import os, types, traceback import json import requests import time from typing import Callable, Optional from litellm.utils import ModelResponse, Usage, Choices, Message import litellm import httpx from .prompt_templates.factory import prompt_factory, custom_prompt class ClarifaiError(Exception): def __init__(self, status_code, message, url): self.status_code = status_code self.message = message self.request = httpx.Request( method="POST", url=url ) self.response = httpx.Response(status_code=status_code, request=self.request) super().__init__( self.message ) class ClarifaiConfig: """ Reference: https://clarifai.com/meta/Llama-2/models/llama2-70b-chat TODO fill in the details """ max_tokens: Optional[int] = None temperature: Optional[int] = None top_k: Optional[int] = None def __init__( self, max_tokens: Optional[int] = None, temperature: Optional[int] = None, top_k: Optional[int] = None, ) -> None: locals_ = locals() for key, value in locals_.items(): if key != "self" and value is not None: setattr(self.__class__, key, value) @classmethod def get_config(cls): return { k: v for k, v in cls.__dict__.items() if not k.startswith("__") and not isinstance( v, ( types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod, ), ) and v is not None } def validate_environment(api_key): headers = { "accept": "application/json", "content-type": "application/json", } if api_key: headers["Authorization"] = f"Bearer {api_key}" return headers def completions_to_model(payload): # if payload["n"] != 1: # raise HTTPException( # status_code=422, # detail="Only one generation is supported. Please set candidate_count to 1.", # ) params = {} if temperature := payload.get("temperature"): params["temperature"] = temperature if max_tokens := payload.get("max_tokens"): params["max_tokens"] = max_tokens return { "inputs": [{"data": {"text": {"raw": payload["prompt"]}}}], "model": {"output_info": {"params": params}}, } def convert_model_to_url(model: str, api_base: str): user_id, app_id, model_id = model.split(".") return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs" def get_prompt_model_name(url: str): clarifai_model_name = url.split("/")[-2] if "claude" in clarifai_model_name: return "anthropic", clarifai_model_name.replace("_", ".") if ("llama" in clarifai_model_name)or ("mistral" in clarifai_model_name): return "", "meta-llama/llama-2-chat" else: return "", clarifai_model_name def completion( model: str, messages: list, api_base: str, model_response: ModelResponse, print_verbose: Callable, encoding, api_key, logging_obj, custom_prompt_dict={}, optional_params=None, litellm_params=None, logger_fn=None, ): headers = validate_environment(api_key) model = convert_model_to_url(model, api_base) prompt = " ".join(message["content"] for message in messages) # TODO ## Load Config config = litellm.ClarifaiConfig.get_config() for k, v in config.items(): if ( k not in optional_params ): optional_params[k] = v custom_llm_provider, orig_model_name = get_prompt_model_name(model) if custom_llm_provider == "anthropic": prompt = prompt_factory( model=orig_model_name, messages=messages, api_key=api_key, custom_llm_provider="clarifai" ) else: prompt = prompt_factory( model=orig_model_name, messages=messages, api_key=api_key, custom_llm_provider=custom_llm_provider ) # print(prompt); exit(0) data = { "prompt": prompt, **optional_params, } data = completions_to_model(data) ## LOGGING logging_obj.pre_call( input=prompt, api_key=api_key, additional_args={ "complete_input_dict": data, "headers": headers, "api_base": api_base, }, ) ## COMPLETION CALL response = requests.post( model, headers=headers, data=json.dumps(data), ) # print(response.content); exit() """ {"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"}}}}]} """ if response.status_code != 200: raise ClarifaiError(status_code=response.status_code, message=response.text, url=model) if "stream" in optional_params and optional_params["stream"] == True: return response.iter_lines() else: logging_obj.post_call( input=prompt, api_key=api_key, original_response=response.text, additional_args={"complete_input_dict": data}, ) ## RESPONSE OBJECT completion_response = response.json() # print(completion_response) try: choices_list = [] for idx, item in enumerate(completion_response["outputs"]): if len(item["data"]["text"]["raw"]) > 0: message_obj = Message(content=item["data"]["text"]["raw"]) else: message_obj = Message(content=None) choice_obj = Choices( finish_reason="stop", index=idx + 1, #check message=message_obj, ) choices_list.append(choice_obj) model_response["choices"] = choices_list except Exception as e: raise ClarifaiError( message=traceback.format_exc(), status_code=response.status_code, url=model ) # Calculate Usage prompt_tokens = len(encoding.encode(prompt)) completion_tokens = len( encoding.encode(model_response["choices"][0]["message"].get("content")) ) model_response["model"] = model model_response["usage"] = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ) return model_response