AWS Generative AI with Weaviate

Weaviate's integration with AWS's SageMaker and Bedrock APIs allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate collection to use a AWS generative AI model, and 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.

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) serverless instances.

For self-hosted users

• Check the cluster metadata to verify if the module is enabled.
• Follow the how-to configure modules guide to enable the module in Weaviate.

API credentials

You must provide access key based AWS credentials to Weaviate for these integrations. Go to AWS 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 API v4

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()

  API docs

JS/TS API v3

import weaviate from 'weaviate-client'

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_APIKEY'),
    headers: {
      'X-AWS-Access-Key': aws_access_key,
      'X-AWS-Secret-Key': aws_secret_key,
    }
  }
)

// Work with Weaviate

client.close()

  API docs

AWS model access

Bedrock

To use a model via Bedrock, it must be available, and AWS must grant you access to it.

Refer to the AWS documentation for the list of available models, and to this document to find out how request access to a model.

SageMaker

To use a model via SageMaker, you must have access to the model's endpoint.

Configure collection

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 for details on how to update the collection configuration.

Configure a Weaviate index as follows to use an AWS generative model:

Bedrock

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

Python API v4

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"
    )
)

  API docs

JS/TS API v3

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

  API docs

SageMaker

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

Python API v4

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>"
    )
)

  API docs

JS/TS API v3

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

  API docs

You can specify one of the available models for Weaviate to use. The default model is used if no model is specified.

Generative parameters

For further details on model parameters, see the relevant AWS documentation.

Retrieval augmented generation

After configuring the generative AI integration, perform RAG operations, either with the single prompt or grouped task method.

Single prompt

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 API v4

collection = client.collections.get("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

  API docs

JS/TS API v3

let myCollection = client.collections.get('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.generated}`);  // Note that the generated output is per object
}

  API docs

Grouped task

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 API v4

collection = client.collections.get("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.generated}")  # Note that the generated output is per query
for obj in response.objects:
    print(obj.properties["title"])

  API docs

JS/TS API v3

let myCollection = client.collections.get('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.generated}`);  // Note that the generated output is per query
for (const obj of groupedTaskResults.objects) {
  console.log(obj.properties['title']);
}

  API docs

References

Available models

Bedrock

• ai21.j2-ultra-v1
• ai21.j2-mid-v1
• amazon.titan-text-lite-v1
• amazon.titan-text-express-v1
• amazon.titan-text-premier-v1:0
• anthropic.claude-v2
• anthropic.claude-v2:1
• anthropic.claude-instant-v1
• anthropic.claude-3-sonnet-20240229-v1:0
• anthropic.claude-3-haiku-20240307-v1:0
• cohere.command-text-v14
• cohere.command-light-text-v14
• cohere.command-r-v1:0
• cohere.command-r-plus-v1:0
• meta.llama3-8b-instruct-v1:0
• meta.llama3-70b-instruct-v1:0
• meta.llama2-13b-chat-v1
• meta.llama2-70b-chat-v1
• mistral.mistral-7b-instruct-v0:2
• mistral.mixtral-8x7b-instruct-v0:1
• mistral.mistral-large-2402-v1:0

Refer to the this document to find out how request access to a model.

SageMaker

Any custom SageMaker URL can be used as an endpoint.

Further resources

Other integrations

• AWS embedding models + Weaviate.

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 data guides show how to perform data operations (i.e. create, update, delete).
• The how-to: search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

References

• AWS Bedrock documentation
• AWS SageMaker documentation

Questions and feedback

If you have any questions or feedback, let us know in the user forum.

• Technical questions
• Documentation feedback