string', isOptional: true, description: 'ID of the vector to update (mutually exclusive with filter)', }, { name: 'filter', type: 'Record<string, any>', isOptional: true, description: 'Metadata filter to identify vector(s) to update (mutually exclusive with id)', }, { name: 'update', type: '{ vector?: number[]; metadata?: Record<string, any>; }', description: 'Object containing the vector and/or metadata to update', }, ]} />

// Update by ID
await vectorStore.updateVector({
  indexName: 'my_vectors',
  id: 'vector123',
  update: {
    vector: [0.1, 0.2, 0.3],
    metadata: { label: 'updated' },
  },
})

// Update by filter
await vectorStore.updateVector({
  indexName: 'my_vectors',
  filter: { category: 'product' },
  update: {
    metadata: { status: 'reviewed' },
  },
})

deleteVector()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the vector', }, { name: 'id', type: 'string', description: 'ID of the vector to delete', }, ]} />

await vectorStore.deleteVector({ indexName: 'my_vectors', id: 'vector123' })

deleteVectors()

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

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the vectors to delete', }, { name: 'ids', type: 'string[]', isOptional: true, description: 'Array of vector IDs to delete (mutually exclusive with filter)', }, { name: 'filter', type: 'Record<string, any>', isOptional: true, description: 'Metadata filter to identify vectors to delete (mutually exclusive with ids)', }, ]} />

// Delete by IDs
await vectorStore.deleteVectors({
  indexName: 'my_vectors',
  ids: ['vec1', 'vec2', 'vec3'],
})

// Delete by filter
await vectorStore.deleteVectors({
  indexName: 'my_vectors',
  filter: { status: 'archived' },
})

Response types

Query results are returned in this format:

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

Metadata filtering

ConvexVector supports metadata filtering with various operators:

// Simple equality
const results = await vectorStore.query({
  indexName: 'my_vectors',
  queryVector: embedding,
  filter: { category: 'documents' },
})

// Comparison operators
const results = await vectorStore.query({
  indexName: 'my_vectors',
  queryVector: embedding,
  filter: {
    price: { $gt: 100 },
    status: { $in: ['active', 'pending'] },
  },
})

// Logical operators
const results = await vectorStore.query({
  indexName: 'my_vectors',
  queryVector: embedding,
  filter: {
    $and: [{ category: 'electronics' }, { price: { $lte: 500 } }],
  },
})

Supported Filter Operators

Architecture

ConvexVector stores vectors in the mastra_vectors table with the following structure:

• id: Unique vector identifier
• indexName: Name of the index
• embedding: The vector data (array of floats)
• metadata: Optional JSON metadata

Vector similarity search is performed using cosine similarity, computed in the Convex function.

Related

• Convex Storage
• Metadata Filters
• Convex Documentation