import sys, os import time import traceback from dotenv import load_dotenv load_dotenv() import os sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path import pytest import litellm from litellm import embedding, completion from litellm.caching import Cache import random # litellm.set_verbose=True messages = [{"role": "user", "content": "who is ishaan Github? "}] # comment messages = [{"role": "user", "content": "who is ishaan 5222"}] def test_caching_v2(): # test in memory cache try: litellm.cache = Cache() response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) print(f"response1: {response1}") print(f"response2: {response2}") litellm.cache = None # disable cache if response2['choices'][0]['message']['content'] != response1['choices'][0]['message']['content']: print(f"response1: {response1}") print(f"response2: {response2}") pytest.fail(f"Error occurred: {e}") except Exception as e: print(f"error occurred: {traceback.format_exc()}") pytest.fail(f"Error occurred: {e}") # test_caching_v2() def test_caching_with_models_v2(): messages = [{"role": "user", "content": "who is ishaan CTO of litellm from litellm 2023"}] litellm.cache = Cache() print("test2 for caching") response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) response3 = completion(model="command-nightly", messages=messages, caching=True) print(f"response1: {response1}") print(f"response2: {response2}") print(f"response3: {response3}") litellm.cache = None if response3['choices'][0]['message']['content'] == response2['choices'][0]['message']['content']: # if models are different, it should not return cached response print(f"response2: {response2}") print(f"response3: {response3}") pytest.fail(f"Error occurred:") if response1['choices'][0]['message']['content'] != response2['choices'][0]['message']['content']: print(f"response1: {response1}") print(f"response2: {response2}") pytest.fail(f"Error occurred:") # test_caching_with_models_v2() embedding_large_text = """ small text """ * 5 # # test_caching_with_models() def test_embedding_caching(): import time litellm.cache = Cache() text_to_embed = [embedding_large_text] start_time = time.time() embedding1 = embedding(model="text-embedding-ada-002", input=text_to_embed, caching=True) end_time = time.time() print(f"Embedding 1 response time: {end_time - start_time} seconds") time.sleep(1) start_time = time.time() embedding2 = embedding(model="text-embedding-ada-002", input=text_to_embed, caching=True) end_time = time.time() print(f"embedding2: {embedding2}") print(f"Embedding 2 response time: {end_time - start_time} seconds") litellm.cache = None assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s if embedding2['data'][0]['embedding'] != embedding1['data'][0]['embedding']: print(f"embedding1: {embedding1}") print(f"embedding2: {embedding2}") pytest.fail("Error occurred: Embedding caching failed") # test_embedding_caching() def test_embedding_caching_azure(): print("Testing azure embedding caching") import time litellm.cache = Cache() text_to_embed = [embedding_large_text] api_key = os.environ['AZURE_API_KEY'] api_base = os.environ['AZURE_API_BASE'] api_version = os.environ['AZURE_API_VERSION'] os.environ['AZURE_API_VERSION'] = "" os.environ['AZURE_API_BASE'] = "" os.environ['AZURE_API_KEY'] = "" start_time = time.time() print("AZURE CONFIGS") print(api_version) print(api_key) print(api_base) embedding1 = embedding( model="azure/azure-embedding-model", input=["good morning from litellm", "this is another item"], api_key=api_key, api_base=api_base, api_version=api_version, caching=True ) end_time = time.time() print(f"Embedding 1 response time: {end_time - start_time} seconds") time.sleep(1) start_time = time.time() embedding2 = embedding( model="azure/azure-embedding-model", input=["good morning from litellm", "this is another item"], api_key=api_key, api_base=api_base, api_version=api_version, caching=True ) end_time = time.time() print(f"Embedding 2 response time: {end_time - start_time} seconds") litellm.cache = None assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s if embedding2['data'][0]['embedding'] != embedding1['data'][0]['embedding']: print(f"embedding1: {embedding1}") print(f"embedding2: {embedding2}") pytest.fail("Error occurred: Embedding caching failed") os.environ['AZURE_API_VERSION'] = api_version os.environ['AZURE_API_BASE'] = api_base os.environ['AZURE_API_KEY'] = api_key # test_embedding_caching_azure() def test_redis_cache_completion(): litellm.set_verbose = False random_number = random.randint(1, 100000) # add a random number to ensure it's always adding / reading from cache messages = [{"role": "user", "content": f"write a one sentence poem about: {random_number}"}] litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) print("test2 for caching") response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=10, seed=1222) response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=10, seed=1222) response3 = completion(model="gpt-3.5-turbo", messages=messages, caching=True, temperature=1) response4 = completion(model="command-nightly", messages=messages, caching=True) print("\nresponse 1", response1) print("\nresponse 2", response2) print("\nresponse 3", response3) print("\nresponse 4", response4) litellm.cache = None """ 1 & 2 should be exactly the same 1 & 3 should be different, since input params are diff 1 & 4 should be diff, since models are diff """ if response1['choices'][0]['message']['content'] != response2['choices'][0]['message']['content']: # 1 and 2 should be the same # 1&2 have the exact same input params. This MUST Be a CACHE HIT print(f"response1: {response1}") print(f"response2: {response2}") pytest.fail(f"Error occurred:") if response1['choices'][0]['message']['content'] == response3['choices'][0]['message']['content']: # if input params like seed, max_tokens are diff it should NOT be a cache hit print(f"response1: {response1}") print(f"response3: {response3}") pytest.fail(f"Response 1 == response 3. Same model, diff params shoudl not cache Error occurred:") if response1['choices'][0]['message']['content'] == response4['choices'][0]['message']['content']: # if models are different, it should not return cached response print(f"response1: {response1}") print(f"response4: {response4}") pytest.fail(f"Error occurred:") # test_redis_cache_completion() # redis cache with custom keys def custom_get_cache_key(*args, **kwargs): # return key to use for your cache: key = kwargs.get("model", "") + str(kwargs.get("messages", "")) + str(kwargs.get("temperature", "")) + str(kwargs.get("logit_bias", "")) return key def test_custom_redis_cache_with_key(): messages = [{"role": "user", "content": "write a one line story"}] litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) litellm.cache.get_cache_key = custom_get_cache_key local_cache = {} def set_cache(key, value): local_cache[key] = value def get_cache(key): if key in local_cache: return local_cache[key] litellm.cache.cache.set_cache = set_cache litellm.cache.cache.get_cache = get_cache # patch this redis cache get and set call response1 = completion(model="gpt-3.5-turbo", messages=messages, temperature=1, caching=True, num_retries=3) response2 = completion(model="gpt-3.5-turbo", messages=messages, temperature=1, caching=True, num_retries=3) response3 = completion(model="gpt-3.5-turbo", messages=messages, temperature=1, caching=False, num_retries=3) print(f"response1: {response1}") print(f"response2: {response2}") print(f"response3: {response3}") if response3['choices'][0]['message']['content'] == response2['choices'][0]['message']['content']: pytest.fail(f"Error occurred:") litellm.cache = None # test_custom_redis_cache_with_key() def test_custom_redis_cache_params(): # test if we can init redis with **kwargs try: litellm.cache = Cache( type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD'], db = 0, ssl=True, ssl_certfile="./redis_user.crt", ssl_keyfile="./redis_user_private.key", ssl_ca_certs="./redis_ca.pem", ) print(litellm.cache.cache.redis_client) litellm.cache = None except Exception as e: pytest.fail(f"Error occurred:", e) # test_custom_redis_cache_params() # def test_redis_cache_with_ttl(): # cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) # sample_model_response_object_str = """{ # "choices": [ # { # "finish_reason": "stop", # "index": 0, # "message": { # "role": "assistant", # "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." # } # } # ], # "created": 1691429984.3852863, # "model": "claude-instant-1", # "usage": { # "prompt_tokens": 18, # "completion_tokens": 23, # "total_tokens": 41 # } # }""" # sample_model_response_object = { # "choices": [ # { # "finish_reason": "stop", # "index": 0, # "message": { # "role": "assistant", # "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." # } # } # ], # "created": 1691429984.3852863, # "model": "claude-instant-1", # "usage": { # "prompt_tokens": 18, # "completion_tokens": 23, # "total_tokens": 41 # } # } # cache.add_cache(cache_key="test_key", result=sample_model_response_object_str, ttl=1) # cached_value = cache.get_cache(cache_key="test_key") # print(f"cached-value: {cached_value}") # assert cached_value['choices'][0]['message']['content'] == sample_model_response_object['choices'][0]['message']['content'] # time.sleep(2) # assert cache.get_cache(cache_key="test_key") is None # # test_redis_cache_with_ttl() # def test_in_memory_cache_with_ttl(): # cache = Cache(type="local") # sample_model_response_object_str = """{ # "choices": [ # { # "finish_reason": "stop", # "index": 0, # "message": { # "role": "assistant", # "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." # } # } # ], # "created": 1691429984.3852863, # "model": "claude-instant-1", # "usage": { # "prompt_tokens": 18, # "completion_tokens": 23, # "total_tokens": 41 # } # }""" # sample_model_response_object = { # "choices": [ # { # "finish_reason": "stop", # "index": 0, # "message": { # "role": "assistant", # "content": "I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." # } # } # ], # "created": 1691429984.3852863, # "model": "claude-instant-1", # "usage": { # "prompt_tokens": 18, # "completion_tokens": 23, # "total_tokens": 41 # } # } # cache.add_cache(cache_key="test_key", result=sample_model_response_object_str, ttl=1) # cached_value = cache.get_cache(cache_key="test_key") # assert cached_value['choices'][0]['message']['content'] == sample_model_response_object['choices'][0]['message']['content'] # time.sleep(2) # assert cache.get_cache(cache_key="test_key") is None # # test_in_memory_cache_with_ttl()