Amazon S3 Vectors Store

⚠️ Amazon S3 Vectors is a Preview service. Preview features may change or be removed without notice and are not covered by AWS SLAs. Behavior, limits, and regional availability can change at any time. This library may introduce breaking changes to stay aligned with AWS.

The S3Vectors class provides vector search using Amazon S3 Vectors (Preview). It stores vectors in vector buckets and performs similarity search in vector indexes, with JSON-based metadata filters.

Installation

npm install @mastra/s3vectors

Usage Example

import { S3Vectors } from "@mastra/s3vectors";

const store = new S3Vectors({
  vectorBucketName: process.env.S3_VECTORS_BUCKET_NAME!, // e.g. "my-vector-bucket"
  clientConfig: {
    region: process.env.AWS_REGION!, // credentials use the default AWS provider chain
  },
  // Optional: mark large/long-text fields as non-filterable at index creation time
  nonFilterableMetadataKeys: ["content"],
});

// Create an index (names are normalized: "_" → "-" and lowercased)
await store.createIndex({
  indexName: "my_index",
  dimension: 1536,
  metric: "cosine", // "euclidean" also supported; "dotproduct" is NOT supported
});

// Upsert vectors (ids auto-generated if omitted). Date values in metadata are serialized to epoch ms.
const ids = await store.upsert({
  indexName: "my_index",
  vectors: [
    [0.1, 0.2 /* … */],
    [0.3, 0.4 /* … */],
  ],
  metadata: [
    {
      text: "doc1",
      genre: "documentary",
      year: 2023,
      createdAt: new Date("2024-01-01"),
    },
    { text: "doc2", genre: "comedy", year: 2021 },
  ],
});

// Query with metadata filters (implicit AND is canonicalized)
const results = await store.query({
  indexName: "my-index",
  queryVector: [0.1, 0.2 /* … */],
  topK: 10, // Service-side limits may apply (commonly 30)
  filter: { genre: { $in: ["documentary", "comedy"] }, year: { $gte: 2020 } },
  includeVector: false, // set true to include raw vectors (may trigger a secondary fetch)
});

// Clean up resources (closes the underlying HTTP handler)
await store.disconnect();

Constructor Options

vectorBucketName:

string

Target S3 Vectors vector bucket name.

clientConfig?:

S3VectorsClientConfig

AWS SDK v3 client options (e.g., `region`, `credentials`).

nonFilterableMetadataKeys?:

string[]

Metadata keys that should NOT be filterable (applied to the index at creation time). Use this for large text fields like `content`.

Methods

createIndex()

Creates a new vector index in the configured vector bucket. If the index already exists, the call validates the schema and becomes a no-op (existing metric and dimension are preserved).

indexName:

string

Logical index name. Normalized internally: underscores are replaced with hyphens and the name is lowercased.

dimension:

number

Vector dimension (must match your embedding model)

metric?:

'cosine' | 'euclidean'

= cosine

Distance metric for similarity search. `dotproduct` is not supported by S3 Vectors.

upsert()

Adds or replaces vectors (full-record put). If ids are not provided, UUIDs are generated.

indexName:

string

Name of the index to upsert into

vectors:

number[][]

Array of embedding vectors

metadata?:

Record<string, any>[]

Metadata for each vector

ids?:

string[]

Optional vector IDs (auto-generated if not provided)

query()

Searches for nearest neighbors with optional metadata filtering.

indexName:

string

Name of the index to query

queryVector:

number[]

Query vector to find similar vectors

topK?:

number

= 10

Number of results to return

filter?:

S3VectorsFilter

JSON-based metadata filter supporting `$and`, `$or`, `$eq`, `$ne`, `$gt`, `$gte`, `$lt`, `$lte`, `$in`, `$nin`, `$exists`.

includeVector?:

boolean

= false

Whether to include vectors in the results

Scoring: Results include score = 1/(1 + distance) so that higher is better while preserving the underlying distance ranking.

describeIndex()

Returns information about the index.

indexName:

string

Index name to describe.

Returns:

interface IndexStats {
  dimension: number;
  count: number; // computed via ListVectors pagination (O(n))
  metric: "cosine" | "euclidean";
}

deleteIndex()

Deletes an index and its data.

indexName:

string

Index to delete.

listIndexes()

Lists all indexes in the configured vector bucket.

Returns: Promise<string[]>

updateVector()

Updates a vector or metadata for a specific ID within an index.

indexName:

string

Index containing the vector.

id:

string

ID to update.

update:

object

Update data containing vector and/or metadata

update.vector?:

number[]

New vector data to update

update.metadata?:

Record<string, any>

New metadata to update

deleteVector()

Deletes a specific vector by ID.

indexName:

string

Index containing the vector.

id:

string

ID to delete.

disconnect()

Closes the underlying AWS SDK HTTP handler to free sockets.

Response Types

Query results are returned in this format:

interface QueryResult {
  id: string;
  score: number; // 1/(1 + distance)
  metadata: Record<string, any>;
  vector?: number[]; // Only included if includeVector is true
}

Filter Syntax

S3 Vectors supports a strict subset of operators and value types. The Mastra filter translator:

• Canonicalizes implicit AND: {a:1,b:2} → { $and: [{a:1},{b:2}] }.
• Normalizes Date values to epoch ms for numeric comparisons and array elements.
• Disallows Date in equality positions (field: value or $eq/$ne); equality values must be string | number | boolean.
• Rejects null/undefined for equality; array equality is not supported (use $in/$nin).
• Only $and / $or are allowed as top-level logical operators.
• Logical operators must contain field conditions (not direct operators).

Supported operators:

• Logical: $and, $or (non-empty arrays)
• Basic: $eq, $ne (string | number | boolean)
• Numeric: $gt, $gte, $lt, $lte (number or Date → epoch ms)
• Array: $in, $nin (non-empty arrays of string | number | boolean; Date → epoch ms)
• Element: $exists (boolean)

Unsupported / disallowed (rejected): $not, $nor, $regex, $all, $elemMatch, $size, $text, etc.

Examples:

// Implicit AND
{ genre: { $in: ["documentary", "comedy"] }, year: { $gte: 2020 } }

// Explicit logicals and ranges
{
  $and: [
    { price: { $gte: 100, $lte: 1000 } },
    { $or: [{ stock: { $gt: 0 } }, { preorder: true }] }
  ]
}

// Dates in range (converted to epoch ms)
{ timestamp: { $gt: new Date("2024-01-01T00:00:00Z") } }

Non-filterable keys: If you set nonFilterableMetadataKeys at index creation, those keys are stored but cannot be used in filters.

Error Handling

The store throws typed errors that can be caught:

try {
  await store.query({
    indexName: "index-name",
    queryVector: queryVector,
  });
} catch (error) {
  if (error instanceof VectorStoreError) {
    console.log(error.code); // 'connection_failed' | 'invalid_dimension' | etc
    console.log(error.details); // Additional error context
  }
}

Environment Variables

Typical environment variables when wiring your app:

• S3_VECTORS_BUCKET_NAME: Your S3 vector bucket name (used to populate vectorBucketName).
• AWS_REGION: AWS region for the S3 Vectors bucket.
• AWS credentials: via the standard AWS SDK provider chain (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_PROFILE, etc.).

Best Practices

• Choose the metric (cosine or euclidean) to match your embedding model; dotproduct is not supported.
• Keep filterable metadata small and structured (string/number/boolean). Store large text (e.g., content) as non-filterable.
• Use dotted paths for nested metadata and explicit $and/$or for complex logic.
• Avoid calling describeIndex() on hot paths—count is computed with paginated ListVectors (O(n)).
• Use includeVector: true only when you need raw vectors.

Related

• Metadata Filters