> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Using AWS Bedrock

> Guide to install and configure MemMachine using AWS Bedrock models.

## 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](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html).

**AWS Bedrock Model Access**

* Access to the necessary **models on AWS Bedrock**.
* *Need help?* Follow this guide: [AWS Foundation Models| Amazon Web Services](https://docs.aws.amazon.com/bedrock/latest/userguide/foundation-models-reference.html).

**MemMachine**

* You can find the latest release here: [GitHub - MemMachine/MemMachine](https://api.github.com/repos/MemMachine/MemMachine/releases/latest).

**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:

```bash theme={null}
[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**

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

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**.

```yaml theme={null}
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.

```yaml theme={null}
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
```

<Note>
  Make sure to restart the MemMachine server for these changes to take effect.
</Note>