import sys, os, uuid 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 import hashlib # litellm.set_verbose=True messages = [{"role": "user", "content": "who is ishaan Github? "}] # comment import random import string def generate_random_word(length=4): letters = string.ascii_lowercase return "".join(random.choice(letters) for _ in range(length)) messages = [{"role": "user", "content": "who is ishaan 5222"}] def test_caching_v2(): # test in memory cache try: litellm.set_verbose = True 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 litellm.success_callback = [] litellm._async_success_callback = [] if ( response2["choices"][0]["message"]["content"] != response1["choices"][0]["message"]["content"] ): print(f"response1: {response1}") print(f"response2: {response2}") pytest.fail(f"Error occurred:") except Exception as e: print(f"error occurred: {traceback.format_exc()}") pytest.fail(f"Error occurred: {e}") # test_caching_v2() def test_caching_with_ttl(): try: litellm.set_verbose = True litellm.cache = Cache() response1 = completion( model="gpt-3.5-turbo", messages=messages, caching=True, ttl=0 ) response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) print(f"response1: {response1}") print(f"response2: {response2}") litellm.cache = None # disable cache litellm.success_callback = [] litellm._async_success_callback = [] assert ( response2["choices"][0]["message"]["content"] != response1["choices"][0]["message"]["content"] ) except Exception as e: print(f"error occurred: {traceback.format_exc()}") pytest.fail(f"Error occurred: {e}") def test_caching_with_cache_controls(): try: litellm.set_verbose = True litellm.cache = Cache() message = [{"role": "user", "content": f"Hey, how's it going? {uuid.uuid4()}"}] ## TTL = 0 response1 = completion( model="gpt-3.5-turbo", messages=messages, cache={"ttl": 0} ) response2 = completion( model="gpt-3.5-turbo", messages=messages, cache={"s-maxage": 10} ) print(f"response1: {response1}") print(f"response2: {response2}") assert ( response2["choices"][0]["message"]["content"] != response1["choices"][0]["message"]["content"] ) message = [{"role": "user", "content": f"Hey, how's it going? {uuid.uuid4()}"}] ## TTL = 5 response1 = completion( model="gpt-3.5-turbo", messages=messages, cache={"ttl": 5} ) response2 = completion( model="gpt-3.5-turbo", messages=messages, cache={"s-maxage": 5} ) print(f"response1: {response1}") print(f"response2: {response2}") assert ( response2["choices"][0]["message"]["content"] == response1["choices"][0]["message"]["content"] ) except Exception as e: print(f"error occurred: {traceback.format_exc()}") pytest.fail(f"Error occurred: {e}") # test_caching_with_cache_controls() 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 litellm.success_callback = [] litellm._async_success_callback = [] 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.set_verbose = True 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 litellm.success_callback = [] litellm._async_success_callback = [] 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 litellm.success_callback = [] litellm._async_success_callback = [] 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 Redis Caching - non streaming") response1 = completion( model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=20 ) response2 = completion( model="gpt-3.5-turbo", messages=messages, caching=True, max_tokens=20 ) response3 = completion( model="gpt-3.5-turbo", messages=messages, caching=True, temperature=0.5 ) 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 litellm.success_callback = [] litellm._async_success_callback = [] """ 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:") assert response1.id == response2.id assert response1.created == response2.created assert response1.choices[0].message.content == response2.choices[0].message.content # test_redis_cache_completion() def test_redis_cache_completion_stream(): try: litellm.success_callback = [] litellm._async_success_callback = [] litellm.callbacks = [] litellm.set_verbose = True 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("test for caching, streaming + completion") response1 = completion( model="gpt-3.5-turbo", messages=messages, max_tokens=40, temperature=0.2, stream=True, ) response_1_content = "" for chunk in response1: print(chunk) response_1_content += chunk.choices[0].delta.content or "" print(response_1_content) time.sleep(0.5) response2 = completion( model="gpt-3.5-turbo", messages=messages, max_tokens=40, temperature=0.2, stream=True, ) response_2_content = "" for chunk in response2: print(chunk) response_2_content += chunk.choices[0].delta.content or "" print("\nresponse 1", response_1_content) print("\nresponse 2", response_2_content) assert ( response_1_content == response_2_content ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" litellm.success_callback = [] litellm.cache = None litellm.success_callback = [] litellm._async_success_callback = [] except Exception as e: print(e) litellm.success_callback = [] raise e """ 1 & 2 should be exactly the same """ test_redis_cache_completion_stream() def test_redis_cache_acompletion_stream(): import asyncio try: litellm.set_verbose = True random_word = generate_random_word() messages = [ { "role": "user", "content": f"write a one sentence poem about: {random_word}", } ] litellm.cache = Cache( type="redis", host=os.environ["REDIS_HOST"], port=os.environ["REDIS_PORT"], password=os.environ["REDIS_PASSWORD"], ) print("test for caching, streaming + completion") response_1_content = "" response_2_content = "" async def call1(): nonlocal response_1_content response1 = await litellm.acompletion( model="gpt-3.5-turbo", messages=messages, max_tokens=40, temperature=1, stream=True, ) async for chunk in response1: print(chunk) response_1_content += chunk.choices[0].delta.content or "" print(response_1_content) asyncio.run(call1()) time.sleep(0.5) print("\n\n Response 1 content: ", response_1_content, "\n\n") async def call2(): nonlocal response_2_content response2 = await litellm.acompletion( model="gpt-3.5-turbo", messages=messages, max_tokens=40, temperature=1, stream=True, ) async for chunk in response2: print(chunk) response_2_content += chunk.choices[0].delta.content or "" print(response_2_content) asyncio.run(call2()) print("\nresponse 1", response_1_content) print("\nresponse 2", response_2_content) assert ( response_1_content == response_2_content ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" litellm.cache = None litellm.success_callback = [] litellm._async_success_callback = [] except Exception as e: print(e) raise e # test_redis_cache_acompletion_stream() def test_redis_cache_acompletion_stream_bedrock(): import asyncio try: litellm.set_verbose = True random_word = generate_random_word() messages = [ { "role": "user", "content": f"write a one sentence poem about: {random_word}", } ] litellm.cache = Cache( type="redis", host=os.environ["REDIS_HOST"], port=os.environ["REDIS_PORT"], password=os.environ["REDIS_PASSWORD"], ) print("test for caching, streaming + completion") response_1_content = "" response_2_content = "" async def call1(): nonlocal response_1_content response1 = await litellm.acompletion( model="bedrock/anthropic.claude-v1", messages=messages, max_tokens=40, temperature=1, stream=True, ) async for chunk in response1: print(chunk) response_1_content += chunk.choices[0].delta.content or "" print(response_1_content) asyncio.run(call1()) time.sleep(0.5) print("\n\n Response 1 content: ", response_1_content, "\n\n") async def call2(): nonlocal response_2_content response2 = await litellm.acompletion( model="bedrock/anthropic.claude-v1", messages=messages, max_tokens=40, temperature=1, stream=True, ) async for chunk in response2: print(chunk) response_2_content += chunk.choices[0].delta.content or "" print(response_2_content) asyncio.run(call2()) print("\nresponse 1", response_1_content) print("\nresponse 2", response_2_content) assert ( response_1_content == response_2_content ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" litellm.cache = None litellm.success_callback = [] litellm._async_success_callback = [] except Exception as e: print(e) raise e def test_s3_cache_acompletion_stream_azure(): import asyncio try: litellm.set_verbose = True random_word = generate_random_word() messages = [ { "role": "user", "content": f"write a one sentence poem about: {random_word}", } ] litellm.cache = Cache( type="s3", s3_bucket_name="cache-bucket-litellm", s3_region_name="us-west-2" ) print("s3 Cache: test for caching, streaming + completion") response_1_content = "" response_2_content = "" response_1_created = "" response_2_created = "" async def call1(): nonlocal response_1_content, response_1_created response1 = await litellm.acompletion( model="azure/chatgpt-v-2", messages=messages, max_tokens=40, temperature=1, stream=True, ) async for chunk in response1: print(chunk) response_1_created = chunk.created response_1_content += chunk.choices[0].delta.content or "" print(response_1_content) asyncio.run(call1()) time.sleep(0.5) print("\n\n Response 1 content: ", response_1_content, "\n\n") async def call2(): nonlocal response_2_content, response_2_created response2 = await litellm.acompletion( model="azure/chatgpt-v-2", messages=messages, max_tokens=40, temperature=1, stream=True, ) async for chunk in response2: print(chunk) response_2_content += chunk.choices[0].delta.content or "" response_2_created = chunk.created print(response_2_content) asyncio.run(call2()) print("\nresponse 1", response_1_content) print("\nresponse 2", response_2_content) assert ( response_1_content == response_2_content ), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" # prioritizing getting a new deploy out - will look at this in the next deploy # print("response 1 created", response_1_created) # print("response 2 created", response_2_created) # assert response_1_created == response_2_created litellm.cache = None litellm.success_callback = [] litellm._async_success_callback = [] except Exception as e: print(e) raise e # test_s3_cache_acompletion_stream_azure() # test_redis_cache_acompletion_stream_bedrock() # 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 litellm.success_callback = [] litellm._async_success_callback = [] # test_custom_redis_cache_with_key() def test_cache_override(): # test if we can override the cache, when `caching=False` but litellm.cache = Cache() is set # in this case it should not return cached responses litellm.cache = Cache() print("Testing cache override") litellm.set_verbose = True # test embedding response1 = embedding( model="text-embedding-ada-002", input=["hello who are you"], caching=False ) start_time = time.time() response2 = embedding( model="text-embedding-ada-002", input=["hello who are you"], caching=False ) end_time = time.time() print(f"Embedding 2 response time: {end_time - start_time} seconds") assert ( end_time - start_time > 0.1 ) # ensure 2nd response comes in over 0.1s. This should not be cached. # test_cache_override() 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 litellm.success_callback = [] litellm._async_success_callback = [] except Exception as e: pytest.fail(f"Error occurred:", e) def test_get_cache_key(): from litellm.caching import Cache try: print("Testing get_cache_key") cache_instance = Cache() cache_key = cache_instance.get_cache_key( **{ "model": "gpt-3.5-turbo", "messages": [ {"role": "user", "content": "write a one sentence poem about: 7510"} ], "max_tokens": 40, "temperature": 0.2, "stream": True, "litellm_call_id": "ffe75e7e-8a07-431f-9a74-71a5b9f35f0b", "litellm_logging_obj": {}, } ) cache_key_2 = cache_instance.get_cache_key( **{ "model": "gpt-3.5-turbo", "messages": [ {"role": "user", "content": "write a one sentence poem about: 7510"} ], "max_tokens": 40, "temperature": 0.2, "stream": True, "litellm_call_id": "ffe75e7e-8a07-431f-9a74-71a5b9f35f0b", "litellm_logging_obj": {}, } ) cache_key_str = "model: gpt-3.5-turbomessages: [{'role': 'user', 'content': 'write a one sentence poem about: 7510'}]temperature: 0.2max_tokens: 40" hash_object = hashlib.sha256(cache_key_str.encode()) # Hexadecimal representation of the hash hash_hex = hash_object.hexdigest() assert cache_key == hash_hex assert ( cache_key_2 == hash_hex ), f"{cache_key} != {cache_key_2}. The same kwargs should have the same cache key across runs" embedding_cache_key = cache_instance.get_cache_key( **{ "model": "azure/azure-embedding-model", "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", "api_key": "", "api_version": "2023-07-01-preview", "timeout": None, "max_retries": 0, "input": ["hi who is ishaan"], "caching": True, "client": "", } ) print(embedding_cache_key) embedding_cache_key_str = ( "model: azure/azure-embedding-modelinput: ['hi who is ishaan']" ) hash_object = hashlib.sha256(embedding_cache_key_str.encode()) # Hexadecimal representation of the hash hash_hex = hash_object.hexdigest() assert ( embedding_cache_key == hash_hex ), f"{embedding_cache_key} != 'model: azure/azure-embedding-modelinput: ['hi who is ishaan']'. The same kwargs should have the same cache key across runs" # Proxy - embedding cache, test if embedding key, gets model_group and not model embedding_cache_key_2 = cache_instance.get_cache_key( **{ "model": "azure/azure-embedding-model", "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", "api_key": "", "api_version": "2023-07-01-preview", "timeout": None, "max_retries": 0, "input": ["hi who is ishaan"], "caching": True, "client": "", "proxy_server_request": { "url": "http://0.0.0.0:8000/embeddings", "method": "POST", "headers": { "host": "0.0.0.0:8000", "user-agent": "curl/7.88.1", "accept": "*/*", "content-type": "application/json", "content-length": "80", }, "body": { "model": "azure-embedding-model", "input": ["hi who is ishaan"], }, }, "user": None, "metadata": { "user_api_key": None, "headers": { "host": "0.0.0.0:8000", "user-agent": "curl/7.88.1", "accept": "*/*", "content-type": "application/json", "content-length": "80", }, "model_group": "EMBEDDING_MODEL_GROUP", "deployment": "azure/azure-embedding-model-ModelID-azure/azure-embedding-modelhttps://openai-gpt-4-test-v-1.openai.azure.com/2023-07-01-preview", }, "model_info": { "mode": "embedding", "base_model": "text-embedding-ada-002", "id": "20b2b515-f151-4dd5-a74f-2231e2f54e29", }, "litellm_call_id": "2642e009-b3cd-443d-b5dd-bb7d56123b0e", "litellm_logging_obj": "", } ) print(embedding_cache_key_2) embedding_cache_key_str_2 = ( "model: EMBEDDING_MODEL_GROUPinput: ['hi who is ishaan']" ) hash_object = hashlib.sha256(embedding_cache_key_str_2.encode()) # Hexadecimal representation of the hash hash_hex = hash_object.hexdigest() assert embedding_cache_key_2 == hash_hex print("passed!") except Exception as e: traceback.print_exc() pytest.fail(f"Error occurred:", e) # test_get_cache_key() def test_cache_context_managers(): litellm.set_verbose = True litellm.cache = Cache(type="redis") # cache is on, disable it litellm.disable_cache() assert litellm.cache == None assert "cache" not in litellm.success_callback assert "cache" not in litellm._async_success_callback # disable a cache that is off litellm.disable_cache() assert litellm.cache == None assert "cache" not in litellm.success_callback assert "cache" not in litellm._async_success_callback litellm.enable_cache( type="redis", host=os.environ["REDIS_HOST"], port=os.environ["REDIS_PORT"], ) assert litellm.cache != None assert litellm.cache.type == "redis" print("VARS of litellm.cache", vars(litellm.cache)) # test_cache_context_managers() # 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()