diff options
Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/llms/bedrock/image/image_handler.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/litellm/llms/bedrock/image/image_handler.py | 314 |
1 files changed, 314 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/bedrock/image/image_handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/bedrock/image/image_handler.py new file mode 100644 index 00000000..8f7762e5 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/llms/bedrock/image/image_handler.py @@ -0,0 +1,314 @@ +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 ( + AsyncHTTPHandler, + HTTPHandler, + _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, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = 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=( + client + if client is not None and isinstance(client, AsyncHTTPHandler) + else None + ), + ) + + if client is None or not isinstance(client, HTTPHandler): + 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, + client: Optional[AsyncHTTPHandler] = None, + ) -> ImageResponse: + """ + Asynchronous handler for bedrock image generation + + Awaits the response from the bedrock image generation endpoint + """ + async_client = client or 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, model + ) + + ### 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, + } + elif provider == "amazon": + return dict(litellm.AmazonNovaCanvasConfig.transform_request_body(text=prompt, optional_params=optional_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.AmazonNovaCanvasConfig if litellm.AmazonNovaCanvasConfig._is_nova_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 |