import json import time import types from typing import Callable, Optional import httpx # type: ignore import litellm from litellm.utils import Choices, Message, ModelResponse, Usage class AlephAlphaError(Exception): def __init__(self, status_code, message): self.status_code = status_code self.message = message self.request = httpx.Request( method="POST", url="https://api.aleph-alpha.com/complete" ) self.response = httpx.Response(status_code=status_code, request=self.request) super().__init__( self.message ) # Call the base class constructor with the parameters it needs class AlephAlphaConfig: """ Reference: https://docs.aleph-alpha.com/api/complete/ The `AlephAlphaConfig` class represents the configuration for the Aleph Alpha API. Here are the properties: - `maximum_tokens` (integer, required): The maximum number of tokens to be generated by the completion. The sum of input tokens and maximum tokens may not exceed 2048. - `minimum_tokens` (integer, optional; default value: 0): Generate at least this number of tokens before an end-of-text token is generated. - `echo` (boolean, optional; default value: false): Whether to echo the prompt in the completion. - `temperature` (number, nullable; default value: 0): Adjusts how creatively the model generates outputs. Use combinations of temperature, top_k, and top_p sensibly. - `top_k` (integer, nullable; default value: 0): Introduces randomness into token generation by considering the top k most likely options. - `top_p` (number, nullable; default value: 0): Adds randomness by considering the smallest set of tokens whose cumulative probability exceeds top_p. - `presence_penalty`, `frequency_penalty`, `sequence_penalty` (number, nullable; default value: 0): Various penalties that can reduce repetition. - `sequence_penalty_min_length` (integer; default value: 2): Minimum number of tokens to be considered as a sequence. - `repetition_penalties_include_prompt`, `repetition_penalties_include_completion`, `use_multiplicative_presence_penalty`,`use_multiplicative_frequency_penalty`,`use_multiplicative_sequence_penalty` (boolean, nullable; default value: false): Various settings that adjust how the repetition penalties are applied. - `penalty_bias` (string, nullable): Text used in addition to the penalized tokens for repetition penalties. - `penalty_exceptions` (string[], nullable): Strings that may be generated without penalty. - `penalty_exceptions_include_stop_sequences` (boolean, nullable; default value: true): Include all stop_sequences in penalty_exceptions. - `best_of` (integer, nullable; default value: 1): The number of completions will be generated on the server side. - `n` (integer, nullable; default value: 1): The number of completions to return. - `logit_bias` (object, nullable): Adjust the logit scores before sampling. - `log_probs` (integer, nullable): Number of top log probabilities for each token generated. - `stop_sequences` (string[], nullable): List of strings that will stop generation if they're generated. - `tokens` (boolean, nullable; default value: false): Flag indicating whether individual tokens of the completion should be returned or not. - `raw_completion` (boolean; default value: false): if True, the raw completion of the model will be returned. - `disable_optimizations` (boolean, nullable; default value: false): Disables any applied optimizations to both your prompt and completion. - `completion_bias_inclusion`, `completion_bias_exclusion` (string[], default value: []): Set of strings to bias the generation of tokens. - `completion_bias_inclusion_first_token_only`, `completion_bias_exclusion_first_token_only` (boolean; default value: false): Consider only the first token for the completion_bias_inclusion/exclusion. - `contextual_control_threshold` (number, nullable): Control over how similar tokens are controlled. - `control_log_additive` (boolean; default value: true): Method of applying control to attention scores. """ maximum_tokens: Optional[int] = ( litellm.max_tokens ) # aleph alpha requires max tokens minimum_tokens: Optional[int] = None echo: Optional[bool] = None temperature: Optional[int] = None top_k: Optional[int] = None top_p: Optional[int] = None presence_penalty: Optional[int] = None frequency_penalty: Optional[int] = None sequence_penalty: Optional[int] = None sequence_penalty_min_length: Optional[int] = None repetition_penalties_include_prompt: Optional[bool] = None repetition_penalties_include_completion: Optional[bool] = None use_multiplicative_presence_penalty: Optional[bool] = None use_multiplicative_frequency_penalty: Optional[bool] = None use_multiplicative_sequence_penalty: Optional[bool] = None penalty_bias: Optional[str] = None penalty_exceptions_include_stop_sequences: Optional[bool] = None best_of: Optional[int] = None n: Optional[int] = None logit_bias: Optional[dict] = None log_probs: Optional[int] = None stop_sequences: Optional[list] = None tokens: Optional[bool] = None raw_completion: Optional[bool] = None disable_optimizations: Optional[bool] = None completion_bias_inclusion: Optional[list] = None completion_bias_exclusion: Optional[list] = None completion_bias_inclusion_first_token_only: Optional[bool] = None completion_bias_exclusion_first_token_only: Optional[bool] = None contextual_control_threshold: Optional[int] = None control_log_additive: Optional[bool] = None def __init__( self, maximum_tokens: Optional[int] = None, minimum_tokens: Optional[int] = None, echo: Optional[bool] = None, temperature: Optional[int] = None, top_k: Optional[int] = None, top_p: Optional[int] = None, presence_penalty: Optional[int] = None, frequency_penalty: Optional[int] = None, sequence_penalty: Optional[int] = None, sequence_penalty_min_length: Optional[int] = None, repetition_penalties_include_prompt: Optional[bool] = None, repetition_penalties_include_completion: Optional[bool] = None, use_multiplicative_presence_penalty: Optional[bool] = None, use_multiplicative_frequency_penalty: Optional[bool] = None, use_multiplicative_sequence_penalty: Optional[bool] = None, penalty_bias: Optional[str] = None, penalty_exceptions_include_stop_sequences: Optional[bool] = None, best_of: Optional[int] = None, n: Optional[int] = None, logit_bias: Optional[dict] = None, log_probs: Optional[int] = None, stop_sequences: Optional[list] = None, tokens: Optional[bool] = None, raw_completion: Optional[bool] = None, disable_optimizations: Optional[bool] = None, completion_bias_inclusion: Optional[list] = None, completion_bias_exclusion: Optional[list] = None, completion_bias_inclusion_first_token_only: Optional[bool] = None, completion_bias_exclusion_first_token_only: Optional[bool] = None, contextual_control_threshold: Optional[int] = None, control_log_additive: Optional[bool] = 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 completion( model: str, messages: list, api_base: str, model_response: ModelResponse, print_verbose: Callable, encoding, api_key, logging_obj, optional_params: dict, litellm_params=None, logger_fn=None, default_max_tokens_to_sample=None, ): headers = validate_environment(api_key) ## Load Config config = litellm.AlephAlphaConfig.get_config() for k, v in config.items(): if ( k not in optional_params ): # completion(top_k=3) > aleph_alpha_config(top_k=3) <- allows for dynamic variables to be passed in optional_params[k] = v completion_url = api_base model = model prompt = "" if "control" in model: # follow the ###Instruction / ###Response format for idx, message in enumerate(messages): if "role" in message: if ( idx == 0 ): # set first message as instruction (required), let later user messages be input prompt += f"###Instruction: {message['content']}" else: if message["role"] == "system": prompt += f"###Instruction: {message['content']}" elif message["role"] == "user": prompt += f"###Input: {message['content']}" else: prompt += f"###Response: {message['content']}" else: prompt += f"{message['content']}" else: prompt = " ".join(message["content"] for message in messages) data = { "model": model, "prompt": prompt, **optional_params, } ## LOGGING logging_obj.pre_call( input=prompt, api_key=api_key, additional_args={"complete_input_dict": data}, ) ## COMPLETION CALL response = litellm.module_level_client.post( completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False, ) if "stream" in optional_params and optional_params["stream"] is True: return response.iter_lines() else: ## LOGGING logging_obj.post_call( input=prompt, api_key=api_key, original_response=response.text, additional_args={"complete_input_dict": data}, ) print_verbose(f"raw model_response: {response.text}") ## RESPONSE OBJECT completion_response = response.json() if "error" in completion_response: raise AlephAlphaError( message=completion_response["error"], status_code=response.status_code, ) else: try: choices_list = [] for idx, item in enumerate(completion_response["completions"]): if len(item["completion"]) > 0: message_obj = Message(content=item["completion"]) else: message_obj = Message(content=None) choice_obj = Choices( finish_reason=item["finish_reason"], index=idx + 1, message=message_obj, ) choices_list.append(choice_obj) model_response.choices = choices_list # type: ignore except Exception: raise AlephAlphaError( message=json.dumps(completion_response), 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"], disallowed_special=(), ) ) model_response.created = int(time.time()) model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ) setattr(model_response, "usage", usage) return model_response def embedding(): # logic for parsing in - calling - parsing out model embedding calls pass