litellm-mirror/litellm/llms/anthropic.py

import os, types
import json
from enum import Enum
import requests
import time
from typing import Callable, Optional
from litellm.utils import ModelResponse, Usage
import litellm 
from .prompt_templates.factory import prompt_factory, custom_prompt
import httpx

class AnthropicConstants(Enum):
    HUMAN_PROMPT = "\n\nHuman: "
    AI_PROMPT = "\n\nAssistant: "

class AnthropicError(Exception):
    def __init__(self, status_code, message):
        self.status_code = status_code
        self.message = message
        self.request = httpx.Request(method="POST", url="https://api.anthropic.com/v1/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 AnthropicConfig():
    """
    Reference: https://docs.anthropic.com/claude/reference/complete_post

    to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
    """
    max_tokens_to_sample: Optional[int]=litellm.max_tokens # anthropic requires a default 
    stop_sequences: Optional[list]=None
    temperature: Optional[int]=None
    top_p: Optional[int]=None
    top_k: Optional[int]=None
    metadata: Optional[dict]=None

    def __init__(self, 
                 max_tokens_to_sample: Optional[int]=256, # anthropic requires a default 
                 stop_sequences: Optional[list]=None,
                 temperature: Optional[int]=None,
                 top_p: Optional[int]=None,
                 top_k: Optional[int]=None,
                 metadata: 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}


# makes headers for API call
def validate_environment(api_key):
    if api_key is None:
        raise ValueError(
            "Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params"
        )
    headers = {
        "accept": "application/json",
        "anthropic-version": "2023-06-01",
        "content-type": "application/json",
        "x-api-key": api_key,
    }
    return headers

def completion(
    model: str,
    messages: list,
    api_base: str,
    custom_prompt_dict: dict,
    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)
    if model in custom_prompt_dict:
            # check if the model has a registered custom prompt
            model_prompt_details = custom_prompt_dict[model]
            prompt = custom_prompt(
                role_dict=model_prompt_details["roles"], 
                initial_prompt_value=model_prompt_details["initial_prompt_value"],  
                final_prompt_value=model_prompt_details["final_prompt_value"], 
                messages=messages
            )
    else:
        prompt = prompt_factory(model=model, messages=messages, custom_llm_provider="anthropic")
        
    ## Load Config
    config = litellm.AnthropicConfig.get_config() 
    for k, v in config.items(): 
        if k not in optional_params: # completion(top_k=3) > anthropic_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
    if "stream" in optional_params and optional_params["stream"] == True:
        response = requests.post(
            api_base,
            headers=headers,
            data=json.dumps(data),
            stream=optional_params["stream"],
        )
        
        if response.status_code != 200:
            raise AnthropicError(status_code=response.status_code, message=response.text)

        return response.iter_lines()
    else:
        response = requests.post(
            api_base, headers=headers, data=json.dumps(data)
        )
        if response.status_code != 200:
            raise AnthropicError(status_code=response.status_code, message=response.text)
        
        ## 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
        try:
            completion_response = response.json()
        except:
            raise AnthropicError(
                message=response.text, status_code=response.status_code
            )
        if "error" in completion_response:
            raise AnthropicError(
                message=str(completion_response["error"]),
                status_code=response.status_code,
            )
        else:
            if len(completion_response["completion"]) > 0:
                model_response["choices"][0]["message"]["content"] = completion_response[
                    "completion"
                ]
            model_response.choices[0].finish_reason = completion_response["stop_reason"]

        ## CALCULATING USAGE
        prompt_tokens = len(
            encoding.encode(prompt)
        )  ##[TODO] use the anthropic tokenizer here
        completion_tokens = len(
            encoding.encode(model_response["choices"][0]["message"].get("content", ""))
        )  ##[TODO] use the anthropic tokenizer here

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

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