Weaviate's integration with Voyage AI's APIs allows you to access their models' capabilities directly from Weaviate.

[Configure a Weaviate vector index](#configure-the-vectorizer) to use an Voyage AI embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Voyage AI 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_voyageai_embedding.png)

## Requirements

### Weaviate configuration

Your Weaviate instance must be configured with the Voyage AI vectorizer integration (`text2vec-voyageai`) module.

:::accordion{title="For Weaviate Cloud (WCD) users"}
This integration is enabled by default on Weaviate Cloud (WCD) instances.
:::

:::accordion{title="For self-hosted users"}
- Check the [cluster metadata](../monitoring-and-logging/status.md#cluster-metadata) to verify if the module is enabled.
- Follow the [how-to configure modules](../how-to-configure-weaviate/modules.md) guide to enable the module in Weaviate.
:::

### API credentials

You must provide a valid Voyage AI API key to Weaviate for this integration. Go to [Voyage AI](https://www.voyageai.com/) to sign up and obtain an API key.

Provide the API key to Weaviate using one of the following methods:

- Set the `VOYAGEAI_APIKEY` environment variable that is available to Weaviate.
- Provide the API key at runtime, as shown in the examples below.

:::code-group{sync="languages"}
```python title="Python"
# Recommended: save sensitive data as environment variables
voyageai_key = os.getenv("VOYAGEAI_API_KEY")
```

```typescript title="JavaScript/TypeScript"
const voyageaiApiKey = process.env.VOYAGEAI_API_KEY || '';  // Replace with your inference API key
```

```goraw title="Go"
"X-VoyageAI-Api-Key": os.Getenv("VOYAGEAI_API_KEY"),
```
:::

## Configure the vectorizer

[Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) as follows to use a Voyage AI embedding 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_voyageai(
            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.text2VecVoyageAI({
      name: 'title_vector',
      sourceProperties: ['title'],
    },
    ),
  ],
  // Additional parameters not shown
});
```

```goraw title="Go" {1-19}
// Define the collection
basicVoyageVectorizerDef := &models.Class{
  Class: "DemoCollection",
  VectorConfig: map[string]models.VectorConfig{
    "title_vector": {
      Vectorizer: map[string]interface{}{
        "text2vec-voyageai": map[string]interface{}{
          "properties": []string{"title"},
        },
      },
    },
  },
}

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

### 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_voyageai(
            name="title_vector",
            source_properties=["title"],
            model="voyage-3-lite"
        )
    ],
    # 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.text2VecVoyageAI({
      name: 'title_vector',
      sourceProperties: ['title'],
      model: 'voyage-3-lite',
    }),
  ],
  // Additional parameters not shown
});
```

```goraw title="Go" {1-20}
// Define the collection
voyageVectorizerWithModelDef := &models.Class{
  Class: "DemoCollection",
  VectorConfig: map[string]models.VectorConfig{
    "title_vector": {
      Vectorizer: map[string]interface{}{
        "text2vec-voyageai": map[string]interface{}{
          "properties": []string{"title"},
          "model":      "voyage-3-lite",
        },
      },
    },
  },
}

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

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

The following examples show how to configure Voyage AI-specific options.

:::code-group{sync="languages"}
```python title="Python" {5-15}
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_voyageai(
            name="title_vector",
            source_properties=["title"],
            # Further options
            # model="voyage-3.5-lite"
            # base_url="<custom_voyageai_url>",
            # truncate=True,
            # dimensions=512,
        )
    ],
    # Additional parameters not shown
)
```

```typescript title="JavaScript/TypeScript" {9-20}
await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectors.text2VecVoyageAI({
      name: 'title_vector',
      sourceProperties: ['title'],
      // Further options
      // model: 'voyage-3.5-lite',
      // baseURL: '<custom_voyageai_url>',
      // truncate: true
      // dimensions: 512
    },
    ),
  ],
  // Additional parameters not shown
});
```

```goraw title="Go" {1-22}
// Define the collection
voyageVectorizerFullDef := &models.Class{
  Class: "DemoCollection",
  VectorConfig: map[string]models.VectorConfig{
    "title_vector": {
      Vectorizer: map[string]interface{}{
        "text2vec-voyageai": map[string]interface{}{
          "properties": []string{"title"},
          // "model":            "voyage-3-lite",
          // "base_url":         "<custom-voyageai-url>",
          // "truncate":         true,
        },
      },
    },
  },
}

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

For further details on model parameters, see the [Voyage AI Embedding API documentation](https://docs.voyageai.com/docs/embeddings).

## Header parameters

You can provide the API key as well as some optional parameters at runtime through additional headers in the request. The following headers are available:

- `X-VoyageAI-Api-Key`: The Voyage AI API key.
- `X-VoyageAI-Baseurl`: The base URL to use (e.g. a proxy) instead of the default Voyage AI URL.

Any additional headers provided at runtime will override the existing Weaviate configuration.

Provide the headers as shown in the [API credentials examples](#api-credentials) above.

## 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)
      }
    }
  }
}
```
:::

:::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 Voyage AI model.

![Embedding integration at search illustration](/assets/docs/weaviate/model-providers/_includes/integration_voyageai_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)
```
:::

### 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)
```
:::

## References

### Available models

- voyage-4
- voyage-4-lite
- voyage-4-large
- voyage-3.5
- voyage-3.5-lite
- voyage-context-3 (contextual embeddings)
- voyage-3-large
- voyage-3 (default)
- voyage-3-lite
- voyage-large-2 (default for <= `v1.24.24`, `v1.25.17`, `v1.26.4`)
- voyage-code-2
- voyage-2
- voyage-law-2
- voyage-large-2-instruct
- voyage-finance-2
- voyage-multilingual-2

:::callout{intent="note" title="Contextual embeddings"}
The `voyage-context-3` model uses Voyage AI's [contextual embeddings API](https://docs.voyageai.com/docs/contextualized-chunk-embeddings). When you configure this model, Weaviate automatically routes requests to the `/contextualizedembeddings` endpoint. This model is optimized for retrieval-augmented generation (RAG) use cases where document context improves retrieval quality.
:::

:::accordion{title="Model support history"}
- `v1.36`:
  - Added `voyage-4`, `voyage-4-lite`, `voyage-4-large`
- Added `voyage-3.5`, `voyage-3.5-lite`, `voyage-context-3`
- `v1.24.25`, `v1.25.18`, `v1.26.5`:
  - Added `voyage-3`, `voyage-3-lite`
  - Default model changed to `voyage-3` from `voyage-large-2`
- `v1.24.14`, `v1.25.1`:
  - Added `voyage-large-2-instruct`
  - Removed `voyage-lite-02-instruct`
- `v1.24.9`:
  - Added `voyage-law-2`, `voyage-lite-02-instruct`
- `v1.24.2`:
  - Introduced `text2vec-voyage`, with `voyage-large-2`, `voyage-code-2`, `voyage-2` support
:::

## Further resources

### Other integrations

- [Voyage AI multimodal embedding models + Weaviate](voyageai-embeddings-multimodal.md).
- [Voyage AI reranker models + Weaviate](voyageai-reranker.md).

### 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.

### External resources

- Voyage AI [Embeddings API documentation](https://docs.voyageai.com/docs/embeddings)

## 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`.