import os import sys import traceback import litellm.cost_calculator sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path import asyncio import os import time from typing import Optional from unittest.mock import AsyncMock, MagicMock, patch import pytest import litellm from litellm import ( TranscriptionResponse, completion_cost, cost_per_token, get_max_tokens, model_cost, open_ai_chat_completion_models, ) from litellm.types.utils import PromptTokensDetails from litellm.litellm_core_utils.litellm_logging import CustomLogger class CustomLoggingHandler(CustomLogger): response_cost: Optional[float] = None def __init__(self): super().__init__() def log_success_event(self, kwargs, response_obj, start_time, end_time): self.response_cost = kwargs["response_cost"] async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): print(f"kwargs - {kwargs}") print(f"kwargs response cost - {kwargs.get('response_cost')}") self.response_cost = kwargs["response_cost"] print(f"response_cost: {self.response_cost} ") def log_failure_event(self, kwargs, response_obj, start_time, end_time): print("Reaches log failure event!") self.response_cost = kwargs["response_cost"] async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): print("Reaches async log failure event!") self.response_cost = kwargs["response_cost"] @pytest.mark.parametrize("sync_mode", [True, False]) @pytest.mark.asyncio async def test_custom_pricing(sync_mode): new_handler = CustomLoggingHandler() litellm.callbacks = [new_handler] if sync_mode: response = litellm.completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], mock_response="What do you want?", input_cost_per_token=0.0, output_cost_per_token=0.0, ) time.sleep(5) else: response = await litellm.acompletion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], mock_response="What do you want?", input_cost_per_token=0.0, output_cost_per_token=0.0, ) await asyncio.sleep(5) print(f"new_handler.response_cost: {new_handler.response_cost}") assert new_handler.response_cost is not None assert new_handler.response_cost == 0 @pytest.mark.parametrize( "sync_mode", [True, False], ) @pytest.mark.asyncio async def test_failure_completion_cost(sync_mode): new_handler = CustomLoggingHandler() litellm.callbacks = [new_handler] if sync_mode: try: response = litellm.completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], mock_response=Exception("this should trigger an error"), ) except Exception: pass time.sleep(5) else: try: response = await litellm.acompletion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], mock_response=Exception("this should trigger an error"), ) except Exception: pass await asyncio.sleep(5) print(f"new_handler.response_cost: {new_handler.response_cost}") assert new_handler.response_cost is not None assert new_handler.response_cost == 0 def test_custom_pricing_as_completion_cost_param(): from litellm import Choices, Message, ModelResponse from litellm.utils import Usage resp = ModelResponse( id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac", choices=[ Choices( finish_reason=None, index=0, message=Message( content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a", role="assistant", ), ) ], created=1700775391, model="ft:gpt-3.5-turbo:my-org:custom_suffix:id", object="chat.completion", system_fingerprint=None, usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38), ) cost = litellm.completion_cost( completion_response=resp, custom_cost_per_token={ "input_cost_per_token": 1000, "output_cost_per_token": 20, }, ) expected_cost = 1000 * 21 + 17 * 20 assert round(cost, 5) == round(expected_cost, 5) def test_get_gpt3_tokens(): max_tokens = get_max_tokens("gpt-3.5-turbo") print(max_tokens) assert max_tokens == 4096 # print(results) # test_get_gpt3_tokens() def test_get_palm_tokens(): # # 🦄🦄🦄🦄🦄🦄🦄🦄 max_tokens = get_max_tokens("palm/chat-bison") assert max_tokens == 4096 print(max_tokens) # test_get_palm_tokens() def test_zephyr_hf_tokens(): max_tokens = get_max_tokens("huggingface/HuggingFaceH4/zephyr-7b-beta") print(max_tokens) assert max_tokens == 32768 # test_zephyr_hf_tokens() def test_cost_ft_gpt_35(): try: # this tests if litellm.completion_cost can calculate cost for ft:gpt-3.5-turbo:my-org:custom_suffix:id # it needs to lookup ft:gpt-3.5-turbo in the litellm model_cost map to get the correct cost from litellm import Choices, Message, ModelResponse from litellm.utils import Usage resp = ModelResponse( id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac", choices=[ Choices( finish_reason=None, index=0, message=Message( content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a", role="assistant", ), ) ], created=1700775391, model="ft:gpt-3.5-turbo:my-org:custom_suffix:id", object="chat.completion", system_fingerprint=None, usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38), ) cost = litellm.completion_cost( completion_response=resp, custom_llm_provider="openai" ) print("\n Calculated Cost for ft:gpt-3.5", cost) input_cost = model_cost["ft:gpt-3.5-turbo"]["input_cost_per_token"] output_cost = model_cost["ft:gpt-3.5-turbo"]["output_cost_per_token"] print(input_cost, output_cost) expected_cost = (input_cost * resp.usage.prompt_tokens) + ( output_cost * resp.usage.completion_tokens ) print("\n Excpected cost", expected_cost) assert cost == expected_cost except Exception as e: pytest.fail( f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}" ) # test_cost_ft_gpt_35() def test_cost_azure_gpt_35(): try: # this tests if litellm.completion_cost can calculate cost for azure/chatgpt-deployment-2 which maps to azure/gpt-3.5-turbo # for this test we check if passing `model` to completion_cost overrides the completion cost from litellm import Choices, Message, ModelResponse from litellm.utils import Usage resp = ModelResponse( id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac", choices=[ Choices( finish_reason=None, index=0, message=Message( content=" Sure! Here is a short poem about the sky:\n\nA canvas of blue, a", role="assistant", ), ) ], model="gpt-35-turbo", # azure always has model written like this usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38), ) cost = litellm.completion_cost( completion_response=resp, model="azure/gpt-35-turbo" ) print("\n Calculated Cost for azure/gpt-3.5-turbo", cost) input_cost = model_cost["azure/gpt-35-turbo"]["input_cost_per_token"] output_cost = model_cost["azure/gpt-35-turbo"]["output_cost_per_token"] expected_cost = (input_cost * resp.usage.prompt_tokens) + ( output_cost * resp.usage.completion_tokens ) print("\n Excpected cost", expected_cost) assert cost == expected_cost except Exception as e: pytest.fail(f"Cost Calc failed for azure/gpt-3.5-turbo. {str(e)}") # test_cost_azure_gpt_35() def test_cost_azure_embedding(): try: import asyncio litellm.set_verbose = True async def _test(): response = await litellm.aembedding( model="azure/azure-embedding-model", input=["good morning from litellm", "gm"], ) print(response) return response response = asyncio.run(_test()) cost = litellm.completion_cost(completion_response=response) print("Cost", cost) expected_cost = float("7e-07") assert cost == expected_cost except Exception as e: pytest.fail( f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}" ) # test_cost_azure_embedding() def test_cost_openai_image_gen(): cost = litellm.completion_cost( model="dall-e-2", size="1024-x-1024", quality="standard", n=1, call_type="image_generation", ) assert cost == 0.019922944 def test_cost_bedrock_pricing(): """ - get pricing specific to region for a model """ from litellm import Choices, Message, ModelResponse from litellm.utils import Usage litellm.set_verbose = True input_tokens = litellm.token_counter( model="bedrock/anthropic.claude-instant-v1", messages=[{"role": "user", "content": "Hey, how's it going?"}], ) print(f"input_tokens: {input_tokens}") output_tokens = litellm.token_counter( model="bedrock/anthropic.claude-instant-v1", text="It's all going well", count_response_tokens=True, ) print(f"output_tokens: {output_tokens}") resp = ModelResponse( id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac", choices=[ Choices( finish_reason=None, index=0, message=Message( content="It's all going well", role="assistant", ), ) ], created=1700775391, model="anthropic.claude-instant-v1", object="chat.completion", system_fingerprint=None, usage=Usage( prompt_tokens=input_tokens, completion_tokens=output_tokens, total_tokens=input_tokens + output_tokens, ), ) resp._hidden_params = { "custom_llm_provider": "bedrock", "region_name": "ap-northeast-1", } cost = litellm.completion_cost( model="anthropic.claude-instant-v1", completion_response=resp, messages=[{"role": "user", "content": "Hey, how's it going?"}], ) predicted_cost = input_tokens * 0.00000223 + 0.00000755 * output_tokens assert cost == predicted_cost def test_cost_bedrock_pricing_actual_calls(): litellm.set_verbose = True model = "anthropic.claude-instant-v1" messages = [{"role": "user", "content": "Hey, how's it going?"}] response = litellm.completion( model=model, messages=messages, mock_response="hello cool one" ) print("response", response) cost = litellm.completion_cost( model="bedrock/anthropic.claude-instant-v1", completion_response=response, messages=[{"role": "user", "content": "Hey, how's it going?"}], ) assert cost > 0 def test_whisper_openai(): litellm.set_verbose = True transcription = TranscriptionResponse( text="Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure." ) transcription._hidden_params = { "model": "whisper-1", "custom_llm_provider": "openai", "optional_params": {}, "model_id": None, } _total_time_in_seconds = 3 transcription._response_ms = _total_time_in_seconds * 1000 cost = litellm.completion_cost(model="whisper-1", completion_response=transcription) print(f"cost: {cost}") print(f"whisper dict: {litellm.model_cost['whisper-1']}") expected_cost = round( litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds, 5, ) assert cost == expected_cost def test_whisper_azure(): litellm.set_verbose = True transcription = TranscriptionResponse( text="Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure." ) transcription._hidden_params = { "model": "whisper-1", "custom_llm_provider": "azure", "optional_params": {}, "model_id": None, } _total_time_in_seconds = 3 transcription._response_ms = _total_time_in_seconds * 1000 cost = litellm.completion_cost( model="azure/azure-whisper", completion_response=transcription ) print(f"cost: {cost}") print(f"whisper dict: {litellm.model_cost['whisper-1']}") expected_cost = round( litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds, 5, ) assert cost == expected_cost def test_dalle_3_azure_cost_tracking(): litellm.set_verbose = True # model = "azure/dall-e-3-test" # response = litellm.image_generation( # model=model, # prompt="A cute baby sea otter", # api_version="2023-12-01-preview", # api_base=os.getenv("AZURE_SWEDEN_API_BASE"), # api_key=os.getenv("AZURE_SWEDEN_API_KEY"), # base_model="dall-e-3", # ) # print(f"response: {response}") response = litellm.ImageResponse( created=1710265780, data=[ { "b64_json": None, "revised_prompt": "A close-up image of an adorable baby sea otter. Its fur is thick and fluffy to provide buoyancy and insulation against the cold water. Its eyes are round, curious and full of life. It's lying on its back, floating effortlessly on the calm sea surface under the warm sun. Surrounding the otter are patches of colorful kelp drifting along the gentle waves, giving the scene a touch of vibrancy. The sea otter has its small paws folded on its chest, and it seems to be taking a break from its play.", "url": "https://dalleprodsec.blob.core.windows.net/private/images/3e5d00f3-700e-4b75-869d-2de73c3c975d/generated_00.png?se=2024-03-13T17%3A49%3A51Z&sig=R9RJD5oOSe0Vp9Eg7ze%2FZ8QR7ldRyGH6XhMxiau16Jc%3D&ske=2024-03-19T11%3A08%3A03Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2024-03-12T11%3A08%3A03Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02", } ], ) response.usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} response._hidden_params = {"model": "dall-e-3", "model_id": None} print(f"response hidden params: {response._hidden_params}") cost = litellm.completion_cost( completion_response=response, call_type="image_generation" ) assert cost > 0 def test_replicate_llama3_cost_tracking(): litellm.set_verbose = True model = "replicate/meta/meta-llama-3-8b-instruct" litellm.register_model( { "replicate/meta/meta-llama-3-8b-instruct": { "input_cost_per_token": 0.00000005, "output_cost_per_token": 0.00000025, "litellm_provider": "replicate", } } ) response = litellm.ModelResponse( id="chatcmpl-cad7282f-7f68-41e7-a5ab-9eb33ae301dc", choices=[ litellm.utils.Choices( finish_reason="stop", index=0, message=litellm.utils.Message( content="I'm doing well, thanks for asking! I'm here to help you with any questions or tasks you may have. How can I assist you today?", role="assistant", ), ) ], created=1714401369, model="replicate/meta/meta-llama-3-8b-instruct", object="chat.completion", system_fingerprint=None, usage=litellm.utils.Usage( prompt_tokens=48, completion_tokens=31, total_tokens=79 ), ) cost = litellm.completion_cost( completion_response=response, messages=[{"role": "user", "content": "Hey, how's it going?"}], ) print(f"cost: {cost}") cost = round(cost, 5) expected_cost = round( litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][ "input_cost_per_token" ] * 48 + litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][ "output_cost_per_token" ] * 31, 5, ) assert cost == expected_cost @pytest.mark.parametrize("is_streaming", [True, False]) # def test_groq_response_cost_tracking(is_streaming): from litellm.utils import ( CallTypes, Choices, Delta, Message, ModelResponse, StreamingChoices, Usage, ) response = ModelResponse( id="chatcmpl-876cce24-e520-4cf8-8649-562a9be11c02", choices=[ Choices( finish_reason="stop", index=0, message=Message( content="Hi! I'm an AI, so I don't have emotions or feelings like humans do, but I'm functioning properly and ready to help with any questions or topics you'd like to discuss! How can I assist you today?", role="assistant", ), ) ], created=1717519830, model="llama3-70b-8192", object="chat.completion", system_fingerprint="fp_c1a4bcec29", usage=Usage(completion_tokens=46, prompt_tokens=17, total_tokens=63), ) response._hidden_params["custom_llm_provider"] = "groq" print(response) response_cost = litellm.response_cost_calculator( response_object=response, model="groq/llama3-70b-8192", custom_llm_provider="groq", call_type=CallTypes.acompletion.value, optional_params={}, ) assert isinstance(response_cost, float) assert response_cost > 0.0 print(f"response_cost: {response_cost}") from litellm.types.utils import CallTypes def test_together_ai_qwen_completion_cost(): input_kwargs = { "completion_response": litellm.ModelResponse( **{ "id": "890db0c33c4ef94b-SJC", "choices": [ { "finish_reason": "eos", "index": 0, "message": { "content": "I am Qwen, a large language model created by Alibaba Cloud.", "role": "assistant", }, } ], "created": 1717900130, "model": "together_ai/qwen/Qwen2-72B-Instruct", "object": "chat.completion", "system_fingerprint": None, "usage": { "completion_tokens": 15, "prompt_tokens": 23, "total_tokens": 38, }, } ), "model": "qwen/Qwen2-72B-Instruct", "prompt": "", "messages": [], "completion": "", "total_time": 0.0, "call_type": "completion", "custom_llm_provider": "together_ai", "region_name": None, "size": None, "quality": None, "n": None, "custom_cost_per_token": None, "custom_cost_per_second": None, } response = litellm.cost_calculator.get_model_params_and_category( model_name="qwen/Qwen2-72B-Instruct", call_type=CallTypes.completion ) assert response == "together-ai-41.1b-80b" @pytest.mark.parametrize("above_128k", [False, True]) @pytest.mark.parametrize("provider", ["gemini"]) def test_gemini_completion_cost(above_128k, provider): """ Check if cost correctly calculated for gemini models based on context window """ os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") if provider == "gemini": model_name = "gemini-1.5-flash-latest" else: model_name = "gemini-1.5-flash-preview-0514" if above_128k: prompt_tokens = 128001.0 output_tokens = 228001.0 else: prompt_tokens = 128.0 output_tokens = 228.0 ## GET MODEL FROM LITELLM.MODEL_INFO model_info = litellm.get_model_info(model=model_name, custom_llm_provider=provider) ## EXPECTED COST if above_128k: assert ( model_info["input_cost_per_token_above_128k_tokens"] is not None ), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format( model_name, model_info ) assert ( model_info["output_cost_per_token_above_128k_tokens"] is not None ), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format( model_name, model_info ) input_cost = ( prompt_tokens * model_info["input_cost_per_token_above_128k_tokens"] ) output_cost = ( output_tokens * model_info["output_cost_per_token_above_128k_tokens"] ) else: input_cost = prompt_tokens * model_info["input_cost_per_token"] output_cost = output_tokens * model_info["output_cost_per_token"] ## CALCULATED COST calculated_input_cost, calculated_output_cost = cost_per_token( model=model_name, prompt_tokens=prompt_tokens, completion_tokens=output_tokens, custom_llm_provider=provider, ) assert calculated_input_cost == input_cost assert calculated_output_cost == output_cost def _count_characters(text): # Remove white spaces and count characters filtered_text = "".join(char for char in text if not char.isspace()) return len(filtered_text) def test_vertex_ai_completion_cost(): os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") text = "The quick brown fox jumps over the lazy dog." characters = _count_characters(text=text) model_info = litellm.get_model_info(model="gemini-1.5-flash") print("\nExpected model info:\n{}\n\n".format(model_info)) expected_input_cost = characters * model_info["input_cost_per_character"] ## CALCULATED COST calculated_input_cost, calculated_output_cost = cost_per_token( model="gemini-1.5-flash", custom_llm_provider="vertex_ai", prompt_characters=characters, completion_characters=0, ) assert round(expected_input_cost, 6) == round(calculated_input_cost, 6) print("expected_input_cost: {}".format(expected_input_cost)) print("calculated_input_cost: {}".format(calculated_input_cost)) @pytest.mark.skip(reason="new test - WIP, working on fixing this") def test_vertex_ai_medlm_completion_cost(): """Test for medlm completion cost .""" with pytest.raises(Exception) as e: model = "vertex_ai/medlm-medium" messages = [{"role": "user", "content": "Test MedLM completion cost."}] predictive_cost = completion_cost( model=model, messages=messages, custom_llm_provider="vertex_ai" ) os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") model = "vertex_ai/medlm-medium" messages = [{"role": "user", "content": "Test MedLM completion cost."}] predictive_cost = completion_cost( model=model, messages=messages, custom_llm_provider="vertex_ai" ) assert predictive_cost > 0 model = "vertex_ai/medlm-large" messages = [{"role": "user", "content": "Test MedLM completion cost."}] predictive_cost = completion_cost(model=model, messages=messages) assert predictive_cost > 0 def test_vertex_ai_claude_completion_cost(): from litellm import Choices, Message, ModelResponse from litellm.utils import Usage os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") litellm.set_verbose = True input_tokens = litellm.token_counter( model="vertex_ai/claude-3-sonnet@20240229", messages=[{"role": "user", "content": "Hey, how's it going?"}], ) print(f"input_tokens: {input_tokens}") output_tokens = litellm.token_counter( model="vertex_ai/claude-3-sonnet@20240229", text="It's all going well", count_response_tokens=True, ) print(f"output_tokens: {output_tokens}") response = ModelResponse( id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac", choices=[ Choices( finish_reason=None, index=0, message=Message( content="It's all going well", role="assistant", ), ) ], created=1700775391, model="vertex_ai/claude-3-sonnet@20240229", object="chat.completion", system_fingerprint=None, usage=Usage( prompt_tokens=input_tokens, completion_tokens=output_tokens, total_tokens=input_tokens + output_tokens, ), ) cost = litellm.completion_cost( model="vertex_ai/claude-3-sonnet@20240229", completion_response=response, messages=[{"role": "user", "content": "Hey, how's it going?"}], ) predicted_cost = input_tokens * 0.000003 + 0.000015 * output_tokens assert cost == predicted_cost def test_vertex_ai_embedding_completion_cost(caplog): """ Relevant issue - https://github.com/BerriAI/litellm/issues/4630 """ os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") text = "The quick brown fox jumps over the lazy dog." input_tokens = litellm.token_counter( model="vertex_ai/textembedding-gecko", text=text ) model_info = litellm.get_model_info(model="vertex_ai/textembedding-gecko") print("\nExpected model info:\n{}\n\n".format(model_info)) expected_input_cost = input_tokens * model_info["input_cost_per_token"] ## CALCULATED COST calculated_input_cost, calculated_output_cost = cost_per_token( model="textembedding-gecko", custom_llm_provider="vertex_ai", prompt_tokens=input_tokens, call_type="aembedding", ) assert round(expected_input_cost, 6) == round(calculated_input_cost, 6) print("expected_input_cost: {}".format(expected_input_cost)) print("calculated_input_cost: {}".format(calculated_input_cost)) captured_logs = [rec.message for rec in caplog.records] for item in captured_logs: print("\nitem:{}\n".format(item)) if ( "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured " in item ): raise Exception("Error log raised for calculating embedding cost") # def test_vertex_ai_embedding_completion_cost_e2e(): # """ # Relevant issue - https://github.com/BerriAI/litellm/issues/4630 # """ # from test_amazing_vertex_completion import load_vertex_ai_credentials # load_vertex_ai_credentials() # os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" # litellm.model_cost = litellm.get_model_cost_map(url="") # text = "The quick brown fox jumps over the lazy dog." # input_tokens = litellm.token_counter( # model="vertex_ai/textembedding-gecko", text=text # ) # model_info = litellm.get_model_info(model="vertex_ai/textembedding-gecko") # print("\nExpected model info:\n{}\n\n".format(model_info)) # expected_input_cost = input_tokens * model_info["input_cost_per_token"] # ## CALCULATED COST # resp = litellm.embedding(model="textembedding-gecko", input=[text]) # calculated_input_cost = resp._hidden_params["response_cost"] # assert round(expected_input_cost, 6) == round(calculated_input_cost, 6) # print("expected_input_cost: {}".format(expected_input_cost)) # print("calculated_input_cost: {}".format(calculated_input_cost)) # assert False def test_completion_azure_ai(): try: os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") litellm.set_verbose = True response = litellm.completion( model="azure_ai/Mistral-large-nmefg", messages=[{"content": "what llm are you", "role": "user"}], max_tokens=15, num_retries=3, api_base=os.getenv("AZURE_AI_MISTRAL_API_BASE"), api_key=os.getenv("AZURE_AI_MISTRAL_API_KEY"), ) print(response) assert "response_cost" in response._hidden_params assert isinstance(response._hidden_params["response_cost"], float) except Exception as e: pytest.fail(f"Error occurred: {e}") @pytest.mark.parametrize("sync_mode", [True, False]) @pytest.mark.asyncio async def test_completion_cost_hidden_params(sync_mode): litellm.return_response_headers = True if sync_mode: response = litellm.completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}], mock_response="Hello world", ) else: response = await litellm.acompletion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}], mock_response="Hello world", ) assert "response_cost" in response._hidden_params assert isinstance(response._hidden_params["response_cost"], float) def test_vertex_ai_gemini_predict_cost(): model = "gemini-1.5-flash" messages = [{"role": "user", "content": "Hey, hows it going???"}] predictive_cost = completion_cost(model=model, messages=messages) assert predictive_cost > 0 def test_vertex_ai_llama_predict_cost(): model = "meta/llama3-405b-instruct-maas" messages = [{"role": "user", "content": "Hey, hows it going???"}] custom_llm_provider = "vertex_ai" predictive_cost = completion_cost( model=model, messages=messages, custom_llm_provider=custom_llm_provider ) assert predictive_cost == 0 @pytest.mark.parametrize("usage", ["litellm_usage", "openai_usage"]) def test_vertex_ai_mistral_predict_cost(usage): from litellm.types.utils import Choices, Message, ModelResponse, Usage if usage == "litellm_usage": response_usage = Usage(prompt_tokens=32, completion_tokens=55, total_tokens=87) else: from openai.types.completion_usage import CompletionUsage response_usage = CompletionUsage( prompt_tokens=32, completion_tokens=55, total_tokens=87 ) response_object = ModelResponse( id="26c0ef045020429d9c5c9b078c01e564", choices=[ Choices( finish_reason="stop", index=0, message=Message( content="Hello! I'm Litellm Bot, your helpful assistant. While I can't provide real-time weather updates, I can help you find a reliable weather service or guide you on how to check the weather on your device. Would you like assistance with that?", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1722124652, model="vertex_ai/mistral-large", object="chat.completion", system_fingerprint=None, usage=response_usage, ) model = "mistral-large@2407" messages = [{"role": "user", "content": "Hey, hows it going???"}] custom_llm_provider = "vertex_ai" predictive_cost = completion_cost( completion_response=response_object, model=model, messages=messages, custom_llm_provider=custom_llm_provider, ) assert predictive_cost > 0 @pytest.mark.parametrize("model", ["openai/tts-1", "azure/tts-1"]) def test_completion_cost_tts(model): os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") cost = completion_cost( model=model, prompt="the quick brown fox jumped over the lazy dogs", call_type="speech", ) assert cost > 0 def test_completion_cost_anthropic(): """ model_name: claude-3-haiku-20240307 litellm_params: model: anthropic/claude-3-haiku-20240307 max_tokens: 4096 """ router = litellm.Router( model_list=[ { "model_name": "claude-3-haiku-20240307", "litellm_params": { "model": "anthropic/claude-3-haiku-20240307", "max_tokens": 4096, }, } ] ) data = { "model": "claude-3-haiku-20240307", "prompt_tokens": 21, "completion_tokens": 20, "response_time_ms": 871.7040000000001, "custom_llm_provider": "anthropic", "region_name": None, "prompt_characters": 0, "completion_characters": 0, "custom_cost_per_token": None, "custom_cost_per_second": None, "call_type": "acompletion", } input_cost, output_cost = cost_per_token(**data) assert input_cost > 0 assert output_cost > 0 print(input_cost) print(output_cost) def test_completion_cost_deepseek(): litellm.set_verbose = True model_name = "deepseek/deepseek-chat" messages_1 = [ { "role": "system", "content": "You are a history expert. The user will provide a series of questions, and your answers should be concise and start with `Answer:`", }, { "role": "user", "content": "In what year did Qin Shi Huang unify the six states?", }, {"role": "assistant", "content": "Answer: 221 BC"}, {"role": "user", "content": "Who was the founder of the Han Dynasty?"}, {"role": "assistant", "content": "Answer: Liu Bang"}, {"role": "user", "content": "Who was the last emperor of the Tang Dynasty?"}, {"role": "assistant", "content": "Answer: Li Zhu"}, { "role": "user", "content": "Who was the founding emperor of the Ming Dynasty?", }, {"role": "assistant", "content": "Answer: Zhu Yuanzhang"}, { "role": "user", "content": "Who was the founding emperor of the Qing Dynasty?", }, ] message_2 = [ { "role": "system", "content": "You are a history expert. The user will provide a series of questions, and your answers should be concise and start with `Answer:`", }, { "role": "user", "content": "In what year did Qin Shi Huang unify the six states?", }, {"role": "assistant", "content": "Answer: 221 BC"}, {"role": "user", "content": "Who was the founder of the Han Dynasty?"}, {"role": "assistant", "content": "Answer: Liu Bang"}, {"role": "user", "content": "Who was the last emperor of the Tang Dynasty?"}, {"role": "assistant", "content": "Answer: Li Zhu"}, { "role": "user", "content": "Who was the founding emperor of the Ming Dynasty?", }, {"role": "assistant", "content": "Answer: Zhu Yuanzhang"}, {"role": "user", "content": "When did the Shang Dynasty fall?"}, ] try: response_1 = litellm.completion(model=model_name, messages=messages_1) response_2 = litellm.completion(model=model_name, messages=message_2) # Add any assertions here to check the response print(response_2) assert response_2.usage.prompt_cache_hit_tokens is not None assert response_2.usage.prompt_cache_miss_tokens is not None assert ( response_2.usage.prompt_tokens == response_2.usage.prompt_cache_miss_tokens + response_2.usage.prompt_cache_hit_tokens ) assert ( response_2.usage._cache_read_input_tokens == response_2.usage.prompt_cache_hit_tokens ) except litellm.APIError as e: pass except Exception as e: pytest.fail(f"Error occurred: {e}") def test_completion_cost_azure_common_deployment_name(): from litellm.utils import ( CallTypes, Choices, Delta, Message, ModelResponse, StreamingChoices, Usage, ) router = litellm.Router( model_list=[ { "model_name": "gpt-4", "litellm_params": { "model": "azure/gpt-4-0314", "max_tokens": 4096, "api_key": os.getenv("AZURE_API_KEY"), "api_base": os.getenv("AZURE_API_BASE"), }, "model_info": {"base_model": "azure/gpt-4"}, } ] ) response = ModelResponse( id="chatcmpl-876cce24-e520-4cf8-8649-562a9be11c02", choices=[ Choices( finish_reason="stop", index=0, message=Message( content="Hi! I'm an AI, so I don't have emotions or feelings like humans do, but I'm functioning properly and ready to help with any questions or topics you'd like to discuss! How can I assist you today?", role="assistant", ), ) ], created=1717519830, model="gpt-4", object="chat.completion", system_fingerprint="fp_c1a4bcec29", usage=Usage(completion_tokens=46, prompt_tokens=17, total_tokens=63), ) response._hidden_params["custom_llm_provider"] = "azure" print(response) with patch.object( litellm.cost_calculator, "completion_cost", new=MagicMock() ) as mock_client: _ = litellm.response_cost_calculator( response_object=response, model="gpt-4-0314", custom_llm_provider="azure", call_type=CallTypes.acompletion.value, optional_params={}, base_model="azure/gpt-4", ) mock_client.assert_called() print(f"mock_client.call_args: {mock_client.call_args.kwargs}") assert "azure/gpt-4" == mock_client.call_args.kwargs["model"] def test_completion_cost_anthropic_prompt_caching(): os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") from litellm.utils import Choices, Message, ModelResponse, Usage model = "anthropic/claude-3-5-sonnet-20240620" ## WRITE TO CACHE ## (MORE EXPENSIVE) response_1 = ModelResponse( id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424", choices=[ Choices( finish_reason="length", index=0, message=Message( content="Hello! I'm doing well, thank you for", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1725036547, model="claude-3-5-sonnet-20240620", object="chat.completion", system_fingerprint=None, usage=Usage( completion_tokens=10, prompt_tokens=114, total_tokens=124, prompt_tokens_details=PromptTokensDetails(cached_tokens=0), cache_creation_input_tokens=100, cache_read_input_tokens=0, ), ) cost_1 = completion_cost(model=model, completion_response=response_1) _model_info = litellm.get_model_info( model="claude-3-5-sonnet-20240620", custom_llm_provider="anthropic" ) expected_cost = ( ( response_1.usage.prompt_tokens - response_1.usage.prompt_tokens_details.cached_tokens ) * _model_info["input_cost_per_token"] + response_1.usage.prompt_tokens_details.cached_tokens * _model_info["cache_read_input_token_cost"] + response_1.usage.cache_creation_input_tokens * _model_info["cache_creation_input_token_cost"] + response_1.usage.completion_tokens * _model_info["output_cost_per_token"] ) # Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing) assert round(expected_cost, 5) == round(cost_1, 5) print(f"expected_cost: {expected_cost}, cost_1: {cost_1}") ## READ FROM CACHE ## (LESS EXPENSIVE) response_2 = ModelResponse( id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424", choices=[ Choices( finish_reason="length", index=0, message=Message( content="Hello! I'm doing well, thank you for", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1725036547, model="claude-3-5-sonnet-20240620", object="chat.completion", system_fingerprint=None, usage=Usage( completion_tokens=10, prompt_tokens=114, total_tokens=134, prompt_tokens_details=PromptTokensDetails(cached_tokens=100), cache_creation_input_tokens=0, cache_read_input_tokens=100, ), ) cost_2 = completion_cost(model=model, completion_response=response_2) assert cost_1 > cost_2 @pytest.mark.parametrize( "model", [ "databricks/databricks-meta-llama-3-1-70b-instruct", "databricks/databricks-meta-llama-3-70b-instruct", "databricks/databricks-dbrx-instruct", "databricks/databricks-mixtral-8x7b-instruct", ], ) def test_completion_cost_databricks(model): os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") model, messages = model, [{"role": "user", "content": "What is 2+2?"}] resp = litellm.completion(model=model, messages=messages) # works fine print(resp) cost = completion_cost(completion_response=resp) @pytest.mark.parametrize( "model", [ "databricks/databricks-bge-large-en", "databricks/databricks-gte-large-en", ], ) def test_completion_cost_databricks_embedding(model): os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") resp = litellm.embedding(model=model, input=["hey, how's it going?"]) # works fine print(resp) cost = completion_cost(completion_response=resp) from litellm.llms.fireworks_ai.cost_calculator import get_base_model_for_pricing @pytest.mark.parametrize( "model, base_model", [ ("fireworks_ai/llama-v3p1-405b-instruct", "fireworks-ai-default"), ("fireworks_ai/mixtral-8x7b-instruct", "fireworks-ai-moe-up-to-56b"), ], ) def test_get_model_params_fireworks_ai(model, base_model): pricing_model = get_base_model_for_pricing(model_name=model) assert base_model == pricing_model @pytest.mark.parametrize( "model", ["fireworks_ai/llama-v3p1-405b-instruct", "fireworks_ai/mixtral-8x7b-instruct"], ) def test_completion_cost_fireworks_ai(model): os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") messages = [{"role": "user", "content": "Hey, how's it going?"}] resp = litellm.completion(model=model, messages=messages) # works fine print(resp) cost = completion_cost(completion_response=resp) def test_cost_azure_openai_prompt_caching(): from litellm.utils import Choices, Message, ModelResponse, Usage from litellm.types.utils import PromptTokensDetails, CompletionTokensDetails from litellm import get_model_info os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") model = "azure/o1-mini" ## LLM API CALL ## (MORE EXPENSIVE) response_1 = ModelResponse( id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424", choices=[ Choices( finish_reason="length", index=0, message=Message( content="Hello! I'm doing well, thank you for", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1725036547, model=model, object="chat.completion", system_fingerprint=None, usage=Usage( completion_tokens=10, prompt_tokens=14, total_tokens=24, completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2), ), ) ## PROMPT CACHE HIT ## (LESS EXPENSIVE) response_2 = ModelResponse( id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424", choices=[ Choices( finish_reason="length", index=0, message=Message( content="Hello! I'm doing well, thank you for", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1725036547, model=model, object="chat.completion", system_fingerprint=None, usage=Usage( completion_tokens=10, prompt_tokens=0, total_tokens=10, prompt_tokens_details=PromptTokensDetails( cached_tokens=14, ), completion_tokens_details=CompletionTokensDetails(reasoning_tokens=2), ), ) cost_1 = completion_cost(model=model, completion_response=response_1) cost_2 = completion_cost(model=model, completion_response=response_2) assert cost_1 > cost_2 model_info = get_model_info(model=model, custom_llm_provider="azure") usage = response_2.usage _expected_cost2 = ( usage.prompt_tokens * model_info["input_cost_per_token"] + usage.completion_tokens * model_info["output_cost_per_token"] + usage.prompt_tokens_details.cached_tokens * model_info["cache_read_input_token_cost"] ) print("_expected_cost2", _expected_cost2) print("cost_2", cost_2) assert cost_2 == _expected_cost2 def test_completion_cost_vertex_llama3(): os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") from litellm.utils import Choices, Message, ModelResponse, Usage response = ModelResponse( id="2024-09-19|14:52:01.823070-07|3.10.13.64|-333502972", choices=[ Choices( finish_reason="stop", index=0, message=Message( content="My name is Litellm Bot, and I'm here to help you with any questions or tasks you may have. As for the weather, I'd be happy to provide you with the current conditions and forecast for your location. However, I'm a large language model, I don't have real-time access to your location, so I'll need you to tell me where you are or provide me with a specific location you're interested in knowing the weather for.\\n\\nOnce you provide me with that information, I can give you the current weather conditions, including temperature, humidity, wind speed, and more, as well as a forecast for the next few days. Just let me know how I can assist you!", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1726782721, model="vertex_ai/meta/llama3-405b-instruct-maas", object="chat.completion", system_fingerprint="", usage=Usage( completion_tokens=152, prompt_tokens=27, total_tokens=179, completion_tokens_details=None, ), ) model = "vertex_ai/meta/llama3-8b-instruct-maas" cost = completion_cost(model=model, completion_response=response) assert cost == 0 def test_cost_openai_prompt_caching(): from litellm.utils import Choices, Message, ModelResponse, Usage from litellm import get_model_info os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") model = "gpt-4o-mini-2024-07-18" ## LLM API CALL ## (MORE EXPENSIVE) response_1 = ModelResponse( id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424", choices=[ Choices( finish_reason="length", index=0, message=Message( content="Hello! I'm doing well, thank you for", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1725036547, model=model, object="chat.completion", system_fingerprint=None, usage=Usage( completion_tokens=10, prompt_tokens=14, total_tokens=24, ), ) ## PROMPT CACHE HIT ## (LESS EXPENSIVE) response_2 = ModelResponse( id="chatcmpl-3f427194-0840-4d08-b571-56bfe38a5424", choices=[ Choices( finish_reason="length", index=0, message=Message( content="Hello! I'm doing well, thank you for", role="assistant", tool_calls=None, function_call=None, ), ) ], created=1725036547, model=model, object="chat.completion", system_fingerprint=None, usage=Usage( completion_tokens=10, prompt_tokens=0, total_tokens=10, prompt_tokens_details=PromptTokensDetails( cached_tokens=14, ), ), ) cost_1 = completion_cost(model=model, completion_response=response_1) cost_2 = completion_cost(model=model, completion_response=response_2) assert cost_1 > cost_2 model_info = get_model_info(model=model, custom_llm_provider="openai") usage = response_2.usage _expected_cost2 = ( usage.prompt_tokens * model_info["input_cost_per_token"] + usage.completion_tokens * model_info["output_cost_per_token"] + usage.prompt_tokens_details.cached_tokens * model_info["cache_read_input_token_cost"] ) print("_expected_cost2", _expected_cost2) print("cost_2", cost_2) assert cost_2 == _expected_cost2 @pytest.mark.parametrize( "model", [ "cohere/rerank-english-v3.0", "azure_ai/cohere-rerank-v3-english", ], ) def test_completion_cost_azure_ai_rerank(model): from litellm import RerankResponse, rerank os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") response = RerankResponse( id="b01dbf2e-63c8-4981-9e69-32241da559ed", results=[ { "document": { "id": "1", "text": "Paris is the capital of France.", }, "index": 0, "relevance_score": 0.990732, }, ], meta={}, ) print("response", response) model = model cost = completion_cost( model=model, completion_response=response, call_type="arerank" ) assert cost > 0 def test_together_ai_embedding_completion_cost(): from litellm.utils import Choices, EmbeddingResponse, Message, ModelResponse, Usage os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") response = EmbeddingResponse( model="togethercomputer/m2-bert-80M-8k-retrieval", data=[ { "embedding": [ -0.18039076, 0.11614138, 0.37174946, 0.27238843, 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custom_llm_provider="together_ai", call_type="embedding", )