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refactor(openai.py): moving openai chat completion calls to http

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Krrish Dholakia 2023-11-08 17:40:32 -08:00
parent da1451e493
commit c57ed0a9d7
6 changed files with 158 additions and 127 deletions
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77
litellm/utils.py
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@ -1358,7 +1358,6 @@ def get_optional_params( # use the openai defaults
frequency_penalty=0,
logit_bias={},
user="",
request_timeout=None,
deployment_id=None,
model=None,
custom_llm_provider="",
@ -1383,7 +1382,6 @@ def get_optional_params( # use the openai defaults
"logit_bias":{},
"user":"",
"deployment_id":None,
"request_timeout":None,
"model":None,
"custom_llm_provider":"",
}
@ -1408,8 +1406,6 @@ def get_optional_params( # use the openai defaults
if k == "n" and n == 1: # langchain sends n=1 as a default value
pass
# Always keeps this in elif code blocks
elif k == "request_timeout": # litellm handles request time outs
pass
else:
unsupported_params[k] = non_default_params[k]
if unsupported_params and not litellm.drop_params:
@ -1761,7 +1757,7 @@ def get_optional_params( # use the openai defaults
if stream:
optional_params["stream"] = stream
elif custom_llm_provider == "deepinfra":
supported_params = ["temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "deployment_id", "request_timeout"]
supported_params = ["temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "deployment_id"]
_check_valid_arg(supported_params=supported_params)
optional_params = non_default_params
if temperature != None:
@ -1769,7 +1765,7 @@ def get_optional_params( # use the openai defaults
temperature = 0.0001 # close to 0
optional_params["temperature"] = temperature
else: # assume passing in params for openai/azure openai
supported_params = ["functions", "function_call", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "deployment_id", "request_timeout"]
supported_params = ["functions", "function_call", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "deployment_id"]
_check_valid_arg(supported_params=supported_params)
optional_params = non_default_params
# if user passed in non-default kwargs for specific providers/models, pass them along
@ -2881,8 +2877,6 @@ def exception_type(
llm_provider="openrouter"
)
original_exception.llm_provider = "openrouter"
else:
original_exception.llm_provider = "openai"
if "This model's maximum context length is" in original_exception._message:
raise ContextWindowExceededError(
message=str(original_exception),
@ -2896,7 +2890,60 @@ def exception_type(
exception_type = type(original_exception).__name__
else:
exception_type = ""
if custom_llm_provider == "anthropic": # one of the anthropics
if custom_llm_provider == "openai":
if "This model's maximum context length is" in error_str:
exception_mapping_worked = True
raise ContextWindowExceededError(
message=f"AzureException - {original_exception.message}",
llm_provider="azure",
model=model
)
elif "invalid_request_error" in error_str:
exception_mapping_worked = True
raise InvalidRequestError(
message=f"AzureException - {original_exception.message}",
llm_provider="azure",
model=model
)
elif hasattr(original_exception, "status_code"):
exception_mapping_worked = True
if original_exception.status_code == 401:
exception_mapping_worked = True
raise AuthenticationError(
message=f"OpenAIException - {original_exception.message}",
llm_provider="openai",
model=model
)
elif original_exception.status_code == 408:
exception_mapping_worked = True
raise Timeout(
message=f"OpenAIException - {original_exception.message}",
model=model,
llm_provider="openai"
)
if original_exception.status_code == 422:
exception_mapping_worked = True
raise InvalidRequestError(
message=f"OpenAIException - {original_exception.message}",
model=model,
llm_provider="openai",
)
elif original_exception.status_code == 429:
exception_mapping_worked = True
raise RateLimitError(
message=f"OpenAIException - {original_exception.message}",
model=model,
llm_provider="openai",
)
else:
exception_mapping_worked = True
raise APIError(
status_code=original_exception.status_code,
message=f"OpenAIException - {original_exception.message}",
llm_provider="openai",
model=model
)
elif custom_llm_provider == "anthropic": # one of the anthropics
if hasattr(original_exception, "message"):
if "prompt is too long" in original_exception.message:
exception_mapping_worked = True
@ -3941,7 +3988,7 @@ class CustomStreamWrapper:
except:
raise ValueError(f"Unable to parse response. Original response: {chunk}")
def handle_custom_openai_chat_completion_chunk(self, chunk):
def handle_openai_chat_completion_chunk(self, chunk):
try:
str_line = chunk.decode("utf-8") # Convert bytes to string
text = ""
@ -3977,12 +4024,6 @@ class CustomStreamWrapper:
except:
raise ValueError(f"Unable to parse response. Original response: {chunk}")
def handle_openai_chat_completion_chunk(self, chunk):
try:
return chunk["choices"][0]["delta"]["content"]
except:
return ""
def handle_baseten_chunk(self, chunk):
try:
chunk = chunk.decode("utf-8")
@ -4187,9 +4228,9 @@ class CustomStreamWrapper:
if "error" in chunk:
exception_type(model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=chunk["error"])
completion_obj = chunk
elif self.custom_llm_provider == "custom_openai":
elif self.custom_llm_provider == "openai":
chunk = next(self.completion_stream)
response_obj = self.handle_custom_openai_chat_completion_chunk(chunk)
response_obj = self.handle_openai_chat_completion_chunk(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]: