šŸ” Mem0 is now SOC 2 and HIPAA compliant! We're committed to the highest standards of data security and privacy, enabling secure memory for enterprises, healthcare, and beyond. Learn more

ā€‹
Setup

  • Before using the AWS Bedrock LLM, make sure you have the appropriate model access from Bedrock Console.
  • You will also need to authenticate the boto3 client by using a method in the AWS documentation
  • You will have to export AWS_REGION, AWS_ACCESS_KEY, and AWS_SECRET_ACCESS_KEY to set environment variables.

ā€‹
Usage

import os
from mem0 import Memory

os.environ['AWS_REGION'] = 'us-west-2'
os.environ["AWS_ACCESS_KEY_ID"] = "xx"
os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"

config = {
    "llm": {
        "provider": "aws_bedrock",
        "config": {
            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
            "temperature": 0.2,
            "max_tokens": 2000,
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
    {"role": "user", "content": "Iā€™m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})

ā€‹
Config

All available parameters for the aws_bedrock config are present in Master List of All Params in Config.