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Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/llms/deprecated_providers/aleph_alpha.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/litellm/llms/deprecated_providers/aleph_alpha.py | 307 |
1 files changed, 307 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/deprecated_providers/aleph_alpha.py b/.venv/lib/python3.12/site-packages/litellm/llms/deprecated_providers/aleph_alpha.py new file mode 100644 index 00000000..81ad1346 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/llms/deprecated_providers/aleph_alpha.py @@ -0,0 +1,307 @@ +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().copy() + 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 |