|
from typing import Optional, Union, Any |
|
import types, requests |
|
from .base import BaseLLM |
|
from litellm.utils import ( |
|
ModelResponse, |
|
Choices, |
|
Message, |
|
CustomStreamWrapper, |
|
convert_to_model_response_object, |
|
) |
|
from typing import Callable, Optional |
|
from litellm import OpenAIConfig |
|
import litellm, json |
|
import httpx |
|
from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport |
|
from openai import AzureOpenAI, AsyncAzureOpenAI |
|
|
|
|
|
class AzureOpenAIError(Exception): |
|
def __init__( |
|
self, |
|
status_code, |
|
message, |
|
request: Optional[httpx.Request] = None, |
|
response: Optional[httpx.Response] = None, |
|
): |
|
self.status_code = status_code |
|
self.message = message |
|
if request: |
|
self.request = request |
|
else: |
|
self.request = httpx.Request(method="POST", url="https://api.openai.com/v1") |
|
if response: |
|
self.response = response |
|
else: |
|
self.response = httpx.Response( |
|
status_code=status_code, request=self.request |
|
) |
|
super().__init__( |
|
self.message |
|
) |
|
|
|
|
|
class AzureOpenAIConfig(OpenAIConfig): |
|
""" |
|
Reference: https://platform.openai.com/docs/api-reference/chat/create |
|
|
|
The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters:: |
|
|
|
- `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition. |
|
|
|
- `function_call` (string or object): This optional parameter controls how the model calls functions. |
|
|
|
- `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs. |
|
|
|
- `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion. |
|
|
|
- `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion. |
|
|
|
- `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message. |
|
|
|
- `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics. |
|
|
|
- `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens. |
|
|
|
- `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2. |
|
|
|
- `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling. |
|
""" |
|
|
|
def __init__( |
|
self, |
|
frequency_penalty: Optional[int] = None, |
|
function_call: Optional[Union[str, dict]] = None, |
|
functions: Optional[list] = None, |
|
logit_bias: Optional[dict] = None, |
|
max_tokens: Optional[int] = None, |
|
n: Optional[int] = None, |
|
presence_penalty: Optional[int] = None, |
|
stop: Optional[Union[str, list]] = None, |
|
temperature: Optional[int] = None, |
|
top_p: Optional[int] = None, |
|
) -> None: |
|
super().__init__( |
|
frequency_penalty, |
|
function_call, |
|
functions, |
|
logit_bias, |
|
max_tokens, |
|
n, |
|
presence_penalty, |
|
stop, |
|
temperature, |
|
top_p, |
|
) |
|
|
|
|
|
class AzureChatCompletion(BaseLLM): |
|
def __init__(self) -> None: |
|
super().__init__() |
|
|
|
def validate_environment(self, api_key, azure_ad_token): |
|
headers = { |
|
"content-type": "application/json", |
|
} |
|
if api_key is not None: |
|
headers["api-key"] = api_key |
|
elif azure_ad_token is not None: |
|
headers["Authorization"] = f"Bearer {azure_ad_token}" |
|
return headers |
|
|
|
def completion( |
|
self, |
|
model: str, |
|
messages: list, |
|
model_response: ModelResponse, |
|
api_key: str, |
|
api_base: str, |
|
api_version: str, |
|
api_type: str, |
|
azure_ad_token: str, |
|
print_verbose: Callable, |
|
timeout, |
|
logging_obj, |
|
optional_params, |
|
litellm_params, |
|
logger_fn, |
|
acompletion: bool = False, |
|
headers: Optional[dict] = None, |
|
client=None, |
|
): |
|
super().completion() |
|
exception_mapping_worked = False |
|
try: |
|
if model is None or messages is None: |
|
raise AzureOpenAIError( |
|
status_code=422, message=f"Missing model or messages" |
|
) |
|
|
|
max_retries = optional_params.pop("max_retries", 2) |
|
|
|
|
|
|
|
if "gateway.ai.cloudflare.com" in api_base: |
|
|
|
if client is None: |
|
if not api_base.endswith("/"): |
|
api_base += "/" |
|
api_base += f"{model}" |
|
|
|
azure_client_params = { |
|
"api_version": api_version, |
|
"base_url": f"{api_base}", |
|
"http_client": litellm.client_session, |
|
"max_retries": max_retries, |
|
"timeout": timeout, |
|
} |
|
if api_key is not None: |
|
azure_client_params["api_key"] = api_key |
|
elif azure_ad_token is not None: |
|
azure_client_params["azure_ad_token"] = azure_ad_token |
|
|
|
if acompletion is True: |
|
client = AsyncAzureOpenAI(**azure_client_params) |
|
else: |
|
client = AzureOpenAI(**azure_client_params) |
|
|
|
data = {"model": None, "messages": messages, **optional_params} |
|
else: |
|
data = { |
|
"model": model, |
|
"messages": messages, |
|
**optional_params, |
|
} |
|
|
|
if acompletion is True: |
|
if optional_params.get("stream", False): |
|
return self.async_streaming( |
|
logging_obj=logging_obj, |
|
api_base=api_base, |
|
data=data, |
|
model=model, |
|
api_key=api_key, |
|
api_version=api_version, |
|
azure_ad_token=azure_ad_token, |
|
timeout=timeout, |
|
client=client, |
|
) |
|
else: |
|
return self.acompletion( |
|
api_base=api_base, |
|
data=data, |
|
model_response=model_response, |
|
api_key=api_key, |
|
api_version=api_version, |
|
model=model, |
|
azure_ad_token=azure_ad_token, |
|
timeout=timeout, |
|
client=client, |
|
logging_obj=logging_obj, |
|
) |
|
elif "stream" in optional_params and optional_params["stream"] == True: |
|
return self.streaming( |
|
logging_obj=logging_obj, |
|
api_base=api_base, |
|
data=data, |
|
model=model, |
|
api_key=api_key, |
|
api_version=api_version, |
|
azure_ad_token=azure_ad_token, |
|
timeout=timeout, |
|
client=client, |
|
) |
|
else: |
|
|
|
logging_obj.pre_call( |
|
input=messages, |
|
api_key=api_key, |
|
additional_args={ |
|
"headers": { |
|
"api_key": api_key, |
|
"azure_ad_token": azure_ad_token, |
|
}, |
|
"api_version": api_version, |
|
"api_base": api_base, |
|
"complete_input_dict": data, |
|
}, |
|
) |
|
if not isinstance(max_retries, int): |
|
raise AzureOpenAIError( |
|
status_code=422, message="max retries must be an int" |
|
) |
|
|
|
azure_client_params = { |
|
"api_version": api_version, |
|
"azure_endpoint": api_base, |
|
"azure_deployment": model, |
|
"http_client": litellm.client_session, |
|
"max_retries": max_retries, |
|
"timeout": timeout, |
|
} |
|
if api_key is not None: |
|
azure_client_params["api_key"] = api_key |
|
elif azure_ad_token is not None: |
|
azure_client_params["azure_ad_token"] = azure_ad_token |
|
if client is None: |
|
azure_client = AzureOpenAI(**azure_client_params) |
|
else: |
|
azure_client = client |
|
response = azure_client.chat.completions.create(**data, timeout=timeout) |
|
stringified_response = response.model_dump() |
|
|
|
logging_obj.post_call( |
|
input=messages, |
|
api_key=api_key, |
|
original_response=stringified_response, |
|
additional_args={ |
|
"headers": headers, |
|
"api_version": api_version, |
|
"api_base": api_base, |
|
}, |
|
) |
|
return convert_to_model_response_object( |
|
response_object=stringified_response, |
|
model_response_object=model_response, |
|
) |
|
except AzureOpenAIError as e: |
|
exception_mapping_worked = True |
|
raise e |
|
except Exception as e: |
|
if hasattr(e, "status_code"): |
|
raise AzureOpenAIError(status_code=e.status_code, message=str(e)) |
|
else: |
|
raise AzureOpenAIError(status_code=500, message=str(e)) |
|
|
|
async def acompletion( |
|
self, |
|
api_key: str, |
|
api_version: str, |
|
model: str, |
|
api_base: str, |
|
data: dict, |
|
timeout: Any, |
|
model_response: ModelResponse, |
|
azure_ad_token: Optional[str] = None, |
|
client=None, |
|
logging_obj=None, |
|
): |
|
response = None |
|
try: |
|
max_retries = data.pop("max_retries", 2) |
|
if not isinstance(max_retries, int): |
|
raise AzureOpenAIError( |
|
status_code=422, message="max retries must be an int" |
|
) |
|
|
|
|
|
azure_client_params = { |
|
"api_version": api_version, |
|
"azure_endpoint": api_base, |
|
"azure_deployment": model, |
|
"http_client": litellm.client_session, |
|
"max_retries": max_retries, |
|
"timeout": timeout, |
|
} |
|
if api_key is not None: |
|
azure_client_params["api_key"] = api_key |
|
elif azure_ad_token is not None: |
|
azure_client_params["azure_ad_token"] = azure_ad_token |
|
if client is None: |
|
azure_client = AsyncAzureOpenAI(**azure_client_params) |
|
else: |
|
azure_client = client |
|
|
|
logging_obj.pre_call( |
|
input=data["messages"], |
|
api_key=azure_client.api_key, |
|
additional_args={ |
|
"headers": {"Authorization": f"Bearer {azure_client.api_key}"}, |
|
"api_base": azure_client._base_url._uri_reference, |
|
"acompletion": True, |
|
"complete_input_dict": data, |
|
}, |
|
) |
|
response = await azure_client.chat.completions.create( |
|
**data, timeout=timeout |
|
) |
|
return convert_to_model_response_object( |
|
response_object=response.model_dump(), |
|
model_response_object=model_response, |
|
) |
|
except AzureOpenAIError as e: |
|
exception_mapping_worked = True |
|
raise e |
|
except Exception as e: |
|
if hasattr(e, "status_code"): |
|
raise e |
|
else: |
|
raise AzureOpenAIError(status_code=500, message=str(e)) |
|
|
|
def streaming( |
|
self, |
|
logging_obj, |
|
api_base: str, |
|
api_key: str, |
|
api_version: str, |
|
data: dict, |
|
model: str, |
|
timeout: Any, |
|
azure_ad_token: Optional[str] = None, |
|
client=None, |
|
): |
|
max_retries = data.pop("max_retries", 2) |
|
if not isinstance(max_retries, int): |
|
raise AzureOpenAIError( |
|
status_code=422, message="max retries must be an int" |
|
) |
|
|
|
azure_client_params = { |
|
"api_version": api_version, |
|
"azure_endpoint": api_base, |
|
"azure_deployment": model, |
|
"http_client": litellm.client_session, |
|
"max_retries": max_retries, |
|
"timeout": timeout, |
|
} |
|
if api_key is not None: |
|
azure_client_params["api_key"] = api_key |
|
elif azure_ad_token is not None: |
|
azure_client_params["azure_ad_token"] = azure_ad_token |
|
if client is None: |
|
azure_client = AzureOpenAI(**azure_client_params) |
|
else: |
|
azure_client = client |
|
|
|
logging_obj.pre_call( |
|
input=data["messages"], |
|
api_key=azure_client.api_key, |
|
additional_args={ |
|
"headers": {"Authorization": f"Bearer {azure_client.api_key}"}, |
|
"api_base": azure_client._base_url._uri_reference, |
|
"acompletion": True, |
|
"complete_input_dict": data, |
|
}, |
|
) |
|
response = azure_client.chat.completions.create(**data, timeout=timeout) |
|
streamwrapper = CustomStreamWrapper( |
|
completion_stream=response, |
|
model=model, |
|
custom_llm_provider="azure", |
|
logging_obj=logging_obj, |
|
) |
|
return streamwrapper |
|
|
|
async def async_streaming( |
|
self, |
|
logging_obj, |
|
api_base: str, |
|
api_key: str, |
|
api_version: str, |
|
data: dict, |
|
model: str, |
|
timeout: Any, |
|
azure_ad_token: Optional[str] = None, |
|
client=None, |
|
): |
|
try: |
|
|
|
azure_client_params = { |
|
"api_version": api_version, |
|
"azure_endpoint": api_base, |
|
"azure_deployment": model, |
|
"http_client": litellm.client_session, |
|
"max_retries": data.pop("max_retries", 2), |
|
"timeout": timeout, |
|
} |
|
if api_key is not None: |
|
azure_client_params["api_key"] = api_key |
|
elif azure_ad_token is not None: |
|
azure_client_params["azure_ad_token"] = azure_ad_token |
|
if client is None: |
|
azure_client = AsyncAzureOpenAI(**azure_client_params) |
|
else: |
|
azure_client = client |
|
|
|
logging_obj.pre_call( |
|
input=data["messages"], |
|
api_key=azure_client.api_key, |
|
additional_args={ |
|
"headers": {"Authorization": f"Bearer {azure_client.api_key}"}, |
|
"api_base": azure_client._base_url._uri_reference, |
|
"acompletion": True, |
|
"complete_input_dict": data, |
|
}, |
|
) |
|
response = await azure_client.chat.completions.create( |
|
**data, timeout=timeout |
|
) |
|
|
|
streamwrapper = CustomStreamWrapper( |
|
completion_stream=response, |
|
model=model, |
|
custom_llm_provider="azure", |
|
logging_obj=logging_obj, |
|
) |
|
return streamwrapper |
|
except Exception as e: |
|
if hasattr(e, "status_code"): |
|
raise AzureOpenAIError(status_code=e.status_code, message=str(e)) |
|
else: |
|
raise AzureOpenAIError(status_code=500, message=str(e)) |
|
|
|
async def aembedding( |
|
self, |
|
data: dict, |
|
model_response: ModelResponse, |
|
azure_client_params: dict, |
|
api_key: str, |
|
input: list, |
|
client=None, |
|
logging_obj=None, |
|
timeout=None, |
|
): |
|
response = None |
|
try: |
|
if client is None: |
|
openai_aclient = AsyncAzureOpenAI(**azure_client_params) |
|
else: |
|
openai_aclient = client |
|
response = await openai_aclient.embeddings.create(**data, timeout=timeout) |
|
stringified_response = response.model_dump() |
|
|
|
logging_obj.post_call( |
|
input=input, |
|
api_key=api_key, |
|
additional_args={"complete_input_dict": data}, |
|
original_response=stringified_response, |
|
) |
|
return convert_to_model_response_object( |
|
response_object=stringified_response, |
|
model_response_object=model_response, |
|
response_type="embedding", |
|
) |
|
except Exception as e: |
|
|
|
logging_obj.post_call( |
|
input=input, |
|
api_key=api_key, |
|
additional_args={"complete_input_dict": data}, |
|
original_response=str(e), |
|
) |
|
raise e |
|
|
|
def embedding( |
|
self, |
|
model: str, |
|
input: list, |
|
api_key: str, |
|
api_base: str, |
|
api_version: str, |
|
timeout: float, |
|
logging_obj=None, |
|
model_response=None, |
|
optional_params=None, |
|
azure_ad_token: Optional[str] = None, |
|
client=None, |
|
aembedding=None, |
|
): |
|
super().embedding() |
|
exception_mapping_worked = False |
|
if self._client_session is None: |
|
self._client_session = self.create_client_session() |
|
try: |
|
data = {"model": model, "input": input, **optional_params} |
|
max_retries = data.pop("max_retries", 2) |
|
if not isinstance(max_retries, int): |
|
raise AzureOpenAIError( |
|
status_code=422, message="max retries must be an int" |
|
) |
|
|
|
|
|
azure_client_params = { |
|
"api_version": api_version, |
|
"azure_endpoint": api_base, |
|
"azure_deployment": model, |
|
"http_client": litellm.client_session, |
|
"max_retries": max_retries, |
|
"timeout": timeout, |
|
} |
|
if api_key is not None: |
|
azure_client_params["api_key"] = api_key |
|
elif azure_ad_token is not None: |
|
azure_client_params["azure_ad_token"] = azure_ad_token |
|
|
|
|
|
logging_obj.pre_call( |
|
input=input, |
|
api_key=api_key, |
|
additional_args={ |
|
"complete_input_dict": data, |
|
"headers": {"api_key": api_key, "azure_ad_token": azure_ad_token}, |
|
}, |
|
) |
|
|
|
if aembedding == True: |
|
response = self.aembedding( |
|
data=data, |
|
input=input, |
|
logging_obj=logging_obj, |
|
api_key=api_key, |
|
model_response=model_response, |
|
azure_client_params=azure_client_params, |
|
timeout=timeout, |
|
) |
|
return response |
|
if client is None: |
|
azure_client = AzureOpenAI(**azure_client_params) |
|
else: |
|
azure_client = client |
|
|
|
response = azure_client.embeddings.create(**data, timeout=timeout) |
|
|
|
logging_obj.post_call( |
|
input=input, |
|
api_key=api_key, |
|
additional_args={"complete_input_dict": data, "api_base": api_base}, |
|
original_response=response, |
|
) |
|
|
|
return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="embedding") |
|
except AzureOpenAIError as e: |
|
exception_mapping_worked = True |
|
raise e |
|
except Exception as e: |
|
if hasattr(e, "status_code"): |
|
raise AzureOpenAIError(status_code=e.status_code, message=str(e)) |
|
else: |
|
raise AzureOpenAIError(status_code=500, message=str(e)) |
|
|
|
async def aimage_generation( |
|
self, |
|
data: dict, |
|
model_response: ModelResponse, |
|
azure_client_params: dict, |
|
api_key: str, |
|
input: list, |
|
client=None, |
|
logging_obj=None, |
|
timeout=None, |
|
): |
|
response = None |
|
try: |
|
if client is None: |
|
client_session = litellm.aclient_session or httpx.AsyncClient( |
|
transport=AsyncCustomHTTPTransport(), |
|
) |
|
openai_aclient = AsyncAzureOpenAI( |
|
http_client=client_session, **azure_client_params |
|
) |
|
else: |
|
openai_aclient = client |
|
response = await openai_aclient.images.generate(**data, timeout=timeout) |
|
stringified_response = response.model_dump() |
|
|
|
logging_obj.post_call( |
|
input=input, |
|
api_key=api_key, |
|
additional_args={"complete_input_dict": data}, |
|
original_response=stringified_response, |
|
) |
|
return convert_to_model_response_object( |
|
response_object=stringified_response, |
|
model_response_object=model_response, |
|
response_type="image_generation", |
|
) |
|
except Exception as e: |
|
|
|
logging_obj.post_call( |
|
input=input, |
|
api_key=api_key, |
|
additional_args={"complete_input_dict": data}, |
|
original_response=str(e), |
|
) |
|
raise e |
|
|
|
def image_generation( |
|
self, |
|
prompt: str, |
|
timeout: float, |
|
model: Optional[str] = None, |
|
api_key: Optional[str] = None, |
|
api_base: Optional[str] = None, |
|
api_version: Optional[str] = None, |
|
model_response: Optional[litellm.utils.ImageResponse] = None, |
|
azure_ad_token: Optional[str] = None, |
|
logging_obj=None, |
|
optional_params=None, |
|
client=None, |
|
aimg_generation=None, |
|
): |
|
exception_mapping_worked = False |
|
try: |
|
if model and len(model) > 0: |
|
model = model |
|
else: |
|
model = None |
|
data = {"model": model, "prompt": prompt, **optional_params} |
|
max_retries = data.pop("max_retries", 2) |
|
if not isinstance(max_retries, int): |
|
raise AzureOpenAIError( |
|
status_code=422, message="max retries must be an int" |
|
) |
|
|
|
|
|
azure_client_params = { |
|
"api_version": api_version, |
|
"azure_endpoint": api_base, |
|
"azure_deployment": model, |
|
"max_retries": max_retries, |
|
"timeout": timeout, |
|
} |
|
if api_key is not None: |
|
azure_client_params["api_key"] = api_key |
|
elif azure_ad_token is not None: |
|
azure_client_params["azure_ad_token"] = azure_ad_token |
|
|
|
if aimg_generation == True: |
|
response = self.aimage_generation(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_key=api_key, client=client, azure_client_params=azure_client_params, timeout=timeout) |
|
return response |
|
|
|
if client is None: |
|
client_session = litellm.client_session or httpx.Client( |
|
transport=CustomHTTPTransport(), |
|
) |
|
azure_client = AzureOpenAI(http_client=client_session, **azure_client_params) |
|
else: |
|
azure_client = client |
|
|
|
|
|
logging_obj.pre_call( |
|
input=prompt, |
|
api_key=azure_client.api_key, |
|
additional_args={ |
|
"headers": {"Authorization": f"Bearer {azure_client.api_key}"}, |
|
"api_base": azure_client._base_url._uri_reference, |
|
"acompletion": False, |
|
"complete_input_dict": data, |
|
}, |
|
) |
|
|
|
|
|
response = azure_client.images.generate(**data, timeout=timeout) |
|
|
|
logging_obj.post_call( |
|
input=input, |
|
api_key=api_key, |
|
additional_args={"complete_input_dict": data}, |
|
original_response=response, |
|
) |
|
|
|
return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="image_generation") |
|
except AzureOpenAIError as e: |
|
exception_mapping_worked = True |
|
raise e |
|
except Exception as e: |
|
if hasattr(e, "status_code"): |
|
raise AzureOpenAIError(status_code=e.status_code, message=str(e)) |
|
else: |
|
raise AzureOpenAIError(status_code=500, message=str(e)) |
|
|
|
async def ahealth_check( |
|
self, |
|
model: Optional[str], |
|
api_key: str, |
|
api_base: str, |
|
api_version: str, |
|
timeout: float, |
|
mode: str, |
|
messages: Optional[list] = None, |
|
input: Optional[list] = None, |
|
prompt: Optional[str] = None, |
|
): |
|
client_session = litellm.aclient_session or httpx.AsyncClient( |
|
transport=AsyncCustomHTTPTransport(), |
|
) |
|
if "gateway.ai.cloudflare.com" in api_base: |
|
|
|
if not api_base.endswith("/"): |
|
api_base += "/" |
|
api_base += f"{model}" |
|
client = AsyncAzureOpenAI( |
|
base_url=api_base, |
|
api_version=api_version, |
|
api_key=api_key, |
|
timeout=timeout, |
|
http_client=client_session, |
|
) |
|
model = None |
|
|
|
else: |
|
client = AsyncAzureOpenAI( |
|
api_version=api_version, |
|
azure_endpoint=api_base, |
|
api_key=api_key, |
|
timeout=timeout, |
|
http_client=client_session, |
|
) |
|
|
|
|
|
if model is None and mode != "image_generation": |
|
raise Exception("model is not set") |
|
|
|
completion = None |
|
|
|
if mode == "completion": |
|
completion = await client.completions.with_raw_response.create( |
|
model=model, |
|
prompt=prompt, |
|
) |
|
elif mode == "chat": |
|
if messages is None: |
|
raise Exception("messages is not set") |
|
completion = await client.chat.completions.with_raw_response.create( |
|
model=model, |
|
messages=messages, |
|
) |
|
elif mode == "embedding": |
|
if input is None: |
|
raise Exception("input is not set") |
|
completion = await client.embeddings.with_raw_response.create( |
|
model=model, |
|
input=input, |
|
) |
|
elif mode == "image_generation": |
|
if prompt is None: |
|
raise Exception("prompt is not set") |
|
completion = await client.images.with_raw_response.generate( |
|
model=model, |
|
prompt=prompt, |
|
) |
|
else: |
|
raise Exception("mode not set") |
|
response = {} |
|
|
|
if completion is None or not hasattr(completion, "headers"): |
|
raise Exception("invalid completion response") |
|
|
|
if ( |
|
completion.headers.get("x-ratelimit-remaining-requests", None) is not None |
|
): |
|
response["x-ratelimit-remaining-requests"] = completion.headers[ |
|
"x-ratelimit-remaining-requests" |
|
] |
|
|
|
if completion.headers.get("x-ratelimit-remaining-tokens", None) is not None: |
|
response["x-ratelimit-remaining-tokens"] = completion.headers[ |
|
"x-ratelimit-remaining-tokens" |
|
] |
|
return response |
|
|
