Weaviate Cloud only

[Configure a Weaviate vector index](#configure-the-vectorizer) to use a Weaviate Embeddings model, and Weaviate will generate embeddings for various operations using the specified model and your Weaviate API key. This feature is called the _vectorizer_.

At [import time](#data-import), Weaviate generates text object embeddings and saves them into the index. For [vector](#vector-near-text-search) and [hybrid](#hybrid-search) search operations, Weaviate converts text queries into embeddings.

![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_wes_embedding.png)

## Requirements

To use Weaviate Embeddings, you need a Weaviate Cloud instance with a Weaviate client library that supports Weaviate Embeddings.

:::callout{intent="info" title="Cloud only"}
Weaviate Embeddings vectorizers are not available for self-hosted users.
:::

### API credentials

Your Weaviate Cloud credentials are automatically used to authorize your access to Weaviate Embeddings.

:::code-group{sync="languages"}
```python title="Python"
import weaviate
from weaviate.classes.init import Auth
import os

# Best practice: store your credentials in environment variables
weaviate_url = os.getenv("WEAVIATE_URL")
weaviate_key = os.getenv("WEAVIATE_API_KEY")

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,                     # Weaviate URL: "REST Endpoint" in Weaviate Cloud console
    auth_credentials=Auth.api_key(weaviate_key),  # Weaviate API key: "ADMIN" API key in Weaviate Cloud console
)

print(client.is_ready())  # Should print: `True`

# Work with Weaviate

client.close()
```

```typescript title="JavaScript/TypeScript"
import weaviate from 'weaviate-client'

// Best practice: store your credentials in environment variables
const weaviateUrl = process.env.WEAVIATE_URL as string;        // Weaviate URL: "REST Endpoint" in Weaviate Cloud console
const weaviateApiKey = process.env.WEAVIATE_API_KEY as string; // Weaviate API key: "ADMIN" API key in Weaviate Cloud console

const client = await weaviate.connectToWeaviateCloud(
  weaviateUrl,  
  {
    authCredentials: new weaviate.ApiKey(weaviateApiKey),  
  }
)

// Work with Weaviate

client.close()
```

```goraw title="Go"
```

```java title="Java" {5-10}
// Best practice: store your credentials in environment variables
String weaviateUrl = System.getenv("WEAVIATE_URL");
String weaviateApiKey = System.getenv("WEAVIATE_API_KEY");

WeaviateClient client = WeaviateClient.connectToWeaviateCloud(weaviateUrl, // Replace with your
    // Weaviate Cloud URL
    weaviateApiKey // Replace with your Weaviate Cloud key
);

System.out.println(client.isReady()); // Should print: `True`

client.close(); // Free up resources
```

```csharp title="C#" {5-11}
// Best practice: store your credentials in environment variables
string weaviateUrl = Environment.GetEnvironmentVariable("WEAVIATE_URL");
string weaviateApiKey = Environment.GetEnvironmentVariable("WEAVIATE_API_KEY");

using var client = await Connect.Cloud(
    weaviateUrl, // Replace with your Weaviate Cloud URL
    weaviateApiKey // Replace with your Weaviate Cloud key
);

var meta = await client.GetMeta();
Console.WriteLine(meta.Version);
```
:::

## Configure the vectorizer

[Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) as follows to use a Weaviate Embeddings model:

:::code-group{sync="languages"}
```python title="Python" {5-10}
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_weaviate(
            name="title_vector",
            source_properties=["title"]
        )
    ],
    # Additional parameters not shown
)
```

```typescript title="JavaScript/TypeScript" {9-15}
await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectors.text2VecWeaviate({
        name: 'title_vector',
        sourceProperties: ['title'],
      },
    ),
  ],
  // Additional parameters not shown
});
```

```goraw title="Go" {1-17}
// Define the collection
basicWeaviateVectorizerDef := &models.Class{
  Class: "DemoCollection",
  VectorConfig: map[string]models.VectorConfig{
    "title_vector": {
      Vectorizer: map[string]interface{}{
        "text2vec-weaviate": map[string]interface{}{},
      },
    },
  },
}

// add the collection
err = client.Schema().ClassCreator().WithClass(basicWeaviateVectorizerDef).Do(ctx)
if err != nil {
  panic(err)
}
```

```java title="Java"
client.collections.create("DemoCollection",
    col -> col
        .vectorConfig(
            VectorConfig.text2vecWeaviate("title_vector", c -> c.sourceProperties("title")))
        .properties(Property.text("title"), Property.text("description")));
```

```csharp title="C#"
await client.Collections.Create(
    new CollectionCreateParams
    {
        Name = "DemoCollection",
        VectorConfig = new VectorConfigList
        {
            Configure.Vector(
                "title_vector",
                v => v.Text2VecWeaviate(),
                sourceProperties: ["title"]
            ),
        },
        Properties = [Property.Text("title"), Property.Text("description")],
    }
);
```
:::

### Select a model

You can specify one of the [available models](#available-models) for the vectorizer to use, as shown in the following configuration example.

:::code-group{sync="languages"}
```python title="Python" {5-11}
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_weaviate(
            name="title_vector",
            source_properties=["title"],
            model="Snowflake/snowflake-arctic-embed-l-v2.0"
        )
    ],
    # Additional parameters not shown
)
```

```typescript title="JavaScript/TypeScript" {9-15}
await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectors.text2VecWeaviate({
      name: 'title_vector',
      sourceProperties: ['title'],
      model: 'Snowflake/snowflake-arctic-embed-l-v2.0',
    }),
  ],
  // Additional parameters not shown
});
```

```goraw title="Go" {1-19}
// Define the collection
weaviateVectorizerWithModelDef := &models.Class{
  Class: "DemoCollection",
  VectorConfig: map[string]models.VectorConfig{
    "title_vector": {
      Vectorizer: map[string]interface{}{
        "text2vec-weaviate": map[string]interface{}{
          "model": "arctic-embed-l-v2.0",
        },
      },
    },
  },
}

// add the collection
err = client.Schema().ClassCreator().WithClass(weaviateVectorizerWithModelDef).Do(ctx)
if err != nil {
  panic(err)
}
```

```java title="Java"
client.collections
    .create("DemoCollection",
        col -> col
            .vectorConfig(VectorConfig.text2vecWeaviate("title_vector",
                c -> c.sourceProperties("title")
                    .model("Snowflake/snowflake-arctic-embed-l-v2.0")))
            .properties(Property.text("title"), Property.text("description")));
```

```csharp title="C#"
await client.Collections.Create(
    new CollectionCreateParams
    {
        Name = "DemoCollection",
        VectorConfig = new VectorConfigList
        {
            Configure.Vector(
                "title_vector",
                v => v.Text2VecWeaviate(model: "Snowflake/snowflake-arctic-embed-l-v2.0"),
                sourceProperties: ["title"]
            ),
        },
        Properties = [Property.Text("title"), Property.Text("description")],
    }
);
```
:::

You can [specify](#vectorizer-parameters) one of the [available models](#available-models) for Weaviate to use. The [default model](#available-models) is used if no model is specified.

:::accordion{title="Vectorization behavior"}
Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects.

Unless specified otherwise in the collection definition, the default behavior is to:

- Only vectorize properties that use the `text` or `text[]` data type (unless [skipped](../how-to-manage-collections/vector-config.md#property-level-settings))
- Sort properties in alphabetical (a-z) order before concatenating values
- If `vectorizePropertyName` is `true` (`false` by default) prepend the property name to each property value
- Join the (prepended) property values with spaces
- Prepend the class name (unless `vectorizeClassName` is `false`)
- Convert the produced string to lowercase

<!-- TODO: Add an actual example -->
:::

### Vectorizer parameters

- `model` (optional): The name of the model to use for embedding generation.
- `dimensions` (optional): The number of dimensions to use for the generated embeddings.
- `base_url` (optional): The base URL for the Weaviate Embeddings service. (Not required in most cases.)

The following examples show how to configure Weaviate Embeddings-specific options.

:::code-group{sync="languages"}
```python title="Python" {5-14}
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_weaviate(
            name="title_vector",
            source_properties=["title"],
            model="Snowflake/snowflake-arctic-embed-m-v1.5",
            # Further options
            # dimensions=256
            # base_url="<custom_weaviate_embeddings_url>",
        )
    ],
    # Additional parameters not shown
)
```

```typescript title="JavaScript/TypeScript" {9-19}
await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectors.text2VecWeaviate({
        name: 'title_vector',
        sourceProperties: ['title'],
        model: 'Snowflake/snowflake-arctic-embed-m-v1.5',
        // Further options
        // dimensions: 256,
        // baseURL: '<custom_weaviate_embeddings_url>',
      },
    ),
  ],
  // Additional parameters not shown
});
```

```goraw title="Go" {1-21}
// Define the collection
weaviateVectorizerArcticEmbedMV15 := &models.Class{
  Class: "DemoCollection",
  VectorConfig: map[string]models.VectorConfig{
    "title_vector": {
      Vectorizer: map[string]interface{}{
        "text2vec-weaviate": map[string]interface{}{
          "model":      "Snowflake/snowflake-arctic-embed-m-v1.5",
          "dimensions": 256, // Or 768
          "base_url":   "<custom_weaviate_url>",
        },
      },
    },
  },
}

// add the collection
err = client.Schema().ClassCreator().WithClass(weaviateVectorizerArcticEmbedMV15).Do(ctx)
if err != nil {
  panic(err)
}
```

```java title="Java"
client.collections.create("DemoCollection",
    col -> col.vectorConfig(VectorConfig.text2vecWeaviate("title_vector",
        c -> c.sourceProperties("title").model("Snowflake/snowflake-arctic-embed-m-v1.5")
    // .inferenceUrl(null)
    // .dimensions(0)
    )).properties(Property.text("title"), Property.text("description")));
```

```csharp title="C#"
await client.Collections.Create(
    new CollectionCreateParams
    {
        Name = "DemoCollection",
        VectorConfig = new VectorConfigList
        {
            Configure.Vector(
                "title_vector",
                v =>
                    v.Text2VecWeaviate(
                        model: "Snowflake/snowflake-arctic-embed-m-v1.5"
                    // baseURL: null,
                    // dimensions: 0
                    ),
                sourceProperties: ["title"]
            ),
        },
        Properties = [Property.Text("title"), Property.Text("description")],
    }
);
```
:::

## Data import

After configuring the vectorizer, [import data](../how-to-manage-objects/import.md) into Weaviate. Weaviate generates embeddings for text objects using the specified model.

:::code-group{sync="languages"}
```python title="Python" {13-20}
source_objects = [
    {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."},
    {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."},
    {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."},
    {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."},
    {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."}
]

collection = client.collections.use("DemoCollection")

with collection.batch.fixed_size(batch_size=200) as batch:
    for src_obj in source_objects:
        # The model provider integration will automatically vectorize the object
        batch.add_object(
            properties={
                "title": src_obj["title"],
                "description": src_obj["description"],
            },
            # vector=vector  # Optionally provide a pre-obtained vector
        )
        if batch.number_errors > 10:
            print("Batch import stopped due to excessive errors.")
            break

failed_objects = collection.batch.failed_objects
if failed_objects:
    print(f"Number of failed imports: {len(failed_objects)}")
    print(f"First failed object: {failed_objects[0]}")
```

```typescript title="JavaScript/TypeScript"
let srcObjects = [
  { title: "The Shawshank Redemption", description: "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places." },
  { title: "The Godfather", description: "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga." },
  { title: "The Dark Knight", description: "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City." },
  { title: "Jingle All the Way", description: "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve." },
  { title: "A Christmas Carol", description: "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption." }
];
```

```goraw title="Go" {9-44}
var sourceObjects = []map[string]string{
  {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."},
  {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."},
  {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."},
  {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."},
  {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."},
}

// Convert items into a slice of models.Object
objects := []models.PropertySchema{}
for i := range sourceObjects {
  objects = append(objects, map[string]interface{}{
    // Populate the object with the data
    "title":       sourceObjects[i]["title"],
    "description": sourceObjects[i]["description"],
  })
}

// Batch write items
batcher := client.Batch().ObjectsBatcher()
for _, dataObj := range objects {
  batcher.WithObjects(&models.Object{
    Class:      "DemoCollection",
    Properties: dataObj,
  })
}

// Flush
batchRes, err := batcher.Do(ctx)

// Error handling
if err != nil {
  panic(err)
}
for _, res := range batchRes {
  if res.Result.Errors != nil {
    for _, err := range res.Result.Errors.Error {
      if err != nil {
        fmt.Printf("Error details: %v\n", *err)
        panic(err.Message)
      }
    }
  }
}
```

```java title="Java"
// Define the source objects
List<Map<String, Object>> sourceObjects = List.of(Map.of("title", "The Shawshank Redemption",
    "description",
    "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."),
    Map.of("title", "The Godfather", "description",
        "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."),
    Map.of("title", "The Dark Knight", "description",
        "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."),
    Map.of("title", "Jingle All the Way", "description",
        "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."),
    Map.of("title", "A Christmas Carol", "description",
        "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."));

// Get a handle to the collection
CollectionHandle<Map<String, Object>> collection = client.collections.use("DemoCollection");

// Insert the data using insertMany
InsertManyResponse response = collection.data.insertMany(sourceObjects.toArray(new Map[0]));

// Check for errors
if (!response.errors().isEmpty()) {
  System.err.printf("Number of failed imports: %d\n", response.errors().size());
  System.err.printf("First failed object error: %s\n", response.errors().get(0));
} else {
  System.out.printf("Successfully inserted %d objects.\n", response.uuids().size());
}
```

```csharp title="C#"
// Define the source objects
var sourceObjects = new[]
{
    new
    {
        title = "The Shawshank Redemption",
        description = "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places.",
    },
    new
    {
        title = "The Godfather",
        description = "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga.",
    },
    new
    {
        title = "The Dark Knight",
        description = "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City.",
    },
    new
    {
        title = "Jingle All the Way",
        description = "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve.",
    },
    new
    {
        title = "A Christmas Carol",
        description = "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption.",
    },
};

// Get a handle to the collection
var collection = client.Collections.Use("DemoCollection");

// Insert the data using insertMany
var response = await collection.Data.InsertMany(sourceObjects);

// Check for errors
if (response.HasErrors)
{
    Console.WriteLine($"Number of failed imports: {response.Errors.Count()}");
    Console.WriteLine($"First failed object error: {response.Errors.First().Message}");
}
else
{
    Console.WriteLine($"Successfully inserted {response.Objects.Count()} objects.");
}
```
:::

:::callout{intent="tip" title="Re-use existing vectors"}
If you already have a compatible model vector available, you can provide it directly to Weaviate. This can be useful if you have already generated embeddings using the same model and want to use them in Weaviate, such as when migrating data from another system.
:::

## Searches

Once the vectorizer is configured, Weaviate will perform vector and hybrid search operations using the specified WED model.

![Embedding integration at search illustration](/assets/docs/weaviate/model-providers/_includes/integration_wes_embedding_search.png)

### Vector (near text) search

When you perform a [vector search](../how-to-query-search/similarity.md#search-with-text), Weaviate converts the text query into an embedding using the specified model and returns the most similar objects from the database.

The query below returns the `n` most similar objects from the database, set by `limit`.

:::code-group{sync="languages"}
```python title="Python" {3-6}
collection = client.collections.use("DemoCollection")

response = collection.query.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])
```

```typescript title="JavaScript/TypeScript"
const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)
```

```goraw title="Go" {1-9}
nearTextResponse, err := client.GraphQL().Get().
  WithClassName("DemoCollection").
  WithFields(
    graphql.Field{Name: "title"},
  ).
  WithNearText(client.GraphQL().NearTextArgBuilder().
    WithConcepts([]string{"A holiday film"})).
  WithLimit(2).
  Do(ctx)

if err != nil {
  panic(err)
}
fmt.Printf("%v", nearTextResponse)
```

```java title="Java" {3-4}
CollectionHandle<Map<String, Object>> collection = client.collections.use("DemoCollection");

var response = collection.query.nearText("A holiday film", // The model provider integration will automatically vectorize the query
    q -> q.limit(2).returnMetadata(Metadata.DISTANCE));

for (var o : response.objects()) {
  System.out.println(o.properties().get("title"));
}
```

```csharp title="C#" {3-7}
var collection = client.Collections.Use("DemoCollection");

var response = await collection.Query.NearText(
    "A holiday film", // The model provider integration will automatically vectorize the query
    limit: 2,
    returnMetadata: MetadataOptions.Distance
);

foreach (var o in response.Objects)
{
    Console.WriteLine(o.Properties["title"]);
}
```
:::

### Hybrid search

:::callout{intent="info" title="What is a hybrid search?"}
A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](../how-to-query-search/hybrid.md) to return the best matching objects from the database.
:::

When you perform a [hybrid search](../how-to-query-search/hybrid.md), Weaviate converts the text query into an embedding using the specified model and returns the best scoring objects from the database.

The query below returns the `n` best scoring objects from the database, set by `limit`.

:::code-group{sync="languages"}
```python title="Python" {3-6}
collection = client.collections.use("DemoCollection")

response = collection.query.hybrid(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])
```

```typescript title="JavaScript/TypeScript"
const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)
```

```goraw title="Go" {1-9}
hybridResponse, err := client.GraphQL().Get().
  WithClassName("DemoCollection").
  WithFields(
    graphql.Field{Name: "title"},
  ).
  WithHybrid(client.GraphQL().HybridArgumentBuilder().
    WithQuery("A holiday film")).
  WithLimit(2).
  Do(ctx)

if err != nil {
  panic(err)
}
fmt.Printf("%v", hybridResponse)
```

```java title="Java" {3-4}
CollectionHandle<Map<String, Object>> collection = client.collections.use("DemoCollection");

QueryResponse<Map<String, Object>> response = collection.query.hybrid("A holiday film", // The model provider integration will automatically vectorize the query
    q -> q.limit(2).returnMetadata(Metadata.DISTANCE));

for (var o : response.objects()) {
  System.out.println(o.properties().get("title"));
}
```

```csharp title="C#" {3-7}
var collection = client.Collections.Use("DemoCollection");

var response = await collection.Query.Hybrid(
    "A holiday film", // The model provider integration will automatically vectorize the query
    limit: 2,
    returnMetadata: MetadataOptions.Distance
);

foreach (var o in response.Objects)
{
    Console.WriteLine(o.Properties["title"]);
}
```
:::

## Available models

### `Snowflake/snowflake-arctic-embed-l-v2.0` (default)

- A 568M parameter, 1024-dimensional model for multilingual enterprise retrieval tasks.
- Trained with Matryoshka Representation Learning to allow vector truncation with minimal loss.
- Quantization-friendly: Using scalar quantization and 256 dimensions provides 99% of unquantized, full-precision performance.
- Read more at the [Snowflake blog](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0), and the Hugging Face [model card](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0)
- Allowable `dimensions`: 1024 (default), 256

***

### `Snowflake/snowflake-arctic-embed-m-v1.5`

- A 109M parameter, 768-dimensional model for enterprise retrieval tasks in English.
- Trained with Matryoshka Representation Learning to allow vector truncation with minimal loss.
- Quantization-friendly: Using scalar quantization and 256 dimensions provides 99% of unquantized, full-precision performance.
- Read more at the [Snowflake blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/), and the Hugging Face [model card](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5)
- Allowable `dimensions`: 768 (default), 256

:::callout{intent="info" title="Input truncation"}
Currently, input exceeding the model's context windows is truncated from the right (i.e. the end of the input).
:::

## Further resources

### Code examples

Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:

- The [How-to: Manage collections](../how-to-manage-collections/index.md) and [How-to: Manage objects](../how-to-manage-objects/index.md) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them).
- The [How-to: Query & Search](../how-to-query-search/index.md) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

### Multimodal embeddings

Looking to embed document images instead of text? See [Weaviate Embeddings: Multimodal](weaviate-embeddings-multimodal.md) for visual document retrieval without OCR or preprocessing.

### References

- Weaviate Embeddings [Documentation](../cloud-weaviate-embeddings/overview.md)
- Weaviate Embeddings [Models](../cloud-weaviate-embeddings/models.md)

### Pricing

Weaviate Embeddings models are charged based on token usage. For more pricing information, see the [Weaviate Cloud pricing page](https://weaviate.io/pricing).

## Questions and feedback

Have a question or feedback? Here's how to reach us.

::::card-grid
:::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"}
Ask questions and connect with other developers on our **Community forum**.
:::

:::card{title="Support" href="/guides/support-overview" icon="life-buoy"}
Weaviate Cloud user or customer? Find the right channel on the **Support page**.
:::
::::

## Related pages

- [Agents](./agents-index.md)
- [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md)
- [APIs](./apis-index.md)
- [Authorization and authentication](./authorization-and-authentication-index.md)
- [Benchmarks](./benchmarks-index.md)
- [Best practices](./best-practices-index.md)
- [Client libraries](./clients-index.md)
- [Client Libraries / SDKs](./client-libraries-index.md)
- [Cloud](./cloud-index.md)
- [Cloud account management](./cloud-account-management-index.md)

# Agent Instructions

This portal answers questions programmatically. To receive a synthesized,
source-cited answer instead of crawling page by page, append the `?ask=`
query parameter to any page URL on this site:

    /guides/quickstart?ask=how+do+I+authenticate

Optional parameters:

- `&goal=<what-you-are-trying-to-do>` steers the answer toward your
  objective (e.g. `&goal=write+a+python+client`).
- `&version=<label>` scopes the answer to a mounted version when the
  portal publishes more than one.

The response is `text/markdown`: the answer followed by a `# Sources` list
of the portal pages it was grounded in. Status codes are the contract:

- `200` — the answer; `402` — the portal owner’s plan or answer credits are
  exhausted (surface this to your operator; do NOT retry); `429` — you are
  rate-limited; back off for the `Retry-After` seconds; `503` — the answer
  lane is temporarily unavailable; fall back to crawling the `.md` pages.

For the full corpus map read `llms.txt` at the site root; for the tool
surface (search + page fetch as MCP tools) see `/mcp`.