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with new caching

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This commit is contained in:
ishaan-jaff 2023-08-28 21:56:57 -07:00
parent 8f37caef6d
commit 3726270d95
4 changed files with 78 additions and 20 deletions
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5
litellm/caching.py
View file

@ -49,7 +49,9 @@ class Cache():
self.cache = RedisCache(type, host, port, password)
if type == "local":
self.cache = InMemoryCache()
if "cache" not in litellm.input_callback:
litellm.input_callback.append("cache")
if "cache" not in litellm.success_callback:
litellm.success_callback.append("cache")
def get_cache_key(self, *args, **kwargs):
@ -88,8 +90,9 @@ class Cache():
def add_cache(self, result, *args, **kwargs):
try:
# print("adding to cache", result)
cache_key = self.get_cache_key(*args, **kwargs)
# print("adding to cache", cache_key, result)
# print(cache_key)
if cache_key is not None:
# print("adding to cache", cache_key, result)

53
litellm/tests/test_caching.py
View file

@ -127,7 +127,7 @@ embedding_large_text = """
small text
""" * 5
# test_caching_with_models()
# # test_caching_with_models()
def test_embedding_caching():
import time
litellm.cache = Cache()
@ -153,18 +153,64 @@ def test_embedding_caching():
# test caching with streaming
messages = [{"role": "user", "content": "tell me a story in 2 sentences"}]
def test_caching_v2_stream():
def test_caching_v2_stream_basic():
try:
litellm.cache = Cache()
# litellm.token="ishaan@berri.ai"
messages = [{"role": "user", "content": "tell me a story in 2 sentences"}]
response1 = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
result_string = ""
for chunk in response1:
print(chunk)
result_string+=chunk['choices'][0]['delta']['content']
# response1_id = chunk['id']
print("current cache")
print(litellm.cache.cache.cache_dict)
result2_string=""
import time
time.sleep(1)
response2 = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
for chunk in response2:
print(chunk)
result2_string+=chunk['choices'][0]['delta']['content']
if result_string != result2_string:
print(result_string)
print(result2_string)
pytest.fail(f"Error occurred: Caching with streaming failed, strings diff")
litellm.cache = None
except Exception as e:
print(f"error occurred: {traceback.format_exc()}")
pytest.fail(f"Error occurred: {e}")
# test_caching_v2_stream_basic()
def test_caching_v2_stream():
try:
litellm.cache = Cache()
# litellm.token="ishaan@berri.ai"
messages = [{"role": "user", "content": "tell me a story in 2 sentences"}]
response1 = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
messages = [{"role": "user", "content": "tell me a chair"}]
response7 = completion(model="command-nightly", messages=messages)
messages = [{"role": "user", "content": "sing a song"}]
response8 = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
result_string = ""
for chunk in response1:
print(chunk)
result_string+=chunk['choices'][0]['delta']['content']
# response1_id = chunk['id']
print("current cache")
messages = [{"role": "user", "content": "tell me a story in 2 sentences"}]
print(litellm.cache.cache.cache_dict)
result2_string=""
response2 = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
for chunk in response2:
@ -174,6 +220,7 @@ def test_caching_v2_stream():
print(result_string)
print(result2_string)
pytest.fail(f"Error occurred: Caching with streaming failed, strings diff")
litellm.cache = None
except Exception as e:
print(f"error occurred: {traceback.format_exc()}")

30
litellm/utils.py
View file

@ -87,7 +87,7 @@ class Choices(OpenAIObject):
class ModelResponse(OpenAIObject):
def __init__(self, choices=None, created=None, model=None, usage=None, **params):
super(ModelResponse, self).__init__(**params)
self.choices = choices if choices else [Choices()]
self.choices = self.choices = choices if choices else [Choices(message=Message())]
self.created = created
self.model = model
self.usage = (
@ -287,17 +287,26 @@ class Logging:
)
if callback == "cache":
try:
# print("entering logger first time")
# print(self.litellm_params["stream_response"])
if litellm.cache != None and self.model_call_details.get('optional_params', {}).get('stream', False) == True:
if self.litellm_params["stream_response"] == None:
self.litellm_params["stream_response"] = ModelResponse()
litellm_call_id = self.litellm_params["litellm_call_id"]
if litellm_call_id in self.litellm_params["stream_response"]:
# append for the given call_id
if self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] == "default":
self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] = original_response # handle first try
else:
#self.litellm_call_id["stream_response"]["id"] = self.litellm_params["litellm_call_id"]
if self.litellm_params["stream_response"]["choices"][0]["message"]["content"] == "default":
self.litellm_params["stream_response"]["choices"][0]["message"]["content"] = original_response # handle first try
else:
self.litellm_params["stream_response"]["choices"][0]["message"]["content"] += original_response
litellm.cache.add_cache(self.litellm_params["stream_response"], **self.model_call_details)
self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] += original_response
else: # init a streaming response for this call id
new_model_response = ModelResponse(choices=[Choices(message=Message(content="default"))])
#print("creating new model response")
#print(new_model_response)
self.litellm_params["stream_response"][litellm_call_id] = new_model_response
#print("adding to cache for", litellm_call_id)
litellm.cache.add_cache(self.litellm_params["stream_response"][litellm_call_id], **self.model_call_details)
except Exception as e:
# print("got exception")
# print(e)
pass
except:
print_verbose(
@ -466,7 +475,6 @@ def client(original_function):
# CRASH REPORTING TELEMETRY
crash_reporting(*args, **kwargs)
# INIT LOGGER - for user-specified integrations
print(f"len args: {len(args)}")
model = args[0] if len(args) > 0 else kwargs["model"]
call_type = original_function.__name__
if call_type == CallTypes.completion.value:
@ -638,7 +646,7 @@ def get_litellm_params(
"custom_api_base": custom_api_base,
"litellm_call_id": litellm_call_id,
"model_alias_map": model_alias_map,
"stream_response": None
"stream_response": {} # litellm_call_id: ModelResponse Dict
}
return litellm_params

2
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm"
version = "0.1.497"
version = "0.1.498"
description = "Library to easily interface with LLM API providers"
authors = ["BerriAI"]
license = "MIT License"