AWS Bedrock

ALL Bedrock models (Anthropic, Meta, Mistral, Amazon, etc.) are Supported

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

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-3-sonnet-20240229-v1:0",
  messages=[{ "content": "Hello, how are you?","role": "user"}]
)

LiteLLM Proxy Usage

Here's how to call Anthropic with the LiteLLM Proxy Server

1. Setup config.yaml

model_list:
  - model_name: bedrock-claude-v1
    litellm_params:
      model: bedrock/anthropic.claude-instant-v1
      aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY
      aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME

All possible auth params:

aws_access_key_id: Optional[str],
aws_secret_access_key: Optional[str],
aws_session_token: Optional[str],
aws_region_name: Optional[str],
aws_session_name: Optional[str],
aws_profile_name: Optional[str],
aws_role_name: Optional[str],
aws_web_identity_token: Optional[str],

2. Start the proxy

litellm --config /path/to/config.yaml

3. Test it

Curl Request
OpenAI v1.0.0+
Langchain

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "bedrock-claude-v1",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="bedrock-claude-v1", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
    model = "bedrock-claude-v1",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

Set temperature, top p, etc.

SDK
PROXY

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-3-sonnet-20240229-v1:0",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  temperature=0.7,
  top_p=1
)

Set on yaml

model_list:
  - model_name: bedrock-claude-v1
    litellm_params:
      model: bedrock/anthropic.claude-instant-v1
      temperature: <your-temp>
      top_p: <your-top-p>

Set on request

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="bedrock-claude-v1", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7,
top_p=1
)

print(response)

Pass provider-specific params

If you pass a non-openai param to litellm, we'll assume it's provider-specific and send it as a kwarg in the request body. See more

SDK
PROXY

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-3-sonnet-20240229-v1:0",
  messages=[{ "content": "Hello, how are you?","role": "user"}],
  top_k=1 # 👈 PROVIDER-SPECIFIC PARAM
)

Set on yaml

model_list:
  - model_name: bedrock-claude-v1
    litellm_params:
      model: bedrock/anthropic.claude-instant-v1
      top_k: 1 # 👈 PROVIDER-SPECIFIC PARAM

Set on request

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="bedrock-claude-v1", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7,
extra_body={
    top_k=1 # 👈 PROVIDER-SPECIFIC PARAM
}
)

print(response)

Usage - Function Calling

LiteLLM uses Bedrock's Converse API for making tool calls

from litellm import completion

# set env
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]

response = completion(
    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
    response.choices[0].message.tool_calls[0].function.arguments, str
)

Usage - Vision

from litellm import completion

# set env
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""


def encode_image(image_path):
    import base64

    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")


image_path = "../proxy/cached_logo.jpg"
# Getting the base64 string
base64_image = encode_image(image_path)
resp = litellm.completion(
    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Whats in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/jpeg;base64," + base64_image
                    },
                },
            ],
        }
    ],
)
print(f"\nResponse: {resp}")

Usage - Bedrock Guardrails

Example of using Bedrock Guardrails with LiteLLM

LiteLLM SDK
Proxy on request
Proxy on config.yaml

from litellm import completion

# set env
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = completion(
    model="anthropic.claude-v2",
    messages=[
        {
            "content": "where do i buy coffee from? ",
            "role": "user",
        }
    ],
    max_tokens=10,
    guardrailConfig={
        "guardrailIdentifier": "ff6ujrregl1q", # The identifier (ID) for the guardrail.
        "guardrailVersion": "DRAFT",           # The version of the guardrail.
        "trace": "disabled",                   # The trace behavior for the guardrail. Can either be "disabled" or "enabled"
    },
)

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="anthropic.claude-v2", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7,
extra_body={
    "guardrailConfig": {
        "guardrailIdentifier": "ff6ujrregl1q", # The identifier (ID) for the guardrail.
        "guardrailVersion": "DRAFT",           # The version of the guardrail.
        "trace": "disabled",                   # The trace behavior for the guardrail. Can either be "disabled" or "enabled"
    },
}
)

print(response)

Update config.yaml

model_list:
  - model_name: bedrock-claude-v1
    litellm_params:
      model: bedrock/anthropic.claude-instant-v1
      aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY
      aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME
      guardrailConfig: {
        "guardrailIdentifier": "ff6ujrregl1q", # The identifier (ID) for the guardrail.
        "guardrailVersion": "DRAFT",           # The version of the guardrail.
        "trace": "disabled",                   # The trace behavior for the guardrail. Can either be "disabled" or "enabled"
    }

Start proxy

litellm --config /path/to/config.yaml

Test it!

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="bedrock-claude-v1", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
],
temperature=0.7
)

print(response)

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
  }
}

Cross-region inferencing

LiteLLM supports Bedrock cross-region inferencing across all supported bedrock models.

SDK
PROXY

from litellm import completion 
import os 


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


litellm.set_verbose = True #  👈 SEE RAW REQUEST 

response = completion(
    model="bedrock/us.anthropic.claude-3-haiku-20240307-v1:0",
    messages=messages,
    max_tokens=10,
    temperature=0.1,
)

print("Final Response: {}".format(response))

1. Setup config.yaml

model_list:
  - model_name: bedrock-claude-haiku
    litellm_params:
      model: bedrock/us.anthropic.claude-3-haiku-20240307-v1:0
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: os.environ/AWS_REGION_NAME

2. Start the proxy

litellm --config /path/to/config.yaml

3. Test it

Curl Request
OpenAI v1.0.0+
Langchain

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "bedrock-claude-haiku",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="bedrock-claude-haiku", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
    model = "bedrock-claude-haiku",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

Alternate user/assistant messages

Use user_continue_message to add a default user message, for cases (e.g. Autogen) where the client might not follow alternating user/assistant messages starting and ending with a user message.

model_list:
  - model_name: "bedrock-claude"
    litellm_params:
      model: "bedrock/anthropic.claude-instant-v1"
      user_continue_message: {"role": "user", "content": "Please continue"}

just set litellm.modify_params=True and LiteLLM will automatically handle this with a default user_continue_message.

model_list:
  - model_name: "bedrock-claude"
    litellm_params:
      model: "bedrock/anthropic.claude-instant-v1"

litellm_settings:
   modify_params: true

Test it!

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "bedrock-claude",
    "messages": [{"role": "assistant", "content": "Hey, how's it going?"}]
}'

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 extra headers + Custom API Endpoints

This can be used to override existing headers (e.g. Authorization) when calling custom api endpoints

SDK
PROXY

import os
import litellm
from litellm import completion

litellm.set_verbose = True # 👈 SEE RAW REQUEST

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="",
            aws_bedrock_runtime_endpoint="https://my-fake-endpoint.com",
            extra_headers={"key": "value"}
)

Setup config.yaml

model_list:
    - model_name: bedrock-model
      litellm_params:
        model: bedrock/anthropic.claude-instant-v1
        aws_access_key_id: "",
        aws_secret_access_key: "",
        aws_region_name: "",
        aws_bedrock_runtime_endpoint: "https://my-fake-endpoint.com",
        extra_headers: {"key": "value"}

Start proxy

litellm --config /path/to/config.yaml --detailed_debug

Test it!

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "bedrock-model",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
      },
      {
        "role": "user",
        "content": "how can I solve 8x + 7 = -23"
      }
    ]
}'

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",
        )

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

danger

This is a deprecated flow. Boto3 is not async. And boto3.client does not let us make the http call through httpx. Pass in your aws params through the method above 👆. See Auth Code Add new auth flow

Experimental - 2024-Jun-23: aws_access_key_id, aws_secret_access_key, and aws_session_token will be extracted from boto3.client and be passed into the httpx client

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,
)

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. For a complete list, refer to the model cost map

Model Name	Command
Anthropic Claude-V3.5 Sonnet	`completion(model='bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0', messages=messages)`
Anthropic Claude-V3 sonnet	`completion(model='bedrock/anthropic.claude-3-sonnet-20240229-v1:0', messages=messages)`
Anthropic Claude-V3 Haiku	`completion(model='bedrock/anthropic.claude-3-haiku-20240307-v1:0', messages=messages)`
Anthropic Claude-V3 Opus	`completion(model='bedrock/anthropic.claude-3-opus-20240229-v1:0', messages=messages)`
Anthropic Claude-V2.1	`completion(model='bedrock/anthropic.claude-v2:1', messages=messages)`
Anthropic Claude-V2	`completion(model='bedrock/anthropic.claude-v2', messages=messages)`
Anthropic Claude-Instant V1	`completion(model='bedrock/anthropic.claude-instant-v1', messages=messages)`
Meta llama3-1-405b	`completion(model='bedrock/meta.llama3-1-405b-instruct-v1:0', messages=messages)`
Meta llama3-1-70b	`completion(model='bedrock/meta.llama3-1-70b-instruct-v1:0', messages=messages)`
Meta llama3-1-8b	`completion(model='bedrock/meta.llama3-1-8b-instruct-v1:0', messages=messages)`
Meta llama3-70b	`completion(model='bedrock/meta.llama3-70b-instruct-v1:0', messages=messages)`
Meta llama3-8b	`completion(model='bedrock/meta.llama3-8b-instruct-v1:0', messages=messages)`
Amazon Titan Lite	`completion(model='bedrock/amazon.titan-text-lite-v1', messages=messages)`
Amazon Titan Express	`completion(model='bedrock/amazon.titan-text-express-v1', messages=messages)`
Cohere Command	`completion(model='bedrock/cohere.command-text-v14', messages=messages)`
AI21 J2-Mid	`completion(model='bedrock/ai21.j2-mid-v1', messages=messages)`
AI21 J2-Ultra	`completion(model='bedrock/ai21.j2-ultra-v1', messages=messages)`
AI21 Jamba-Instruct	`completion(model='bedrock/ai21.jamba-instruct-v1:0', messages=messages)`
Meta Llama 2 Chat 13b	`completion(model='bedrock/meta.llama2-13b-chat-v1', messages=messages)`
Meta Llama 2 Chat 70b	`completion(model='bedrock/meta.llama2-70b-chat-v1', messages=messages)`
Mistral 7B Instruct	`completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)`
Mixtral 8x7B Instruct	`completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', 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 Name	Usage	Supported Additional OpenAI params
Titan Embeddings V2	`embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)`	here
Titan Embeddings - V1	`embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)`	here
Titan Multimodal Embeddings	`embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)`	here
Cohere Embeddings - English	`embedding(model="bedrock/cohere.embed-english-v3", input=input)`	here
Cohere Embeddings - Multilingual	`embedding(model="bedrock/cohere.embed-multilingual-v3", input=input)`	here

Advanced - Drop Unsupported Params

Advanced - Pass model/provider-specific Params

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 Name	Function Call
Stable Diffusion - v0	`embedding(model="bedrock/stability.stable-diffusion-xl-v0", prompt=prompt)`
Stable Diffusion - v0	`embedding(model="bedrock/stability.stable-diffusion-xl-v1", prompt=prompt)`

AWS Bedrock

Required Environment Variables​

Usage​

LiteLLM Proxy Usage​

1. Setup config.yaml​

2. Start the proxy​

3. Test it​

Set temperature, top p, etc.​

Pass provider-specific params​

Usage - Function Calling​

Usage - Vision​

Usage - Bedrock Guardrails​

Usage - "Assistant Pre-fill"​

Example prompt sent to Claude​

Usage - "System" messages​

Example prompt sent to Claude​

Usage - Streaming​

Example Streaming Output Chunk​

Cross-region inferencing​

1. Setup config.yaml​

2. Start the proxy​

3. Test it​

Alternate user/assistant messages​

Boto3 - Authentication​

Passing credentials as parameters - Completion()​

Passing extra headers + Custom API Endpoints​

SSO Login (AWS Profile)​

STS based Auth​

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

Provisioned throughput models​

Supported AWS Bedrock Models​

Bedrock Embedding​

API keys​

Usage​

Supported AWS Bedrock Embedding Models​

Advanced - Drop Unsupported Params​

Advanced - Pass model/provider-specific Params​

Image Generation​

Usage​

Supported AWS Bedrock Image Generation Models​

Required Environment Variables

Usage

LiteLLM Proxy Usage

1. Setup config.yaml

2. Start the proxy

3. Test it

Set temperature, top p, etc.

Pass provider-specific params

Usage - Function Calling

Usage - Vision

Usage - Bedrock Guardrails

Usage - "Assistant Pre-fill"

Example prompt sent to Claude

Usage - "System" messages

Example prompt sent to Claude

Usage - Streaming

Example Streaming Output Chunk

Cross-region inferencing

1. Setup config.yaml

2. Start the proxy

3. Test it

Alternate user/assistant messages

Boto3 - Authentication

Passing credentials as parameters - Completion()

Passing extra headers + Custom API Endpoints

SSO Login (AWS Profile)

STS based Auth

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

Provisioned throughput models

Supported AWS Bedrock Models

Bedrock Embedding

API keys

Usage

Supported AWS Bedrock Embedding Models

Advanced - Drop Unsupported Params

Advanced - Pass model/provider-specific Params

Image Generation

Usage

Supported AWS Bedrock Image Generation Models