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 vector index](#configure-the-vectorizer) 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](#data-import), Weaviate generates text object embeddings and saves them into the index. For [vector](#vector-near-text-search) and [hybrid](#hybrid-search) search operations, Weaviate converts text queries into embeddings. ![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_aws_embedding.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the AWS vectorizer integration (`text2vec-aws`) module. :::accordion{title="For Weaviate Cloud (WCD) users"} This integration is enabled by default on Weaviate Cloud (WCD) instances. ::: :::accordion{title="For self-hosted users"} - Check the [cluster metadata](../monitoring-and-logging/status.md#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](../how-to-configure-weaviate/modules.md) 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. :::code-group{sync="languages"} ```python title="Python" # Recommended: save sensitive data as environment variables aws_access_key = os.getenv("AWS_ACCESS_KEY") aws_secret_key = os.getenv("AWS_SECRET_KEY") ``` ```typescript title="JavaScript/TypeScript" 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 ``` ```goraw title="Go" "X-AWS-Access-Key": os.Getenv("AWS_ACCESS_KEY"), "X-AWS-Secret-Key": os.Getenv("AWS_SECRET_KEY"), ``` ::: ### 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 the vectorizer [Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) 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. :::code-group{sync="languages"} ```python title="Python" {5-13} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_aws( name="title_vector", region="us-east-1", source_properties=["title"], service="bedrock", model="amazon.titan-embed-text-v2:0", ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-17} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecAWS({ name: 'title_vector', sourceProperties: ['title'], region: 'us-east-1', service: 'bedrock', // default service model: 'amazon.titan-embed-text-v1', }), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-22} // Define the collection basicAWSBedrockVectorizerDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-aws": map[string]interface{}{ "properties": []string{"title"}, "region": "us-east-1", "service": "bedrock", "model": "cohere.embed-multilingual-v3", }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(basicAWSBedrockVectorizerDef).Do(ctx) if err != nil { panic(err) } ``` ::: ### SageMaker For SageMaker, you must provide the endpoint address in the vectorizer configuration. :::code-group{sync="languages"} ```python title="Python" {5-13} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_aws( name="title_vector", region="us-east-1", source_properties=["title"], service="sagemaker", endpoint="", # e.g., "tei-xxx" ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-17} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecAWS({ name: 'title_vector', sourceProperties: ['title'], region: 'us-east-1', service: 'sagemaker', model: '', }), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-22} // Define the collection basicAWSSagemakerVectorizerDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-aws": map[string]interface{}{ "properties": []string{"title"}, "region": "us-east-1", "service": "sagemaker", "endpoint": "", }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(basicAWSSagemakerVectorizerDef).Do(ctx) if err != nil { panic(err) } ``` ::: :::accordion{title="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](../how-to-manage-collections/vector-config.md#property-level-settings)) - 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 **Common parameters:** - `service` (Optional): The AWS service to use, either `bedrock` or `sagemaker`. Defaults to `bedrock`. - `region` (Required): The AWS region to send requests to, e.g. `us-east-1`. **Bedrock parameters:** - `model` (Required): The full Bedrock model identifier, e.g. `amazon.titan-embed-text-v2:0`. - `dimensions` (Optional): The size of the embedding to request from the model, e.g. `512`. **SageMaker parameters:** - `endpoint` (Required): The name of the SageMaker endpoint to invoke, e.g. `tei-xxx`. - `targetModel` (Optional): The model to target on a multi-model endpoint. - `targetVariant` (Optional): The production variant to target on the endpoint. #### `service` Weaviate falls back to `bedrock` when `service` is not set, so a SageMaker configuration has to set it to `sagemaker`. Any value other than `bedrock` or `sagemaker` is rejected when the collection is created. Some clients offer service-specific constructors, such as `text2vec_aws_bedrock` and `text2vec_aws_sagemaker`, which select the service for you. Examples built on those constructors do not pass `service` at all. Every other example on this page sets it explicitly, as does any configuration written directly against the collection definition. #### `dimensions` `dimensions` was added in `v1.36.19`, and is also available from `v1.37.10`, `v1.38.2`, and `v1.39.0` onward. Weaviate only forwards `dimensions` to Amazon models on Bedrock, such as the Titan and Nova embedding families. Cohere models on Bedrock, and SageMaker endpoints, accept the setting in the collection configuration but ignore it when embeddings are generated, so their vectors keep the model's default size. Check the model's documentation for the sizes it supports. #### Example configuration The following examples show how to configure AWS-specific options for each service. :::code-group{sync="languages"} ```python title="Python" {6-15,25-35} from weaviate.classes.config import Configure # For Bedrock client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_aws_bedrock( name="title_vector", region="us-east-1", source_properties=["title"], model="amazon.titan-embed-text-v2:0", # Required # Further options # dimensions=512, # Amazon models only ) ], # Additional parameters not shown ) # clean up client.collections.delete("DemoCollection") # For SageMaker client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_aws_sagemaker( name="title_vector", region="us-east-1", source_properties=["title"], endpoint="", # Required # Further options # target_model="", # target_variant="", ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-19} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecAWS({ name: 'title_vector', sourceProperties: ['title'], region: 'us-east-1', service: 'bedrock', model: 'cohere.embed-multilingual-v3', // If using Bedrock // endpoint: '', // If using SageMaker // vectorizeClassName: true, }), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-23} // Define the collection awsVectorizerFullDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-aws": map[string]interface{}{ "properties": []string{"title"}, "region": "us-east-1", "service": "bedrock", // "bedrock" or "sagemaker" "model": "cohere.embed-multilingual-v3", // If using `bedrock`, this is required // "endpoint": "", // If using `sagemaker`, this is required }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(awsVectorizerFullDef).Do(ctx) if err != nil { panic(err) } ``` ::: ## Data import After configuring the vectorizer, [import data](../how-to-manage-objects/import.md) into Weaviate. Weaviate generates embeddings for text objects using the specified model. :::code-group{sync="languages"} ```python title="Python" {13-20} source_objects = [ {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."}, {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."}, {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."}, {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."}, {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."} ] collection = client.collections.use("DemoCollection") with collection.batch.fixed_size(batch_size=200) as batch: for src_obj in source_objects: # The model provider integration will automatically vectorize the object batch.add_object( properties={ "title": src_obj["title"], "description": src_obj["description"], }, # vector=vector # Optionally provide a pre-obtained vector ) if batch.number_errors > 10: print("Batch import stopped due to excessive errors.") break failed_objects = collection.batch.failed_objects if failed_objects: print(f"Number of failed imports: {len(failed_objects)}") print(f"First failed object: {failed_objects[0]}") ``` ```typescript title="JavaScript/TypeScript" let srcObjects = [ { title: "The Shawshank Redemption", description: "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places." }, { title: "The Godfather", description: "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga." }, { title: "The Dark Knight", description: "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City." }, { title: "Jingle All the Way", description: "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve." }, { title: "A Christmas Carol", description: "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption." } ]; ``` ```goraw title="Go" {9-44} var sourceObjects = []map[string]string{ {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."}, {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."}, {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."}, {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."}, {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."}, } // Convert items into a slice of models.Object objects := []models.PropertySchema{} for i := range sourceObjects { objects = append(objects, map[string]interface{}{ // Populate the object with the data "title": sourceObjects[i]["title"], "description": sourceObjects[i]["description"], }) } // Batch write items batcher := client.Batch().ObjectsBatcher() for _, dataObj := range objects { batcher.WithObjects(&models.Object{ Class: "DemoCollection", Properties: dataObj, }) } // Flush batchRes, err := batcher.Do(ctx) // Error handling if err != nil { panic(err) } for _, res := range batchRes { if res.Result.Errors != nil { for _, err := range res.Result.Errors.Error { if err != nil { fmt.Printf("Error details: %v\n", *err) panic(err.Message) } } } } ``` ::: :::callout{intent="tip" title="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](/assets/docs/weaviate/model-providers/_includes/integration_aws_embedding_search.png) ### Vector (near text) search When you perform a [vector search](../how-to-query-search/similarity.md#search-with-text), 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`. :::code-group{sync="languages"} ```python title="Python" {3-6} collection = client.collections.use("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"]) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ```goraw title="Go" {1-9} nearTextResponse, err := client.GraphQL().Get(). WithClassName("DemoCollection"). WithFields( graphql.Field{Name: "title"}, ). WithNearText(client.GraphQL().NearTextArgBuilder(). WithConcepts([]string{"A holiday film"})). WithLimit(2). Do(ctx) if err != nil { panic(err) } fmt.Printf("%v", nearTextResponse) ``` ::: ### Hybrid search :::callout{intent="info" title="What is a hybrid search?"} A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](../how-to-query-search/hybrid.md) to return the best matching objects from the database. ::: When you perform a [hybrid search](../how-to-query-search/hybrid.md), 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`. :::code-group{sync="languages"} ```python title="Python" {3-6} collection = client.collections.use("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"]) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ```goraw title="Go" {1-9} hybridResponse, err := client.GraphQL().Get(). WithClassName("DemoCollection"). WithFields( graphql.Field{Name: "title"}, ). WithHybrid(client.GraphQL().HybridArgumentBuilder(). WithQuery("A holiday film")). WithLimit(2). Do(ctx) if err != nil { panic(err) } fmt.Printf("%v", hybridResponse) ``` ::: ## 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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-usage.html) to find out if the model is available in your region and how request access to a model. ### SageMaker Any custom SageMaker name (e.g., "TEI-xxx") can be used as an endpoint. ## Further resources ### Other integrations - [AWS generative models + Weaviate](aws-generative.md). ### 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](../how-to-manage-collections/index.md) and [How-to: Manage objects](../how-to-manage-objects/index.md) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](../how-to-query-search/index.md) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation. ### External resources - AWS [Bedrock documentation](https://docs.aws.amazon.com/bedrock/) - AWS [SageMaker documentation](https://docs.aws.amazon.com/sagemaker/) ## Questions and feedback Have a question or feedback? 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To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=