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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 CloudflareError(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.cloudflare.com") |
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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 CloudflareConfig: |
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max_tokens: Optional[int] = None |
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stream: Optional[bool] = None |
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def __init__( |
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self, |
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max_tokens: Optional[int] = None, |
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stream: Optional[bool] = None, |
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) -> 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 { |
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k: v |
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for k, v in cls.__dict__.items() |
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if not k.startswith("__") |
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and not isinstance( |
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v, |
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( |
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types.FunctionType, |
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types.BuiltinFunctionType, |
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classmethod, |
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staticmethod, |
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), |
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) |
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and v is not None |
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} |
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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 CloudflareError API Key - A call is being made to cloudflare 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.CloudflareConfig.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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api_base = api_base + model |
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data = { |
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"messages": messages, |
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**optional_params, |
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} |
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logging_obj.pre_call( |
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input=messages, |
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api_key=api_key, |
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additional_args={ |
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"headers": headers, |
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"api_base": api_base, |
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"complete_input_dict": data, |
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}, |
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) |
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if "stream" in optional_params and optional_params["stream"] == True: |
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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"], |
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) |
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return response.iter_lines() |
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else: |
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response = requests.post(api_base, headers=headers, data=json.dumps(data)) |
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logging_obj.post_call( |
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input=messages, |
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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 CloudflareError( |
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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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model_response["choices"][0]["message"]["content"] = completion_response[ |
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"result" |
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]["response"] |
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print_verbose( |
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f"CALCULATING CLOUDFLARE TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}" |
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) |
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prompt_tokens = litellm.utils.get_token_count(messages=messages, model=model) |
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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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model_response["created"] = int(time.time()) |
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model_response["model"] = "cloudflare/" + 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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