cohere.py

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

class CohereError(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 CohereConfig():
    """
    Reference: https://docs.cohere.com/reference/generate

    The class `CohereConfig` provides configuration for the Cohere's API interface. Below are the parameters:
        
    - `num_generations` (integer): Maximum number of generations returned. Default is 1, with a minimum value of 1 and a maximum value of 5.
        
    - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default value is 20.
        
    - `truncate` (string): Specifies how the API handles inputs longer than maximum token length. Options include NONE, START, END. Default is END.
        
    - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.75.
        
    - `preset` (string): Identifier of a custom preset, a combination of parameters such as prompt, temperature etc.
        
    - `end_sequences` (array of strings): The generated text gets cut at the beginning of the earliest occurrence of an end sequence, which will be excluded from the text.
        
    - `stop_sequences` (array of strings): The generated text gets cut at the end of the earliest occurrence of a stop sequence, which will be included in the text.
        
    - `k` (integer): Limits generation at each step to top `k` most likely tokens. Default is 0.
        
    - `p` (number): Limits generation at each step to most likely tokens with total probability mass of `p`. Default is 0.
        
    - `frequency_penalty` (number): Reduces repetitiveness of generated tokens. Higher values apply stronger penalties to previously occurred tokens.
        
    - `presence_penalty` (number): Reduces repetitiveness of generated tokens. Similar to frequency_penalty, but this penalty applies equally to all tokens that have already appeared.
        
    - `return_likelihoods` (string): Specifies how and if token likelihoods are returned with the response. Options include GENERATION, ALL and NONE.
        
    - `logit_bias` (object): Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. e.g. {"hello_world": 1233}
    """
    num_generations: Optional[int]=None
    max_tokens: Optional[int]=None
    truncate: Optional[str]=None
    temperature: Optional[int]=None
    preset: Optional[str]=None
    end_sequences: Optional[list]=None
    stop_sequences: Optional[list]=None
    k: Optional[int]=None
    p: Optional[int]=None
    frequency_penalty: Optional[int]=None
    presence_penalty: Optional[int]=None
    return_likelihoods: Optional[str]=None
    logit_bias: Optional[dict]=None
    
    def __init__(self,
                 num_generations: Optional[int]=None,
                 max_tokens: Optional[int]=None,
                 truncate: Optional[str]=None,
                 temperature: Optional[int]=None,
                 preset: Optional[str]=None,
                 end_sequences: Optional[list]=None,
                 stop_sequences: Optional[list]=None,
                 k: Optional[int]=None,
                 p: Optional[int]=None,
                 frequency_penalty: Optional[int]=None,
                 presence_penalty: Optional[int]=None,
                 return_likelihoods: Optional[str]=None,
                 logit_bias: Optional[dict]=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,
    model_response: ModelResponse,
    print_verbose: Callable,
    encoding,
    api_key,
    logging_obj,
    optional_params=None,
    litellm_params=None,
    logger_fn=None,
):
    headers = validate_environment(api_key)
    completion_url = "https://api.cohere.ai/v1/generate"
    model = model
    prompt = " ".join(message["content"] for message in messages)

    ## Load Config
    config=litellm.CohereConfig.get_config()
    for k, v in config.items():
        if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
            optional_params[k] = v

    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 = requests.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"] == 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 CohereError(
                message=completion_response["error"],
                status_code=response.status_code,
            )
        else:
            try:
                model_response["choices"][0]["message"]["content"] = completion_response["generations"][0]["text"]
            except:
                raise CohereError(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"])
        )

        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(
    model: str,
    input: list,
    api_key: str,
    logging_obj=None,
    model_response=None,
    encoding=None,
):
    headers = validate_environment(api_key)
    embed_url = "https://api.cohere.ai/v1/embed"
    model = model
    data = {
        "model": model,
        "texts": input,
    }

    ## LOGGING
    logging_obj.pre_call(
            input=input,
            api_key=api_key,
            additional_args={"complete_input_dict": data},
        )
    ## COMPLETION CALL
    response = requests.post(
        embed_url, headers=headers, data=json.dumps(data)
    )

    ## LOGGING
    logging_obj.post_call(
            input=input,
            api_key=api_key,
            additional_args={"complete_input_dict": data},
            original_response=response,
        )
    # print(response.json())
    """
        response 
        {
            'object': "list",
            'data': [
            
            ]
            'model', 
            'usage'
        }
    """
    embeddings = response.json()['embeddings']
    output_data = []
    for idx, embedding in enumerate(embeddings):
        output_data.append(
            {
                "object": "embedding",
                "index": idx,
                "embedding": embedding
            }
        )
    model_response["object"] = "list"
    model_response["data"] = output_data
    model_response["model"] = model
    input_tokens = 0
    for text in input:
        input_tokens+=len(encoding.encode(text)) 

    model_response["usage"] = { 
        "prompt_tokens": input_tokens, 
        "total_tokens": input_tokens,
    }
    return model_response