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import os, types |
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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, Optional |
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import litellm |
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import httpx |
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from litellm.utils import ModelResponse, Usage |
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from .prompt_templates.factory import prompt_factory, custom_prompt |
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class TogetherAIError(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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self.request = httpx.Request(method="POST", url="https://api.together.xyz/inference") |
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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 TogetherAIConfig(): |
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""" |
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Reference: https://docs.together.ai/reference/inference |
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The class `TogetherAIConfig` provides configuration for the TogetherAI's API interface. Here are the parameters: |
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- `max_tokens` (int32, required): The maximum number of tokens to generate. |
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- `stop` (string, optional): A string sequence that will truncate (stop) the inference text output. For example, "\n\n" will stop generation as soon as the model generates two newlines. |
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- `temperature` (float, optional): A decimal number that determines the degree of randomness in the response. A value of 1 will always yield the same output. A temperature less than 1 favors more correctness and is appropriate for question answering or summarization. A value greater than 1 introduces more randomness in the output. |
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- `top_p` (float, optional): The `top_p` (nucleus) parameter is used to dynamically adjust the number of choices for each predicted token based on the cumulative probabilities. It specifies a probability threshold, below which all less likely tokens are filtered out. This technique helps to maintain diversity and generate more fluent and natural-sounding text. |
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- `top_k` (int32, optional): The `top_k` parameter is used to limit the number of choices for the next predicted word or token. It specifies the maximum number of tokens to consider at each step, based on their probability of occurrence. This technique helps to speed up the generation process and can improve the quality of the generated text by focusing on the most likely options. |
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- `repetition_penalty` (float, optional): A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition. |
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- `logprobs` (int32, optional): This parameter is not described in the prompt. |
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""" |
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max_tokens: Optional[int]=None |
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stop: Optional[str]=None |
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temperature:Optional[int]=None |
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top_p: Optional[float]=None |
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top_k: Optional[int]=None |
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repetition_penalty: Optional[float]=None |
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logprobs: Optional[int]=None |
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def __init__(self, |
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max_tokens: Optional[int]=None, |
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stop: Optional[str]=None, |
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temperature:Optional[int]=None, |
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top_p: Optional[float]=None, |
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top_k: Optional[int]=None, |
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repetition_penalty: Optional[float]=None, |
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logprobs: Optional[int]=None) -> 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 {k: v for k, v in cls.__dict__.items() |
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if not k.startswith('__') |
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and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) |
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and v is not None} |
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def validate_environment(api_key): |
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if api_key is None: |
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raise ValueError( |
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"Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params" |
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) |
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headers = { |
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"accept": "application/json", |
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"content-type": "application/json", |
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"Authorization": "Bearer " + api_key, |
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} |
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return headers |
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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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config = litellm.TogetherAIConfig.get_config() |
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for k, v in config.items(): |
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if k not in optional_params: |
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optional_params[k] = v |
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print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}") |
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if model in custom_prompt_dict: |
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model_prompt_details = custom_prompt_dict[model] |
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prompt = custom_prompt( |
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role_dict=model_prompt_details.get("roles", {}), |
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initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), |
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final_prompt_value=model_prompt_details.get("final_prompt_value", ""), |
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bos_token=model_prompt_details.get("bos_token", ""), |
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eos_token=model_prompt_details.get("eos_token", ""), |
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messages=messages, |
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) |
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else: |
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prompt = prompt_factory(model=model, messages=messages) |
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data = { |
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"model": model, |
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"prompt": prompt, |
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"request_type": "language-model-inference", |
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**optional_params, |
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} |
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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, "headers": headers, "api_base": api_base}, |
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) |
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if ( |
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"stream_tokens" in optional_params |
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and optional_params["stream_tokens"] == True |
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): |
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response = requests.post( |
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api_base, |
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headers=headers, |
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data=json.dumps(data), |
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stream=optional_params["stream_tokens"], |
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) |
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return response.iter_lines() |
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else: |
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response = requests.post( |
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api_base, |
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headers=headers, |
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data=json.dumps(data) |
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) |
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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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if response.status_code != 200: |
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raise TogetherAIError( |
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status_code=response.status_code, message=response.text |
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) |
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completion_response = response.json() |
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if "error" in completion_response: |
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raise TogetherAIError( |
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message=json.dumps(completion_response), |
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status_code=response.status_code, |
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) |
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elif "error" in completion_response["output"]: |
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raise TogetherAIError( |
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message=json.dumps(completion_response["output"]), status_code=response.status_code |
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) |
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if len(completion_response["output"]["choices"][0]["text"]) > 0: |
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model_response["choices"][0]["message"]["content"] = completion_response["output"]["choices"][0]["text"] |
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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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if "finish_reason" in completion_response["output"]["choices"][0]: |
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model_response.choices[0].finish_reason = completion_response["output"]["choices"][0]["finish_reason"] |
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model_response["created"] = int(time.time()) |
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model_response["model"] = model |
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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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model_response.usage = usage |
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return model_response |
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def embedding(): |
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pass |
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