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AWS Bedrock

Anthropic, Amazon Titan, A121 LLMs are Supported on Bedrock

Pre-Requisites​

LiteLLM requires boto3 to be installed on your system for Bedrock requests

pip install boto3>=1.28.57

Required Environment Variables​

os.environ["AWS_ACCESS_KEY_ID"] = ""  # Access key
os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key
os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2

Usage​

Open In Colab
import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
  model="bedrock/anthropic.claude-instant-v1",
  messages=[{ "content": "Hello, how are you?","role": "user"}]
)

Usage - "Assistant Pre-fill"​

If you're using Anthropic's Claude with Bedrock, you can "put words in Claude's mouth" by including an assistant role message as the last item in the messages array.

[!IMPORTANT] The returned completion will not include your "pre-fill" text, since it is part of the prompt itself. Make sure to prefix Claude's completion with your pre-fill.

import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

messages = [
    {"role": "user", "content": "How do you say 'Hello' in German? Return your answer as a JSON object, like this:\n\n{ \"Hello\": \"Hallo\" }"},
    {"role": "assistant", "content": "{"},
]
response = completion(model="bedrock/anthropic.claude-v2", messages=messages)

Example prompt sent to Claude​


Human: How do you say 'Hello' in German? Return your answer as a JSON object, like this:

{ "Hello": "Hallo" }

Assistant: {

Usage - "System" messages​

If you're using Anthropic's Claude 2.1 with Bedrock, system role messages are properly formatted for you.

import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

messages = [
    {"role": "system", "content": "You are a snarky assistant."},
    {"role": "user", "content": "How do I boil water?"},
]
response = completion(model="bedrock/anthropic.claude-v2:1", messages=messages)

Example prompt sent to Claude​

You are a snarky assistant.

Human: How do I boil water?

Assistant:

Usage - Streaming​

import os
from litellm import completion

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
  model="bedrock/anthropic.claude-instant-v1",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  stream=True
)
for chunk in response:
  print(chunk)

Example Streaming Output Chunk​

{
  "choices": [
    {
      "finish_reason": null,
      "index": 0,
      "delta": {
        "content": "ase can appeal the case to a higher federal court. If a higher federal court rules in a way that conflicts with a ruling from a lower federal court or conflicts with a ruling from a higher state court, the parties involved in the case can appeal the case to the Supreme Court. In order to appeal a case to the Sup"
      }
    }
  ],
  "created": null,
  "model": "anthropic.claude-instant-v1",
  "usage": {
    "prompt_tokens": null,
    "completion_tokens": null,
    "total_tokens": null
  }
}

Boto3 - Authentication​

Passing credentials as parameters - Completion()​

Pass AWS credentials as parameters to litellm.completion

import os
from litellm import completion

response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            aws_access_key_id="",
            aws_secret_access_key="",
            aws_region_name="",
)

Passing an external BedrockRuntime.Client as a parameter - Completion()​

Pass an external BedrockRuntime.Client object as a parameter to litellm.completion. Useful when using an AWS credentials profile, SSO session, assumed role session, or if environment variables are not available for auth.

Create a client from session credentials:

import boto3
from litellm import completion

bedrock = boto3.client(
            service_name="bedrock-runtime",
            region_name="us-east-1",
            aws_access_key_id="",
            aws_secret_access_key="",
            aws_session_token="",
)

response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            aws_bedrock_client=bedrock,
)

Create a client from AWS profile in ~/.aws/config:

import boto3
from litellm import completion

dev_session = boto3.Session(profile_name="dev-profile")
bedrock = dev_session.client(
            service_name="bedrock-runtime",
            region_name="us-east-1",
)

response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            aws_bedrock_client=bedrock,
)

SSO Login (AWS Profile)​

  • Set AWS_PROFILE environment variable
  • Make bedrock completion call
import os
from litellm import completion

response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=[{ "content": "Hello, how are you?","role": "user"}]
)

or pass aws_profile_name:

import os
from litellm import completion

response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=[{ "content": "Hello, how are you?","role": "user"}],
            aws_profile_name="dev-profile",
)

STS based Auth​

  • Set aws_role_name and aws_session_name in completion() / embedding() function

Make the bedrock completion call

from litellm import completion

response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=messages,
            max_tokens=10,
            temperature=0.1,
            aws_role_name=aws_role_name,
            aws_session_name="my-test-session",
        )

If you also need to dynamically set the aws user accessing the role, add the additional args in the completion()/embedding() function

from litellm import completion

response = completion(
            model="bedrock/anthropic.claude-instant-v1",
            messages=messages,
            max_tokens=10,
            temperature=0.1,
            aws_region_name=aws_region_name,
            aws_access_key_id=aws_access_key_id,
            aws_secret_access_key=aws_secret_access_key,
            aws_role_name=aws_role_name,
            aws_session_name="my-test-session",
        )

Provisioned throughput models​

To use provisioned throughput Bedrock models pass

  • model=bedrock/<base-model>, example model=bedrock/anthropic.claude-v2. Set model to any of the Supported AWS models
  • model_id=provisioned-model-arn

Completion

import litellm
response = litellm.completion(
    model="bedrock/anthropic.claude-instant-v1",
    model_id="provisioned-model-arn",
    messages=[{"content": "Hello, how are you?", "role": "user"}]
)

Embedding

import litellm
response = litellm.embedding(
    model="bedrock/amazon.titan-embed-text-v1",
    model_id="provisioned-model-arn",
    input=["hi"],
)

Supported AWS Bedrock Models​

Here's an example of using a bedrock model with LiteLLM

Model NameCommand
Anthropic Claude-V2.1completion(model='bedrock/anthropic.claude-v2:1', messages=messages)
Anthropic Claude-V2completion(model='bedrock/anthropic.claude-v2', messages=messages)
Anthropic Claude-Instant V1completion(model='bedrock/anthropic.claude-instant-v1', messages=messages)
Anthropic Claude-V1completion(model='bedrock/anthropic.claude-v1', messages=messages)
Amazon Titan Litecompletion(model='bedrock/amazon.titan-text-lite-v1', messages=messages)
Amazon Titan Expresscompletion(model='bedrock/amazon.titan-text-express-v1', messages=messages)
Cohere Commandcompletion(model='bedrock/cohere.command-text-v14', messages=messages)
AI21 J2-Midcompletion(model='bedrock/ai21.j2-mid-v1', messages=messages)
AI21 J2-Ultracompletion(model='bedrock/ai21.j2-ultra-v1', messages=messages)
Meta Llama 2 Chat 13bcompletion(model='bedrock/meta.llama2-13b-chat-v1', messages=messages)
Meta Llama 2 Chat 70bcompletion(model='bedrock/meta.llama2-70b-chat-v1', messages=messages)

Bedrock Embedding​

API keys​

This can be set as env variables or passed as params to litellm.embedding()

import os
os.environ["AWS_ACCESS_KEY_ID"] = ""        # Access key
os.environ["AWS_SECRET_ACCESS_KEY"] = ""    # Secret access key
os.environ["AWS_REGION_NAME"] = ""           # us-east-1, us-east-2, us-west-1, us-west-2

Usage​

from litellm import embedding
response = embedding(
    model="bedrock/amazon.titan-embed-text-v1",
    input=["good morning from litellm"],
)
print(response)

Supported AWS Bedrock Embedding Models​

Model NameFunction Call
Titan Embeddings - G1embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)
Cohere Embeddings - Englishembedding(model="bedrock/cohere.embed-english-v3", input=input)
Cohere Embeddings - Multilingualembedding(model="bedrock/cohere.embed-multilingual-v3", input=input)

Image Generation​

Use this for stable diffusion on bedrock

Usage​

import os
from litellm import image_generation

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = image_generation(
            prompt="A cute baby sea otter",
            model="bedrock/stability.stable-diffusion-xl-v0",
        )
print(f"response: {response}")

Set optional params

import os
from litellm import image_generation

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = image_generation(
            prompt="A cute baby sea otter",
            model="bedrock/stability.stable-diffusion-xl-v0",
            ### OPENAI-COMPATIBLE ###
            size="128x512", # width=128, height=512
            ### PROVIDER-SPECIFIC ### see `AmazonStabilityConfig` in bedrock.py for all params
            seed=30
        )
print(f"response: {response}")

Supported AWS Bedrock Image Generation Models​

Model NameFunction Call
Stable Diffusion - v0embedding(model="bedrock/stability.stable-diffusion-xl-v0", prompt=prompt)
Stable Diffusion - v0embedding(model="bedrock/stability.stable-diffusion-xl-v1", prompt=prompt)