forked from phoenix/litellm-mirror
Merge branch 'main' into litellm_return-response_headers
This commit is contained in:
Ishaan Jaff
GitHub
commit
f6225623e9
37 changed files with 570 additions and 89 deletions
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@ -831,6 +831,7 @@ class OpenAIChatCompletion(BaseLLM):
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client=None,
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organization: Optional[str] = None,
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custom_llm_provider: Optional[str] = None,
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drop_params: Optional[bool] = None,
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):
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super().completion()
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exception_mapping_worked = False
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@ -886,6 +887,7 @@ class OpenAIChatCompletion(BaseLLM):
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client=client,
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max_retries=max_retries,
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organization=organization,
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drop_params=drop_params,
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)
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else:
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return self.acompletion(
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@ -899,6 +901,7 @@ class OpenAIChatCompletion(BaseLLM):
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client=client,
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max_retries=max_retries,
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organization=organization,
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drop_params=drop_params,
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)
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elif optional_params.get("stream", False):
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return self.streaming(
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@ -962,6 +965,33 @@ class OpenAIChatCompletion(BaseLLM):
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model_response_object=model_response,
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_response_headers=headers,
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)
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except openai.UnprocessableEntityError as e:
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## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
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if litellm.drop_params is True or drop_params is True:
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if e.body is not None and e.body.get("detail"): # type: ignore
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detail = e.body.get("detail") # type: ignore
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invalid_params: List[str] = []
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if (
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isinstance(detail, List)
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and len(detail) > 0
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and isinstance(detail[0], dict)
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):
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for error_dict in detail:
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if (
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error_dict.get("loc")
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and isinstance(error_dict.get("loc"), list)
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and len(error_dict.get("loc")) == 2
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):
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invalid_params.append(error_dict["loc"][1])
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new_data = {}
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for k, v in optional_params.items():
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if k not in invalid_params:
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new_data[k] = v
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optional_params = new_data
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else:
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raise e
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# e.message
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except Exception as e:
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if print_verbose is not None:
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print_verbose(f"openai.py: Received openai error - {str(e)}")
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@ -1019,30 +1049,36 @@ class OpenAIChatCompletion(BaseLLM):
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client=None,
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max_retries=None,
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headers=None,
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drop_params: Optional[bool] = None,
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):
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response = None
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try:
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openai_aclient = self._get_openai_client(
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is_async=True,
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api_key=api_key,
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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client=client,
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)
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for _ in range(
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2
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): # if call fails due to alternating messages, retry with reformatted message
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try:
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openai_aclient = self._get_openai_client(
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is_async=True,
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api_key=api_key,
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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client=client,
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)
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## LOGGING
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logging_obj.pre_call(
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input=data["messages"],
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api_key=openai_aclient.api_key,
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additional_args={
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"headers": {"Authorization": f"Bearer {openai_aclient.api_key}"},
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"api_base": openai_aclient._base_url._uri_reference,
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"acompletion": True,
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"complete_input_dict": data,
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},
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)
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## LOGGING
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logging_obj.pre_call(
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input=data["messages"],
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api_key=openai_aclient.api_key,
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additional_args={
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"headers": {
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"Authorization": f"Bearer {openai_aclient.api_key}"
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},
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"api_base": openai_aclient._base_url._uri_reference,
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"acompletion": True,
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"complete_input_dict": data,
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},
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)
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headers, response = await self.make_openai_chat_completion_request(
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openai_aclient=openai_aclient, data=data, timeout=timeout
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@ -1063,6 +1099,51 @@ class OpenAIChatCompletion(BaseLLM):
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)
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except Exception as e:
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raise e
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headers, response = await self.make_openai_chat_completion_request(
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openai_aclient=openai_aclient, data=data, timeout=timeout
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)
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stringified_response = response.model_dump()
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logging_obj.post_call(
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input=data["messages"],
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api_key=api_key,
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original_response=stringified_response,
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additional_args={"complete_input_dict": data},
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)
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logging_obj.model_call_details["response_headers"] = headers
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return convert_to_model_response_object(
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response_object=stringified_response,
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model_response_object=model_response,
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hidden_params={"headers": headers},
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)
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except openai.UnprocessableEntityError as e:
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## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
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if litellm.drop_params is True or drop_params is True:
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if e.body is not None and e.body.get("detail"): # type: ignore
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detail = e.body.get("detail") # type: ignore
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invalid_params: List[str] = []
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if (
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isinstance(detail, List)
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and len(detail) > 0
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and isinstance(detail[0], dict)
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):
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for error_dict in detail:
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if (
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error_dict.get("loc")
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and isinstance(error_dict.get("loc"), list)
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and len(error_dict.get("loc")) == 2
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):
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invalid_params.append(error_dict["loc"][1])
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new_data = {}
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for k, v in data.items():
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if k not in invalid_params:
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new_data[k] = v
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data = new_data
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else:
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raise e
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# e.message
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except Exception as e:
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raise e
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def streaming(
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self,
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@ -1126,29 +1207,31 @@ class OpenAIChatCompletion(BaseLLM):
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client=None,
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max_retries=None,
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headers=None,
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drop_params: Optional[bool] = None,
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):
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response = None
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try:
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openai_aclient = self._get_openai_client(
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is_async=True,
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api_key=api_key,
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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client=client,
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)
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## LOGGING
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logging_obj.pre_call(
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input=data["messages"],
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api_key=api_key,
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additional_args={
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"headers": headers,
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"api_base": api_base,
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"acompletion": True,
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"complete_input_dict": data,
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},
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)
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for _ in range(2):
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try:
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openai_aclient = self._get_openai_client(
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is_async=True,
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api_key=api_key,
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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client=client,
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)
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## LOGGING
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logging_obj.pre_call(
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input=data["messages"],
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api_key=api_key,
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additional_args={
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"headers": headers,
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"api_base": api_base,
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"acompletion": True,
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"complete_input_dict": data,
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},
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)
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headers, response = await self.make_openai_chat_completion_request(
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openai_aclient=openai_aclient, data=data, timeout=timeout
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@ -1170,14 +1253,63 @@ class OpenAIChatCompletion(BaseLLM):
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raise OpenAIError(
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status_code=500,
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message=f"{str(e)}\n\nOriginal Response: {response.text}",
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headers, response = await self.make_openai_chat_completion_request(
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openai_aclient=openai_aclient, data=data, timeout=timeout
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)
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else:
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if type(e).__name__ == "ReadTimeout":
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raise OpenAIError(status_code=408, message=f"{type(e).__name__}")
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elif hasattr(e, "status_code"):
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raise OpenAIError(status_code=e.status_code, message=str(e))
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logging_obj.model_call_details["response_headers"] = headers
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streamwrapper = CustomStreamWrapper(
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completion_stream=response,
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model=model,
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custom_llm_provider="openai",
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logging_obj=logging_obj,
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stream_options=data.get("stream_options", None),
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)
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return streamwrapper
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except openai.UnprocessableEntityError as e:
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## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
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if litellm.drop_params is True or drop_params is True:
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if e.body is not None and e.body.get("detail"): # type: ignore
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detail = e.body.get("detail") # type: ignore
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invalid_params: List[str] = []
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if (
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isinstance(detail, List)
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and len(detail) > 0
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and isinstance(detail[0], dict)
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):
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for error_dict in detail:
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if (
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error_dict.get("loc")
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and isinstance(error_dict.get("loc"), list)
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and len(error_dict.get("loc")) == 2
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):
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invalid_params.append(error_dict["loc"][1])
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new_data = {}
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for k, v in data.items():
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if k not in invalid_params:
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new_data[k] = v
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data = new_data
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else:
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raise OpenAIError(status_code=500, message=f"{str(e)}")
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raise e
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except (
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Exception
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) as e: # need to exception handle here. async exceptions don't get caught in sync functions.
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if response is not None and hasattr(response, "text"):
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raise OpenAIError(
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status_code=500,
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message=f"{str(e)}\n\nOriginal Response: {response.text}",
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)
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else:
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if type(e).__name__ == "ReadTimeout":
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raise OpenAIError(
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status_code=408, message=f"{type(e).__name__}"
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)
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elif hasattr(e, "status_code"):
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raise OpenAIError(status_code=e.status_code, message=str(e))
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else:
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raise OpenAIError(status_code=500, message=f"{str(e)}")
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# Embedding
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async def make_openai_embedding_request(
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