---
title: "xAI Generative AI with Weaviate"
description: "Weaviate's integrations with xAI's API allows you to access their models' capabilities directly from Weaviate."
url: https://docs.weaviate.io/weaviate/model-providers/xai/generative
---

# xAI Generative AI with Weaviate

> [!NOTE]
>
> **Added in `v1.30.0`**

Weaviate's integrations with xAI's API allows you to access their models' capabilities directly from Weaviate.

[Configure a Weaviate collection](https://docs.weaviate.io/weaviate/model-providers/xai/generative#configure-collection) to use a generative AI model with xAI. Weaviate will perform Retrieval Augmented Generation (RAG) using the specified model using your xAI API Key.

More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the generative model on xAI to generate outputs.

![RAG integration illustration](https://docs.weaviate.io/assets/images/integration_xai_rag-1628af82ed840486a25f4dc8bb6897f2.png)

## Requirements

### Weaviate configuration

Your Weaviate instance must be configured with the xAI generative (`generative-xai`) module.

**For Weaviate Cloud (WCD) users**

This integration is enabled by default on Weaviate Cloud (WCD) instances.

**For self-hosted users**

- Check the [cluster metadata](https://docs.weaviate.io/deploy/configuration/status#cluster-metadata) to verify if the module is enabled.
- Follow the [how-to configure modules](https://docs.weaviate.io/weaviate/configuration/modules) guide to enable the module in Weaviate.

### API credentials

You must provide a valid API key to Weaviate for this integration. Go to [xAI](https://console.x.ai/) to sign up and obtain an API key.

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

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

**Python**

```py
import weaviate
from weaviate.classes.init import Auth
import os

# Recommended: save sensitive data as environment variables
xai_key = os.getenv("XAI_API_KEY")
headers = {
    "X-Xai-Api-Key": xai_key,
}

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,                       # `weaviate_url`: your Weaviate URL
    auth_credentials=Auth.api_key(weaviate_key),      # `weaviate_key`: your Weaviate API key
    headers=headers
)

# Work with Weaviate

client.close()
```

**JavaScript/TypeScript**

```ts
import weaviate from '@weaviate/node'

const xaiApiKey = process.env.XAI_API_KEY || '';  // Replace with your inference API key

const client = await weaviate.connectToWeaviateCloud(
  'WEAVIATE_INSTANCE_URL',  // Replace with your instance URL
  {
    authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_API_KEY'),
    headers: {
      'X-Xai-Api-Key': xaiApiKey,
    }
  }
)

// Work with Weaviate

client.close()
```

## Configure collection

> [!NOTE]
>
> **Generative model integration mutability**
>
> A collection's `generative` model integration configuration is mutable from `v1.25.23`, `v1.26.8` and `v1.27.1`. See [this section](https://docs.weaviate.io/weaviate/manage-collections/generative-reranker-models#update-the-generative-model-integration) for details on how to update the collection configuration.

[Configure a Weaviate index](https://docs.weaviate.io/weaviate/manage-collections/generative-reranker-models#specify-a-generative-model-integration) as follows to use an xAI generative AI model:

**Python**

```py
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.xai()
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.xai(),
  // Additional parameters not shown
});
```

### Select a model

You can specify one of the [available models](https://docs.weaviate.io/weaviate/model-providers/xai/generative#available-models) for Weaviate to use, as shown in the following configuration example:

**Python**

```py
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.xai(
        model="grok-2-latest"
    )
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.xai({
    model: 'grok-2-latest'
  }),
  // Additional parameters not shown
});
```

You can [specify](https://docs.weaviate.io/weaviate/model-providers/xai/generative#generative-parameters) one of the [available models](https://docs.weaviate.io/weaviate/model-providers/xai/generative#available-models) for Weaviate to use. The [default model](https://docs.weaviate.io/weaviate/model-providers/xai/generative#available-models) is used if no model is specified.

### Generative parameters

Configure the following generative parameters to customize the model behavior.

**Python**

```py
from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.xai(
        # # These parameters are optional
        # base_url="https://api.x.ai/v1"
        # model="grok-2-latest",
        # max_tokens=500,
        # temperature=0.7,
    )
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.xai({
    // // These parameters are optional
    // baseURL: 'https://api.x.ai/v1',
    // model: 'grok-2-latest',
    // maxTokens: 512,
    // temperature: 0.7,
  }),
  // Additional parameters not shown
});
```

For further details on model parameters, see the [xAI API documentation](https://docs.x.ai/docs/guides/chat#parameters).

## Select a model at runtime

Aside from setting the default model provider when creating the collection, you can also override it at query time.

**Python**

```py
from weaviate.classes.config import Configure
from weaviate.classes.generate import GenerativeConfig

collection = client.collections.use("DemoCollection")
response = collection.generate.near_text(
    query="A holiday film",
    limit=2,
    grouped_task="Write a tweet promoting these two movies",
    generative_provider=GenerativeConfig.xai(
        # # These parameters are optional
        # base_url="https://api.x.ai/v1"
        # model="grok-2-latest",
        # max_tokens=500,
        # temperature=0.7,
    ),
    # Additional parameters not shown
)
```

**JavaScript/TypeScript**

```ts
import { generativeParameters } from '@weaviate/node';

let response;
const myCollection = client.collections.use("DemoCollection");

response = await myCollection.generate.nearText("A holiday film", {
  groupedTask: "Write a tweet promoting these two movies",
  config: generativeParameters.xai({
    // These parameters are optional
    // baseURL: 'https://api.x.ai/v1',
    // model: 'grok-2-latest',
    // maxTokens: 512,
    // temperature: 0.7,
  }),
}, {
  limit: 2,
}
  // Additional parameters not shown
);
```

## Retrieval augmented generation

After configuring the generative AI integration, perform RAG operations, either with the [single prompt](https://docs.weaviate.io/weaviate/model-providers/xai/generative#single-prompt) or [grouped task](https://docs.weaviate.io/weaviate/model-providers/xai/generative#grouped-task) method.

### Single prompt

![Single prompt RAG integration generates individual outputs per search result](https://docs.weaviate.io/assets/images/integration_xai_rag-1628af82ed840486a25f4dc8bb6897f2.png)

To generate text for each object in the search results, use the single prompt method.

The example below generates outputs for each of the `n` search results, where `n` is specified by the `limit` parameter.

When creating a single prompt query, use braces `{}` to interpolate the object properties you want Weaviate to pass on to the language model. For example, to pass on the object's `title` property, include `{title}` in the query.

**Python**

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

response = collection.generate.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    single_prompt="Translate this into French: {title}",
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])
    print(f"Generated output: {obj.generated}")  # Note that the generated output is per object
```

**JavaScript/TypeScript**

```ts
let response;
const myCollection = client.collections.use("DemoCollection");

let myCollection = client.collections.use('DemoCollection');

const singlePromptResults = await myCollection.generate.nearText('A holiday film', {
  singlePrompt: `Translate this into French: {title}`,
}, {
  limit: 2,
});

for (const obj of singlePromptResults.objects) {
  console.log(obj.properties['title']);
  console.log(`Generated output: ${obj.generative?.text}`);  // Note that the generated output is per object
}
```

### Grouped task

![Grouped task RAG integration generates one output for the set of search results](https://docs.weaviate.io/assets/images/integration_xai_rag-1628af82ed840486a25f4dc8bb6897f2.png)

To generate one text for the entire set of search results, use the grouped task method.

In other words, when you have `n` search results, the generative model generates one output for the entire group.

**Python**

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

response = collection.generate.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    grouped_task="Write a fun tweet to promote readers to check out these films.",
    limit=2
)

print(f"Generated output: {response.generative.text}")  # Note that the generated output is per query
for obj in response.objects:
    print(obj.properties["title"])
```

**JavaScript/TypeScript**

```ts
let response;
const myCollection = client.collections.use("DemoCollection");

let myCollection = client.collections.use('DemoCollection');

const groupedTaskResults = await myCollection.generate.nearText('A holiday film', {
  groupedTask: `Write a fun tweet to promote readers to check out these films.`,
}, {
  limit: 2,
});

console.log(`Generated output: ${groupedTaskResults.generative?.text}`);  // Note that the generated output is per query
for (const obj of groupedTaskResults.objects) {
  console.log(obj.properties['title']);
}
```

## References

### Available models

You can use any generative model [on xAI's API](https://docs.x.ai/docs/models) with Weaviate.

The default model is `grok-2-latest`.

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

### References

- [xAI API Documentation](https://docs.x.ai/docs/introduction)