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my-project-public — gitlawb

#1	---
#2	title: AWS Bedrock
#3	---
#4
#5	### Setup
#6	- Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
#7	- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
#8	- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
#9
#10	### Usage
#11
#12	```python
#13	import os
#14	from mem0 import Memory
#15
#16	os.environ['AWS_REGION'] = 'us-west-2'
#17	os.environ["AWS_ACCESS_KEY_ID"] = "xx"
#18	os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"
#19
#20	config = {
#21	"llm": {
#22	"provider": "aws_bedrock",
#23	"config": {
#24	"model": "anthropic.claude-3-5-haiku-20241022-v1:0",
#25	"temperature": 0.2,
#26	"max_tokens": 2000,
#27	}
#28	}
#29	}
#30
#31	m = Memory.from_config(config)
#32	messages = [
#33	{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
#34	{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
#35	{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
#36	{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
#37	]
#38	m.add(messages, user_id="alice", metadata={"category": "movies"})
#39	```
#40
#41	### Config
#42
#43	All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).

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