AWS Embeddings 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 vector index to use an AWS embedding model, and Weaviate will generate embeddings for various operations using the specified model and your AWS API credentials. This feature is called the vectorizer.

At import time, Weaviate generates text object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts text queries into embeddings.

Requirements

Weaviate configuration

Your Weaviate instance must be configured with the AWS vectorizer integration (text2vec-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()

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()

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 the vectorizer

Configure a Weaviate index as follows to use an AWS embedding model.

The required parameters for the Bedrock and the SageMaker models are different.

Bedrock

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

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_aws(
            name="title_vector",
            region="us-east-1",
            source_properties=["title"],
            service="bedrock",
            model="cohere.embed-multilingual-v3",
        )
    ],
    # Additional parameters not shown
)

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectorizer.text2VecAWS({
      name: 'title_vector',
      sourceProperties: ['title'],
      region: 'us-east-1',
      service: 'bedrock', // default service
      model: 'amazon.titan-embed-text-v1',
    }),
  ],
  // Additional parameters not shown
});

SageMaker

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

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_aws(
            name="title_vector",
            region="us-east-1",
            source_properties=["title"],
            service="sagemaker",
            endpoint="<custom_sagemaker_url>",
        )
    ],
    # Additional parameters not shown
)

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectorizer.text2VecAWS({
      name: 'title_vector',
      sourceProperties: ['title'],
      region: 'us-east-1',
      service: 'sagemaker',
      model: '<custom_sagemaker_url>',
    }),
  ],
  // Additional parameters not shown
});

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)
• 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

Vectorizer parameters

The following examples show how to configure AWS-specific options.

The AWS region setting is required for all AWS integrations.

• Bedrock users must set service to bedrock and provide the model name.
• SageMaker users must set service to sagemaker and provide the endpoint address.

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_aws(
            name="title_vector",
            region="us-east-1",
            source_properties=["title"],
            service="bedrock",                      # `bedrock` or `sagemaker`
            model="cohere.embed-multilingual-v3",   # If using `bedrock`, this is required
            # endpoint="<sagemaker_endpoint>",        # If using `sagemaker`, this is required
        )
    ],
    # Additional parameters not shown
)

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectorizer.text2VecAWS({
      name: 'title_vector',
      sourceProperties: ['title'],
      region: 'us-east-1',
      service: 'bedrock',
      model: 'cohere.embed-multilingual-v3', // If using Bedrock
      // endpoint: '<custom_sagemaker_url>',  // If using SageMaker
      // vectorizeClassName: true,
    }),
  ],
  // Additional parameters not shown
});

Data import

After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.

Python API v4

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

with collection.batch.dynamic() as batch:
    for src_obj in source_objects:
        weaviate_obj = {
            "title": src_obj["title"],
            "description": src_obj["description"],
        }

        # The model provider integration will automatically vectorize the object
        batch.add_object(
            properties=weaviate_obj,
            # vector=vector  # Optionally provide a pre-obtained vector
        )

JS/TS API v3

const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)

let dataObjects = []

for (let srcObject of srcObjects) {
  dataObject.push({
    title: srcObject.title,
    description: srcObject.description,
  });
}

const response = await myCollection.data.insertMany(dataObjects);

console.log(response);

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 AWS model.

Vector (near text) search

When you perform a vector search, 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.

Python API v4

collection = client.collections.get("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"])

JS/TS API v3

const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)

let result;

result = await myCollection.query.nearText(
  'A holiday film',  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

Hybrid search

What is a hybrid search?

A hybrid search performs a vector search and a keyword (BM25) search, before combining the results to return the best matching objects from the database.

When you perform a hybrid search, 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.

Python API v4

collection = client.collections.get("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"])

JS/TS API v3

const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)

result = await myCollection.query.hybrid(
  'A holiday film',  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

References

Available models

Bedrock

• amazon.titan-embed-text-v1
• amazon.titan-embed-text-v2:0
• cohere.embed-english-v3
• cohere.embed-multilingual-v3

Refer to 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 generative 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.

External resources

• 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