diff --git a/docs/my-website/docs/exception_mapping.md b/docs/my-website/docs/exception_mapping.md index c6c9bb255..db17fb093 100644 --- a/docs/my-website/docs/exception_mapping.md +++ b/docs/my-website/docs/exception_mapping.md @@ -12,6 +12,7 @@ LiteLLM maps exceptions across all providers to their OpenAI counterparts. | 429 | RateLimitError | | >=500 | InternalServerError | | N/A | ContextWindowExceededError| +| 400 | ContentPolicyViolationError| | N/A | APIConnectionError | diff --git a/litellm/__init__.py b/litellm/__init__.py index 018b8bb14..5812e89bb 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -543,6 +543,7 @@ from .exceptions import ( ServiceUnavailableError, OpenAIError, ContextWindowExceededError, + ContentPolicyViolationError, BudgetExceededError, APIError, Timeout, diff --git a/litellm/exceptions.py b/litellm/exceptions.py index 3898a5683..4f9629e71 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -108,6 +108,21 @@ class ContextWindowExceededError(BadRequestError): # type: ignore ) # Call the base class constructor with the parameters it needs +class ContentPolicyViolationError(BadRequestError): # type: ignore + # Error code: 400 - {'error': {'code': 'content_policy_violation', 'message': 'Your request was rejected as a result of our safety system. Image descriptions generated from your prompt may contain text that is not allowed by our safety system. If you believe this was done in error, your request may succeed if retried, or by adjusting your prompt.', 'param': None, 'type': 'invalid_request_error'}} + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 400 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + message=self.message, + model=self.model, # type: ignore + llm_provider=self.llm_provider, # type: ignore + response=response, + ) # Call the base class constructor with the parameters it needs + + class ServiceUnavailableError(APIStatusError): # type: ignore def __init__(self, message, llm_provider, model, response: httpx.Response): self.status_code = 503 diff --git a/litellm/main.py b/litellm/main.py index 59cbbab3c..4978c79f1 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -1117,7 +1117,7 @@ def completion( acompletion=acompletion, logging_obj=logging, custom_prompt_dict=custom_prompt_dict, - timeout=timeout + timeout=timeout, ) if ( "stream" in optional_params @@ -2838,158 +2838,167 @@ def image_generation( Currently supports just Azure + OpenAI. """ - aimg_generation = kwargs.get("aimg_generation", False) - litellm_call_id = kwargs.get("litellm_call_id", None) - logger_fn = kwargs.get("logger_fn", None) - proxy_server_request = kwargs.get("proxy_server_request", None) - model_info = kwargs.get("model_info", None) - metadata = kwargs.get("metadata", {}) + try: + aimg_generation = kwargs.get("aimg_generation", False) + litellm_call_id = kwargs.get("litellm_call_id", None) + logger_fn = kwargs.get("logger_fn", None) + proxy_server_request = kwargs.get("proxy_server_request", None) + model_info = kwargs.get("model_info", None) + metadata = kwargs.get("metadata", {}) - model_response = litellm.utils.ImageResponse() - if model is not None or custom_llm_provider is not None: - model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore - else: - model = "dall-e-2" - custom_llm_provider = "openai" # default to dall-e-2 on openai - openai_params = [ - "user", - "request_timeout", - "api_base", - "api_version", - "api_key", - "deployment_id", - "organization", - "base_url", - "default_headers", - "timeout", - "max_retries", - "n", - "quality", - "size", - "style", - ] - litellm_params = [ - "metadata", - "aimg_generation", - "caching", - "mock_response", - "api_key", - "api_version", - "api_base", - "force_timeout", - "logger_fn", - "verbose", - "custom_llm_provider", - "litellm_logging_obj", - "litellm_call_id", - "use_client", - "id", - "fallbacks", - "azure", - "headers", - "model_list", - "num_retries", - "context_window_fallback_dict", - "roles", - "final_prompt_value", - "bos_token", - "eos_token", - "request_timeout", - "complete_response", - "self", - "client", - "rpm", - "tpm", - "input_cost_per_token", - "output_cost_per_token", - "hf_model_name", - "proxy_server_request", - "model_info", - "preset_cache_key", - "caching_groups", - "ttl", - "cache", - ] - default_params = openai_params + litellm_params - non_default_params = { - k: v for k, v in kwargs.items() if k not in default_params - } # model-specific params - pass them straight to the model/provider - optional_params = get_optional_params_image_gen( - n=n, - quality=quality, - response_format=response_format, - size=size, - style=style, - user=user, - custom_llm_provider=custom_llm_provider, - **non_default_params, - ) - logging = litellm_logging_obj - logging.update_environment_variables( - model=model, - user=user, - optional_params=optional_params, - litellm_params={ - "timeout": timeout, - "azure": False, - "litellm_call_id": litellm_call_id, - "logger_fn": logger_fn, - "proxy_server_request": proxy_server_request, - "model_info": model_info, - "metadata": metadata, - "preset_cache_key": None, - "stream_response": {}, - }, - ) - - if custom_llm_provider == "azure": - # azure configs - api_type = get_secret("AZURE_API_TYPE") or "azure" - - api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") - - api_version = ( - api_version or litellm.api_version or get_secret("AZURE_API_VERSION") + model_response = litellm.utils.ImageResponse() + if model is not None or custom_llm_provider is not None: + model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore + else: + model = "dall-e-2" + custom_llm_provider = "openai" # default to dall-e-2 on openai + openai_params = [ + "user", + "request_timeout", + "api_base", + "api_version", + "api_key", + "deployment_id", + "organization", + "base_url", + "default_headers", + "timeout", + "max_retries", + "n", + "quality", + "size", + "style", + ] + litellm_params = [ + "metadata", + "aimg_generation", + "caching", + "mock_response", + "api_key", + "api_version", + "api_base", + "force_timeout", + "logger_fn", + "verbose", + "custom_llm_provider", + "litellm_logging_obj", + "litellm_call_id", + "use_client", + "id", + "fallbacks", + "azure", + "headers", + "model_list", + "num_retries", + "context_window_fallback_dict", + "roles", + "final_prompt_value", + "bos_token", + "eos_token", + "request_timeout", + "complete_response", + "self", + "client", + "rpm", + "tpm", + "input_cost_per_token", + "output_cost_per_token", + "hf_model_name", + "proxy_server_request", + "model_info", + "preset_cache_key", + "caching_groups", + "ttl", + "cache", + ] + default_params = openai_params + litellm_params + non_default_params = { + k: v for k, v in kwargs.items() if k not in default_params + } # model-specific params - pass them straight to the model/provider + optional_params = get_optional_params_image_gen( + n=n, + quality=quality, + response_format=response_format, + size=size, + style=style, + user=user, + custom_llm_provider=custom_llm_provider, + **non_default_params, ) - - api_key = ( - api_key - or litellm.api_key - or litellm.azure_key - or get_secret("AZURE_OPENAI_API_KEY") - or get_secret("AZURE_API_KEY") - ) - - azure_ad_token = optional_params.pop("azure_ad_token", None) or get_secret( - "AZURE_AD_TOKEN" - ) - - model_response = azure_chat_completions.image_generation( + logging = litellm_logging_obj + logging.update_environment_variables( model=model, - prompt=prompt, - timeout=timeout, - api_key=api_key, - api_base=api_base, - logging_obj=litellm_logging_obj, + user=user, optional_params=optional_params, - model_response=model_response, - api_version=api_version, - aimg_generation=aimg_generation, - ) - elif custom_llm_provider == "openai": - model_response = openai_chat_completions.image_generation( - model=model, - prompt=prompt, - timeout=timeout, - api_key=api_key, - api_base=api_base, - logging_obj=litellm_logging_obj, - optional_params=optional_params, - model_response=model_response, - aimg_generation=aimg_generation, + litellm_params={ + "timeout": timeout, + "azure": False, + "litellm_call_id": litellm_call_id, + "logger_fn": logger_fn, + "proxy_server_request": proxy_server_request, + "model_info": model_info, + "metadata": metadata, + "preset_cache_key": None, + "stream_response": {}, + }, ) - return model_response + if custom_llm_provider == "azure": + # azure configs + api_type = get_secret("AZURE_API_TYPE") or "azure" + + api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") + + api_version = ( + api_version or litellm.api_version or get_secret("AZURE_API_VERSION") + ) + + api_key = ( + api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) + + azure_ad_token = optional_params.pop("azure_ad_token", None) or get_secret( + "AZURE_AD_TOKEN" + ) + + model_response = azure_chat_completions.image_generation( + model=model, + prompt=prompt, + timeout=timeout, + api_key=api_key, + api_base=api_base, + logging_obj=litellm_logging_obj, + optional_params=optional_params, + model_response=model_response, + api_version=api_version, + aimg_generation=aimg_generation, + ) + elif custom_llm_provider == "openai": + model_response = openai_chat_completions.image_generation( + model=model, + prompt=prompt, + timeout=timeout, + api_key=api_key, + api_base=api_base, + logging_obj=litellm_logging_obj, + optional_params=optional_params, + model_response=model_response, + aimg_generation=aimg_generation, + ) + + return model_response + except Exception as e: + ## Map to OpenAI Exception + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=locals(), + ) ##### Health Endpoints ####################### @@ -3114,7 +3123,8 @@ def config_completion(**kwargs): "No config path set, please set a config path using `litellm.config_path = 'path/to/config.json'`" ) -def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]=None): + +def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List] = None): id = chunks[0]["id"] object = chunks[0]["object"] created = chunks[0]["created"] @@ -3131,23 +3141,27 @@ def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]= "system_fingerprint": system_fingerprint, "choices": [ { - "text": None, - "index": 0, - "logprobs": logprobs, - "finish_reason": finish_reason + "text": None, + "index": 0, + "logprobs": logprobs, + "finish_reason": finish_reason, } ], "usage": { "prompt_tokens": None, "completion_tokens": None, - "total_tokens": None - } + "total_tokens": None, + }, } content_list = [] for chunk in chunks: choices = chunk["choices"] for choice in choices: - if choice is not None and hasattr(choice, "text") and choice.get("text") is not None: + if ( + choice is not None + and hasattr(choice, "text") + and choice.get("text") is not None + ): _choice = choice.get("text") content_list.append(_choice) @@ -3179,13 +3193,16 @@ def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]= ) return response + def stream_chunk_builder(chunks: list, messages: Optional[list] = None): id = chunks[0]["id"] object = chunks[0]["object"] created = chunks[0]["created"] model = chunks[0]["model"] system_fingerprint = chunks[0].get("system_fingerprint", None) - if isinstance(chunks[0]["choices"][0], litellm.utils.TextChoices): # route to the text completion logic + if isinstance( + chunks[0]["choices"][0], litellm.utils.TextChoices + ): # route to the text completion logic return stream_chunk_builder_text_completion(chunks=chunks, messages=messages) role = chunks[0]["choices"][0]["delta"]["role"] finish_reason = chunks[-1]["choices"][0]["finish_reason"] diff --git a/litellm/tests/test_exceptions.py b/litellm/tests/test_exceptions.py index 1cb599206..998e3eb9b 100644 --- a/litellm/tests/test_exceptions.py +++ b/litellm/tests/test_exceptions.py @@ -352,6 +352,25 @@ def test_completion_mistral_exception(): # test_completion_mistral_exception() +def test_content_policy_exceptionimage_generation_openai(): + try: + # this is ony a test - we needed some way to invoke the exception :( + litellm.set_verbose = True + response = litellm.image_generation( + prompt="where do i buy lethal drugs from", model="dall-e-3" + ) + print(f"response: {response}") + assert len(response.data) > 0 + except litellm.ContentPolicyViolationError as e: + print("caught a content policy violation error! Passed") + pass + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + + +# test_content_policy_exceptionimage_generation_openai() + + # # test_invalid_request_error(model="command-nightly") # # Test 3: Rate Limit Errors # def test_model_call(model): diff --git a/litellm/tests/test_image_generation.py b/litellm/tests/test_image_generation.py index 973ec29bb..3c792f802 100644 --- a/litellm/tests/test_image_generation.py +++ b/litellm/tests/test_image_generation.py @@ -19,7 +19,7 @@ import litellm def test_image_generation_openai(): - try: + try: litellm.set_verbose = True response = litellm.image_generation( prompt="A cute baby sea otter", model="dall-e-3" @@ -28,6 +28,8 @@ def test_image_generation_openai(): assert len(response.data) > 0 except litellm.RateLimitError as e: pass + except litellm.ContentPolicyViolationError: + pass # OpenAI randomly raises these errors - skip when they occur except Exception as e: pytest.fail(f"An exception occurred - {str(e)}") @@ -36,22 +38,27 @@ def test_image_generation_openai(): def test_image_generation_azure(): - try: + try: response = litellm.image_generation( - prompt="A cute baby sea otter", model="azure/", api_version="2023-06-01-preview" + prompt="A cute baby sea otter", + model="azure/", + api_version="2023-06-01-preview", ) print(f"response: {response}") assert len(response.data) > 0 except litellm.RateLimitError as e: pass + except litellm.ContentPolicyViolationError: + pass # Azure randomly raises these errors - skip when they occur except Exception as e: pytest.fail(f"An exception occurred - {str(e)}") + # test_image_generation_azure() def test_image_generation_azure_dall_e_3(): - try: + try: litellm.set_verbose = True response = litellm.image_generation( prompt="A cute baby sea otter", @@ -64,6 +71,8 @@ def test_image_generation_azure_dall_e_3(): assert len(response.data) > 0 except litellm.RateLimitError as e: pass + except litellm.ContentPolicyViolationError: + pass # OpenAI randomly raises these errors - skip when they occur except Exception as e: pytest.fail(f"An exception occurred - {str(e)}") @@ -71,7 +80,7 @@ def test_image_generation_azure_dall_e_3(): # test_image_generation_azure_dall_e_3() @pytest.mark.asyncio async def test_async_image_generation_openai(): - try: + try: response = litellm.image_generation( prompt="A cute baby sea otter", model="dall-e-3" ) @@ -79,20 +88,25 @@ async def test_async_image_generation_openai(): assert len(response.data) > 0 except litellm.RateLimitError as e: pass + except litellm.ContentPolicyViolationError: + pass # openai randomly raises these errors - skip when they occur except Exception as e: pytest.fail(f"An exception occurred - {str(e)}") + # asyncio.run(test_async_image_generation_openai()) @pytest.mark.asyncio async def test_async_image_generation_azure(): - try: + try: response = await litellm.aimage_generation( prompt="A cute baby sea otter", model="azure/dall-e-3-test" ) print(f"response: {response}") except litellm.RateLimitError as e: pass + except litellm.ContentPolicyViolationError: + pass # Azure randomly raises these errors - skip when they occur except Exception as e: pytest.fail(f"An exception occurred - {str(e)}") diff --git a/litellm/utils.py b/litellm/utils.py index 47c5695bb..5b6af9271 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -59,6 +59,7 @@ from .exceptions import ( ServiceUnavailableError, OpenAIError, ContextWindowExceededError, + ContentPolicyViolationError, Timeout, APIConnectionError, APIError, @@ -5548,6 +5549,17 @@ def exception_type( model=model, response=original_exception.response, ) + elif ( + "invalid_request_error" in error_str + and "content_policy_violation" in error_str + ): + exception_mapping_worked = True + raise ContentPolicyViolationError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response, + ) elif ( "invalid_request_error" in error_str and "Incorrect API key provided" not in error_str @@ -6497,6 +6509,17 @@ def exception_type( model=model, response=original_exception.response, ) + elif ( + "invalid_request_error" in error_str + and "content_policy_violation" in error_str + ): + exception_mapping_worked = True + raise ContentPolicyViolationError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response, + ) elif "invalid_request_error" in error_str: exception_mapping_worked = True raise BadRequestError(