---
title: "AWS Generative AI with Weaviate"
description: "AWS Generative Model Provider"
url: https://docs.weaviate.io/weaviate/model-providers/aws/generative
---

# AWS Generative AI with Weaviate

Weaviate's integration with AWS's [SageMaker](https://aws.amazon.com/sagemaker/) and [Bedrock](https://aws.amazon.com/bedrock/) APIs allows you to access their models' capabilities directly from Weaviate.

[Configure a Weaviate collection](https://docs.weaviate.io/weaviate/model-providers/aws/generative#configure-collection) to use a generative AI model with AWS. Weaviate will perform retrieval augmented generation (RAG) using the specified model and your AWS API credentials.

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

![RAG integration illustration](https://docs.weaviate.io/assets/images/integration_aws_rag-b5fc747d33ccaa80665e0be5f175afd6.png)

## Requirements

### Weaviate configuration

Your Weaviate instance must be configured with the AWS generative AI integration (`generative-aws`) 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 [access key based AWS credentials](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html) to Weaviate for these integrations. Go to [AWS](https://aws.amazon.com/) to sign up and obtain an AWS access key ID and a corresponding AWS secret access key.

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

- Set the `AWS_ACCESS_KEY` and `AWS_SECRET_KEY` environment variables that are available to Weaviate.
- Provide the API credentials 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
aws_access_key = os.getenv("AWS_ACCESS_KEY")
aws_secret_key = os.getenv("AWS_SECRET_KEY")
headers = {
    "X-AWS-Access-Key": aws_access_key,
    "X-AWS-Secret-Key": aws_secret_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 aws_access_key = process.env.AWS_ACCESS_KEY || '';  // Replace with your AWS access key
const aws_secret_key = process.env.AWS_SECRET_KEY || '';  // Replace with your AWS secret key

const client = await weaviate.connectToWeaviateCloud(
  'WEAVIATE_INSTANCE_URL',  // Replace with your instance URL
  {
    authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_API_KEY'),
    headers: {
      'X-AWS-Access-Key': aws_access_key,
      'X-AWS-Secret-Key': aws_secret_key,
    }
  }
)

// Work with Weaviate

client.close()
```

### AWS model access

#### Bedrock

To use a model via [Bedrock](https://aws.amazon.com/bedrock/), it must be available, and AWS must grant you access to it.

Refer to the [AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) for the list of available models, and to [this document](https://docs.aws.amazon.com/bedrock/latest/userguide/model-usage.html) to find out how request access to a model.

#### SageMaker

To use a model via [SageMaker](https://aws.amazon.com/sagemaker/), you must have access to the model's endpoint.

## 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 AWS generative model:

### Bedrock

For Bedrock, you must provide the model name in the generative AI configuration.

**Python**

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

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.aws(
        region="us-east-1",
        service="bedrock",
        model="cohere.command-r-plus-v1:0"
    )
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.aws({
    region: 'us-east-1',
    service: 'bedrock',
    model: 'cohere.command-r-plus-v1:0',
  }),
})
```

### SageMaker

For SageMaker, you must provide the endpoint address in the generative AI configuration.

**Python**

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

client.collections.create(
    "DemoCollection",
    generative_config=Configure.Generative.aws(
        region="us-east-1",
        service="sagemaker",
        endpoint="<custom_sagemaker_url>"
    )
)
```

**JavaScript/TypeScript**

```ts
await client.collections.create({
  name: 'DemoCollection',
  generative: weaviate.configure.generative.aws({
    region: 'us-east-1',
    service: 'sagemaker',
    endpoint: '<custom_sagemaker_url>'
  }),
})
```

You can [specify](https://docs.weaviate.io/weaviate/model-providers/aws/generative#generative-parameters) which [model](https://docs.weaviate.io/weaviate/model-providers/aws/generative#available-models) Weaviate uses.

### Generative parameters

For further details on model parameters, see the [relevant AWS documentation](https://docs.weaviate.io/weaviate/model-providers/aws/generative#further-resources).

## 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.aws(
        region="us-east-1",
        service="bedrock", # You can also use sagemaker
        model="cohere.command-r-plus-v1:0"
    ),
    # 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.aws({
    region: 'us-east-1',
    service: 'bedrock', // You can also use sagemaker
    model: 'cohere.command-r-plus-v1:0',
  }),
}, {
  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/aws/generative#single-prompt) or [grouped task](https://docs.weaviate.io/weaviate/model-providers/aws/generative#grouped-task) method.

### Single prompt

![Single prompt RAG integration generates individual outputs per search result](https://docs.weaviate.io/assets/images/integration_aws_rag_single-5712810b2933bea42af13e7bf9248cc1.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_aws_rag_grouped-4783a4946a74639059fb82f476a5fbc8.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']);
}
```

### RAG with images

You can also supply images as a part of the input when performing retrieval augmented generation in both single prompts and grouped tasks.

**Python**

```py
import base64
import requests
from weaviate.classes.generate import GenerativeConfig, GenerativeParameters

src_img_path = "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Winter_forest_silver.jpg/960px-Winter_forest_silver.jpg"
base64_image = base64.b64encode(requests.get(src_img_path).content).decode('utf-8')

prompt = GenerativeParameters.grouped_task(
    prompt="Which movie is closest to the image in terms of atmosphere",
    images=[base64_image],      # A list of base64 encoded strings of the image bytes
    # image_properties=["img"], # Properties containing images in Weaviate
)

jeopardy = client.collections.use("DemoCollection")
response = jeopardy.generate.near_text(
    query="Movies",
    limit=5,
    grouped_task=prompt,
    generative_provider=GenerativeConfig.aws(
        region="us-east-1",
        service="bedrock", # You can also use sagemaker
        model="cohere.command-r-plus-v1:0"
    ),
)

# Print the source property and the generated response
for o in response.objects:
    print(f"Title property: {o.properties['title']}")
print(f"Grouped task result: {response.generative.text}")
```

**JavaScript/TypeScript**

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

function arrayBufferToBase64(buffer: ArrayBuffer): string {
  const bytes = new Uint8Array(buffer);
  let binary = '';
  const chunkSize = 1024; // Process in chunks to avoid call stack issues

  for (let i = 0; i < bytes.length; i += chunkSize) {
    const chunk = bytes.slice(i, Math.min(i + chunkSize, bytes.length));
    binary += String.fromCharCode.apply(null, Array.from(chunk));
  }

  return btoa(binary);
}

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

const srcImgPath = "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Winter_forest_silver.jpg/960px-Winter_forest_silver.jpg"
const responseImg = await fetch(srcImgPath);
const image = await responseImg.arrayBuffer();

const base64String = arrayBufferToBase64(image);

const prompt = {
  prompt: "Which movie is closest to the image in terms of atmosphere",
  images: [base64String],      // A list of base64 encoded strings of the image bytes
  // imageProperties: ["img"], // Properties containing images in Weaviate
}

response = await myCollection.generate.nearText("Movies", {
  groupedTask: prompt,
  config: generativeParameters.aws({
    region: 'us-east-1',
    service: 'bedrock', // You can also use sagemaker
    model: 'cohere.command-r-plus-v1:0',
  }),
}, {
  limit: 5,
})

// Print the source property and the generated response
for (const item of response.objects) {
  console.log("Title property:", item.properties['title'])
}

console.log("Grouped task result:", response.generative?.text)
```

## References

### Available models

#### Bedrock

Weaviate passes the `model` value through to Amazon Bedrock, so any Bedrock text generation model that your AWS account and region has access to can be used. Weaviate recognizes the model families offered by AI21 Labs, Amazon (Titan and Nova), Anthropic, Cohere, Meta, and Mistral AI, including their cross-region inference profile IDs.

For the current model IDs, see the [Amazon Bedrock supported foundation models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) documentation. Refer to [this document](https://docs.aws.amazon.com/bedrock/latest/userguide/model-usage.html) to find out how to request access to a model.

#### SageMaker

Any custom SageMaker URL can be used as an endpoint.

## Further resources

### Other integrations

- [AWS embedding models + Weaviate](https://docs.weaviate.io/weaviate/model-providers/aws/embeddings).

### 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

- AWS [Bedrock documentation](https://docs.aws.amazon.com/bedrock/)
- AWS [SageMaker documentation](https://docs.aws.amazon.com/sagemaker/)