mirror of
https://github.com/BerriAI/litellm.git
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103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
import os
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import json
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from enum import Enum
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import requests
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import time
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from typing import Callable
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from litellm.utils import ModelResponse
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class SagemakerError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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super().__init__(
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self.message
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) # Call the base class constructor with the parameters it needs
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def completion(
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model: str,
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messages: list,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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):
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model = model
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prompt = ""
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for message in messages:
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if "role" in message:
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if message["role"] == "user":
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prompt += (
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f"{message['content']}"
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)
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else:
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prompt += (
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f"{message['content']}"
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)
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else:
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prompt += f"{message['content']}"
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data = {
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"prompt": prompt,
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# "instruction": prompt, # some baseten models require the prompt to be passed in via the 'instruction' kwarg
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**optional_params,
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}
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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)
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## COMPLETION CALL
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response = requests.post(
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"https://api.ai21.com/studio/v1/" + model + "/complete", headers=headers, data=json.dumps(data)
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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return response.iter_lines()
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else:
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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completion_response = response.json()
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if "error" in completion_response:
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raise SagemakerError(
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message=completion_response["error"],
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status_code=response.status_code,
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)
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else:
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try:
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model_response["choices"][0]["message"]["content"] = completion_response["completions"][0]["data"]["text"]
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except:
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raise SagemakerError(message=json.dumps(completion_response), status_code=response.status_code)
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## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
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prompt_tokens = len(
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encoding.encode(prompt)
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)
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"]["content"])
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)
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model_response["created"] = time.time()
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model_response["model"] = model
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model_response["usage"] = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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}
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return model_response
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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