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Clarifai-LiteLLM integration (#1)
* intg v1 clarifai-litellm * Added more community models and testcase * Clarifai-updated markdown docs
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litellm/llms/clarifai.py
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litellm/llms/clarifai.py
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import os, types, traceback
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import json
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import requests
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import time
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from typing import Callable, Optional
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from litellm.utils import ModelResponse, Usage, Choices, Message
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import litellm
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import httpx
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class ClarifaiError(Exception):
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def __init__(self, status_code, message, url):
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self.status_code = status_code
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self.message = message
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self.request = httpx.Request(
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method="POST", url=url
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)
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self.response = httpx.Response(status_code=status_code, request=self.request)
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super().__init__(
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self.message
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)
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class ClarifaiConfig:
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"""
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Reference: https://clarifai.com/meta/Llama-2/models/llama2-70b-chat
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TODO fill in the details
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"""
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max_tokens: Optional[int] = None
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temperature: Optional[int] = None
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top_k: Optional[int] = None
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def __init__(
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self,
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max_tokens: Optional[int] = None,
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temperature: Optional[int] = None,
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top_k: Optional[int] = None,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def validate_environment(api_key):
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headers = {
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"accept": "application/json",
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"content-type": "application/json",
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}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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return headers
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def completions_to_model(payload):
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# if payload["n"] != 1:
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# raise HTTPException(
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# status_code=422,
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# detail="Only one generation is supported. Please set candidate_count to 1.",
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# )
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params = {}
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if temperature := payload.get("temperature"):
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params["temperature"] = temperature
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if max_tokens := payload.get("max_tokens"):
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params["max_tokens"] = max_tokens
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return {
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"inputs": [{"data": {"text": {"raw": payload["prompt"]}}}],
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"model": {"output_info": {"params": params}},
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}
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def convert_model_to_url(model: str, api_base: str):
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user_id, app_id, model_id = model.split(".")
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return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
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def get_prompt_model_name(url: str):
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clarifai_model_name = url.split("/")[-2]
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if "claude" in clarifai_model_name:
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return "anthropic", clarifai_model_name.replace("_", ".")
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if ("llama" in clarifai_model_name)or ("mistral" in clarifai_model_name):
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return "", "meta-llama/llama-2-chat"
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else:
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return "", clarifai_model_name
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def completion(
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model: str,
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messages: list,
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api_base: str,
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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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custom_prompt_dict={},
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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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headers = validate_environment(api_key)
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model = convert_model_to_url(model, api_base)
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prompt = " ".join(message["content"] for message in messages) # TODO
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## Load Config
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config = litellm.ClarifaiConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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):
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optional_params[k] = v
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custom_llm_provider, orig_model_name = get_prompt_model_name(model)
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if custom_llm_provider == "anthropic":
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prompt = prompt_factory(
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model=orig_model_name,
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messages=messages,
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api_key=api_key,
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custom_llm_provider="clarifai"
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)
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else:
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prompt = prompt_factory(
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model=orig_model_name,
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messages=messages,
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api_key=api_key,
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custom_llm_provider=custom_llm_provider
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)
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# print(prompt); exit(0)
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data = {
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"prompt": prompt,
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**optional_params,
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}
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data = completions_to_model(data)
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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={
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"complete_input_dict": data,
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"headers": headers,
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"api_base": api_base,
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},
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)
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## COMPLETION CALL
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response = requests.post(
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model,
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headers=headers,
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data=json.dumps(data),
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)
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# print(response.content); exit()
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"""
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{"status":{"code":10000,"description":"Ok","req_id":"d914cf7e097487997910650cde954a37"},"outputs":[{"id":"c2baa668174b4547bd4d2e9f8996198d","status":{"code":10000,"description":"Ok"},"created_at":"2024-02-07T10:57:52.917990493Z","model":{"id":"GPT-4","name":"GPT-4","created_at":"2023-06-08T17:40:07.964967Z","modified_at":"2023-12-04T11:39:54.587604Z","app_id":"chat-completion","model_version":{"id":"5d7a50b44aec4a01a9c492c5a5fcf387","created_at":"2023-11-09T19:57:56.961259Z","status":{"code":21100,"description":"Model is trained and ready"},"completed_at":"2023-11-09T20:00:48.933172Z","visibility":{"gettable":50},"app_id":"chat-completion","user_id":"openai","metadata":{}},"user_id":"openai","model_type_id":"text-to-text","visibility":{"gettable":50},"toolkits":[],"use_cases":[],"languages":[],"languages_full":[],"check_consents":[],"workflow_recommended":false,"image":{"url":"https://data.clarifai.com/small/users/openai/apps/chat-completion/inputs/image/34326a9914d361bb93ae8e5381689755","hosted":{"prefix":"https://data.clarifai.com","suffix":"users/openai/apps/chat-completion/inputs/image/34326a9914d361bb93ae8e5381689755","sizes":["small"],"crossorigin":"use-credentials"}}},"input":{"id":"fba1f22a332743f083ddae0a7eb443ae","data":{"text":{"raw":"what\'s the weather in SF","url":"https://samples.clarifai.com/placeholder.gif"}}},"data":{"text":{"raw":"As an AI, I\'m unable to provide real-time information or updates. Please check a reliable weather website or app for the current weather in San Francisco.","text_info":{"encoding":"UnknownTextEnc"}}}}]}
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"""
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if response.status_code != 200:
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raise ClarifaiError(status_code=response.status_code, message=response.text, url=model)
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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_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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## RESPONSE OBJECT
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completion_response = response.json()
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# print(completion_response)
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try:
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choices_list = []
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for idx, item in enumerate(completion_response["outputs"]):
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if len(item["data"]["text"]["raw"]) > 0:
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message_obj = Message(content=item["data"]["text"]["raw"])
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else:
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message_obj = Message(content=None)
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choice_obj = Choices(
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finish_reason="stop",
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index=idx + 1, #check
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message=message_obj,
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)
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choices_list.append(choice_obj)
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model_response["choices"] = choices_list
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except Exception as e:
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raise ClarifaiError(
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message=traceback.format_exc(), status_code=response.status_code, url=model
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)
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# Calculate Usage
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prompt_tokens = len(encoding.encode(prompt))
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"].get("content"))
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)
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model_response["model"] = model
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model_response["usage"] = 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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