forked from phoenix/litellm-mirror
(feat) v0 adding cloudflare
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ishaan-jaff
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commit
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3 changed files with 234 additions and 0 deletions
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@ -44,6 +44,7 @@ huggingface_key: Optional[str] = None
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vertex_project: Optional[str] = None
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vertex_location: Optional[str] = None
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togetherai_api_key: Optional[str] = None
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cloudflare_api_key: Optional[str] = None
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baseten_key: Optional[str] = None
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aleph_alpha_key: Optional[str] = None
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nlp_cloud_key: Optional[str] = None
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@ -390,6 +391,7 @@ provider_list: List = [
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"mistral",
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"maritalk",
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"voyage",
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"cloudflare",
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"custom", # custom apis
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]
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@ -491,6 +493,7 @@ from .llms.replicate import ReplicateConfig
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from .llms.cohere import CohereConfig
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from .llms.ai21 import AI21Config
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from .llms.together_ai import TogetherAIConfig
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from .llms.cloudflare import CloudflareConfig
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from .llms.palm import PalmConfig
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from .llms.gemini import GeminiConfig
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from .llms.nlp_cloud import NLPCloudConfig
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185
litellm/llms/cloudflare.py
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185
litellm/llms/cloudflare.py
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@ -0,0 +1,185 @@
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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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) # Call the base class constructor with the parameters it needs
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class CloudflareConfig:
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max_tokens: Optional[int] = 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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) -> 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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## Load Config
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config = litellm.CloudflareConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
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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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# check if the model has a registered custom prompt
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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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else:
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prompt = prompt_factory(
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model=model,
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messages=messages,
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api_key=api_key,
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custom_llm_provider="together_ai",
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) # api key required to query together ai model list
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data = {
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"model": model,
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"prompt": prompt,
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"request_type": "language-model-inference",
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**optional_params,
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}
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"headers": headers,
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"api_base": api_base,
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},
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)
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## COMPLETION CALL
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if "stream_tokens" in optional_params and optional_params["stream_tokens"] == 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_tokens"],
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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
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logging_obj.post_call(
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input=prompt,
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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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## RESPONSE OBJECT
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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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if len(completion_response["output"]["choices"][0]["text"]) >= 0:
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model_response["choices"][0]["message"]["content"] = completion_response[
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"output"
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]["choices"][0]["text"]
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## CALCULATING USAGE
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print_verbose(
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f"CALCULATING TOGETHERAI 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 = len(encoding.encode(prompt))
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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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if "finish_reason" in completion_response["output"]["choices"][0]:
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model_response.choices[0].finish_reason = completion_response["output"][
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"choices"
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][0]["finish_reason"]
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model_response["created"] = int(time.time())
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model_response["model"] = "together_ai/" + 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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# logic for parsing in - calling - parsing out model embedding calls
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pass
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@ -50,6 +50,7 @@ from .llms import (
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vllm,
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ollama,
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ollama_chat,
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cloudflare,
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cohere,
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petals,
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oobabooga,
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@ -1564,6 +1565,51 @@ def completion(
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return generator
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response = generator
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elif custom_llm_provider == "cloudflare":
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api_key = (
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api_key
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or litellm.cloudflare_api_key
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or litellm.api_key
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or get_secret("CLOUDFLARE_API_KEY")
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)
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# api_base = (
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# api_base
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# or litellm.api_base
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# or get_secret("CLOUDFLARE_API_BASE")
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# or "https://api.anthropic.com/v1/complete"
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# )
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custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
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response = cloudflare.completion(
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model=model,
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messages=messages,
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api_base=api_base,
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custom_prompt_dict=litellm.custom_prompt_dict,
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model_response=model_response,
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print_verbose=print_verbose,
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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encoding=encoding, # for calculating input/output tokens
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api_key=api_key,
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logging_obj=logging,
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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response = CustomStreamWrapper(
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response,
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model,
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custom_llm_provider="anthropic",
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logging_obj=logging,
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)
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if optional_params.get("stream", False) or acompletion == True:
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## LOGGING
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logging.post_call(
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input=messages,
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api_key=api_key,
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original_response=response,
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)
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response = response
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elif (
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custom_llm_provider == "baseten"
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or litellm.api_base == "https://app.baseten.co"
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