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AWS

Introduction​

Amazon Bedrock is a fully managed service that provides a selection of high-performance foundation models (FMs) developed by leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. It offers a unified API for building generative AI applications with essential features including security, privacy, and responsible AI.

With Amazon Bedrock, users can easily experiment with and assess various FMs tailored to their specific needs. They can privately customize these models using techniques like fine-tuning and Retrieval Augmented Generation (RAG), enabling the creation of agents capable of performing tasks using enterprise systems and data sources.

Being serverless, Amazon Bedrock eliminates the need for managing infrastructure, allowing seamless integration and deployment of generative AI capabilities using familiar AWS services.

Deploying your model​

Amazon Bedrock offers a number of partners to pick and choose your foundation models, you can select the best for your use case.

LLM Model​

To start building on AWS console, you will need to setup your account first. Follow the instructions here to get started.

Usage with MAAP​

To use Amazon FM with MAAP framework, you would need to the below components to it.

  • Config File :​

    Provided below are the values required to be added in config.yaml file in LLM section.

    llms:
        class_name: Bedrock
        model_name: <check_references_below>
        max_tokens: <integer_value>
        temperature: <integer_value>

  • Environment Variable :​

    Below value(s) are to be added in .env file, present at builder/partnerproduct/.

      BEDROCK_AWS_REGION = <check_references_below>
      BEDROCK_AWS_ACCESS_KEY_ID = <check_references_below>
      BEDROCK_AWS_SECRET_ACCESS_KEY = <check_references_below>

Embedding Model​

To use Amazon powered embedding model with MAAP framework, use the below configurations.

Usage with MAAP​

To use AWS Titan embedding with MAAP framework, you would need to feed below values.

  • Config File :​

    Provided below are the values required to be added in config.yaml file in embedding section.

    embedding:
        class_name: Bedrock
        model_name: `amazon.titan-embed-image-v1` or `amazon.titan-embed-text-v2:0`

  • Environment Variable :​

    Below value(s) are to be added in .env file, present at builder/partnerproduct/.

      BEDROCK_AWS_REGION = <check_references_below>
      BEDROCK_AWS_ACCESS_KEY_ID = <check_references_below>
      BEDROCK_AWS_SECRET_ACCESS_KEY = <check_references_below>

References​

Provided below are the instructions on how to procure the right values for building your MAAP framework.

  • Model Name​

    Based on the provider you are using, you can use the Model ID for the model name. The list of base models can be found here.

  • BEDROCK_AWS_REGION​

    The aws-region you are using. Listed here are the supported AWS Regions.

  • BEDROCK_AWS_ACCESS_KEY_ID and BEDROCK_AWS_SECRET_ACCESS_KEY​

    You can follow any method listed here to get your access key ID and its secret.