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litellm/tests/local_testing/test_caching.py
Ishaan Jaff 4d1b4beb3d
(refactor) caching use LLMCachingHandler for async_get_cache and set_cache (#6208) 
* use folder for caching

* fix importing caching

* fix clickhouse pyright

* fix linting

* fix correctly pass kwargs and args

* fix test case for embedding

* fix linting

* fix embedding caching logic

* fix refactor handle utils.py

* fix test_embedding_caching_azure_individual_items_reordered
2024-10-14 16:34:01 +05:30

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import os
import sys
import time
import traceback
import uuid
from dotenv import load_dotenv
from test_rerank import assert_response_shape
load_dotenv()
import os
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import asyncio
import hashlib
import random
import pytest
import litellm
from litellm import aembedding, completion, embedding
from litellm.caching.caching import Cache
from unittest.mock import AsyncMock, patch, MagicMock
import datetime
# 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"}]
@pytest.mark.asyncio
async def test_dual_cache_async_batch_get_cache():
"""
Unit testing for Dual Cache async_batch_get_cache()
- 2 item query
- in_memory result has a partial hit (1/2)
- hit redis for the other -> expect to return None
- expect result = [in_memory_result, None]
"""
from litellm.caching.caching import DualCache, InMemoryCache, RedisCache
in_memory_cache = InMemoryCache()
redis_cache = RedisCache() # get credentials from environment
dual_cache = DualCache(in_memory_cache=in_memory_cache, redis_cache=redis_cache)
in_memory_cache.set_cache(key="test_value", value="hello world")
result = await dual_cache.async_batch_get_cache(keys=["test_value", "test_value_2"])
assert result[0] == "hello world"
assert result[1] == None
def test_dual_cache_batch_get_cache():
"""
Unit testing for Dual Cache batch_get_cache()
- 2 item query
- in_memory result has a partial hit (1/2)
- hit redis for the other -> expect to return None
- expect result = [in_memory_result, None]
"""
from litellm.caching.caching import DualCache, InMemoryCache, RedisCache
in_memory_cache = InMemoryCache()
redis_cache = RedisCache() # get credentials from environment
dual_cache = DualCache(in_memory_cache=in_memory_cache, redis_cache=redis_cache)
in_memory_cache.set_cache(key="test_value", value="hello world")
result = dual_cache.batch_get_cache(keys=["test_value", "test_value_2"])
assert result[0] == "hello world"
assert result[1] == None
# @pytest.mark.skip(reason="")
def test_caching_dynamic_args(): # test in memory cache
try:
litellm.set_verbose = True
_redis_host_env = os.environ.pop("REDIS_HOST")
_redis_port_env = os.environ.pop("REDIS_PORT")
_redis_password_env = os.environ.pop("REDIS_PASSWORD")
litellm.cache = Cache(
type="redis",
host=_redis_host_env,
port=_redis_port_env,
password=_redis_password_env,
)
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:")
os.environ["REDIS_HOST"] = _redis_host_env
os.environ["REDIS_PORT"] = _redis_port_env
os.environ["REDIS_PASSWORD"] = _redis_password_env
except Exception as e:
print(f"error occurred: {traceback.format_exc()}")
pytest.fail(f"Error occurred: {e}")
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_default_ttl():
try:
litellm.set_verbose = True
litellm.cache = Cache(ttl=0)
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 = []
assert response2["id"] != response1["id"]
except Exception as e:
print(f"error occurred: {traceback.format_exc()}")
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize(
"sync_flag",
[True, False],
)
@pytest.mark.asyncio
async def test_caching_with_cache_controls(sync_flag):
try:
litellm.set_verbose = True
litellm.cache = Cache()
message = [{"role": "user", "content": f"Hey, how's it going? {uuid.uuid4()}"}]
if sync_flag:
## 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}
)
assert response2["id"] != response1["id"]
else:
## TTL = 0
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
cache={"ttl": 0},
mock_response="Hello world",
)
await asyncio.sleep(10)
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
cache={"s-maxage": 10},
mock_response="Hello world",
)
assert response2["id"] != response1["id"]
message = [{"role": "user", "content": f"Hey, how's it going? {uuid.uuid4()}"}]
## TTL = 5
if sync_flag:
response1 = completion(
model="gpt-3.5-turbo",
messages=messages,
cache={"ttl": 5},
mock_response="Hello world",
)
response2 = completion(
model="gpt-3.5-turbo",
messages=messages,
cache={"s-maxage": 5},
mock_response="Hello world",
)
print(f"response1: {response1}")
print(f"response2: {response2}")
assert response2["id"] == response1["id"]
else:
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
cache={"ttl": 25},
mock_response="Hello world",
)
await asyncio.sleep(10)
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
cache={"s-maxage": 25},
mock_response="Hello world",
)
print(f"response1: {response1}")
print(f"response2: {response2}")
assert response2["id"] == response1["id"]
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")
litellm.set_verbose = True
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="azure/chatgpt-v-2", 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()
def c():
litellm.enable_caching_on_provider_specific_optional_params = True
messages = [
{"role": "user", "content": "who is ishaan CTO of litellm from litellm 2023"}
]
litellm.cache = Cache()
print("test2 for caching")
litellm.set_verbose = True
response1 = completion(
model="gpt-3.5-turbo",
messages=messages,
top_k=10,
caching=True,
mock_response="Hello: {}".format(uuid.uuid4()),
)
response2 = completion(
model="gpt-3.5-turbo",
messages=messages,
top_k=10,
caching=True,
mock_response="Hello: {}".format(uuid.uuid4()),
)
response3 = completion(
model="gpt-3.5-turbo",
messages=messages,
top_k=9,
caching=True,
mock_response="Hello: {}".format(uuid.uuid4()),
)
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:")
litellm.enable_caching_on_provider_specific_optional_params = False
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()
@pytest.mark.asyncio
async def test_embedding_caching_azure_individual_items():
"""
Tests caching for individual items in an embedding list
- Cache an item
- call aembedding(..) with the item + 1 unique item
- compare to a 2nd aembedding (...) with 2 unique items
```
embedding_1 = ["hey how's it going", "I'm doing well"]
embedding_val_1 = embedding(...)
embedding_2 = ["hey how's it going", "I'm fine"]
embedding_val_2 = embedding(...)
assert embedding_val_1[0]["id"] == embedding_val_2[0]["id"]
```
"""
litellm.cache = Cache()
common_msg = f"hey how's it going {uuid.uuid4()}"
common_msg_2 = f"hey how's it going {uuid.uuid4()}"
embedding_1 = [common_msg]
embedding_2 = [
common_msg,
f"I'm fine {uuid.uuid4()}",
]
embedding_val_1 = await aembedding(
model="azure/azure-embedding-model", input=embedding_1, caching=True
)
embedding_val_2 = await aembedding(
model="azure/azure-embedding-model", input=embedding_2, caching=True
)
print(f"embedding_val_2._hidden_params: {embedding_val_2._hidden_params}")
assert embedding_val_2._hidden_params["cache_hit"] == True
@pytest.mark.asyncio
async def test_embedding_caching_azure_individual_items_reordered():
"""
Tests caching for individual items in an embedding list
- Cache an item
- call aembedding(..) with the item + 1 unique item
- compare to a 2nd aembedding (...) with 2 unique items
```
embedding_1 = ["hey how's it going", "I'm doing well"]
embedding_val_1 = embedding(...)
embedding_2 = ["hey how's it going", "I'm fine"]
embedding_val_2 = embedding(...)
assert embedding_val_1[0]["id"] == embedding_val_2[0]["id"]
```
"""
litellm.set_verbose = True
litellm.cache = Cache()
common_msg = f"{uuid.uuid4()}"
common_msg_2 = f"hey how's it going {uuid.uuid4()}"
embedding_1 = [common_msg_2, common_msg]
embedding_2 = [
common_msg,
f"I'm fine {uuid.uuid4()}",
]
embedding_val_1 = await aembedding(
model="azure/azure-embedding-model", input=embedding_1, caching=True
)
print("embedding val 1", embedding_val_1)
embedding_val_2 = await aembedding(
model="azure/azure-embedding-model", input=embedding_2, caching=True
)
print("embedding val 2", embedding_val_2)
print(f"embedding_val_2._hidden_params: {embedding_val_2._hidden_params}")
assert embedding_val_2._hidden_params["cache_hit"] == True
assert embedding_val_2.data[0]["embedding"] == embedding_val_1.data[1]["embedding"]
assert embedding_val_2.data[0]["index"] != embedding_val_1.data[1]["index"]
assert embedding_val_2.data[0]["index"] == 0
assert embedding_val_1.data[1]["index"] == 1
@pytest.mark.asyncio
@pytest.mark.flaky(retries=3, delay=1)
async def test_embedding_caching_base_64():
""" """
litellm.set_verbose = True
litellm.cache = Cache(
type="redis",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
)
import uuid
inputs = [
f"{uuid.uuid4()} hello this is ishaan",
f"{uuid.uuid4()} hello this is ishaan again",
]
embedding_val_1 = await aembedding(
model="azure/azure-embedding-model",
input=inputs,
caching=True,
encoding_format="base64",
)
await asyncio.sleep(5)
print("\n\nCALL2\n\n")
embedding_val_2 = await aembedding(
model="azure/azure-embedding-model",
input=inputs,
caching=True,
encoding_format="base64",
)
assert embedding_val_2._hidden_params["cache_hit"] == True
print(embedding_val_2)
print(embedding_val_1)
assert embedding_val_2.data[0]["embedding"] == embedding_val_1.data[0]["embedding"]
assert embedding_val_2.data[1]["embedding"] == embedding_val_1.data[1]["embedding"]
@pytest.mark.asyncio
async def test_embedding_caching_redis_ttl():
"""
Test default_in_redis_ttl is used for embedding caching
issue: https://github.com/BerriAI/litellm/issues/6010
"""
litellm.set_verbose = True
# Create a mock for the pipeline
mock_pipeline = AsyncMock()
mock_set = AsyncMock()
mock_pipeline.__aenter__.return_value.set = mock_set
# Patch the Redis class to return our mock
with patch("redis.asyncio.Redis.pipeline", return_value=mock_pipeline):
# Simulate the context manager behavior for the pipeline
litellm.cache = Cache(
type="redis",
host="dummy_host",
password="dummy_password",
default_in_redis_ttl=2.5,
)
inputs = [
f"{uuid.uuid4()} hello this is ishaan",
f"{uuid.uuid4()} hello this is ishaan again",
]
# Call the embedding method
embedding_val_1 = await litellm.aembedding(
model="azure/azure-embedding-model",
input=inputs,
encoding_format="base64",
)
await asyncio.sleep(3) # Wait for TTL to expire
# Check if set was called on the pipeline
mock_set.assert_called()
# Check if the TTL was set correctly
for call in mock_set.call_args_list:
args, kwargs = call
print(f"redis pipeline set args: {args}")
print(f"redis pipeline set kwargs: {kwargs}")
assert kwargs.get("ex") == datetime.timedelta(
seconds=2.5
) # Check if TTL is set to 2.5 seconds
@pytest.mark.asyncio
async def test_redis_cache_basic():
"""
Init redis client
- write to client
- read from client
"""
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"],
)
response1 = completion(
model="gpt-3.5-turbo",
messages=messages,
)
cache_key = litellm.cache.get_cache_key(
model="gpt-3.5-turbo",
messages=messages,
)
print(f"cache_key: {cache_key}")
litellm.cache.add_cache(result=response1, cache_key=cache_key)
print(f"cache key pre async get: {cache_key}")
stored_val = await litellm.cache.async_get_cache(
model="gpt-3.5-turbo",
messages=messages,
)
print(f"stored_val: {stored_val}")
assert stored_val["id"] == response1.id
@pytest.mark.asyncio
async def test_redis_batch_cache_write():
"""
Init redis client
- write to client
- read from client
"""
litellm.set_verbose = True
import uuid
messages = [
{"role": "user", "content": f"write a one sentence poem about: {uuid.uuid4()}"},
]
litellm.cache = Cache(
type="redis",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
password=os.environ["REDIS_PASSWORD"],
redis_flush_size=2,
)
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
)
response2 = await litellm.acompletion(
model="anthropic/claude-3-opus-20240229",
messages=messages,
mock_response="good morning from this test",
)
# we hit the flush size, this will now send to redis
await asyncio.sleep(2)
response4 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
)
assert response1.id == response4.id
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="azure/chatgpt-v-2", 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_id = ""
for chunk in response1:
print(chunk)
response_1_id = chunk.id
time.sleep(0.5)
response2 = completion(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=40,
temperature=0.2,
stream=True,
)
response_2_id = ""
for chunk in response2:
print(chunk)
response_2_id = chunk.id
assert (
response_1_id == response_2_id
), f"Response 1 != Response 2. Same params, Response 1{response_1_id} != Response 2{response_2_id}"
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()
@pytest.mark.skip(reason="Local test. Requires running redis cluster locally.")
@pytest.mark.asyncio
async def test_redis_cache_cluster_init_unit_test():
try:
from redis.asyncio import RedisCluster as AsyncRedisCluster
from redis.cluster import RedisCluster
from litellm.caching.caching import RedisCache
litellm.set_verbose = True
# List of startup nodes
startup_nodes = [
{"host": "127.0.0.1", "port": "7001"},
]
resp = RedisCache(startup_nodes=startup_nodes)
assert isinstance(resp.redis_client, RedisCluster)
assert isinstance(resp.init_async_client(), AsyncRedisCluster)
resp = litellm.Cache(type="redis", redis_startup_nodes=startup_nodes)
assert isinstance(resp.cache, RedisCache)
assert isinstance(resp.cache.redis_client, RedisCluster)
assert isinstance(resp.cache.init_async_client(), AsyncRedisCluster)
except Exception as e:
print(f"{str(e)}\n\n{traceback.format_exc()}")
raise e
@pytest.mark.asyncio
@pytest.mark.skip(reason="Local test. Requires running redis cluster locally.")
async def test_redis_cache_cluster_init_with_env_vars_unit_test():
try:
import json
from redis.asyncio import RedisCluster as AsyncRedisCluster
from redis.cluster import RedisCluster
from litellm.caching.caching import RedisCache
litellm.set_verbose = True
# List of startup nodes
startup_nodes = [
{"host": "127.0.0.1", "port": "7001"},
{"host": "127.0.0.1", "port": "7003"},
{"host": "127.0.0.1", "port": "7004"},
{"host": "127.0.0.1", "port": "7005"},
{"host": "127.0.0.1", "port": "7006"},
{"host": "127.0.0.1", "port": "7007"},
]
# set startup nodes in environment variables
os.environ["REDIS_CLUSTER_NODES"] = json.dumps(startup_nodes)
print("REDIS_CLUSTER_NODES", os.environ["REDIS_CLUSTER_NODES"])
# unser REDIS_HOST, REDIS_PORT, REDIS_PASSWORD
os.environ.pop("REDIS_HOST", None)
os.environ.pop("REDIS_PORT", None)
os.environ.pop("REDIS_PASSWORD", None)
resp = RedisCache()
print("response from redis cache", resp)
assert isinstance(resp.redis_client, RedisCluster)
assert isinstance(resp.init_async_client(), AsyncRedisCluster)
resp = litellm.Cache(type="redis")
assert isinstance(resp.cache, RedisCache)
assert isinstance(resp.cache.redis_client, RedisCluster)
assert isinstance(resp.cache.init_async_client(), AsyncRedisCluster)
except Exception as e:
print(f"{str(e)}\n\n{traceback.format_exc()}")
raise e
@pytest.mark.asyncio
async def test_redis_cache_acompletion_stream():
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 = ""
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=40,
temperature=1,
stream=True,
)
async for chunk in response1:
response_1_content += chunk.choices[0].delta.content or ""
print(response_1_content)
time.sleep(0.5)
print("\n\n Response 1 content: ", response_1_content, "\n\n")
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=40,
temperature=1,
stream=True,
)
async for chunk in response2:
response_2_content += chunk.choices[0].delta.content or ""
print(response_2_content)
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(f"{str(e)}\n\n{traceback.format_exc()}")
raise e
# test_redis_cache_acompletion_stream()
@pytest.mark.asyncio
async def test_redis_cache_atext_completion():
try:
litellm.set_verbose = True
prompt = f"write a one sentence poem about: {uuid.uuid4()}"
litellm.cache = Cache(
type="redis",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
password=os.environ["REDIS_PASSWORD"],
supported_call_types=["atext_completion"],
)
print("test for caching, atext_completion")
response1 = await litellm.atext_completion(
model="gpt-3.5-turbo-instruct", prompt=prompt, max_tokens=40, temperature=1
)
await asyncio.sleep(0.5)
print("\n\n Response 1 content: ", response1, "\n\n")
response2 = await litellm.atext_completion(
model="gpt-3.5-turbo-instruct", prompt=prompt, max_tokens=40, temperature=1
)
print(response2)
assert response1.id == response2.id
except Exception as e:
print(f"{str(e)}\n\n{traceback.format_exc()}")
raise e
@pytest.mark.asyncio
async 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")
print("test for caching, streaming + completion")
response_1_content = ""
response_2_content = ""
response1 = await litellm.acompletion(
model="bedrock/anthropic.claude-v2",
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)
time.sleep(0.5)
print("\n\n Response 1 content: ", response_1_content, "\n\n")
response2 = await litellm.acompletion(
model="bedrock/anthropic.claude-v2",
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)
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_disk_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="disk",
)
response1 = completion(
model="gpt-3.5-turbo",
messages=messages,
caching=True,
max_tokens=20,
mock_response="This number is so great!",
)
# response2 is mocked to a different response from response1,
# but the completion from the cache should be used instead of the mock
# response since the input is the same as response1
response2 = completion(
model="gpt-3.5-turbo",
messages=messages,
caching=True,
max_tokens=20,
mock_response="This number is awful!",
)
# Since the parameters are not the same as response1, response3 should actually
# be the mock response
response3 = completion(
model="gpt-3.5-turbo",
messages=messages,
caching=True,
temperature=0.5,
mock_response="This number is awful!",
)
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
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 max_tokens, temperature 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"
f" occurred:"
)
assert response1.id == response2.id
assert response1.created == response2.created
assert response1.choices[0].message.content == response2.choices[0].message.content
# @pytest.mark.skip(reason="AWS Suspended Account")
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_s3_cache_stream_azure(sync_mode):
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="litellm-proxy",
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 = ""
if sync_mode:
response1 = litellm.completion(
model="azure/chatgpt-v-2",
messages=messages,
max_tokens=40,
temperature=1,
stream=True,
)
for chunk in response1:
print(chunk)
response_1_created = chunk.created
response_1_content += chunk.choices[0].delta.content or ""
print(response_1_content)
else:
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)
if sync_mode:
time.sleep(0.5)
else:
await asyncio.sleep(0.5)
print("\n\n Response 1 content: ", response_1_content, "\n\n")
if sync_mode:
response2 = litellm.completion(
model="azure/chatgpt-v-2",
messages=messages,
max_tokens=40,
temperature=1,
stream=True,
)
for chunk in response2:
print(chunk)
response_2_content += chunk.choices[0].delta.content or ""
response_2_created = chunk.created
print(response_2_content)
else:
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)
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()
@pytest.mark.skip(reason="AWS Suspended Account")
@pytest.mark.asyncio
async def test_s3_cache_acompletion_azure():
import asyncio
import logging
import tracemalloc
tracemalloc.start()
logging.basicConfig(level=logging.DEBUG)
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="litellm-my-test-bucket-2",
s3_region_name="us-east-1",
)
print("s3 Cache: test for caching, streaming + completion")
response1 = await litellm.acompletion(
model="azure/chatgpt-v-2",
messages=messages,
max_tokens=40,
temperature=1,
)
print(response1)
time.sleep(2)
response2 = await litellm.acompletion(
model="azure/chatgpt-v-2",
messages=messages,
max_tokens=40,
temperature=1,
)
print(response2)
assert response1.id == response2.id
litellm.cache = None
litellm.success_callback = []
litellm._async_success_callback = []
except Exception as e:
print(e)
raise e
# 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 > 0.05
) # ensure 2nd response comes in over 0.05s. This should not be cached.
# test_cache_override()
@pytest.mark.asyncio
async def test_cache_control_overrides():
# we use the cache controls to ensure there is no cache hit on this test
litellm.cache = Cache(
type="redis",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
password=os.environ["REDIS_PASSWORD"],
)
print("Testing cache override")
litellm.set_verbose = True
import uuid
unique_num = str(uuid.uuid4())
start_time = time.time()
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "hello who are you" + unique_num,
}
],
caching=True,
)
print(response1)
await asyncio.sleep(2)
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "hello who are you" + unique_num,
}
],
caching=True,
cache={"no-cache": True},
)
print(response2)
assert response1.id != response2.id
def test_sync_cache_control_overrides():
# we use the cache controls to ensure there is no cache hit on this test
litellm.cache = Cache(
type="redis",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
password=os.environ["REDIS_PASSWORD"],
)
print("Testing cache override")
litellm.set_verbose = True
import uuid
unique_num = str(uuid.uuid4())
start_time = time.time()
response1 = litellm.completion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "hello who are you" + unique_num,
}
],
caching=True,
)
print(response1)
time.sleep(2)
response2 = litellm.completion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "hello who are you" + unique_num,
}
],
caching=True,
cache={"no-cache": True},
)
print(response2)
assert response1.id != response2.id
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,
)
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: {str(e)}")
def test_get_cache_key():
from litellm.caching.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'}]max_tokens: 40temperature: 0.2stream: True"
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": "<openai.lib.azure.AsyncAzureOpenAI object at 0x12b6a1060>",
}
)
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": "<openai.lib.azure.AsyncAzureOpenAI object at 0x12b6a1060>",
"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": "<litellm.utils.Logging object at 0x12f1bddb0>",
}
)
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()
@pytest.mark.skip(reason="beta test - new redis semantic cache")
def test_redis_semantic_cache_completion():
litellm.set_verbose = True
import logging
logging.basicConfig(level=logging.DEBUG)
random_number = random.randint(
1, 100000
) # add a random number to ensure it's always adding /reading from cache
print("testing semantic caching")
litellm.cache = Cache(
type="redis-semantic",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
password=os.environ["REDIS_PASSWORD"],
similarity_threshold=0.8,
redis_semantic_cache_embedding_model="text-embedding-ada-002",
)
response1 = completion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
max_tokens=20,
)
print(f"response1: {response1}")
random_number = random.randint(1, 100000)
response2 = completion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
max_tokens=20,
)
print(f"response2: {response1}")
assert response1.id == response2.id
# test_redis_cache_completion()
@pytest.mark.skip(reason="beta test - new redis semantic cache")
@pytest.mark.asyncio
async def test_redis_semantic_cache_acompletion():
litellm.set_verbose = True
import logging
logging.basicConfig(level=logging.DEBUG)
random_number = random.randint(
1, 100000
) # add a random number to ensure it's always adding / reading from cache
print("testing semantic caching")
litellm.cache = Cache(
type="redis-semantic",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
password=os.environ["REDIS_PASSWORD"],
similarity_threshold=0.8,
redis_semantic_cache_use_async=True,
)
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
max_tokens=5,
)
print(f"response1: {response1}")
random_number = random.randint(1, 100000)
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
max_tokens=5,
)
print(f"response2: {response2}")
assert response1.id == response2.id
def test_caching_redis_simple(caplog, capsys):
"""
Relevant issue - https://github.com/BerriAI/litellm/issues/4511
"""
litellm.set_verbose = True ## REQUIRED FOR TEST.
litellm.cache = Cache(
type="redis", url=os.getenv("REDIS_SSL_URL")
) # passing `supported_call_types = ["completion"]` has no effect
s = time.time()
uuid_str = str(uuid.uuid4())
x = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": f"Hello, how are you? Wink {uuid_str}"}],
stream=True,
)
for m in x:
print(m)
print(time.time() - s)
s2 = time.time()
x = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": f"Hello, how are you? Wink {uuid_str}"}],
stream=True,
)
for m in x:
print(m)
print(time.time() - s2)
redis_async_caching_error = False
redis_service_logging_error = False
captured = capsys.readouterr()
captured_logs = [rec.message for rec in caplog.records]
print(f"captured_logs: {captured_logs}")
for item in captured_logs:
if (
"Error connecting to Async Redis client" in item
or "Set ASYNC Redis Cache" in item
):
redis_async_caching_error = True
if "ServiceLogging.async_service_success_hook" in item:
redis_service_logging_error = True
assert redis_async_caching_error is False
assert redis_service_logging_error is False
assert "async success_callback: reaches cache for logging" not in captured.out
@pytest.mark.asyncio
async def test_qdrant_semantic_cache_acompletion():
litellm.set_verbose = True
random_number = random.randint(
1, 100000
) # add a random number to ensure it's always adding /reading from cache
print("Testing Qdrant Semantic Caching with acompletion")
litellm.cache = Cache(
type="qdrant-semantic",
_host_type="cloud",
qdrant_api_base=os.getenv("QDRANT_URL"),
qdrant_api_key=os.getenv("QDRANT_API_KEY"),
qdrant_collection_name="test_collection",
similarity_threshold=0.8,
qdrant_quantization_config="binary",
)
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
mock_response="hello",
max_tokens=20,
)
print(f"Response1: {response1}")
random_number = random.randint(1, 100000)
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
max_tokens=20,
)
print(f"Response2: {response2}")
assert response1.id == response2.id
@pytest.mark.asyncio
async def test_qdrant_semantic_cache_acompletion_stream():
try:
random_word = generate_random_word()
messages = [
{
"role": "user",
"content": f"write a joke about: {random_word}",
}
]
litellm.cache = Cache(
type="qdrant-semantic",
qdrant_api_base=os.getenv("QDRANT_URL"),
qdrant_api_key=os.getenv("QDRANT_API_KEY"),
qdrant_collection_name="test_collection",
similarity_threshold=0.8,
qdrant_quantization_config="binary",
)
print("Test Qdrant Semantic Caching with streaming + acompletion")
response_1_content = ""
response_2_content = ""
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=40,
temperature=1,
stream=True,
mock_response="hi",
)
async for chunk in response1:
response_1_id = chunk.id
response_1_content += chunk.choices[0].delta.content or ""
time.sleep(2)
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=40,
temperature=1,
stream=True,
)
async for chunk in response2:
response_2_id = chunk.id
response_2_content += chunk.choices[0].delta.content or ""
print("\nResponse 1", response_1_content, "\nResponse 1 id", response_1_id)
print("\nResponse 2", response_2_content, "\nResponse 2 id", response_2_id)
assert (
response_1_content == response_2_content
), f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}"
assert (
response_1_id == response_2_id
), f"Response 1 id != Response 2 id, Response 1 id: {response_1_id} != Response 2 id: {response_2_id}"
litellm.cache = None
litellm.success_callback = []
litellm._async_success_callback = []
except Exception as e:
print(f"{str(e)}\n\n{traceback.format_exc()}")
raise e
@pytest.mark.asyncio()
async def test_cache_default_off_acompletion():
litellm.set_verbose = True
import logging
from litellm._logging import verbose_logger
verbose_logger.setLevel(logging.DEBUG)
from litellm.caching.caching import CacheMode
random_number = random.randint(
1, 100000
) # add a random number to ensure it's always adding /reading from cache
litellm.cache = Cache(
type="local",
mode=CacheMode.default_off,
)
### No Cache hits when it's default off
response1 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
mock_response="hello",
max_tokens=20,
)
print(f"Response1: {response1}")
response2 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
max_tokens=20,
)
print(f"Response2: {response2}")
assert response1.id != response2.id
## Cache hits when it's default off and then opt in
response3 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
mock_response="hello",
cache={"use-cache": True},
metadata={"key": "value"},
max_tokens=20,
)
print(f"Response3: {response3}")
await asyncio.sleep(2)
response4 = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": f"write a one sentence poem about: {random_number}",
}
],
cache={"use-cache": True},
metadata={"key": "value"},
max_tokens=20,
)
print(f"Response4: {response4}")
assert response3.id == response4.id
@pytest.mark.asyncio()
@pytest.mark.skip(reason="dual caching should first prioritze local cache")
async def test_dual_cache_uses_redis():
"""
- Store diff values in redis and in memory cache
- call get cache
- Assert that value from redis is used
"""
litellm.set_verbose = True
from litellm.caching.caching import DualCache, RedisCache
current_usage = uuid.uuid4()
_cache_obj = DualCache(redis_cache=RedisCache(), always_read_redis=True)
# set cache
await _cache_obj.async_set_cache(key=f"current_usage: {current_usage}", value=10)
# modify value of in memory cache
_cache_obj.in_memory_cache.cache_dict[f"current_usage: {current_usage}"] = 1
# get cache
value = await _cache_obj.async_get_cache(key=f"current_usage: {current_usage}")
print("value from dual cache", value)
assert value == 10
@pytest.mark.asyncio()
async def test_proxy_logging_setup():
"""
Assert always_read_redis is True when used by internal usage cache
"""
from litellm.caching.caching import DualCache
from litellm.proxy.utils import ProxyLogging
pl_obj = ProxyLogging(user_api_key_cache=DualCache())
assert pl_obj.internal_usage_cache.dual_cache.always_read_redis is True
@pytest.mark.skip(reason="local test. Requires sentinel setup.")
@pytest.mark.asyncio
async def test_redis_sentinel_caching():
"""
Init redis client
- write to client
- read from client
"""
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"],
service_name="mymaster",
sentinel_nodes=[("localhost", 26379)],
)
response1 = completion(
model="gpt-3.5-turbo",
messages=messages,
)
cache_key = litellm.cache.get_cache_key(
model="gpt-3.5-turbo",
messages=messages,
)
print(f"cache_key: {cache_key}")
litellm.cache.add_cache(result=response1, cache_key=cache_key)
print(f"cache key pre async get: {cache_key}")
stored_val = litellm.cache.get_cache(
model="gpt-3.5-turbo",
messages=messages,
)
print(f"stored_val: {stored_val}")
assert stored_val["id"] == response1.id
stored_val_2 = await litellm.cache.async_get_cache(
model="gpt-3.5-turbo",
messages=messages,
)
print(f"stored_val: {stored_val}")
assert stored_val_2["id"] == response1.id
@pytest.mark.asyncio
async def test_redis_proxy_batch_redis_get_cache():
"""
Tests batch_redis_get.py
- make 1st call -> expect miss
- make 2nd call -> expect hit
"""
from litellm.caching.caching import Cache, DualCache
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.hooks.batch_redis_get import _PROXY_BatchRedisRequests
litellm.cache = Cache(
type="redis",
host=os.getenv("REDIS_HOST"),
port=os.getenv("REDIS_PORT"),
password=os.getenv("REDIS_PASSWORD"),
namespace="test_namespace",
)
batch_redis_get_obj = (
_PROXY_BatchRedisRequests()
) # overrides the .async_get_cache method
user_api_key_cache = DualCache()
import uuid
batch_redis_get_obj.in_memory_cache = user_api_key_cache.in_memory_cache
messages = [{"role": "user", "content": "hi {}".format(uuid.uuid4())}]
# 1st call -> expect miss
response = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
mock_response="hello",
)
assert response is not None
assert "cache_key" not in response._hidden_params
print(response._hidden_params)
await asyncio.sleep(1)
# 2nd call -> expect hit
response = await litellm.acompletion(
model="gpt-3.5-turbo",
messages=messages,
mock_response="hello",
)
print(response._hidden_params)
assert "cache_key" in response._hidden_params
def test_logging_turn_off_message_logging_streaming():
litellm.turn_off_message_logging = True
mock_obj = Cache(type="local")
litellm.cache = mock_obj
with patch.object(mock_obj, "add_cache", new=MagicMock()) as mock_client:
print(f"mock_obj.add_cache: {mock_obj.add_cache}")
resp = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "hi"}],
mock_response="hello",
stream=True,
)
for chunk in resp:
continue
time.sleep(1)
mock_client.assert_called_once()
assert mock_client.call_args.args[0].choices[0].message.content == "hello"
@pytest.mark.asyncio()
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.parametrize(
"top_n_1, top_n_2, expect_cache_hit",
[
(3, 3, True),
(3, None, False),
],
)
async def test_basic_rerank_caching(sync_mode, top_n_1, top_n_2, expect_cache_hit):
litellm.set_verbose = True
litellm.cache = Cache(type="local")
if sync_mode is True:
for idx in range(2):
if idx == 0:
top_n = top_n_1
else:
top_n = top_n_2
response = litellm.rerank(
model="cohere/rerank-english-v3.0",
query="hello",
documents=["hello", "world"],
top_n=top_n,
)
else:
for idx in range(2):
if idx == 0:
top_n = top_n_1
else:
top_n = top_n_2
response = await litellm.arerank(
model="cohere/rerank-english-v3.0",
query="hello",
documents=["hello", "world"],
top_n=top_n,
)
await asyncio.sleep(1)
if expect_cache_hit is True:
assert "cache_key" in response._hidden_params
else:
assert "cache_key" not in response._hidden_params
print("re rank response: ", response)
assert response.id is not None
assert response.results is not None
assert_response_shape(response, custom_llm_provider="cohere")
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