litellm-mirror/litellm/llms/aleph_alpha.py

import os, json
from enum import Enum
import requests
import time
from typing import Callable
from litellm.utils import ModelResponse

class AlephAlphaError(Exception):
    def __init__(self, status_code, message):
        self.status_code = status_code
        self.message = message
        super().__init__(
            self.message
        )  # Call the base class constructor with the parameters it needs


class AlephAlphaLLM:
    def __init__(
        self, encoding, default_max_tokens_to_sample, logging_obj, api_key=None
    ):
        self.encoding = encoding
        self.default_max_tokens_to_sample = default_max_tokens_to_sample
        self.completion_url = "https://api.aleph-alpha.com/complete"
        self.api_key = api_key
        self.logging_obj = logging_obj
        self.validate_environment(api_key=api_key)

    def validate_environment(
        self, api_key
    ):  # set up the environment required to run the model
        # set the api key
        if self.api_key == None:
            raise ValueError(
                "Missing Aleph Alpha API Key - A call is being made to Aleph Alpha but no key is set either in the environment variables or via params"
            )
        self.api_key = api_key
        self.headers = {
            "accept": "application/json",
            "content-type": "application/json",
            "Authorization": "Bearer " + self.api_key,
        }

    def completion(
        self,
        model: str,
        messages: list,
        model_response: ModelResponse,
        print_verbose: Callable,
        optional_params=None,
        litellm_params=None,
        logger_fn=None,
    ):  # logic for parsing in - calling - parsing out model completion calls
        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,
            "maximum_tokens": optional_params["maximum_tokens"] if  "maximum_tokens" in optional_params else self.default_max_tokens_to_sample, # required input
            **optional_params,
        }

        ## LOGGING
        self.logging_obj.pre_call(
            input=prompt,
            api_key=self.api_key,
            additional_args={"complete_input_dict": data},
        )
        ## COMPLETION CALL
        response = requests.post(
            self.completion_url, headers=self.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"] == True:
            return response.iter_lines()
        else:
            ## LOGGING
            self.logging_obj.post_call(
                input=prompt,
                api_key=self.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:
                    model_response["choices"][0]["message"]["content"] = completion_response["completions"][0]["completion"]
                except:
                    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(
                self.encoding.encode(prompt)
            ) 
            completion_tokens = len(
                self.encoding.encode(model_response["choices"][0]["message"]["content"])
            )

            model_response["created"] = time.time()
            model_response["model"] = model
            model_response["usage"] = {
                "prompt_tokens": prompt_tokens,
                "completion_tokens": completion_tokens,
                "total_tokens": prompt_tokens + completion_tokens,
            }
            return model_response

    def embedding(
        self,
    ):  # logic for parsing in - calling - parsing out model embedding calls
        pass