---
layout: article
title: VectorsDB
description: Manage Appwrite VectorsDB databases, collections, indexes, and embeddings with the official Terraform provider.
---

VectorsDB stores embeddings in collections and searches them by vector similarity. Its Terraform resources mirror [DocumentsDB](/docs/tooling/terraform/resources/documentsdb), since the two products share one implementation. One difference matters. A VectorsDB collection has a required `dimension` and takes no typed attributes.

For full generated schemas, see the Terraform Registry: [vectorsdb](https://registry.terraform.io/providers/appwrite/appwrite/latest/docs/resources/vectorsdb), [vectorsdb_collection](https://registry.terraform.io/providers/appwrite/appwrite/latest/docs/resources/vectorsdb_collection), [vectorsdb_index](https://registry.terraform.io/providers/appwrite/appwrite/latest/docs/resources/vectorsdb_index), and [vectorsdb_document](https://registry.terraform.io/providers/appwrite/appwrite/latest/docs/resources/vectorsdb_document).

# Resources

| Resource | Purpose |
|----------|---------|
| `appwrite_vectorsdb` | Create a VectorsDB database in your project |
| `appwrite_vectorsdb_collection` | Create a collection of fixed-dimension embeddings |
| `appwrite_vectorsdb_index` | Index one or more document attributes |
| `appwrite_vectorsdb_document` | Manage seed and reference embeddings |

# Data sources

| Data source | Purpose |
|-------------|---------|
| `appwrite_vectorsdb` | Look up a database by ID |
| `appwrite_vectorsdb_specifications` | List the compute specifications your billing plan allows |

**VectorsDB has its own API key scopes**

VectorsDB does not use the TablesDB scopes (`tables.*`, `rows.*`) or the deprecated `collections.*` and `documents.*` ones. Give the key `vectorsdb.read` and `vectorsdb.write` for databases, `vectorsdb.collections.read` and `vectorsdb.collections.write` for collections and indexes, and `vectorsdb.documents.read` and `vectorsdb.documents.write` for documents.

# Creating a database

```hcl
resource "appwrite_vectorsdb" "main" {
  name = "embeddings"
}
```

Setting `specification` places the database on dedicated infrastructure reserved for your project, which is billed separately. Size it from the VectorsDB catalog, since each product publishes its own:

```hcl
data "appwrite_vectorsdb_specifications" "available" {}

output "available_specifications" {
  value = [
    for s in data.appwrite_vectorsdb_specifications.available.specifications :
    { slug = s.slug, cpu = s.cpu, memory = s.memory, price = s.price }
    if s.enabled
  ]
}

resource "appwrite_vectorsdb" "production" {
  name          = "embeddings"
  specification = "s-2vcpu-4gb"
  replicas      = 1
  sync_mode     = "sync"
}
```

Set the slug you want rather than deriving one from the catalog output. For a `precondition` that fails the plan when a slug is not enabled on your billing plan, see [asserting a specification at plan time](/docs/tooling/terraform/resources/dedicated-databases#sizing-from-the-specifications-data-source).

**Some deployments require a specification**

Omitting `specification` runs the database on the deployment's shared pool. Not every deployment has one configured. Where none is, the API rejects creation with `dedicated_database_required`, and `specification` becomes required.

`replicas` does not count the primary, and `sync_mode` (`async`, `sync`, or `quorum`) applies only when `replicas` is greater than 0. Creating a database with a dedicated backing waits for that backing to finish provisioning. Read-only attributes report `type`, `engine`, `status`, `created_at`, and `updated_at`. `engine` and `status` are empty when there is no dedicated backing.

# Collections

`dimension` is required and must match the model producing your embeddings. `text-embedding-3-small` emits 1536 values, for example. Changing it later re-indexes the collection.

```hcl
resource "appwrite_vectorsdb_collection" "articles" {
  database_id = appwrite_vectorsdb.main.id
  id          = "article-embeddings"
  name        = "Article embeddings"
  dimension   = 1536

  permissions       = ["read(\"any\")"]
  document_security = true
}
```

VectorsDB collections take no typed attribute definitions. `attributes` is read-only here, and exists so both products share one state shape. In the other direction, the provider rejects `dimension` at plan time on a DocumentsDB collection.

# Indexes

```hcl
resource "appwrite_vectorsdb_index" "by_source" {
  database_id   = appwrite_vectorsdb.main.id
  collection_id = appwrite_vectorsdb_collection.articles.id
  key           = "by_source"
  type          = "key"
  attributes    = ["source_id"]
}
```

`orders` (`ASC` or `DESC`) and `lengths` are positional, matching `attributes` entry for entry. Indexes have no update route, so changing any argument replaces the index. Terraform waits for a new index to become available, and `status` reports `available`, `processing`, `deleting`, `stuck`, or `failed`.

# Documents

A VectorsDB document carries its embedding, which must have exactly the collection's `dimension` values.

```hcl
resource "appwrite_vectorsdb_collection" "toy" {
  database_id = appwrite_vectorsdb.main.id
  name        = "Toy embeddings"
  dimension   = 4
}

resource "appwrite_vectorsdb_document" "seed" {
  database_id   = appwrite_vectorsdb.main.id
  collection_id = appwrite_vectorsdb_collection.toy.id
  id            = "seed"

  data = jsonencode({
    embedding = [0.1, 0.2, 0.3, 0.4]
    source_id = "article-1"
  })
}
```

Embeddings come from a model, so your application normally writes them rather than pinning them in configuration. Keep this resource for seed and reference records. Terraform tracks only the keys present in `data`, so fields written by other clients do not show as drift.

# Looking up a database

```hcl
data "appwrite_vectorsdb" "existing" {
  id = "embeddings"
}

resource "appwrite_vectorsdb_collection" "example" {
  database_id = data.appwrite_vectorsdb.existing.id
  name        = "Example"
  dimension   = 1536
}
```

# Importing

```bash
terraform import appwrite_vectorsdb.main <database-id>
terraform import appwrite_vectorsdb_collection.articles <database-id>/<collection-id>
terraform import appwrite_vectorsdb_index.by_source <database-id>/<collection-id>/<key>
terraform import appwrite_vectorsdb_document.seed <database-id>/<collection-id>/<document-id>
```

# Related

- [DocumentsDB](/docs/tooling/terraform/resources/documentsdb): the same shape, for schemaless JSON
- [Dedicated databases](/docs/tooling/terraform/resources/dedicated-databases): PostgreSQL, MySQL, and MongoDB
- [TablesDB](/docs/tooling/terraform/resources/databases): the relational product on shared infrastructure
- [Configuration](/docs/tooling/terraform/provider): authentication and endpoints