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import json
from copy import deepcopy
from typing import Callable, Optional, Union
import httpx
from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.utils import ModelResponse, get_secret
from ..common_utils import AWSEventStreamDecoder
from .transformation import SagemakerChatConfig
class SagemakerChatHandler(BaseAWSLLM):
def _load_credentials(
self,
optional_params: dict,
):
try:
from botocore.credentials import Credentials
except ImportError:
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
## CREDENTIALS ##
# pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
aws_access_key_id = optional_params.pop("aws_access_key_id", None)
aws_session_token = optional_params.pop("aws_session_token", None)
aws_region_name = optional_params.pop("aws_region_name", None)
aws_role_name = optional_params.pop("aws_role_name", None)
aws_session_name = optional_params.pop("aws_session_name", None)
aws_profile_name = optional_params.pop("aws_profile_name", None)
optional_params.pop(
"aws_bedrock_runtime_endpoint", None
) # https://bedrock-runtime.{region_name}.amazonaws.com
aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None)
### SET REGION NAME ###
if aws_region_name is None:
# check env #
litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
if litellm_aws_region_name is not None and isinstance(
litellm_aws_region_name, str
):
aws_region_name = litellm_aws_region_name
standard_aws_region_name = get_secret("AWS_REGION", None)
if standard_aws_region_name is not None and isinstance(
standard_aws_region_name, str
):
aws_region_name = standard_aws_region_name
if aws_region_name is None:
aws_region_name = "us-west-2"
credentials: Credentials = self.get_credentials(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
aws_region_name=aws_region_name,
aws_session_name=aws_session_name,
aws_profile_name=aws_profile_name,
aws_role_name=aws_role_name,
aws_web_identity_token=aws_web_identity_token,
aws_sts_endpoint=aws_sts_endpoint,
)
return credentials, aws_region_name
def _prepare_request(
self,
credentials,
model: str,
data: dict,
optional_params: dict,
aws_region_name: str,
extra_headers: Optional[dict] = None,
):
try:
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest
except ImportError:
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
sigv4 = SigV4Auth(credentials, "sagemaker", aws_region_name)
if optional_params.get("stream") is True:
api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations-response-stream"
else:
api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations"
sagemaker_base_url = optional_params.get("sagemaker_base_url", None)
if sagemaker_base_url is not None:
api_base = sagemaker_base_url
encoded_data = json.dumps(data).encode("utf-8")
headers = {"Content-Type": "application/json"}
if extra_headers is not None:
headers = {"Content-Type": "application/json", **extra_headers}
request = AWSRequest(
method="POST", url=api_base, data=encoded_data, headers=headers
)
sigv4.add_auth(request)
if (
extra_headers is not None and "Authorization" in extra_headers
): # prevent sigv4 from overwriting the auth header
request.headers["Authorization"] = extra_headers["Authorization"]
prepped_request = request.prepare()
return prepped_request
def completion(
self,
model: str,
messages: list,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
logging_obj,
optional_params: dict,
litellm_params: dict,
timeout: Optional[Union[float, httpx.Timeout]] = None,
custom_prompt_dict={},
logger_fn=None,
acompletion: bool = False,
headers: dict = {},
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
):
# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
credentials, aws_region_name = self._load_credentials(optional_params)
inference_params = deepcopy(optional_params)
stream = inference_params.pop("stream", None)
from litellm.llms.openai_like.chat.handler import OpenAILikeChatHandler
openai_like_chat_completions = OpenAILikeChatHandler()
inference_params["stream"] = True if stream is True else False
_data = SagemakerChatConfig().transform_request(
model=model,
messages=messages,
optional_params=inference_params,
litellm_params=litellm_params,
headers=headers,
)
prepared_request = self._prepare_request(
model=model,
data=_data,
optional_params=optional_params,
credentials=credentials,
aws_region_name=aws_region_name,
)
custom_stream_decoder = AWSEventStreamDecoder(model="", is_messages_api=True)
return openai_like_chat_completions.completion(
model=model,
messages=messages,
api_base=prepared_request.url,
api_key=None,
custom_prompt_dict=custom_prompt_dict,
model_response=model_response,
print_verbose=print_verbose,
logging_obj=logging_obj,
optional_params=inference_params,
acompletion=acompletion,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout,
encoding=encoding,
headers=prepared_request.headers, # type: ignore
custom_endpoint=True,
custom_llm_provider="sagemaker_chat",
streaming_decoder=custom_stream_decoder, # type: ignore
client=client,
)
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