(feat) v0 adding cloudflare

litellm/__init__.py

@@ -44,6 +44,7 @@ huggingface_key: Optional[str] = None
 vertex_project: Optional[str] = None
 vertex_location: Optional[str] = None
 togetherai_api_key: Optional[str] = None
+cloudflare_api_key: Optional[str] = None
 baseten_key: Optional[str] = None
 aleph_alpha_key: Optional[str] = None
 nlp_cloud_key: Optional[str] = None
@@ -390,6 +391,7 @@ provider_list: List = [
     "mistral",
     "maritalk",
     "voyage",
+    "cloudflare",
     "custom",  # custom apis
 ]
 
@@ -491,6 +493,7 @@ from .llms.replicate import ReplicateConfig
 from .llms.cohere import CohereConfig
 from .llms.ai21 import AI21Config
 from .llms.together_ai import TogetherAIConfig
+from .llms.cloudflare import CloudflareConfig
 from .llms.palm import PalmConfig
 from .llms.gemini import GeminiConfig
 from .llms.nlp_cloud import NLPCloudConfig

litellm/llms/cloudflare.py

@@ -0,0 +1,185 @@
+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
+
+    def __init__(
+        self,
+        max_tokens: Optional[int] = 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
+        ):  # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
+            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,
+        )
+    else:
+        prompt = prompt_factory(
+            model=model,
+            messages=messages,
+            api_key=api_key,
+            custom_llm_provider="together_ai",
+        )  # api key required to query together ai model list
+
+    data = {
+        "model": model,
+        "prompt": prompt,
+        "request_type": "language-model-inference",
+        **optional_params,
+    }
+
+    ## LOGGING
+    logging_obj.pre_call(
+        input=prompt,
+        api_key=api_key,
+        additional_args={
+            "complete_input_dict": data,
+            "headers": headers,
+            "api_base": api_base,
+        },
+    )
+    ## COMPLETION CALL
+    if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True:
+        response = requests.post(
+            api_base,
+            headers=headers,
+            data=json.dumps(data),
+            stream=optional_params["stream_tokens"],
+        )
+        return response.iter_lines()
+    else:
+        response = requests.post(api_base, headers=headers, data=json.dumps(data))
+        ## 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
+        if response.status_code != 200:
+            raise CloudflareError(
+                status_code=response.status_code, message=response.text
+            )
+        completion_response = response.json()
+
+        if len(completion_response["output"]["choices"][0]["text"]) >= 0:
+            model_response["choices"][0]["message"]["content"] = completion_response[
+                "output"
+            ]["choices"][0]["text"]
+
+        ## CALCULATING USAGE
+        print_verbose(
+            f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}"
+        )
+        prompt_tokens = len(encoding.encode(prompt))
+        completion_tokens = len(
+            encoding.encode(model_response["choices"][0]["message"].get("content", ""))
+        )
+        if "finish_reason" in completion_response["output"]["choices"][0]:
+            model_response.choices[0].finish_reason = completion_response["output"][
+                "choices"
+            ][0]["finish_reason"]
+        model_response["created"] = int(time.time())
+        model_response["model"] = "together_ai/" + 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

litellm/main.py

@@ -50,6 +50,7 @@ from .llms import (
     vllm,
     ollama,
     ollama_chat,
+    cloudflare,
     cohere,
     petals,
     oobabooga,
@@ -1564,6 +1565,51 @@ def completion(
                 return generator
 
             response = generator
+        elif custom_llm_provider == "cloudflare":
+            api_key = (
+                api_key
+                or litellm.cloudflare_api_key
+                or litellm.api_key
+                or get_secret("CLOUDFLARE_API_KEY")
+            )
+            # api_base = (
+            #     api_base
+            #     or litellm.api_base
+            #     or get_secret("CLOUDFLARE_API_BASE")
+            #     or "https://api.anthropic.com/v1/complete"
+            # )
+            custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
+            response = cloudflare.completion(
+                model=model,
+                messages=messages,
+                api_base=api_base,
+                custom_prompt_dict=litellm.custom_prompt_dict,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                encoding=encoding,  # for calculating input/output tokens
+                api_key=api_key,
+                logging_obj=logging,
+            )
+            if "stream" in optional_params and optional_params["stream"] == True:
+                # don't try to access stream object,
+                response = CustomStreamWrapper(
+                    response,
+                    model,
+                    custom_llm_provider="anthropic",
+                    logging_obj=logging,
+                )
+
+            if optional_params.get("stream", False) or acompletion == True:
+                ## LOGGING
+                logging.post_call(
+                    input=messages,
+                    api_key=api_key,
+                    original_response=response,
+                )
+            response = response
         elif (
             custom_llm_provider == "baseten"
             or litellm.api_base == "https://app.baseten.co"