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import os, types
import json
from enum import Enum
import requests
import time
from typing import Callable, Optional
import litellm
import httpx
from litellm.utils import ModelResponse, Usage
from .prompt_templates.factory import prompt_factory, custom_prompt
class CloudflareError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
self.request = httpx.Request(method="POST", url="https://api.cloudflare.com")
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 CloudflareConfig:
max_tokens: Optional[int] = None
stream: Optional[bool] = None
def __init__(
self,
max_tokens: Optional[int] = None,
stream: Optional[bool] = 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):
if api_key is None:
raise ValueError(
"Missing CloudflareError API Key - A call is being made to cloudflare but no key is set either in the environment variables or via params"
)
headers = {
"accept": "application/json",
"content-type": "application/json",
"Authorization": "Bearer " + api_key,
}
return headers
def completion(
model: str,
messages: list,
api_base: str,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
custom_prompt_dict={},
optional_params=None,
litellm_params=None,
logger_fn=None,
):
headers = validate_environment(api_key)
## Load Config
config = litellm.CloudflareConfig.get_config()
for k, v in config.items():
if k not in optional_params:
optional_params[k] = v
print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}")
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.get("roles", {}),
initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
bos_token=model_prompt_details.get("bos_token", ""),
eos_token=model_prompt_details.get("eos_token", ""),
messages=messages,
)
# cloudflare adds the model to the api base
api_base = api_base + model
data = {
"messages": messages,
**optional_params,
}
## LOGGING
logging_obj.pre_call(
input=messages,
api_key=api_key,
additional_args={
"headers": headers,
"api_base": api_base,
"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"],
)
return response.iter_lines()
else:
response = requests.post(api_base, headers=headers, data=json.dumps(data))
## LOGGING
logging_obj.post_call(
input=messages,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT
if response.status_code != 200:
raise CloudflareError(
status_code=response.status_code, message=response.text
)
completion_response = response.json()
model_response["choices"][0]["message"]["content"] = completion_response[
"result"
]["response"]
## CALCULATING USAGE
print_verbose(
f"CALCULATING CLOUDFLARE TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}"
)
prompt_tokens = litellm.utils.get_token_count(messages=messages, model=model)
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
model_response["created"] = int(time.time())
model_response["model"] = "cloudflare/" + 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
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