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import copy
import json
import os
from typing import TYPE_CHECKING, Any, Optional, Union
import httpx
from pydantic import BaseModel
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging
from litellm.llms.custom_httpx.http_handler import (
_get_httpx_client,
get_async_httpx_client,
)
from litellm.types.utils import ImageResponse
from ..base_aws_llm import BaseAWSLLM
from ..common_utils import BedrockError
if TYPE_CHECKING:
from botocore.awsrequest import AWSPreparedRequest
else:
AWSPreparedRequest = Any
class BedrockImagePreparedRequest(BaseModel):
"""
Internal/Helper class for preparing the request for bedrock image generation
"""
endpoint_url: str
prepped: AWSPreparedRequest
body: bytes
data: dict
class BedrockImageGeneration(BaseAWSLLM):
"""
Bedrock Image Generation handler
"""
def image_generation(
self,
model: str,
prompt: str,
model_response: ImageResponse,
optional_params: dict,
logging_obj: LitellmLogging,
timeout: Optional[Union[float, httpx.Timeout]],
aimg_generation: bool = False,
api_base: Optional[str] = None,
extra_headers: Optional[dict] = None,
):
prepared_request = self._prepare_request(
model=model,
optional_params=optional_params,
api_base=api_base,
extra_headers=extra_headers,
logging_obj=logging_obj,
prompt=prompt,
)
if aimg_generation is True:
return self.async_image_generation(
prepared_request=prepared_request,
timeout=timeout,
model=model,
logging_obj=logging_obj,
prompt=prompt,
model_response=model_response,
)
client = _get_httpx_client()
try:
response = client.post(url=prepared_request.endpoint_url, headers=prepared_request.prepped.headers, data=prepared_request.body) # type: ignore
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
raise BedrockError(status_code=error_code, message=err.response.text)
except httpx.TimeoutException:
raise BedrockError(status_code=408, message="Timeout error occurred.")
### FORMAT RESPONSE TO OPENAI FORMAT ###
model_response = self._transform_response_dict_to_openai_response(
model_response=model_response,
model=model,
logging_obj=logging_obj,
prompt=prompt,
response=response,
data=prepared_request.data,
)
return model_response
async def async_image_generation(
self,
prepared_request: BedrockImagePreparedRequest,
timeout: Optional[Union[float, httpx.Timeout]],
model: str,
logging_obj: LitellmLogging,
prompt: str,
model_response: ImageResponse,
) -> ImageResponse:
"""
Asynchronous handler for bedrock image generation
Awaits the response from the bedrock image generation endpoint
"""
async_client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.BEDROCK,
params={"timeout": timeout},
)
try:
response = await async_client.post(url=prepared_request.endpoint_url, headers=prepared_request.prepped.headers, data=prepared_request.body) # type: ignore
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
raise BedrockError(status_code=error_code, message=err.response.text)
except httpx.TimeoutException:
raise BedrockError(status_code=408, message="Timeout error occurred.")
### FORMAT RESPONSE TO OPENAI FORMAT ###
model_response = self._transform_response_dict_to_openai_response(
model=model,
logging_obj=logging_obj,
prompt=prompt,
response=response,
data=prepared_request.data,
model_response=model_response,
)
return model_response
def _prepare_request(
self,
model: str,
optional_params: dict,
api_base: Optional[str],
extra_headers: Optional[dict],
logging_obj: LitellmLogging,
prompt: str,
) -> BedrockImagePreparedRequest:
"""
Prepare the request body, headers, and endpoint URL for the Bedrock Image Generation API
Args:
model (str): The model to use for the image generation
optional_params (dict): The optional parameters for the image generation
api_base (Optional[str]): The base URL for the Bedrock API
extra_headers (Optional[dict]): The extra headers to include in the request
logging_obj (LitellmLogging): The logging object to use for logging
prompt (str): The prompt to use for the image generation
Returns:
BedrockImagePreparedRequest: The prepared request object
The BedrockImagePreparedRequest contains:
endpoint_url (str): The endpoint URL for the Bedrock Image Generation API
prepped (httpx.Request): The prepared request object
body (bytes): The request body
"""
try:
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest
except ImportError:
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
boto3_credentials_info = self._get_boto_credentials_from_optional_params(
optional_params
)
### SET RUNTIME ENDPOINT ###
modelId = model
_, proxy_endpoint_url = self.get_runtime_endpoint(
api_base=api_base,
aws_bedrock_runtime_endpoint=boto3_credentials_info.aws_bedrock_runtime_endpoint,
aws_region_name=boto3_credentials_info.aws_region_name,
)
proxy_endpoint_url = f"{proxy_endpoint_url}/model/{modelId}/invoke"
sigv4 = SigV4Auth(
boto3_credentials_info.credentials,
"bedrock",
boto3_credentials_info.aws_region_name,
)
data = self._get_request_body(
model=model, prompt=prompt, optional_params=optional_params
)
# Make POST Request
body = 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=proxy_endpoint_url, data=body, 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.prepare()
## LOGGING
logging_obj.pre_call(
input=prompt,
api_key="",
additional_args={
"complete_input_dict": data,
"api_base": proxy_endpoint_url,
"headers": prepped.headers,
},
)
return BedrockImagePreparedRequest(
endpoint_url=proxy_endpoint_url,
prepped=prepped,
body=body,
data=data,
)
def _get_request_body(
self,
model: str,
prompt: str,
optional_params: dict,
) -> dict:
"""
Get the request body for the Bedrock Image Generation API
Checks the model/provider and transforms the request body accordingly
Returns:
dict: The request body to use for the Bedrock Image Generation API
"""
provider = model.split(".")[0]
inference_params = copy.deepcopy(optional_params)
inference_params.pop(
"user", None
) # make sure user is not passed in for bedrock call
data = {}
if provider == "stability":
if litellm.AmazonStability3Config._is_stability_3_model(model):
request_body = litellm.AmazonStability3Config.transform_request_body(
prompt=prompt, optional_params=optional_params
)
return dict(request_body)
else:
prompt = prompt.replace(os.linesep, " ")
## LOAD CONFIG
config = litellm.AmazonStabilityConfig.get_config()
for k, v in config.items():
if (
k not in inference_params
): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
inference_params[k] = v
data = {
"text_prompts": [{"text": prompt, "weight": 1}],
**inference_params,
}
else:
raise BedrockError(
status_code=422, message=f"Unsupported model={model}, passed in"
)
return data
def _transform_response_dict_to_openai_response(
self,
model_response: ImageResponse,
model: str,
logging_obj: LitellmLogging,
prompt: str,
response: httpx.Response,
data: dict,
) -> ImageResponse:
"""
Transforms the Image Generation response from Bedrock to OpenAI format
"""
## LOGGING
if logging_obj is not None:
logging_obj.post_call(
input=prompt,
api_key="",
original_response=response.text,
additional_args={"complete_input_dict": data},
)
verbose_logger.debug("raw model_response: %s", response.text)
response_dict = response.json()
if response_dict is None:
raise ValueError("Error in response object format, got None")
config_class = (
litellm.AmazonStability3Config
if litellm.AmazonStability3Config._is_stability_3_model(model=model)
else litellm.AmazonStabilityConfig
)
config_class.transform_response_dict_to_openai_response(
model_response=model_response,
response_dict=response_dict,
)
return model_response
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