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--- | |
title: "Storing Embeddings in A Vector Database | Mastra Docs" | |
description: 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 | |
<Tabs items={['Pg Vector', 'Pinecone', 'Qdrant', 'Chroma', 'Astra', 'LibSQL', 'Upstash', 'Cloudflare', 'MongoDB']}> | |
<Tabs.Tab> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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](https://github.com/pgvector/pgvector). | |
</Tabs.Tab> | |
<Tabs.Tab> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
```ts filename="vector-store.ts" showLineNumbers copy | |
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> | |
</Tabs> | |
## 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: | |
```ts filename="store-embeddings.ts" showLineNumbers copy | |
// 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> | |
</Tabs> | |
### Upserting Embeddings | |
After creating an index, you can store embeddings along with their basic metadata: | |
```ts filename="store-embeddings.ts" showLineNumbers copy | |
// 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](../../examples/rag/upsert/upsert-embeddings.mdx) 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. | |
```ts showLineNumbers copy | |
// 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`) | |
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