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AWS Embeddings with Weaviate

New Documentation

The model provider integration pages are new and still undergoing improvements. We appreciate any feedback on this forum thread.

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.

Embedding integration illustration

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

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.
import weaviate
from weaviate.auth import AuthApiKey
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_wcs(
    cluster_url=weaviate_url,                       # `weaviate_url`: your Weaviate URL
    auth_credentials=AuthApiKey(weaviate_key),      # `weaviate_key`: your Weaviate API key
    headers=headers
)

# 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 to use an AWS embedding model by setting the vectorizer as follows. Note that the required parameters differ between Bedrock and SageMaker models.

Bedrock​

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

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
)

SageMaker​

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

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
)

Data import​

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

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
        )
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.

Embedding integration at search illustration

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.

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"])
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.

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"])

References​

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.
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
)

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​

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​

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