import os import json from enum import Enum import requests import time from typing import Callable from litellm.utils import ModelResponse, get_secret import sys class BedrockError(Exception): def __init__(self, status_code, message): self.status_code = status_code self.message = message super().__init__( self.message ) # Call the base class constructor with the parameters it needs def init_bedrock_client(region_name): import sys import boto3 import subprocess try: client = boto3.client( service_name="bedrock", region_name=region_name, endpoint_url=f'https://bedrock.{region_name}.amazonaws.com' ) except Exception as e: try: command1 = "python3 -m pip install https://github.com/BerriAI/litellm/raw/main/cookbook/bedrock_resources/boto3-1.28.21-py3-none-any.whl" subprocess.run(command1, shell=True, check=True) # Command 2: Install boto3 from URL command2 = "python3 -m pip install https://github.com/BerriAI/litellm/raw/main/cookbook/bedrock_resources/botocore-1.31.21-py3-none-any.whl" subprocess.run(command2, shell=True, check=True) import boto3 client = boto3.client( service_name="bedrock", region_name=region_name, endpoint_url=f'https://bedrock.{region_name}.amazonaws.com' ) except Exception as e: raise e return client """ BEDROCK AUTH Keys/Vars os.environ['AWS_ACCESS_KEY_ID'] = "" os.environ['AWS_SECRET_ACCESS_KEY'] = "" """ # set os.environ['AWS_REGION_NAME'] = def completion( model: str, messages: list, model_response: ModelResponse, print_verbose: Callable, encoding, logging_obj, optional_params=None, litellm_params=None, logger_fn=None, ): region_name = ( get_secret("AWS_REGION_NAME") or "us-west-2" # default to us-west-2 ) client = init_bedrock_client(region_name) model = model provider = model.split(".")[0] prompt = "" for message in messages: if "role" in message: if message["role"] == "user": prompt += ( f"{message['content']}" ) else: prompt += ( f"{message['content']}" ) else: prompt += f"{message['content']}" if provider == "ai21": data = json.dumps({ "prompt": prompt, }) else: # amazon titan data = json.dumps({ "inputText": prompt, "textGenerationConfig":{ "maxTokenCount":4096, "stopSequences":[], "temperature":0, "topP":0.9 } }) ## LOGGING logging_obj.pre_call( input=prompt, api_key="", additional_args={"complete_input_dict": data}, ) ## COMPLETION CALL accept = 'application/json' contentType = 'application/json' response = client.invoke_model( body=data, modelId=model, accept=accept, contentType=contentType ) response_body = json.loads(response.get('body').read()) if "stream" in optional_params and optional_params["stream"] == True: return response.iter_lines() else: ## LOGGING logging_obj.post_call( input=prompt, api_key="", original_response=response, additional_args={"complete_input_dict": data}, ) print_verbose(f"raw model_response: {response}") ## RESPONSE OBJECT outputText = "default" if provider == "ai21": outputText = response_body.get('completions')[0].get('data').get('text') else: # amazon titan outputText = response_body.get('results')[0].get('outputText') if "error" in outputText: raise BedrockError( message=outputText, status_code=response.status_code, ) else: try: model_response["choices"][0]["message"]["content"] = outputText except: raise BedrockError(message=json.dumps(outputText), status_code=response.status_code) ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. prompt_tokens = len( encoding.encode(prompt) ) completion_tokens = len( encoding.encode(model_response["choices"][0]["message"]["content"]) ) model_response["created"] = time.time() model_response["model"] = model model_response["usage"] = { "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, "total_tokens": prompt_tokens + completion_tokens, } return model_response def embedding(): # logic for parsing in - calling - parsing out model embedding calls pass