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Merge pull request #2124 from BerriAI/litellm_streaming_caching_logging

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fix(utils.py): support streaming cached response logging
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Krish Dholakia 2024-02-21 22:06:04 -08:00 • committed by GitHub
commit bfdacf6d6b
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5 changed files with 140 additions and 38 deletions
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4
litellm/caching.py
View file

@ -124,7 +124,9 @@ class RedisCache(BaseCache):
self.redis_client.set(name=key, value=str(value), ex=ttl)
except Exception as e:
# NON blocking - notify users Redis is throwing an exception
print_verbose("LiteLLM Caching: set() - Got exception from REDIS : ", e)
print_verbose(
f"LiteLLM Caching: set() - Got exception from REDIS : {str(e)}"
)
async def async_set_cache(self, key, value, **kwargs):
_redis_client = self.init_async_client()

6
litellm/tests/test_completion.py
View file

@ -1986,6 +1986,8 @@ def test_completion_gemini():
response = completion(model=model_name, messages=messages)
# Add any assertions here to check the response
print(response)
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@ -2015,6 +2017,8 @@ def test_completion_palm():
response = completion(model=model_name, messages=messages)
# Add any assertions here to check the response
print(response)
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@ -2037,6 +2041,8 @@ def test_completion_palm_stream():
# Add any assertions here to check the response
for chunk in response:
print(chunk)
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")

49
litellm/tests/test_custom_callback_input.py
View file

@ -2,7 +2,7 @@
## This test asserts the type of data passed into each method of the custom callback handler
import sys, os, time, inspect, asyncio, traceback
from datetime import datetime
import pytest
import pytest, uuid
from pydantic import BaseModel
sys.path.insert(0, os.path.abspath("../.."))
@ -795,6 +795,53 @@ async def test_async_completion_azure_caching():
assert len(customHandler_caching.states) == 4 # pre, post, success, success
@pytest.mark.asyncio
async def test_async_completion_azure_caching_streaming():
import copy
litellm.set_verbose = True
customHandler_caching = CompletionCustomHandler()
litellm.cache = Cache(
type="redis",
host=os.environ["REDIS_HOST"],
port=os.environ["REDIS_PORT"],
password=os.environ["REDIS_PASSWORD"],
)
litellm.callbacks = [customHandler_caching]
unique_time = uuid.uuid4()
response1 = await litellm.acompletion(
model="azure/chatgpt-v-2",
messages=[
{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}
],
caching=True,
stream=True,
)
async for chunk in response1:
print(f"chunk in response1: {chunk}")
await asyncio.sleep(1)
initial_customhandler_caching_states = len(customHandler_caching.states)
print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}")
response2 = await litellm.acompletion(
model="azure/chatgpt-v-2",
messages=[
{"role": "user", "content": f"Hi 👋 - i'm async azure {unique_time}"}
],
caching=True,
stream=True,
)
async for chunk in response2:
print(f"chunk in response2: {chunk}")
await asyncio.sleep(1) # success callbacks are done in parallel
print(
f"customHandler_caching.states post-cache hit: {customHandler_caching.states}"
)
assert len(customHandler_caching.errors) == 0
assert (
len(customHandler_caching.states) > initial_customhandler_caching_states
) # pre, post, streaming .., success, success
@pytest.mark.asyncio
async def test_async_embedding_azure_caching():
print("Testing custom callback input - Azure Caching")

6
litellm/tests/test_streaming.py
View file

@ -392,6 +392,8 @@ def test_completion_palm_stream():
if complete_response.strip() == "":
raise Exception("Empty response received")
print(f"completion_response: {complete_response}")
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@ -425,6 +427,8 @@ def test_completion_gemini_stream():
if complete_response.strip() == "":
raise Exception("Empty response received")
print(f"completion_response: {complete_response}")
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@ -461,6 +465,8 @@ async def test_acompletion_gemini_stream():
print(f"completion_response: {complete_response}")
if complete_response.strip() == "":
raise Exception("Empty response received")
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")

113
litellm/utils.py
View file

@ -1411,7 +1411,7 @@ class Logging:
print_verbose(
f"success_callback: reaches cache for logging, there is no complete_streaming_response. Kwargs={kwargs}\n\n"
)
return
pass
else:
print_verbose(
"success_callback: reaches cache for logging, there is a complete_streaming_response. Adding to cache"
@ -1616,7 +1616,7 @@ class Logging:
print_verbose(
f"async success_callback: reaches cache for logging, there is no complete_streaming_response. Kwargs={kwargs}\n\n"
)
return
pass
else:
print_verbose(
"async success_callback: reaches cache for logging, there is a complete_streaming_response. Adding to cache"
@ -1625,8 +1625,10 @@ class Logging:
# only add to cache once we have a complete streaming response
litellm.cache.add_cache(result, **kwargs)
if isinstance(callback, CustomLogger): # custom logger class
print_verbose(f"Async success callbacks: {callback}")
if self.stream:
print_verbose(
f"Async success callbacks: {callback}; self.stream: {self.stream}; complete_streaming_response: {self.model_call_details.get('complete_streaming_response', None)}"
)
if self.stream == True:
if "complete_streaming_response" in self.model_call_details:
await callback.async_log_success_event(
kwargs=self.model_call_details,
@ -2328,6 +2330,13 @@ def client(original_function):
model_response_object=ModelResponse(),
stream=kwargs.get("stream", False),
)
if kwargs.get("stream", False) == True:
cached_result = CustomStreamWrapper(
completion_stream=cached_result,
model=model,
custom_llm_provider="cached_response",
logging_obj=logging_obj,
)
elif call_type == CallTypes.embedding.value and isinstance(
cached_result, dict
):
@ -2624,28 +2633,6 @@ def client(original_function):
cached_result, list
):
print_verbose(f"Cache Hit!")
call_type = original_function.__name__
if call_type == CallTypes.acompletion.value and isinstance(
cached_result, dict
):
if kwargs.get("stream", False) == True:
cached_result = convert_to_streaming_response_async(
response_object=cached_result,
)
else:
cached_result = convert_to_model_response_object(
response_object=cached_result,
model_response_object=ModelResponse(),
)
elif call_type == CallTypes.aembedding.value and isinstance(
cached_result, dict
):
cached_result = convert_to_model_response_object(
response_object=cached_result,
model_response_object=EmbeddingResponse(),
response_type="embedding",
)
# LOG SUCCESS
cache_hit = True
end_time = datetime.datetime.now()
(
@ -2685,15 +2672,44 @@ def client(original_function):
additional_args=None,
stream=kwargs.get("stream", False),
)
asyncio.create_task(
logging_obj.async_success_handler(
cached_result, start_time, end_time, cache_hit
call_type = original_function.__name__
if call_type == CallTypes.acompletion.value and isinstance(
cached_result, dict
):
if kwargs.get("stream", False) == True:
cached_result = convert_to_streaming_response_async(
response_object=cached_result,
)
cached_result = CustomStreamWrapper(
completion_stream=cached_result,
model=model,
custom_llm_provider="cached_response",
logging_obj=logging_obj,
)
else:
cached_result = convert_to_model_response_object(
response_object=cached_result,
model_response_object=ModelResponse(),
)
elif call_type == CallTypes.aembedding.value and isinstance(
cached_result, dict
):
cached_result = convert_to_model_response_object(
response_object=cached_result,
model_response_object=EmbeddingResponse(),
response_type="embedding",
)
)
threading.Thread(
target=logging_obj.success_handler,
args=(cached_result, start_time, end_time, cache_hit),
).start()
if kwargs.get("stream", False) == False:
# LOG SUCCESS
asyncio.create_task(
logging_obj.async_success_handler(
cached_result, start_time, end_time, cache_hit
)
)
threading.Thread(
target=logging_obj.success_handler,
args=(cached_result, start_time, end_time, cache_hit),
).start()
return cached_result
elif (
call_type == CallTypes.aembedding.value
@ -4296,7 +4312,9 @@ def get_optional_params(
parameters=tool["function"].get("parameters", {}),
)
gtool_func_declarations.append(gtool_func_declaration)
optional_params["tools"] = [generative_models.Tool(function_declarations=gtool_func_declarations)]
optional_params["tools"] = [
generative_models.Tool(function_declarations=gtool_func_declarations)
]
elif custom_llm_provider == "sagemaker":
## check if unsupported param passed in
supported_params = ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
@ -6795,7 +6813,7 @@ def exception_type(
llm_provider="vertex_ai",
request=original_exception.request,
)
elif custom_llm_provider == "palm":
elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
if "503 Getting metadata" in error_str:
# auth errors look like this
# 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate.
@ -6814,6 +6832,15 @@ def exception_type(
llm_provider="palm",
response=original_exception.response,
)
if "500 An internal error has occurred." in error_str:
exception_mapping_worked = True
raise APIError(
status_code=original_exception.status_code,
message=f"PalmException - {original_exception.message}",
llm_provider="palm",
model=model,
request=original_exception.request,
)
if hasattr(original_exception, "status_code"):
if original_exception.status_code == 400:
exception_mapping_worked = True
@ -8524,6 +8551,19 @@ class CustomStreamWrapper:
]
elif self.custom_llm_provider == "text-completion-openai":
response_obj = self.handle_openai_text_completion_chunk(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
model_response.choices[0].finish_reason = response_obj[
"finish_reason"
]
elif self.custom_llm_provider == "cached_response":
response_obj = {
"text": chunk.choices[0].delta.content,
"is_finished": True,
"finish_reason": chunk.choices[0].finish_reason,
}
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
@ -8732,6 +8772,7 @@ class CustomStreamWrapper:
or self.custom_llm_provider == "vertex_ai"
or self.custom_llm_provider == "sagemaker"
or self.custom_llm_provider == "gemini"
or self.custom_llm_provider == "cached_response"
or self.custom_llm_provider in litellm.openai_compatible_endpoints
):
async for chunk in self.completion_stream: