AWS + Weaviate
The model provider integration pages are new and still undergoing improvements. We appreciate any feedback on this forum thread.
AWS offers a wide range of models for natural language processing and generation. Weaviate seamlessly integrates with AWS's APIs, allowing users to leverage AWS's models directly within the Weaviate database.
Weaviate integrates with both AWS Sagemaker and Bedrock.
These integrations empower developers to build sophisticated AI-driven applications with ease.
Amazon SageMaker is a fully managed service where you can build, train and deploy ML models. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies.
Integrations with AWS
Embedding models for semantic search
AWS's embedding models transform text data into high-dimensional vector representations, capturing semantic meaning and context.
Weaviate integrates with AWS's embedding models to enable seamless vectorization of data. This integration allows users to perform semantic and hybrid search operations without the need for additional preprocessing or data transformation steps.
AWS embedding integration page
Generative AI models for RAG
AWS's generative AI models can generate human-like text based on given prompts and contexts.
Weaviate's generative AI integration enables users to perform retrieval augmented generation (RAG) directly within the Weaviate database. This combines Weaviate's efficient storage and fast retrieval capabilities with AWS's generative AI models to generate personalized and context-aware responses.
AWS generative AI integration page
Summary
These integrations enable developers to leverage AWS's powerful models directly within Weaviate.
In turn, they simplify the process of building AI-driven applications to speed up your development process, so that you can focus on creating innovative solutions.
Get started
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 and a corresponding AWS secret access key.
Then, go to the relevant integration page to learn how to configure Weaviate with the AWS models and start using them in your applications.
If you have any questions or feedback, let us know in the user forum.