Using AWS Bedrock

Prerequisites

Before beginning, ensure you have the following access and components: AWS Credentials

AWS Access key ID and AWS Secret Access Key.
Need help? Follow this guide: Create an AWS access key| Amazon Web Services.

AWS Bedrock Model Access

Access to the necessary models on AWS Bedrock.
Need help? Follow this guide: AWS Foundation Models| Amazon Web Services.

MemMachine

You can find the latest release here: GitHub - MemMachine/MemMachine.

Docker

The Docker containerization platform must be installed and running.

Installation: QuickStart Configuration

The installation script will automatically guide you through setting up your Large Language Model (LLM) provider. When prompted, you must select Bedrock to integrate with AWS Bedrock. Your prompt input should match the following example:

[PROMPT] Which provider would you like to use? (OpenAI/Bedrock/Ollama) [OpenAI]: Bedrock
[INFO] Selected provider: BEDROCK

After selecting Bedrock, you will be prompted to enter the necessary AWS credentials and configuration details:

AWS Access Key ID
AWS Secret Access Key
AWS Region (e.g., us-east-1 or eu-central-1)
Choice of LLM
Choice of Embedding Model

If you are unsure about model selection, simply press Enter at the respective prompts to use the recommended default options.

Congratulations! You have now successfully deployed MemMachine using AWS Bedrock!

Manually Configuring MemMachine to use AWS Bedrock

To manually configure MemMachine for AWS Bedrock, you need to define your resources in the resources section of your cfg.yml file and then reference them in the memory configuration sections.

1. Define Bedrock Resources

Add or update the resources block in your cfg.yml file. You will need to configure a Language Model, an Embedder, and optionally a Reranker.

resources:
  # LLM Configuration
  language_models:
    my-bedrock-llm:
      provider: amazon-bedrock
      config:
        region: us-east-1
        aws_access_key_id: <YOUR_AWS_ACCESS_KEY>
        aws_secret_access_key: <YOUR_AWS_SECRET_KEY>
        model_id: anthropic.claude-3-sonnet-20240229-v1:0
        inference_config:
          max_tokens: 2000
          temperature: 0.7

  # Embedder Configuration
  embedders:
    my-bedrock-embedder:
      provider: amazon-bedrock
      config:
        region: us-east-1
        aws_access_key_id: <YOUR_AWS_ACCESS_KEY>
        aws_secret_access_key: <YOUR_AWS_SECRET_KEY>
        model_id: amazon.titan-embed-text-v2:0
  
  # Reranker Configuration (Optional)
  rerankers:
    my-bedrock-reranker:
      provider: amazon-bedrock
      config:
        region: us-east-1
        aws_access_key_id: <YOUR_AWS_ACCESS_KEY>
        aws_secret_access_key: <YOUR_AWS_SECRET_KEY>
        model_id: amazon.rerank-v1:0

2. Update Memory Configuration

Now, reference these resource IDs in your episodic_memory and semantic_memory sections.

episodic_memory:
  enabled: true
  long_term_memory:
    vector_graph_store: my-neo4j-db # Assumes you defined this in resources.databases
    embedder: my-bedrock-embedder
    reranker: my-bedrock-reranker
  short_term_memory:
    llm_model: my-bedrock-llm

semantic_memory:
  database: my-postgres-db # Assumes you defined this in resources.databases
  llm_model: my-bedrock-llm
  embedding_model: my-bedrock-embedder

Make sure to restart the MemMachine server for these changes to take effect.

Getting Started

Install Guides

Integrations

Tools

Core Concepts

Open Source

Prerequisites

Installation: QuickStart Configuration

Manually Configuring MemMachine to use AWS Bedrock

1. Define Bedrock Resources

2. Update Memory Configuration

​Prerequisites

​Installation: QuickStart Configuration

​Manually Configuring MemMachine to use AWS Bedrock

​1. Define Bedrock Resources

​2. Update Memory Configuration

Prerequisites

Installation: QuickStart Configuration

Manually Configuring MemMachine to use AWS Bedrock

1. Define Bedrock Resources

2. Update Memory Configuration