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](#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](/assets/docs/weaviate/model-providers/_includes/integration_aws_rag.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the AWS generative AI integration (`generative-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 ``` ::: ### 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 :::callout{intent="info" title="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](../how-to-manage-collections/generative-reranker-models.md#update-the-generative-model-integration) for details on how to update the collection configuration. ::: [Configure a Weaviate index](../how-to-manage-collections/generative-reranker-models.md#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. :::code-group{sync="languages"} ```python title="Python" {5-9} 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" ) ) ``` ```typescript title="JavaScript/TypeScript" {3-7} 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. :::code-group{sync="languages"} ```python title="Python" {5-9} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", generative_config=Configure.Generative.aws( region="us-east-1", service="sagemaker", endpoint="" ) ) ``` ```typescript title="JavaScript/TypeScript" {3-7} await client.collections.create({ name: 'DemoCollection', generative: weaviate.configure.generative.aws({ region: 'us-east-1', service: 'sagemaker', endpoint: '' }), }) ``` ::: You can [specify](#generative-parameters) which [model](#available-models) Weaviate uses. ### Generative parameters For further details on model parameters, see the [relevant AWS documentation](#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. :::code-group{sync="languages"} ```python title="Python" {9-14} 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 ) ``` ```typescript title="JavaScript/TypeScript" import { generativeParameters } from 'weaviate-client'; ``` ::: ## Retrieval augmented generation After configuring the generative AI integration, perform RAG operations, either with the [single prompt](#single-prompt) or [grouped task](#grouped-task) method. ### Single prompt ![Single prompt RAG integration generates individual outputs per search result](/assets/docs/weaviate/model-providers/_includes/integration_aws_rag_single.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. :::code-group{sync="languages"} ```python title="Python" {5-6} 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 ``` ```typescript title="JavaScript/TypeScript" let response; const myCollection = client.collections.use("DemoCollection"); ``` ::: ### Grouped task ![Grouped task RAG integration generates one output for the set of search results](/assets/docs/weaviate/model-providers/_includes/integration_aws_rag_grouped.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. :::code-group{sync="languages"} ```python title="Python" {5-6} 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"]) ``` ```typescript title="JavaScript/TypeScript" let response; const myCollection = client.collections.use("DemoCollection"); ``` ::: ### 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. :::code-group{sync="languages"} ```python title="Python" {9-11,18} 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}") ``` ```typescript title="JavaScript/TypeScript" import { generativeParameters } from 'weaviate-client'; ``` ::: ## 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](aws-embeddings.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. ### References - 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? Here's how to reach us. ::::card-grid :::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"} Ask questions and connect with other developers on our **Community forum**. ::: :::card{title="Support" href="/guides/support-overview" icon="life-buoy"} Weaviate Cloud user or customer? Find the right channel on the **Support page**. ::: :::: ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. 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=