AWS Bedrock Models¶

The Redis Agent Memory Server supports Amazon Bedrock for both embedding models and LLM generation models. This allows you to use AWS-native AI models while keeping your data within the AWS ecosystem.

Overview¶

Amazon Bedrock provides access to a wide variety of foundation models from leading AI providers. The Redis Agent Memory Server supports using Bedrock for:

Embedding Models - For semantic search and memory retrieval
LLM Generation Models - For memory extraction, summarization, and topic modeling

Supported Embedding Models¶

Model ID	Provider	Dimensions	Description
`amazon.titan-embed-text-v2:0`	Amazon	1024	Latest Titan embedding model
`amazon.titan-embed-text-v1`	Amazon	1536	Original Titan embedding model
`cohere.embed-english-v3`	Cohere	1024	English-focused embeddings
`cohere.embed-multilingual-v3`	Cohere	1024	Multilingual embeddings

Pre-configured LLM Generation Models¶

The following models are pre-configured in the codebase:

Model ID	Provider	Max Tokens	Description
`anthropic.claude-sonnet-4-5-20250929-v1:0`	Anthropic	200,000	Claude 4.5 Sonnet
`anthropic.claude-haiku-4-5-20251001-v1:0`	Anthropic	200,000	Claude 4.5 Haiku
`anthropic.claude-opus-4-5-20251101-v1:0`	Anthropic	200,000	Claude 4.5 Opus

Installation¶

AWS Bedrock support requires additional dependencies. Install them with:

pip install agent-memory-server[aws]

This installs:

langchain-aws - LangChain integration for AWS services
boto3 - AWS SDK for Python
botocore - Low-level AWS client library

Configuration¶

Environment Variables¶

Configure the following environment variables to use Bedrock models:

# Required: AWS region where Bedrock is available
REGION_NAME=us-east-1

# For Bedrock Embedding Models
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0

# For Bedrock LLM Generation Models (optional)
GENERATION_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0
FAST_MODEL=anthropic.claude-haiku-4-5-20251001-v1:0
SLOW_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0
TOPIC_MODEL=anthropic.claude-haiku-4-5-20251001-v1:0

# AWS Credentials (choose one method below)

AWS Credentials¶

There are several ways to provide AWS credentials:

Option 1: Environment Variables (Explicit)¶

export AWS_ACCESS_KEY_ID=your-access-key-id
export AWS_SECRET_ACCESS_KEY=your-secret-access-key
export AWS_SESSION_TOKEN=your-session-token  # Optional, for temporary credentials

Option 2: AWS Credentials File¶

The server will automatically use credentials from ~/.aws/credentials:

[default]
aws_access_key_id = your-access-key-id
aws_secret_access_key = your-secret-access-key

Option 3: IAM Role (Recommended for AWS deployments)¶

When running on AWS infrastructure (EC2, ECS, Lambda, etc.), use IAM roles for automatic credential management. No explicit credentials are needed.

Option 4: AWS SSO / AWS CLI Profile¶

If you've configured AWS SSO or named profiles:

# First, login via SSO
aws sso login --profile your-profile

# The server will use the default profile, or set explicitly
export AWS_PROFILE=your-profile

Docker Configuration¶

The Docker image supports two build targets:

standard (default): OpenAI/Anthropic support only
aws: Includes AWS Bedrock embedding models support

Building the AWS-enabled Image¶

# Build directly with Docker
docker build --target aws -t agent-memory-server:aws .

# Or use Docker Compose with the DOCKER_TARGET variable
DOCKER_TARGET=aws docker-compose up --build

Docker Compose Configuration¶

When using Docker Compose, set the DOCKER_TARGET environment variable to aws:

# Start with AWS Bedrock support
DOCKER_TARGET=aws docker-compose up --build

# Or for the production-like setup
DOCKER_TARGET=aws docker-compose -f docker-compose-task-workers.yml up --build

Create a .env file with your credentials and configuration:

# Docker build target
DOCKER_TARGET=aws

# Embedding model
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0

# AWS credentials
REGION_NAME=us-east-1
AWS_ACCESS_KEY_ID=your-access-key-id
AWS_SECRET_ACCESS_KEY=your-secret-access-key
AWS_SESSION_TOKEN=your-session-token  # Optional

The Docker Compose files already include the AWS environment variables, so you only need to set them in your .env file or environment.

Required IAM Permissions¶

The AWS credentials must have permissions to invoke Bedrock models. Here's a minimal IAM policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "bedrock:InvokeModel",
                "bedrock:ListFoundationModels"
            ],
            "Resource": "*"
        }
    ]
}

For production, scope down the Resource to specific model ARNs:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "bedrock:InvokeModel",
            "Resource": [
                "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0",
                "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-sonnet-4-5-20250929-v1:0",
                "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-haiku-4-5-20251001-v1:0"
            ]
        },
        {
            "Effect": "Allow",
            "Action": "bedrock:ListFoundationModels",
            "Resource": "*"
        }
    ]
}

Note: When using Bedrock LLM models for generation tasks (memory extraction, summarization, topic modeling), ensure your IAM policy includes permissions for all the models you've configured (GENERATION_MODEL, FAST_MODEL, SLOW_MODEL, TOPIC_MODEL).

Vector Dimensions¶

When using Bedrock embedding models, make sure to update the vector dimensions setting to match your chosen model:

# For Titan v2 and Cohere models (1024 dimensions)
REDISVL_VECTOR_DIMENSIONS=1024

# For Titan v1 (1536 dimensions)
REDISVL_VECTOR_DIMENSIONS=1536

Complete Configuration Examples¶

Example 1: Bedrock Embeddings with OpenAI Generation¶

# Embedding model (Bedrock)
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0

# AWS Configuration
REGION_NAME=us-east-1
AWS_ACCESS_KEY_ID=your-access-key-id
AWS_SECRET_ACCESS_KEY=your-secret-access-key

# Vector store dimensions (must match embedding model)
REDISVL_VECTOR_DIMENSIONS=1024

# Generation model (OpenAI)
GENERATION_MODEL=gpt-4o
OPENAI_API_KEY=your-openai-key

# Other settings
REDIS_URL=redis://localhost:6379

Example 2: Full Bedrock Stack (Recommended for AWS-only deployments)¶

# AWS Configuration
REGION_NAME=us-east-1
AWS_ACCESS_KEY_ID=your-access-key-id
AWS_SECRET_ACCESS_KEY=your-secret-access-key

# Embedding model (Bedrock Titan)
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
REDISVL_VECTOR_DIMENSIONS=1024

# Generation models (Bedrock Claude)
GENERATION_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0
FAST_MODEL=anthropic.claude-haiku-4-5-20251001-v1:0
SLOW_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0
TOPIC_MODEL=anthropic.claude-haiku-4-5-20251001-v1:0

# Other settings
REDIS_URL=redis://localhost:6379

YAML Configuration¶

# config.yaml - Full Bedrock Stack
region_name: us-east-1
embedding_model: amazon.titan-embed-text-v2:0
redisvl_vector_dimensions: 1024
generation_model: anthropic.claude-sonnet-4-5-20250929-v1:0
fast_model: anthropic.claude-haiku-4-5-20251001-v1:0
slow_model: anthropic.claude-sonnet-4-5-20250929-v1:0
topic_model: anthropic.claude-haiku-4-5-20251001-v1:0
redis_url: redis://localhost:6379

Model Validation¶

The server validates that the specified Bedrock embedding model exists in your configured region at startup. If the model is not found, you'll see an error like:

ValueError: Bedrock embedding model amazon.titan-embed-text-v2:0 not found in region us-east-1.

This helps catch configuration errors early. Common causes:

Model not enabled: You may need to enable the model in the Bedrock console
Wrong region: The model may not be available in your configured region
Typo in model ID: Double-check the model ID spelling

Enabling Bedrock Models in AWS Console¶

Before using a Bedrock model, you must enable it in the AWS Console:

Navigate to Amazon Bedrock in the AWS Console
Select Model access from the left navigation
Click Manage model access
Enable the embedding models you want to use
Wait for access to be granted (usually immediate for Amazon models)

Mixing Providers¶

You can mix and match providers for different use cases:

Bedrock Embeddings with OpenAI Generation¶

# Embeddings via Bedrock
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
REGION_NAME=us-east-1

# Generation via OpenAI
GENERATION_MODEL=gpt-4o
OPENAI_API_KEY=your-openai-key

Full Bedrock Stack (Embeddings + Generation)¶

# All AWS - keep everything within your AWS environment
REGION_NAME=us-east-1

# Embeddings via Bedrock
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
REDISVL_VECTOR_DIMENSIONS=1024

# Generation via Bedrock Claude
GENERATION_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0
FAST_MODEL=anthropic.claude-haiku-4-5-20251001-v1:0
SLOW_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0
TOPIC_MODEL=anthropic.claude-haiku-4-5-20251001-v1:0

OpenAI Embeddings with Bedrock Generation¶

# Embeddings via OpenAI
EMBEDDING_MODEL=text-embedding-3-small
OPENAI_API_KEY=your-openai-key

# Generation via Bedrock
REGION_NAME=us-east-1
GENERATION_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0

This flexibility allows you to: - Keep all data within AWS for compliance requirements - Use the best model for each task - Optimize costs by choosing appropriate models for different operations

Troubleshooting¶

Install the AWS extras:

pip install agent-memory-server[aws]

"Missing environment variable 'REGION_NAME'"¶

Set the AWS region:

export REGION_NAME=us-east-1

"Bedrock embedding model not found"¶

Verify the model ID is correct
Check the model is available in your region
Ensure the model is enabled in Bedrock console
Verify your IAM permissions include bedrock:ListFoundationModels

"Bedrock LLM model not responding correctly"¶

Verify the model ID matches exactly (including version suffix like :0)
Check the model is enabled in your Bedrock console
Verify your IAM permissions include bedrock:InvokeModel for the specific model
Some models may have different regional availability - check AWS documentation

"Error creating chat completion with Bedrock"¶

Check that the model ID is correct and the model is enabled
Verify your AWS credentials have bedrock:InvokeModel permission
Check the request isn't exceeding the model's token limits
Review CloudWatch logs for detailed error messages

Credential Errors¶

If you see authentication errors:

Verify your AWS credentials are correctly set
Check the credentials have the required IAM permissions
If using temporary credentials, ensure they haven't expired
Try running aws sts get-caller-identity to verify your credentials work

Model-Specific Issues¶

Model IDs: Ensure you're using the Bedrock-specific model IDs (e.g., anthropic.claude-sonnet-4-5-20250929-v1:0) not the direct provider API model IDs.

Converse API: The implementation uses ChatBedrockConverse from langchain-aws, which provides a unified interface for all Bedrock models via the Converse API. Model-specific formatting is handled automatically.

Performance Considerations¶

Latency: Bedrock API calls may have different latency characteristics than OpenAI
Rate limits: Check your Bedrock service quotas for your region
Caching: The server caches model existence checks for 1 hour to reduce API calls
Cost: Review Bedrock pricing for your chosen embedding model

Configuration - Full configuration reference
Vector Store Backends - Custom vector store setup
Getting Started - Initial setup guide