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from typing import Optional, Union, Any
import types, time, json
import httpx
from .base import BaseLLM
from litellm.utils import (
ModelResponse,
Choices,
Message,
CustomStreamWrapper,
convert_to_model_response_object,
Usage,
)
from typing import Callable, Optional
import aiohttp, requests
import litellm
from .prompt_templates.factory import prompt_factory, custom_prompt
from openai import OpenAI, AsyncOpenAI
class OpenAIError(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
) # Call the base class constructor with the parameters it needs
class OpenAIConfig:
"""
Reference: https://platform.openai.com/docs/api-reference/chat/create
The class `OpenAIConfig` provides configuration for the OpenAI's Chat API interface. 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.
"""
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
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:
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
}
class OpenAITextCompletionConfig:
"""
Reference: https://platform.openai.com/docs/api-reference/completions/create
The class `OpenAITextCompletionConfig` provides configuration for the OpenAI's text completion API interface. Below are the parameters:
- `best_of` (integer or null): This optional parameter generates server-side completions and returns the one with the highest log probability per token.
- `echo` (boolean or null): This optional parameter will echo back the prompt in addition to the completion.
- `frequency_penalty` (number or null): Defaults to 0. It is a numbers from -2.0 to 2.0, where positive values decrease the model's likelihood to repeat the same line.
- `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
- `logprobs` (integer or null): This optional parameter includes the log probabilities on the most likely tokens as well as the chosen tokens.
- `max_tokens` (integer or null): This optional parameter sets the maximum number of tokens to generate in the completion.
- `n` (integer or null): This optional parameter sets how many completions to generate for each prompt.
- `presence_penalty` (number or null): Defaults to 0 and can be between -2.0 and 2.0. Positive values increase 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.
- `suffix` (string or null): Defines the suffix that comes after a completion of inserted text.
- `temperature` (number or null): This optional parameter defines the sampling temperature to use.
- `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
"""
best_of: Optional[int] = None
echo: Optional[bool] = None
frequency_penalty: Optional[int] = None
logit_bias: Optional[dict] = None
logprobs: Optional[int] = None
max_tokens: Optional[int] = None
n: Optional[int] = None
presence_penalty: Optional[int] = None
stop: Optional[Union[str, list]] = None
suffix: Optional[str] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
def __init__(
self,
best_of: Optional[int] = None,
echo: Optional[bool] = None,
frequency_penalty: Optional[int] = None,
logit_bias: Optional[dict] = None,
logprobs: Optional[int] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[int] = None,
stop: Optional[Union[str, list]] = None,
suffix: Optional[str] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = 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
}
class OpenAIChatCompletion(BaseLLM):
def __init__(self) -> None:
super().__init__()
def completion(
self,
model_response: ModelResponse,
timeout: float,
model: Optional[str] = None,
messages: Optional[list] = None,
print_verbose: Optional[Callable] = None,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
acompletion: bool = False,
logging_obj=None,
optional_params=None,
litellm_params=None,
logger_fn=None,
headers: Optional[dict] = None,
custom_prompt_dict: dict = {},
client=None,
):
super().completion()
exception_mapping_worked = False
try:
if headers:
optional_params["extra_headers"] = headers
if model is None or messages is None:
raise OpenAIError(status_code=422, message=f"Missing model or messages")
if not isinstance(timeout, float):
raise OpenAIError(
status_code=422, message=f"Timeout needs to be a float"
)
for _ in range(
2
): # if call fails due to alternating messages, retry with reformatted message
data = {"model": model, "messages": messages, **optional_params}
try:
max_retries = data.pop("max_retries", 2)
if acompletion is True:
if optional_params.get("stream", False):
return self.async_streaming(
logging_obj=logging_obj,
headers=headers,
data=data,
model=model,
api_base=api_base,
api_key=api_key,
timeout=timeout,
client=client,
max_retries=max_retries,
)
else:
return self.acompletion(
data=data,
headers=headers,
logging_obj=logging_obj,
model_response=model_response,
api_base=api_base,
api_key=api_key,
timeout=timeout,
client=client,
max_retries=max_retries,
)
elif optional_params.get("stream", False):
return self.streaming(
logging_obj=logging_obj,
headers=headers,
data=data,
model=model,
api_base=api_base,
api_key=api_key,
timeout=timeout,
client=client,
max_retries=max_retries,
)
else:
if not isinstance(max_retries, int):
raise OpenAIError(
status_code=422, message="max retries must be an int"
)
if client is None:
openai_client = OpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.client_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_client = client
## LOGGING
logging_obj.pre_call(
input=messages,
api_key=openai_client.api_key,
additional_args={
"headers": headers,
"api_base": openai_client._base_url._uri_reference,
"acompletion": acompletion,
"complete_input_dict": data,
},
)
response = openai_client.chat.completions.create(**data, timeout=timeout) # type: ignore
stringified_response = response.model_dump()
logging_obj.post_call(
input=messages,
api_key=api_key,
original_response=stringified_response,
additional_args={"complete_input_dict": data},
)
return convert_to_model_response_object(
response_object=stringified_response,
model_response_object=model_response,
)
except Exception as e:
if "Conversation roles must alternate user/assistant" in str(
e
) or "user and assistant roles should be alternating" in str(e):
# reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility
new_messages = []
for i in range(len(messages) - 1):
new_messages.append(messages[i])
if messages[i]["role"] == messages[i + 1]["role"]:
if messages[i]["role"] == "user":
new_messages.append(
{"role": "assistant", "content": ""}
)
else:
new_messages.append({"role": "user", "content": ""})
new_messages.append(messages[-1])
messages = new_messages
elif "Last message must have role `user`" in str(e):
new_messages = messages
new_messages.append({"role": "user", "content": ""})
messages = new_messages
else:
raise e
except OpenAIError as e:
exception_mapping_worked = True
raise e
except Exception as e:
if hasattr(e, "status_code"):
raise OpenAIError(status_code=e.status_code, message=str(e))
else:
raise OpenAIError(status_code=500, message=str(e))
async def acompletion(
self,
data: dict,
model_response: ModelResponse,
timeout: float,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client=None,
max_retries=None,
logging_obj=None,
headers=None,
):
response = None
try:
if client is None:
openai_aclient = AsyncOpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.aclient_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_aclient = client
## LOGGING
logging_obj.pre_call(
input=data["messages"],
api_key=openai_aclient.api_key,
additional_args={
"headers": {"Authorization": f"Bearer {openai_aclient.api_key}"},
"api_base": openai_aclient._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
response = await openai_aclient.chat.completions.create(
**data, timeout=timeout
)
stringified_response = response.model_dump()
logging_obj.post_call(
input=data["messages"],
api_key=api_key,
original_response=stringified_response,
additional_args={"complete_input_dict": data},
)
return convert_to_model_response_object(
response_object=stringified_response,
model_response_object=model_response,
)
except Exception as e:
raise e
def streaming(
self,
logging_obj,
timeout: float,
data: dict,
model: str,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client=None,
max_retries=None,
headers=None,
):
if client is None:
openai_client = OpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.client_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_client = client
## LOGGING
logging_obj.pre_call(
input=data["messages"],
api_key=api_key,
additional_args={
"headers": headers,
"api_base": api_base,
"acompletion": False,
"complete_input_dict": data,
},
)
response = openai_client.chat.completions.create(**data, timeout=timeout)
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="openai",
logging_obj=logging_obj,
)
return streamwrapper
async def async_streaming(
self,
logging_obj,
timeout: float,
data: dict,
model: str,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client=None,
max_retries=None,
headers=None,
):
response = None
try:
if client is None:
openai_aclient = AsyncOpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.aclient_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_aclient = client
## LOGGING
logging_obj.pre_call(
input=data["messages"],
api_key=api_key,
additional_args={
"headers": headers,
"api_base": api_base,
"acompletion": True,
"complete_input_dict": data,
},
)
response = await openai_aclient.chat.completions.create(
**data, timeout=timeout
)
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="openai",
logging_obj=logging_obj,
)
return streamwrapper
except (
Exception
) as e: # need to exception handle here. async exceptions don't get caught in sync functions.
if response is not None and hasattr(response, "text"):
raise OpenAIError(
status_code=500,
message=f"{str(e)}\n\nOriginal Response: {response.text}",
)
else:
if type(e).__name__ == "ReadTimeout":
raise OpenAIError(status_code=408, message=f"{type(e).__name__}")
elif hasattr(e, "status_code"):
raise OpenAIError(status_code=e.status_code, message=str(e))
else:
raise OpenAIError(status_code=500, message=f"{str(e)}")
async def aembedding(
self,
input: list,
data: dict,
model_response: ModelResponse,
timeout: float,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client=None,
max_retries=None,
logging_obj=None,
):
response = None
try:
if client is None:
openai_aclient = AsyncOpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.aclient_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_aclient = client
response = await openai_aclient.embeddings.create(**data, timeout=timeout) # type: ignore
stringified_response = response.model_dump()
## LOGGING
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") # type: ignore
except Exception as e:
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
original_response=str(e),
)
raise e
def embedding(
self,
model: str,
input: list,
timeout: float,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
model_response: Optional[litellm.utils.EmbeddingResponse] = None,
logging_obj=None,
optional_params=None,
client=None,
aembedding=None,
):
super().embedding()
exception_mapping_worked = False
try:
model = model
data = {"model": model, "input": input, **optional_params}
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise OpenAIError(status_code=422, message="max retries must be an int")
## LOGGING
logging_obj.pre_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data, "api_base": api_base},
)
if aembedding == True:
response = self.aembedding(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore
return response
if client is None:
openai_client = OpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.client_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_client = client
## COMPLETION CALL
response = openai_client.embeddings.create(**data, timeout=timeout) # type: ignore
## LOGGING
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="embedding") # type: ignore
except OpenAIError as e:
exception_mapping_worked = True
raise e
except Exception as e:
if hasattr(e, "status_code"):
raise OpenAIError(status_code=e.status_code, message=str(e))
else:
raise OpenAIError(status_code=500, message=str(e))
async def aimage_generation(
self,
prompt: str,
data: dict,
model_response: ModelResponse,
timeout: float,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client=None,
max_retries=None,
logging_obj=None,
):
response = None
try:
if client is None:
openai_aclient = AsyncOpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.aclient_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_aclient = client
response = await openai_aclient.images.generate(**data, timeout=timeout) # type: ignore
stringified_response = response.model_dump()
## LOGGING
logging_obj.post_call(
input=prompt,
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") # type: ignore
except Exception as e:
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
original_response=str(e),
)
raise e
def image_generation(
self,
model: Optional[str],
prompt: str,
timeout: float,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
model_response: Optional[litellm.utils.ImageResponse] = None,
logging_obj=None,
optional_params=None,
client=None,
aimg_generation=None,
):
exception_mapping_worked = False
try:
model = model
data = {"model": model, "prompt": prompt, **optional_params}
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise OpenAIError(status_code=422, message="max retries must be an int")
if aimg_generation == True:
response = self.aimage_generation(data=data, prompt=prompt, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore
return response
if client is None:
openai_client = OpenAI(
api_key=api_key,
base_url=api_base,
http_client=litellm.client_session,
timeout=timeout,
max_retries=max_retries,
)
else:
openai_client = client
## LOGGING
logging_obj.pre_call(
input=prompt,
api_key=openai_client.api_key,
additional_args={
"headers": {"Authorization": f"Bearer {openai_client.api_key}"},
"api_base": openai_client._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
## COMPLETION CALL
response = openai_client.images.generate(**data, timeout=timeout) # type: ignore
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=response,
)
# return response
return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="image_generation") # type: ignore
except OpenAIError as e:
exception_mapping_worked = True
raise e
except Exception as e:
if hasattr(e, "status_code"):
raise OpenAIError(status_code=e.status_code, message=str(e))
else:
raise OpenAIError(status_code=500, message=str(e))
async def ahealth_check(
self,
model: Optional[str],
api_key: str,
timeout: float,
mode: str,
messages: Optional[list] = None,
input: Optional[list] = None,
prompt: Optional[str] = None,
):
client = AsyncOpenAI(api_key=api_key, timeout=timeout)
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, # type: ignore
prompt=prompt, # type: ignore
)
elif mode == "chat":
if messages is None:
raise Exception("messages is not set")
completion = await client.chat.completions.with_raw_response.create(
model=model, # type: ignore
messages=messages, # type: ignore
)
elif mode == "embedding":
if input is None:
raise Exception("input is not set")
completion = await client.embeddings.with_raw_response.create(
model=model, # type: ignore
input=input, # type: ignore
)
elif mode == "image_generation":
if prompt is None:
raise Exception("prompt is not set")
completion = await client.images.with_raw_response.generate(
model=model, # type: ignore
prompt=prompt, # type: ignore
)
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
): # not provided for dall-e requests
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
class OpenAITextCompletion(BaseLLM):
_client_session: httpx.Client
def __init__(self) -> None:
super().__init__()
self._client_session = self.create_client_session()
def validate_environment(self, api_key):
headers = {
"content-type": "application/json",
}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
return headers
def convert_to_model_response_object(
self,
response_object: Optional[dict] = None,
model_response_object: Optional[ModelResponse] = None,
):
try:
## RESPONSE OBJECT
if response_object is None or model_response_object is None:
raise ValueError("Error in response object format")
choice_list = []
for idx, choice in enumerate(response_object["choices"]):
message = Message(content=choice["text"], role="assistant")
choice = Choices(
finish_reason=choice["finish_reason"], index=idx, message=message
)
choice_list.append(choice)
model_response_object.choices = choice_list
if "usage" in response_object:
model_response_object.usage = response_object["usage"]
if "id" in response_object:
model_response_object.id = response_object["id"]
if "model" in response_object:
model_response_object.model = response_object["model"]
model_response_object._hidden_params[
"original_response"
] = response_object # track original response, if users make a litellm.text_completion() request, we can return the original response
return model_response_object
except Exception as e:
raise e
def completion(
self,
model_response: ModelResponse,
api_key: str,
model: str,
messages: list,
timeout: float,
print_verbose: Optional[Callable] = None,
api_base: Optional[str] = None,
logging_obj=None,
acompletion: bool = False,
optional_params=None,
litellm_params=None,
logger_fn=None,
headers: Optional[dict] = None,
):
super().completion()
exception_mapping_worked = False
try:
if headers is None:
headers = self.validate_environment(api_key=api_key)
if model is None or messages is None:
raise OpenAIError(status_code=422, message=f"Missing model or messages")
api_base = f"{api_base}/completions"
if (
len(messages) > 0
and "content" in messages[0]
and type(messages[0]["content"]) == list
):
prompt = messages[0]["content"]
else:
prompt = " ".join([message["content"] for message in messages]) # type: ignore
# don't send max retries to the api, if set
optional_params.pop("max_retries", None)
data = {"model": model, "prompt": prompt, **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,
},
)
if acompletion == True:
if optional_params.get("stream", False):
return self.async_streaming(
logging_obj=logging_obj,
api_base=api_base,
data=data,
headers=headers,
model_response=model_response,
model=model,
timeout=timeout,
)
else:
return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, prompt=prompt, api_key=api_key, logging_obj=logging_obj, model=model, timeout=timeout) # type: ignore
elif optional_params.get("stream", False):
return self.streaming(
logging_obj=logging_obj,
api_base=api_base,
data=data,
headers=headers,
model_response=model_response,
model=model,
timeout=timeout,
)
else:
response = httpx.post(
url=f"{api_base}", json=data, headers=headers, timeout=timeout
)
if response.status_code != 200:
raise OpenAIError(
status_code=response.status_code, message=response.text
)
## LOGGING
logging_obj.post_call(
input=prompt,
api_key=api_key,
original_response=response,
additional_args={
"headers": headers,
"api_base": api_base,
},
)
## RESPONSE OBJECT
return self.convert_to_model_response_object(
response_object=response.json(),
model_response_object=model_response,
)
except Exception as e:
raise e
async def acompletion(
self,
logging_obj,
api_base: str,
data: dict,
headers: dict,
model_response: ModelResponse,
prompt: str,
api_key: str,
model: str,
timeout: float,
):
async with httpx.AsyncClient(timeout=timeout) as client:
try:
response = await client.post(
api_base,
json=data,
headers=headers,
timeout=litellm.request_timeout,
)
response_json = response.json()
if response.status_code != 200:
raise OpenAIError(
status_code=response.status_code, message=response.text
)
## LOGGING
logging_obj.post_call(
input=prompt,
api_key=api_key,
original_response=response,
additional_args={
"headers": headers,
"api_base": api_base,
},
)
## RESPONSE OBJECT
return self.convert_to_model_response_object(
response_object=response_json, model_response_object=model_response
)
except Exception as e:
raise e
def streaming(
self,
logging_obj,
api_base: str,
data: dict,
headers: dict,
model_response: ModelResponse,
model: str,
timeout: float,
):
with httpx.stream(
url=f"{api_base}",
json=data,
headers=headers,
method="POST",
timeout=timeout,
) as response:
if response.status_code != 200:
raise OpenAIError(
status_code=response.status_code, message=response.text
)
streamwrapper = CustomStreamWrapper(
completion_stream=response.iter_lines(),
model=model,
custom_llm_provider="text-completion-openai",
logging_obj=logging_obj,
)
for transformed_chunk in streamwrapper:
yield transformed_chunk
async def async_streaming(
self,
logging_obj,
api_base: str,
data: dict,
headers: dict,
model_response: ModelResponse,
model: str,
timeout: float,
):
client = httpx.AsyncClient()
async with client.stream(
url=f"{api_base}",
json=data,
headers=headers,
method="POST",
timeout=timeout,
) as response:
try:
if response.status_code != 200:
raise OpenAIError(
status_code=response.status_code, message=response.text
)
streamwrapper = CustomStreamWrapper(
completion_stream=response.aiter_lines(),
model=model,
custom_llm_provider="text-completion-openai",
logging_obj=logging_obj,
)
async for transformed_chunk in streamwrapper:
yield transformed_chunk
except Exception as e:
raise e
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