import sys, os import traceback from dotenv import load_dotenv load_dotenv() import os, io sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path import pytest import litellm from litellm import embedding, completion, completion_cost, Timeout from litellm import RateLimitError litellm.num_retries = 3 litellm.cache = None user_message = "Write a short poem about the sky" messages = [{"content": user_message, "role": "user"}] def logger_fn(user_model_dict): print(f"user_model_dict: {user_model_dict}") def test_completion_custom_provider_model_name(): try: litellm.cache = None response = completion( model="together_ai/togethercomputer/llama-2-70b-chat", messages=messages, logger_fn=logger_fn, ) # Add any assertions here to check the response print(response) print(response['choices'][0]['finish_reason']) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_custom_provider_model_name() def test_completion_claude(): litellm.set_verbose = True litellm.cache = None litellm.AnthropicConfig(max_tokens_to_sample=200, metadata={"user_id": "1224"}) messages = [{"role": "system", "content": """You are an upbeat, enthusiastic personal fitness coach named Sam. Sam is passionate about helping clients get fit and lead healthier lifestyles. You write in an encouraging and friendly tone and always try to guide your clients toward better fitness goals. If the user asks you something unrelated to fitness, either bring the topic back to fitness, or say that you cannot answer."""},{"content": user_message, "role": "user"}] try: # test without max tokens response = completion( model="claude-instant-1", messages=messages, request_timeout=10, ) # Add any assertions here to check the response print(response) print(response.usage) print(response.usage.completion_tokens) print(response["usage"]["completion_tokens"]) # print("new cost tracking") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_claude() def test_completion_claude2_1(): try: print("claude2.1 test request") # test without max tokens response = completion( model="claude-2.1", messages=messages, request_timeout=10, max_tokens=10 ) # Add any assertions here to check the response print(response) print(response.usage) print(response.usage.completion_tokens) print(response["usage"]["completion_tokens"]) # print("new cost tracking") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_claude2_1() # def test_completion_oobabooga(): # try: # response = completion( # model="oobabooga/vicuna-1.3b", messages=messages, api_base="http://127.0.0.1:5000" # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_oobabooga() # aleph alpha # def test_completion_aleph_alpha(): # try: # response = completion( # model="luminous-base", messages=messages, logger_fn=logger_fn # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_aleph_alpha() # def test_completion_aleph_alpha_control_models(): # try: # response = completion( # model="luminous-base-control", messages=messages, logger_fn=logger_fn # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_aleph_alpha_control_models() import openai def test_completion_gpt4_turbo(): try: response = completion( model="gpt-4-1106-preview", messages=messages, max_tokens=10, ) print(response) except openai.RateLimitError: print("got a rate liimt error") pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_gpt4_turbo() def test_completion_gpt4_vision(): try: litellm.set_verbose=True response = completion( model="gpt-4-vision-preview", messages=[ { "role": "user", "content": [ { "type": "text", "text": "Whats in this image?" }, { "type": "image_url", "image_url": { "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" } } ] } ], ) print(response) except openai.RateLimitError: print("got a rate liimt error") pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_gpt4_vision() def test_completion_perplexity_api(): try: # litellm.set_verbose=True messages=[{ "role": "system", "content": "You're a good bot" },{ "role": "user", "content": "Hey", },{ "role": "user", "content": "Hey", }] response = completion( model="mistral-7b-instruct", messages=messages, api_base="https://api.perplexity.ai") print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_perplexity_api() def test_completion_perplexity_api_2(): try: # litellm.set_verbose=True messages=[{ "role": "system", "content": "You're a good bot" },{ "role": "user", "content": "Hey", },{ "role": "user", "content": "Hey", }] response = completion( model="perplexity/mistral-7b-instruct", messages=messages ) print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_perplexity_api_2() # commenting out as this is a flaky test on circle ci # def test_completion_nlp_cloud(): # try: # messages = [ # {"role": "system", "content": "You are a helpful assistant."}, # { # "role": "user", # "content": "how does a court case get to the Supreme Court?", # }, # ] # response = completion(model="dolphin", messages=messages, logger_fn=logger_fn) # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_nlp_cloud() ######### HUGGING FACE TESTS ######################## ##################################################### """ HF Tests we should pass - TGI: - Pro Inference API - Deployed Endpoint - Coversational - Free Inference API - Deployed Endpoint - Neither TGI or Coversational - Free Inference API - Deployed Endpoint """ ##################################################### ##################################################### # Test util to sort models to TGI, conv, None def test_get_hf_task_for_model(): model = "glaiveai/glaive-coder-7b" model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) print(f"model:{model}, model type: {model_type}") assert(model_type == "text-generation-inference") model = "meta-llama/Llama-2-7b-hf" model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) print(f"model:{model}, model type: {model_type}") assert(model_type == "text-generation-inference") model = "facebook/blenderbot-400M-distill" model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) print(f"model:{model}, model type: {model_type}") assert(model_type == "conversational") model = "facebook/blenderbot-3B" model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) print(f"model:{model}, model type: {model_type}") assert(model_type == "conversational") # neither Conv or None model = "roneneldan/TinyStories-3M" model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model) print(f"model:{model}, model type: {model_type}") assert(model_type == None) # test_get_hf_task_for_model() # litellm.set_verbose=False # ################### Hugging Face TGI models ######################## # # TGI model # # this is a TGI model https://huggingface.co/glaiveai/glaive-coder-7b def hf_test_completion_tgi(): # litellm.set_verbose=True try: response = completion( model = 'huggingface/HuggingFaceH4/zephyr-7b-beta', messages = [{ "content": "Hello, how are you?","role": "user"}], ) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # hf_test_completion_tgi() # ################### Hugging Face Conversational models ######################## # def hf_test_completion_conv(): # try: # response = litellm.completion( # model="huggingface/facebook/blenderbot-3B", # messages=[{ "content": "Hello, how are you?","role": "user"}], # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # hf_test_completion_conv() # ################### Hugging Face Neither TGI or Conversational models ######################## # # Neither TGI or Conversational # def hf_test_completion_none_task(): # try: # user_message = "My name is Merve and my favorite" # messages = [{ "content": user_message,"role": "user"}] # response = completion( # model="huggingface/roneneldan/TinyStories-3M", # messages=messages, # api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # hf_test_completion_none_task() ########################### End of Hugging Face Tests ############################################## # def test_completion_hf_api(): # # failing on circle ci commenting out # try: # user_message = "write some code to find the sum of two numbers" # messages = [{ "content": user_message,"role": "user"}] # api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud" # response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base) # # Add any assertions here to check the response # print(response) # except Exception as e: # if "loading" in str(e): # pass # pytest.fail(f"Error occurred: {e}") # test_completion_hf_api() # def test_completion_hf_api_best_of(): # # failing on circle ci commenting out # try: # user_message = "write some code to find the sum of two numbers" # messages = [{ "content": user_message,"role": "user"}] # api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud" # response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base, n=2) # # Add any assertions here to check the response # print(response) # except Exception as e: # if "loading" in str(e): # pass # pytest.fail(f"Error occurred: {e}") # test_completion_hf_api_best_of() # def test_completion_hf_deployed_api(): # try: # user_message = "There's a llama in my garden 😱 What should I do?" # messages = [{ "content": user_message,"role": "user"}] # response = completion(model="huggingface/https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud", messages=messages, logger_fn=logger_fn) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # this should throw an exception, to trigger https://logs.litellm.ai/ # def hf_test_error_logs(): # try: # litellm.set_verbose=True # user_message = "My name is Merve and my favorite" # messages = [{ "content": user_message,"role": "user"}] # response = completion( # model="huggingface/roneneldan/TinyStories-3M", # messages=messages, # api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # hf_test_error_logs() def test_completion_cohere(): # commenting for now as the cohere endpoint is being flaky try: litellm.CohereConfig(max_tokens=1000, stop_sequences=["a"]) response = completion( model="command-nightly", messages=messages, logger_fn=logger_fn ) # Add any assertions here to check the response print(response) response_str = response["choices"][0]["message"]["content"] response_str_2 = response.choices[0].message.content if type(response_str) != str: pytest.fail(f"Error occurred: {e}") if type(response_str_2) != str: pytest.fail(f"Error occurred: {e}") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_cohere() def test_completion_openai(): try: litellm.set_verbose=True print(f"api key: {os.environ['OPENAI_API_KEY']}") litellm.api_key = os.environ['OPENAI_API_KEY'] response = completion( model="gpt-3.5-turbo", messages=messages, max_tokens=10, request_timeout=1, metadata = {"hi": "bye"} ) print("This is the response object\n", response) response_str = response["choices"][0]["message"]["content"] response_str_2 = response.choices[0].message.content cost = completion_cost(completion_response=response) print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}") assert response_str == response_str_2 assert type(response_str) == str assert len(response_str) > 1 litellm.api_key = None except Timeout as e: pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_openai() def test_completion_text_openai(): try: # litellm.set_verbose = True response = completion(model="gpt-3.5-turbo-instruct", messages=messages) print(response["choices"][0]["message"]["content"]) except Exception as e: print(e) pytest.fail(f"Error occurred: {e}") # test_completion_text_openai() def custom_callback( kwargs, # kwargs to completion completion_response, # response from completion start_time, end_time # start/end time ): # Your custom code here try: print("LITELLM: in custom callback function") print("\nkwargs\n", kwargs) model = kwargs["model"] messages = kwargs["messages"] user = kwargs.get("user") ################################################# print( f""" Model: {model}, Messages: {messages}, User: {user}, Seed: {kwargs["seed"]}, temperature: {kwargs["temperature"]}, """ ) assert kwargs["user"] == "ishaans app" assert kwargs["model"] == "gpt-3.5-turbo-1106" assert kwargs["seed"] == 12 assert kwargs["temperature"] == 0.5 except Exception as e: pytest.fail(f"Error occurred: {e}") def test_completion_openai_with_optional_params(): # [Proxy PROD TEST] WARNING: DO NOT DELETE THIS TEST # assert that `user` gets passed to the completion call # Note: This tests that we actually send the optional params to the completion call # We use custom callbacks to test this try: litellm.set_verbose = True litellm.success_callback = [custom_callback] response = completion( model="gpt-3.5-turbo-1106", messages=[ { "role": "user", "content": "respond in valid, json - what is the day" } ], temperature=0.5, top_p=0.1, seed=12, response_format={ "type": "json_object" }, logit_bias=None, user = "ishaans app" ) # Add any assertions here to check the response print(response) litellm.success_callback = [] # unset callbacks except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_openai_with_optional_params() def test_completion_openai_litellm_key(): try: litellm.set_verbose = True litellm.num_retries = 0 litellm.api_key = os.environ['OPENAI_API_KEY'] # ensure key is set to None in .env and in openai.api_key os.environ['OPENAI_API_KEY'] = "" import openai openai.api_key = "" ########################################################## response = completion( model="gpt-3.5-turbo", messages=messages, temperature=0.5, top_p=0.1, max_tokens=10, user="ishaan_dev@berri.ai", ) # Add any assertions here to check the response print(response) ###### reset environ key os.environ['OPENAI_API_KEY'] = litellm.api_key ##### unset litellm var litellm.api_key = None except Timeout as e: pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_openai_litellm_key() def test_completion_openrouter1(): try: response = completion( model="openrouter/google/palm-2-chat-bison", messages=messages, max_tokens=5, ) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_openrouter1() def test_completion_hf_model_no_provider(): try: response = completion( model="WizardLM/WizardLM-70B-V1.0", messages=messages, max_tokens=5, ) # Add any assertions here to check the response print(response) pytest.fail(f"Error occurred: {e}") except Exception as e: pass # test_completion_hf_model_no_provider() # def test_completion_openai_azure_with_functions(): # function1 = [ # { # "name": "get_current_weather", # "description": "Get the current weather in a given location", # "parameters": { # "type": "object", # "properties": { # "location": { # "type": "string", # "description": "The city and state, e.g. San Francisco, CA", # }, # "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, # }, # "required": ["location"], # }, # } # ] # try: # messages = [{"role": "user", "content": "What is the weather like in Boston?"}] # response = completion( # model="azure/chatgpt-functioncalling", messages=messages, functions=function1 # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_openai_azure_with_functions() def test_completion_azure_key_completion_arg(): # this tests if we can pass api_key to completion, when it's not in the env # DO NOT REMOVE THIS TEST. No MATTER WHAT Happens. # If you want to remove it, speak to Ishaan! # Ishaan will be very disappointed if this test is removed -> this is a standard way to pass api_key + the router + proxy use this old_key = os.environ["AZURE_API_KEY"] os.environ.pop("AZURE_API_KEY", None) try: print("azure gpt-3.5 test\n\n") litellm.set_verbose=False ## Test azure call response = completion( model="azure/chatgpt-v-2", messages=messages, api_key=old_key, max_tokens=10, ) print(f"response: {response}") os.environ["AZURE_API_KEY"] = old_key except Exception as e: os.environ["AZURE_API_KEY"] = old_key pytest.fail(f"Error occurred: {e}") # test_completion_azure_key_completion_arg() async def test_re_use_azure_async_client(): try: print("azure gpt-3.5 ASYNC with clie nttest\n\n") litellm.set_verbose=True import openai client = openai.AsyncAzureOpenAI( azure_endpoint=os.environ['AZURE_API_BASE'], api_key=os.environ["AZURE_API_KEY"], api_version="2023-07-01-preview", ) ## Test azure call for _ in range(3): response = await litellm.acompletion( model="azure/chatgpt-v-2", messages=messages, client=client ) print(f"response: {response}") except Exception as e: pytest.fail("got Exception", e) # import asyncio # asyncio.run( # test_re_use_azure_async_client() # ) def test_re_use_openaiClient(): try: print("gpt-3.5 with client test\n\n") litellm.set_verbose=True import openai client = openai.OpenAI( api_key=os.environ["OPENAI_API_KEY"], ) ## Test OpenAI call for _ in range(2): response = litellm.completion( model="gpt-3.5-turbo", messages=messages, client=client ) print(f"response: {response}") except Exception as e: pytest.fail("got Exception", e) # test_re_use_openaiClient() def test_completion_azure(): try: print("azure gpt-3.5 test\n\n") litellm.set_verbose=False ## Test azure call response = completion( model="azure/chatgpt-v-2", messages=messages, api_key="os.environ/AZURE_API_KEY" ) print(f"response: {response}") ## Test azure flag for backwards compatibility # response = completion( # model="chatgpt-v-2", # messages=messages, # azure=True, # max_tokens=10 # ) # Add any assertions here to check the response print(response) cost = completion_cost(completion_response=response) print("Cost for azure completion request", cost) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_azure() def test_azure_openai_ad_token(): # this tests if the azure ad token is set in the request header # the request can fail since azure ad tokens expire after 30 mins, but the header MUST have the azure ad token # we use litellm.input_callbacks for this test def tester( kwargs, # kwargs to completion ): print(kwargs["additional_args"]) if kwargs["additional_args"]["headers"]["Authorization"] != 'Bearer gm': pytest.fail("AZURE AD TOKEN Passed but not set in request header") return litellm.input_callback = [tester] try: response = litellm.completion( model="azure/chatgpt-v-2", # e.g. gpt-35-instant messages=[ { "role": "user", "content": "what is your name", }, ], azure_ad_token="gm" ) print("azure ad token respoonse\n") print(response) litellm.input_callback = [] except: litellm.input_callback = [] pass # test_azure_openai_ad_token() # test_completion_azure() def test_completion_azure2(): # test if we can pass api_base, api_version and api_key in compleition() try: print("azure gpt-3.5 test\n\n") litellm.set_verbose=False api_base = os.environ["AZURE_API_BASE"] api_key = os.environ["AZURE_API_KEY"] api_version = os.environ["AZURE_API_VERSION"] os.environ["AZURE_API_BASE"] = "" os.environ["AZURE_API_VERSION"] = "" os.environ["AZURE_API_KEY"] = "" ## Test azure call response = completion( model="azure/chatgpt-v-2", messages=messages, api_base = api_base, api_key = api_key, api_version = api_version, max_tokens=10, ) # Add any assertions here to check the response print(response) os.environ["AZURE_API_BASE"] = api_base os.environ["AZURE_API_VERSION"] = api_version os.environ["AZURE_API_KEY"] = api_key except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_azure2() def test_completion_azure3(): # test if we can pass api_base, api_version and api_key in compleition() try: print("azure gpt-3.5 test\n\n") litellm.set_verbose=True litellm.api_base = os.environ["AZURE_API_BASE"] litellm.api_key = os.environ["AZURE_API_KEY"] litellm.api_version = os.environ["AZURE_API_VERSION"] os.environ["AZURE_API_BASE"] = "" os.environ["AZURE_API_VERSION"] = "" os.environ["AZURE_API_KEY"] = "" ## Test azure call response = completion( model="azure/chatgpt-v-2", messages=messages, max_tokens=10, ) # Add any assertions here to check the response print(response) os.environ["AZURE_API_BASE"] = litellm.api_base os.environ["AZURE_API_VERSION"] = litellm.api_version os.environ["AZURE_API_KEY"] = litellm.api_key except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_azure3() # new azure test for using litellm. vars, # use the following vars in this test and make an azure_api_call # litellm.api_type = self.azure_api_type # litellm.api_base = self.azure_api_base # litellm.api_version = self.azure_api_version # litellm.api_key = self.api_key def test_completion_azure_with_litellm_key(): try: print("azure gpt-3.5 test\n\n") import openai #### set litellm vars litellm.api_type = "azure" litellm.api_base = os.environ['AZURE_API_BASE'] litellm.api_version = os.environ['AZURE_API_VERSION'] litellm.api_key = os.environ['AZURE_API_KEY'] ######### UNSET ENV VARs for this ################ os.environ['AZURE_API_BASE'] = "" os.environ['AZURE_API_VERSION'] = "" os.environ['AZURE_API_KEY'] = "" ######### UNSET OpenAI vars for this ############## openai.api_type = "" openai.api_base = "gm" openai.api_version = "333" openai.api_key = "ymca" response = completion( model="azure/chatgpt-v-2", messages=messages, ) # Add any assertions here to check the response print(response) ######### RESET ENV VARs for this ################ os.environ['AZURE_API_BASE'] = litellm.api_base os.environ['AZURE_API_VERSION'] = litellm.api_version os.environ['AZURE_API_KEY'] = litellm.api_key ######### UNSET litellm vars litellm.api_type = None litellm.api_base = None litellm.api_version = None litellm.api_key = None except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_azure() def test_completion_azure_deployment_id(): try: litellm.set_verbose = True response = completion( deployment_id="chatgpt-v-2", model="gpt-3.5-turbo", messages=messages, ) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_azure_deployment_id() # Only works for local endpoint # def test_completion_anthropic_openai_proxy(): # try: # response = completion( # model="custom_openai/claude-2", # messages=messages, # api_base="http://0.0.0.0:8000" # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_anthropic_openai_proxy() def test_completion_replicate_vicuna(): print("TESTING REPLICATE") litellm.set_verbose=True model_name = "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b" try: response = completion( model=model_name, messages=messages, temperature=0.5, top_k=20, repetition_penalty=1, min_tokens=1, seed=-1, max_tokens=20, ) print(response) # Add any assertions here to check the response response_str = response["choices"][0]["message"]["content"] print("RESPONSE STRING\n", response_str) if type(response_str) != str: pytest.fail(f"Error occurred: {e}") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_replicate_vicuna() # commenting out - flaky test # def test_completion_replicate_llama2_stream(): # litellm.set_verbose=False # model_name = "replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0" # try: # response = completion( # model=model_name, # messages=[ # { # "role": "user", # "content": "what is yc write 1 paragraph", # } # ], # stream=True, # max_tokens=20, # num_retries=3 # ) # print(f"response: {response}") # # Add any assertions here to check the response # complete_response = "" # for i, chunk in enumerate(response): # complete_response += chunk.choices[0].delta["content"] # # if i == 0: # # assert len(chunk.choices[0].delta["content"]) > 2 # # print(chunk) # assert len(complete_response) > 5 # print(f"complete_response: {complete_response}") # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_replicate_llama2_stream() def test_replicate_custom_prompt_dict(): litellm.set_verbose = True model_name = "replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0" litellm.register_prompt_template( model="replicate/meta/llama-2-7b-chat:13c3cdee13ee059ab779f0291d29054dab00a47dad8261375654de5540165fb0", initial_prompt_value="You are a good assistant", # [OPTIONAL] roles={ "system": { "pre_message": "[INST] <>\n", # [OPTIONAL] "post_message": "\n<>\n [/INST]\n" # [OPTIONAL] }, "user": { "pre_message": "[INST] ", # [OPTIONAL] "post_message": " [/INST]" # [OPTIONAL] }, "assistant": { "pre_message": "\n", # [OPTIONAL] "post_message": "\n" # [OPTIONAL] } }, final_prompt_value="Now answer as best you can:" # [OPTIONAL] ) response = completion( model=model_name, messages=[ { "role": "user", "content": "what is yc write 1 paragraph", } ], num_retries=3 ) print(f"response: {response}") litellm.custom_prompt_dict = {} # reset # test_replicate_custom_prompt_dict() # commenthing this out since we won't be always testing a custom replicate deployment # def test_completion_replicate_deployments(): # print("TESTING REPLICATE") # litellm.set_verbose=False # model_name = "replicate/deployments/ishaan-jaff/ishaan-mistral" # try: # response = completion( # model=model_name, # messages=messages, # temperature=0.5, # seed=-1, # ) # print(response) # # Add any assertions here to check the response # response_str = response["choices"][0]["message"]["content"] # print("RESPONSE STRING\n", response_str) # if type(response_str) != str: # pytest.fail(f"Error occurred: {e}") # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_replicate_deployments() ######## Test TogetherAI ######## def test_completion_together_ai(): model_name = "together_ai/togethercomputer/llama-2-70b-chat" try: response = completion(model=model_name, messages=messages, max_tokens=256, n=1, logger_fn=logger_fn) # Add any assertions here to check the response print(response) cost = completion_cost(completion_response=response) print("Cost for completion call together-computer/llama-2-70b: ", f"${float(cost):.10f}") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_together_ai() def test_customprompt_together_ai(): try: litellm.set_verbose = False litellm.num_retries = 0 response = completion( model="together_ai/togethercomputer/llama-2-70b-chat", messages=messages, roles={"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}} ) print(response) except litellm.exceptions.Timeout as e: print(f"Timeout Error") litellm.num_retries = 3 # reset retries pass except Exception as e: print(f"ERROR TYPE {type(e)}") pytest.fail(f"Error occurred: {e}") # test_customprompt_together_ai() def test_completion_sagemaker(): try: print("testing sagemaker") litellm.set_verbose=True response = completion( model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", messages=messages, temperature=0.2, max_tokens=80, ) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_sagemaker() def test_completion_chat_sagemaker(): try: print("testing sagemaker") litellm.set_verbose=True response = completion( model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f", messages=messages, stream=True, ) # Add any assertions here to check the response print(response) for chunk in response: print(chunk) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_chat_sagemaker() def test_completion_bedrock_titan(): try: response = completion( model="bedrock/amazon.titan-tg1-large", messages=messages, temperature=0.2, max_tokens=200, top_p=0.8, logger_fn=logger_fn ) # Add any assertions here to check the response print(response) except RateLimitError: pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_bedrock_titan() def test_completion_bedrock_claude(): print("calling claude") try: response = completion( model="anthropic.claude-instant-v1", messages=messages, max_tokens=10, temperature=0.1, logger_fn=logger_fn ) # Add any assertions here to check the response print(response) except RateLimitError: pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_bedrock_claude() def test_completion_bedrock_cohere(): print("calling bedrock cohere") litellm.set_verbose = True try: response = completion( model="bedrock/cohere.command-text-v14", messages=[{"role": "user", "content": "hi"}], temperature=0.1, max_tokens=10, stream=True ) # Add any assertions here to check the response print(response) for chunk in response: print(chunk) except RateLimitError: pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_bedrock_cohere() def test_completion_bedrock_claude_completion_auth(): print("calling bedrock claude completion params auth") import os aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"] aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"] aws_region_name = os.environ["AWS_REGION_NAME"] os.environ["AWS_ACCESS_KEY_ID"] = "" os.environ["AWS_SECRET_ACCESS_KEY"] = "" os.environ["AWS_REGION_NAME"] = "" try: response = completion( model="bedrock/anthropic.claude-instant-v1", messages=messages, max_tokens=10, temperature=0.1, logger_fn=logger_fn, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, aws_region_name=aws_region_name, ) # Add any assertions here to check the response print(response) os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key os.environ["AWS_REGION_NAME"] = aws_region_name except RateLimitError: pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_bedrock_claude_completion_auth() # def test_completion_bedrock_claude_external_client_auth(): # print("calling bedrock claude external client auth") # import os # aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"] # aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"] # aws_region_name = os.environ["AWS_REGION_NAME"] # os.environ["AWS_ACCESS_KEY_ID"] = "" # os.environ["AWS_SECRET_ACCESS_KEY"] = "" # os.environ["AWS_REGION_NAME"] = "" # try: # import boto3 # bedrock = boto3.client( # service_name="bedrock-runtime", # region_name=aws_region_name, # aws_access_key_id=aws_access_key_id, # aws_secret_access_key=aws_secret_access_key, # endpoint_url=f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" # ) # response = completion( # model="bedrock/anthropic.claude-instant-v1", # messages=messages, # max_tokens=10, # temperature=0.1, # logger_fn=logger_fn, # aws_bedrock_client=bedrock, # ) # # Add any assertions here to check the response # print(response) # os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id # os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key # os.environ["AWS_REGION_NAME"] = aws_region_name # except RateLimitError: # pass # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_bedrock_claude_external_client_auth() # def test_completion_bedrock_claude_stream(): # print("calling claude") # litellm.set_verbose = False # try: # response = completion( # model="bedrock/anthropic.claude-instant-v1", # messages=messages, # stream=True # ) # # Add any assertions here to check the response # print(response) # for chunk in response: # print(chunk) # except RateLimitError: # pass # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_bedrock_claude_stream() # def test_completion_bedrock_ai21(): # try: # litellm.set_verbose = False # response = completion( # model="bedrock/ai21.j2-mid", # messages=messages, # temperature=0.2, # top_p=0.2, # max_tokens=20 # ) # # Add any assertions here to check the response # print(response) # except RateLimitError: # pass # except Exception as e: # pytest.fail(f"Error occurred: {e}") ######## Test VLLM ######## # def test_completion_vllm(): # try: # response = completion( # model="vllm/facebook/opt-125m", # messages=messages, # temperature=0.2, # max_tokens=80, # ) # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_vllm() # def test_completion_hosted_chatCompletion(): # # this tests calling a server where vllm is hosted # # this should make an openai.Completion() call to the specified api_base # # send a request to this proxy server: https://replit.com/@BerriAI/openai-proxy#main.py # # it checks if model == facebook/opt-125m and returns test passed # try: # litellm.set_verbose = True # response = completion( # model="facebook/opt-125m", # messages=messages, # temperature=0.2, # max_tokens=80, # api_base="https://openai-proxy.berriai.repl.co", # custom_llm_provider="openai" # ) # print(response) # if response['choices'][0]['message']['content'] != "passed": # # see https://replit.com/@BerriAI/openai-proxy#main.py # pytest.fail(f"Error occurred: proxy server did not respond") # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_hosted_chatCompletion() # def test_completion_custom_api_base(): # try: # response = completion( # model="custom/meta-llama/Llama-2-13b-hf", # messages=messages, # temperature=0.2, # max_tokens=10, # api_base="https://api.autoai.dev/inference", # request_timeout=300, # ) # # Add any assertions here to check the response # print("got response\n", response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_completion_custom_api_base() # def test_vertex_ai(): # test_models = ["codechat-bison"] + litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models # # test_models = ["chat-bison"] # for model in test_models: # try: # if model in ["code-gecko@001", "code-gecko@latest"]: # # our account does not have access to this model # continue # print("making request", model) # response = completion(model=model, messages=[{'role': 'user', 'content': 'hi'}]) # print(response) # print(response.usage.completion_tokens) # print(response['usage']['completion_tokens']) # assert type(response.choices[0].message.content) == str # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_vertex_ai() # def test_vertex_ai_stream(): # litellm.set_verbose=False # test_models = litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models # for model in test_models: # try: # if model in ["code-gecko@001", "code-gecko@latest"]: # # our account does not have access to this model # continue # print("making request", model) # response = completion(model=model, messages=[{"role": "user", "content": "write 100 line code code for saying hi"}], stream=True) # for chunk in response: # print(chunk) # # pass # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_vertex_ai_stream() def test_completion_with_fallbacks(): print(f"RUNNING TEST COMPLETION WITH FALLBACKS - test_completion_with_fallbacks") fallbacks = ["gpt-3.5-turbo", "gpt-3.5-turbo", "command-nightly"] try: response = completion( model="bad-model", messages=messages, force_timeout=120, fallbacks=fallbacks ) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_with_fallbacks() def test_completion_anyscale_api(): try: # litellm.set_verbose=True messages=[{ "role": "system", "content": "You're a good bot" },{ "role": "user", "content": "Hey", },{ "role": "user", "content": "Hey", }] response = completion( model="anyscale/meta-llama/Llama-2-7b-chat-hf", messages=messages,) print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_anyscale_api() def test_azure_cloudflare_api(): try: messages = [ { "role": "user", "content": "How do I output all files in a directory using Python?", }, ] response = completion(model="azure/gpt-turbo", messages=messages, base_url=os.getenv("CLOUDFLARE_AZURE_BASE_URL"), api_key=os.getenv("AZURE_FRANCE_API_KEY")) print(f"response: {response}") except Exception as e: traceback.print_exc() pass # test_azure_cloudflare_api() def test_completion_anyscale_2(): try: # litellm.set_verbose=True messages=[{ "role": "system", "content": "You're a good bot" },{ "role": "user", "content": "Hey", },{ "role": "user", "content": "Hey", }] response = completion( model="anyscale/meta-llama/Llama-2-7b-chat-hf", messages=messages ) print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") def test_mistral_anyscale_stream(): litellm.set_verbose=False response = completion( model = 'anyscale/mistralai/Mistral-7B-Instruct-v0.1', messages = [{ "content": "hello, good morning","role": "user"}], stream=True, ) for chunk in response: # print(chunk) print(chunk["choices"][0]["delta"].get("content", ""), end="") # test_mistral_anyscale_stream() # test_completion_anyscale_2() # def test_completion_with_fallbacks_multiple_keys(): # print(f"backup key 1: {os.getenv('BACKUP_OPENAI_API_KEY_1')}") # print(f"backup key 2: {os.getenv('BACKUP_OPENAI_API_KEY_2')}") # backup_keys = [{"api_key": os.getenv("BACKUP_OPENAI_API_KEY_1")}, {"api_key": os.getenv("BACKUP_OPENAI_API_KEY_2")}] # try: # api_key = "bad-key" # response = completion( # model="gpt-3.5-turbo", messages=messages, force_timeout=120, fallbacks=backup_keys, api_key=api_key # ) # # Add any assertions here to check the response # print(response) # except Exception as e: # error_str = traceback.format_exc() # pytest.fail(f"Error occurred: {error_str}") # test_completion_with_fallbacks_multiple_keys() # def test_petals(): # try: # response = completion(model="petals-team/StableBeluga2", messages=messages) # # Add any assertions here to check the response # print(response) # response = completion(model="petals-team/StableBeluga2", messages=messages) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # def test_baseten(): # try: # response = completion(model="baseten/7qQNLDB", messages=messages, logger_fn=logger_fn) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_baseten() # def test_baseten_falcon_7bcompletion(): # model_name = "qvv0xeq" # try: # response = completion(model=model_name, messages=messages, custom_llm_provider="baseten") # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_baseten_falcon_7bcompletion() # def test_baseten_falcon_7bcompletion_withbase(): # model_name = "qvv0xeq" # litellm.api_base = "https://app.baseten.co" # try: # response = completion(model=model_name, messages=messages) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # litellm.api_base = None # test_baseten_falcon_7bcompletion_withbase() # def test_baseten_wizardLMcompletion_withbase(): # model_name = "q841o8w" # litellm.api_base = "https://app.baseten.co" # try: # response = completion(model=model_name, messages=messages) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_baseten_wizardLMcompletion_withbase() # def test_baseten_mosaic_ML_completion_withbase(): # model_name = "31dxrj3" # litellm.api_base = "https://app.baseten.co" # try: # response = completion(model=model_name, messages=messages) # # Add any assertions here to check the response # print(response) # except Exception as e: # pytest.fail(f"Error occurred: {e}") #### Test A121 ################### def test_completion_ai21(): print("running ai21 j2light test") litellm.set_verbose=True model_name = "j2-light" try: response = completion(model=model_name, messages=messages, max_tokens=100, temperature=0.8) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_ai21() # test_completion_ai21() ## test deep infra def test_completion_deep_infra(): litellm.set_verbose = False model_name = "deepinfra/meta-llama/Llama-2-70b-chat-hf" try: response = completion( model=model_name, messages=messages, temperature=0, max_tokens=10 ) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_deep_infra() def test_completion_deep_infra_mistral(): print("deep infra test with temp=0") model_name = "deepinfra/mistralai/Mistral-7B-Instruct-v0.1" try: response = completion( model=model_name, messages=messages, temperature=0.01, # mistrail fails with temperature=0 max_tokens=10 ) # Add any assertions here to check the response print(response) except litellm.exceptions.Timeout as e: pass except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_deep_infra_mistral() # Palm tests def test_completion_palm(): litellm.set_verbose = True model_name = "palm/chat-bison" messages = [{"role": "user", "content": "Hey, how's it going?"}] try: response = completion(model=model_name, messages=messages) # Add any assertions here to check the response print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_palm() # test palm with streaming def test_completion_palm_stream(): # litellm.set_verbose = True model_name = "palm/chat-bison" try: response = completion( model=model_name, messages=messages, stop=["stop"], stream=True, max_tokens=20 ) # Add any assertions here to check the response for chunk in response: print(chunk) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_palm_stream() # test_completion_deep_infra() # test_completion_ai21() # test config file with completion # # def test_completion_openai_config(): # try: # litellm.config_path = "../config.json" # litellm.set_verbose = True # response = litellm.config_completion(messages=messages) # # Add any assertions here to check the response # print(response) # litellm.config_path = None # except Exception as e: # pytest.fail(f"Error occurred: {e}") # def test_maritalk(): # messages = [{"role": "user", "content": "Hey"}] # try: # response = completion("maritalk", messages=messages) # print(f"response: {response}") # except Exception as e: # pytest.fail(f"Error occurred: {e}") # test_maritalk() def test_completion_together_ai_stream(): user_message = "Write 1pg about YC & litellm" messages = [{ "content": user_message,"role": "user"}] try: response = completion( model="together_ai/togethercomputer/llama-2-70b-chat", messages=messages, stream=True, max_tokens=5 ) print(response) for chunk in response: print(chunk) # print(string_response) except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_together_ai_stream() # async def get_response(generator): # async for elem in generator: # print(elem) # return # test_completion_together_ai_stream() def test_moderation(): import openai openai.api_type = "azure" openai.api_version = "GM" response = litellm.moderation(input="i'm ishaan cto of litellm") print(response) output = response.results[0] print(output) return output # test_moderation()