Upstash vector store

The UpstashVector class provides vector search using Upstash Vector, a serverless vector database service that provides vector similarity search with metadata filtering capabilities and hybrid search support.

Constructor options

id:

string

Unique identifier for this vector store instance

url:

string

Upstash Vector database URL

token:

string

Upstash Vector API token

Methods

createIndex()

Note: This method is a no-op for Upstash as indexes are created automatically.

indexName:

string

Name of the index to create

dimension:

number

Vector dimension (must match your embedding model)

metric?:

'cosine' | 'euclidean' | 'dotproduct'

= cosine

Distance metric for similarity search

upsert()

indexName:

string

Name of the index to upsert into

vectors:

number[][]

Array of embedding vectors

sparseVectors?:

{ indices: number[], values: number[] }[]

Array of sparse vectors for hybrid search. Each sparse vector must have matching indices and values arrays.

metadata?:

Record<string, any>[]

Metadata for each vector

ids?:

string[]

Optional vector IDs (auto-generated if not provided)

query()

indexName:

string

Name of the index to query

queryVector:

number[]

Query vector to find similar vectors

sparseVector?:

{ indices: number[], values: number[] }

Optional sparse vector for hybrid search. Must have matching indices and values arrays.

topK?:

number

= 10

Number of results to return

filter?:

Record<string, any>

Metadata filters for the query

includeVector?:

boolean

= false

Whether to include vectors in the results

fusionAlgorithm?:

FusionAlgorithm

Algorithm used to combine dense and sparse search results in hybrid search (e.g., RRF - Reciprocal Rank Fusion)

queryMode?:

QueryMode

Search mode: 'DENSE' for dense-only, 'SPARSE' for sparse-only, or 'HYBRID' for combined search

listIndexes()

Returns an array of index names (namespaces) as strings.

describeIndex()

indexName:

string

Name of the index to describe

Returns:

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

deleteIndex()

indexName:

string

Name of the index (namespace) to delete

updateVector()

indexName:

string

Name of the index to update

id:

string

ID of the item to update

update:

object

Update object containing vector, sparse vector, and/or metadata

The update object can have the following properties:

• vector (optional): An array of numbers representing the new dense vector.
• sparseVector (optional): A sparse vector object with indices and values arrays for hybrid indexes.
• metadata (optional): A record of key-value pairs for metadata.

deleteVector()

indexName:

string

Name of the index from which to delete the item

id:

string

ID of the item to delete

Attempts to delete an item by its ID from the specified index. Logs an error message if the deletion fails.

Hybrid vector search

Upstash Vector supports hybrid search that combines semantic search (dense vectors) with keyword-based search (sparse vectors) for improved relevance and accuracy.

Basic Hybrid Usage

import { UpstashVector } from '@mastra/upstash'

const vectorStore = new UpstashVector({
  id: 'upstash-vector',
  url: process.env.UPSTASH_VECTOR_URL,
  token: process.env.UPSTASH_VECTOR_TOKEN,
})

// Upsert vectors with both dense and sparse components
const denseVectors = [
  [0.1, 0.2, 0.3],
  [0.4, 0.5, 0.6],
]
const sparseVectors = [
  { indices: [1, 5, 10], values: [0.8, 0.6, 0.4] },
  { indices: [2, 6, 11], values: [0.7, 0.5, 0.3] },
]

await vectorStore.upsert({
  indexName: 'hybrid-index',
  vectors: denseVectors,
  sparseVectors: sparseVectors,
  metadata: [{ title: 'Document 1' }, { title: 'Document 2' }],
})

// Query with hybrid search
const results = await vectorStore.query({
  indexName: 'hybrid-index',
  queryVector: [0.1, 0.2, 0.3],
  sparseVector: { indices: [1, 5], values: [0.9, 0.7] },
  topK: 10,
})

Advanced Hybrid Search Options

import { FusionAlgorithm, QueryMode } from '@upstash/vector'

// Query with specific fusion algorithm
const fusionResults = await vectorStore.query({
  indexName: 'hybrid-index',
  queryVector: [0.1, 0.2, 0.3],
  sparseVector: { indices: [1, 5], values: [0.9, 0.7] },
  fusionAlgorithm: FusionAlgorithm.RRF,
  topK: 10,
})

// Dense-only search
const denseResults = await vectorStore.query({
  indexName: 'hybrid-index',
  queryVector: [0.1, 0.2, 0.3],
  queryMode: QueryMode.DENSE,
  topK: 10,
})

// Sparse-only search
const sparseResults = await vectorStore.query({
  indexName: 'hybrid-index',
  queryVector: [0.1, 0.2, 0.3], // Still required for index structure
  sparseVector: { indices: [1, 5], values: [0.9, 0.7] },
  queryMode: QueryMode.SPARSE,
  topK: 10,
})

Updating Hybrid Vectors

// Update both dense and sparse components
await vectorStore.updateVector({
  indexName: 'hybrid-index',
  id: 'vector-id',
  update: {
    vector: [0.2, 0.3, 0.4],
    sparseVector: { indices: [2, 7, 12], values: [0.9, 0.8, 0.6] },
    metadata: { title: 'Updated Document' },
  },
})

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
}

Error handling

The store throws MastraError instances from @mastra/core/error:

import { MastraError } from '@mastra/core/error'

try {
  await store.query({
    indexName: 'index_name',
    queryVector: queryVector,
  })
} catch (error) {
  if (error instanceof MastraError) {
    console.log(error.id) // 'MASTRA_VECTOR_UPSTASH_QUERY_FAILED'
    console.log(error.domain) // 'STORAGE'
    console.log(error.category) // 'USER' | 'THIRD_PARTY'
    console.log(error.details) // Additional error context
  }
}

Environment variables

Required environment variables:

• UPSTASH_VECTOR_URL: Your Upstash Vector database URL
• UPSTASH_VECTOR_TOKEN: Your Upstash Vector API token

Usage example

Local embeddings with fastembed

Embeddings are numeric vectors used by memory's semanticRecall to retrieve related messages by meaning (not keywords). This setup uses @mastra/fastembed to generate vector embeddings.

Install fastembed to get started:

npm

npm install @mastra/fastembed@latest

pnpm

pnpm add @mastra/fastembed@latest

Yarn

yarn add @mastra/fastembed@latest

Bun

bun add @mastra/fastembed@latest

Add the following to your agent:

src/mastra/agents/example-upstash-agent.ts

import { Memory } from '@mastra/memory'
import { Agent } from '@mastra/core/agent'
import { UpstashStore, UpstashVector } from '@mastra/upstash'
import { fastembed } from '@mastra/fastembed'

export const upstashAgent = new Agent({
  id: 'upstash-agent',
  name: 'Upstash Agent',
  instructions:
    'You are an AI agent with the ability to automatically recall memories from previous interactions.',
  model: 'openai/gpt-5.6-sol',
  memory: new Memory({
    storage: new UpstashStore({
      id: 'upstash-agent-storage',
      url: process.env.UPSTASH_REDIS_REST_URL!,
      token: process.env.UPSTASH_REDIS_REST_TOKEN!,
    }),
    vector: new UpstashVector({
      id: 'upstash-agent-vector',
      url: process.env.UPSTASH_VECTOR_REST_URL!,
      token: process.env.UPSTASH_VECTOR_REST_TOKEN!,
    }),
    embedder: fastembed,
    options: {
      lastMessages: 10,
      semanticRecall: {
        topK: 3,
        messageRange: 2,
      },
    },
  }),
})

Related

• Metadata Filters