The vector databases reference

title	description
Storing Embeddings in A Vector Database \| Mastra Docs	Guide on vector storage options in Mastra, including embedded and dedicated vector databases for similarity search.

import { Tabs } from "nextra/components";

Storing Embeddings in A Vector Database

After generating embeddings, you need to store them in a database that supports vector similarity search. Mastra provides a consistent interface for storing and querying embeddings across different vector databases.

Supported Databases

import { PgVector } from '@mastra/pg';

const store = new PgVector(process.env.POSTGRES_CONNECTION_STRING)
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

Using PostgreSQL with pgvector

PostgreSQL with the pgvector extension is a good solution for teams already using PostgreSQL who want to minimize infrastructure complexity. For detailed setup instructions and best practices, see the official pgvector repository. </Tabs.Tab> <Tabs.Tab>

import { PineconeVector } from '@mastra/pinecone'

const store = new PineconeVector(process.env.PINECONE_API_KEY)
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab> <Tabs.Tab>

import { QdrantVector } from '@mastra/qdrant'

const store = new QdrantVector({
  url: process.env.QDRANT_URL,
  apiKey: process.env.QDRANT_API_KEY
})
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab> <Tabs.Tab>

import { ChromaVector } from '@mastra/chroma'

const store = new ChromaVector()
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab> <Tabs.Tab>

import { AstraVector } from '@mastra/astra'

const store = new AstraVector({
  token: process.env.ASTRA_DB_TOKEN,
  endpoint: process.env.ASTRA_DB_ENDPOINT,
  keyspace: process.env.ASTRA_DB_KEYSPACE
})
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab> <Tabs.Tab>

import { LibSQLVector } from "@mastra/core/vector/libsql";

const store = new LibSQLVector({
  connectionUrl: process.env.DATABASE_URL,
  authToken: process.env.DATABASE_AUTH_TOKEN // Optional: for Turso cloud databases
})
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab> <Tabs.Tab>

import { UpstashVector } from '@mastra/upstash'

const store = new UpstashVector({
  url: process.env.UPSTASH_URL,
  token: process.env.UPSTASH_TOKEN
})
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab> <Tabs.Tab>

import { CloudflareVector } from '@mastra/vectorize'

const store = new CloudflareVector({
  accountId: process.env.CF_ACCOUNT_ID,
  apiToken: process.env.CF_API_TOKEN
})
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab> <Tabs.Tab>

import { MongoDBVector } from '@mastra/mongodb'

const store = new MongoDBVector({
  url: process.env.MONGODB_URL,
  database: process.env.MONGODB_DATABASE
})
await store.createIndex({
  indexName: "myCollection",
  dimension: 1536,
});
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map(chunk => ({ text: chunk.text })),
});

</Tabs.Tab>

Using Vector Storage

Once initialized, all vector stores share the same interface for creating indexes, upserting embeddings, and querying.

Creating Indexes

Before storing embeddings, you need to create an index with the appropriate dimension size for your embedding model:

// Create an index with dimension 1536 (for text-embedding-3-small)
await store.createIndex({
  indexName: 'myCollection',
  dimension: 1536,
});

The dimension size must match the output dimension of your chosen embedding model. Common dimension sizes are:

OpenAI text-embedding-3-small: 1536 dimensions (or custom, e.g., 256)
Cohere embed-multilingual-v3: 1024 dimensions
Google text-embedding-004: 768 dimensions (or custom)

Important: Index dimensions cannot be changed after creation. To use a different model, delete and recreate the index with the new dimension size.

Naming Rules for Databases

Each vector database enforces specific naming conventions for indexes and collections to ensure compatibility and prevent conflicts.

<Tabs items={['Pg Vector', 'Pinecone', 'Qdrant', 'Chroma', 'Astra', 'LibSQL', 'Upstash', 'Cloudflare', 'MongoDB']}> <Tabs.Tab> Index names must: - Start with a letter or underscore - Contain only letters, numbers, and underscores - Example: my_index_123 is valid - Example: my-index is not valid (contains hyphen) </Tabs.Tab> <Tabs.Tab> Index names must: - Use only lowercase letters, numbers, and dashes - Not contain dots (used for DNS routing) - Not use non-Latin characters or emojis - Have a combined length (with project ID) under 52 characters - Example: my-index-123 is valid - Example: my.index is not valid (contains dot) </Tabs.Tab> <Tabs.Tab> Collection names must: - Be 1-255 characters long - Not contain any of these special characters: - < > : " / \ | ? * - Null character (\0) - Unit separator (\u{1F}) - Example: my_collection_123 is valid - Example: my/collection is not valid (contains slash) </Tabs.Tab> <Tabs.Tab> Collection names must: - Be 3-63 characters long - Start and end with a letter or number - Contain only letters, numbers, underscores, or hyphens - Not contain consecutive periods (..) - Not be a valid IPv4 address - Example: my-collection-123 is valid - Example: my..collection is not valid (consecutive periods) </Tabs.Tab> <Tabs.Tab> Collection names must: - Not be empty - Be 48 characters or less - Contain only letters, numbers, and underscores - Example: my_collection_123 is valid - Example: my-collection is not valid (contains hyphen) </Tabs.Tab> <Tabs.Tab> Index names must: - Start with a letter or underscore - Contain only letters, numbers, and underscores - Example: my_index_123 is valid - Example: my-index is not valid (contains hyphen) </Tabs.Tab> <Tabs.Tab> Namespace names must: - Be 2-100 characters long - Contain only: - Alphanumeric characters (a-z, A-Z, 0-9) - Underscores, hyphens, dots - Not start or end with special characters (_, -, .) - Can be case-sensitive - Example: MyNamespace123 is valid - Example: _namespace is not valid (starts with underscore) </Tabs.Tab> <Tabs.Tab> Index names must: - Start with a letter - Be shorter than 32 characters - Contain only lowercase ASCII letters, numbers, and dashes - Use dashes instead of spaces - Example: my-index-123 is valid - Example: My_Index is not valid (uppercase and underscore) </Tabs.Tab> <Tabs.Tab> Collection (index) names must: - Start with a letter or underscore - Be up to 120 bytes long - Contain only letters, numbers, underscores, or dots - Cannot contain $ or the null character - Example: my_collection.123 is valid - Example: my-index is not valid (contains hyphen) - Example: My$Collection is not valid (contains $) </Tabs.Tab>

Upserting Embeddings

After creating an index, you can store embeddings along with their basic metadata:

// Store embeddings with their corresponding metadata
await store.upsert({
  indexName: 'myCollection',  // index name
  vectors: embeddings,       // array of embedding vectors
  metadata: chunks.map(chunk => ({
    text: chunk.text,  // The original text content
    id: chunk.id       // Optional unique identifier
  }))
});

The upsert operation:

Takes an array of embedding vectors and their corresponding metadata
Updates existing vectors if they share the same ID
Creates new vectors if they don't exist
Automatically handles batching for large datasets

For complete examples of upserting embeddings in different vector stores, see the Upsert Embeddings guide.

Adding Metadata

Vector stores support rich metadata (any JSON-serializable fields) for filtering and organization. Since metadata is stored with no fixed schema, use consistent field naming to avoid unexpected query results.

Important: Metadata is crucial for vector storage - without it, you'd only have numerical embeddings with no way to return the original text or filter results. Always store at least the source text as metadata.

// Store embeddings with rich metadata for better organization and filtering
await store.upsert({
  indexName: "myCollection",
  vectors: embeddings,
  metadata: chunks.map((chunk) => ({
    // Basic content
    text: chunk.text,
    id: chunk.id,
    
    // Document organization
    source: chunk.source,
    category: chunk.category,
    
    // Temporal metadata
    createdAt: new Date().toISOString(),
    version: "1.0",
    
    // Custom fields
    language: chunk.language,
    author: chunk.author,
    confidenceScore: chunk.score,
  })),
});

Key metadata considerations:

Be strict with field naming - inconsistencies like 'category' vs 'Category' will affect queries
Only include fields you plan to filter or sort by - extra fields add overhead
Add timestamps (e.g., 'createdAt', 'lastUpdated') to track content freshness

Best Practices

Create indexes before bulk insertions
Use batch operations for large insertions (the upsert method handles batching automatically)
Only store metadata you'll query against
Match embedding dimensions to your model (e.g., 1536 for text-embedding-3-small)

adeleke5140/en-vector-databases.mdx

Storing Embeddings in A Vector Database

Supported Databases

Using PostgreSQL with pgvector

Using Vector Storage

Creating Indexes

Naming Rules for Databases

Upserting Embeddings

Adding Metadata

Best Practices