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Revert "Log errors in Traceloop Integration"

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Ishaan Jaff 2024-05-24 21:25:17 -07:00 • committed by GitHub
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commit 0083776a14
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3 changed files with 135 additions and 168 deletions
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217
litellm/integrations/traceloop.py
View file

@ -1,149 +1,114 @@
import traceback
from litellm._logging import verbose_logger
import litellm
class TraceloopLogger: class TraceloopLogger:
def __init__(self): def __init__(self):
try: from traceloop.sdk.tracing.tracing import TracerWrapper
from traceloop.sdk.tracing.tracing import TracerWrapper from traceloop.sdk import Traceloop
from traceloop.sdk import Traceloop
except ModuleNotFoundError as e:
verbose_logger.error(
f"Traceloop not installed, try running 'pip install traceloop-sdk' to fix this error: {e}\n{traceback.format_exc()}"
)
Traceloop.init( Traceloop.init(app_name="Litellm-Server", disable_batch=True)
app_name="Litellm-Server",
disable_batch=True,
)
self.tracer_wrapper = TracerWrapper() self.tracer_wrapper = TracerWrapper()
def log_event( def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
self, from opentelemetry.trace import SpanKind
kwargs,
response_obj,
start_time,
end_time,
user_id,
print_verbose,
level="DEFAULT",
status_message=None,
):
from opentelemetry import trace
from opentelemetry.trace import SpanKind, Status, StatusCode
from opentelemetry.semconv.ai import SpanAttributes from opentelemetry.semconv.ai import SpanAttributes
try: try:
print_verbose(
f"Traceloop Logging - Enters logging function for model {kwargs}"
)
tracer = self.tracer_wrapper.get_tracer() tracer = self.tracer_wrapper.get_tracer()
model = kwargs.get("model")
# LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK # LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK
if kwargs.get("litellm_params").get("custom_llm_provider") == "openai": if kwargs.get("litellm_params").get("custom_llm_provider") == "openai":
return return
optional_params = kwargs.get("optional_params", {}) optional_params = kwargs.get("optional_params", {})
span = tracer.start_span( with tracer.start_as_current_span(
"litellm.completion", kind=SpanKind.CLIENT, start_time=start_time "litellm.completion",
) kind=SpanKind.CLIENT,
) as span:
if span.is_recording():
span.set_attribute(
SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model")
)
if "stop" in optional_params:
span.set_attribute(
SpanAttributes.LLM_CHAT_STOP_SEQUENCES,
optional_params.get("stop"),
)
if "frequency_penalty" in optional_params:
span.set_attribute(
SpanAttributes.LLM_FREQUENCY_PENALTY,
optional_params.get("frequency_penalty"),
)
if "presence_penalty" in optional_params:
span.set_attribute(
SpanAttributes.LLM_PRESENCE_PENALTY,
optional_params.get("presence_penalty"),
)
if "top_p" in optional_params:
span.set_attribute(
SpanAttributes.LLM_TOP_P, optional_params.get("top_p")
)
if "tools" in optional_params or "functions" in optional_params:
span.set_attribute(
SpanAttributes.LLM_REQUEST_FUNCTIONS,
optional_params.get(
"tools", optional_params.get("functions")
),
)
if "user" in optional_params:
span.set_attribute(
SpanAttributes.LLM_USER, optional_params.get("user")
)
if "max_tokens" in optional_params:
span.set_attribute(
SpanAttributes.LLM_REQUEST_MAX_TOKENS,
kwargs.get("max_tokens"),
)
if "temperature" in optional_params:
span.set_attribute(
SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature")
)
if span.is_recording(): for idx, prompt in enumerate(kwargs.get("messages")):
span.set_attribute( span.set_attribute(
SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model") f"{SpanAttributes.LLM_PROMPTS}.{idx}.role",
) prompt.get("role"),
if "stop" in optional_params: )
span.set_attribute( span.set_attribute(
SpanAttributes.LLM_CHAT_STOP_SEQUENCES, f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
optional_params.get("stop"), prompt.get("content"),
) )
if "frequency_penalty" in optional_params:
span.set_attribute(
SpanAttributes.LLM_FREQUENCY_PENALTY,
optional_params.get("frequency_penalty"),
)
if "presence_penalty" in optional_params:
span.set_attribute(
SpanAttributes.LLM_PRESENCE_PENALTY,
optional_params.get("presence_penalty"),
)
if "top_p" in optional_params:
span.set_attribute(
SpanAttributes.LLM_TOP_P, optional_params.get("top_p")
)
if "tools" in optional_params or "functions" in optional_params:
span.set_attribute(
SpanAttributes.LLM_REQUEST_FUNCTIONS,
optional_params.get("tools", optional_params.get("functions")),
)
if "user" in optional_params:
span.set_attribute(
SpanAttributes.LLM_USER, optional_params.get("user")
)
if "max_tokens" in optional_params:
span.set_attribute(
SpanAttributes.LLM_REQUEST_MAX_TOKENS,
kwargs.get("max_tokens"),
)
if "temperature" in optional_params:
span.set_attribute(
SpanAttributes.LLM_REQUEST_TEMPERATURE,
kwargs.get("temperature"),
)
for idx, prompt in enumerate(kwargs.get("messages")):
span.set_attribute( span.set_attribute(
f"{SpanAttributes.LLM_PROMPTS}.{idx}.role", SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model")
prompt.get("role"),
)
span.set_attribute(
f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
prompt.get("content"),
) )
usage = response_obj.get("usage")
if usage:
span.set_attribute(
SpanAttributes.LLM_USAGE_TOTAL_TOKENS,
usage.get("total_tokens"),
)
span.set_attribute(
SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
usage.get("completion_tokens"),
)
span.set_attribute(
SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
usage.get("prompt_tokens"),
)
span.set_attribute( for idx, choice in enumerate(response_obj.get("choices")):
SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model") span.set_attribute(
) f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason",
usage = response_obj.get("usage") choice.get("finish_reason"),
if usage: )
span.set_attribute( span.set_attribute(
SpanAttributes.LLM_USAGE_TOTAL_TOKENS, f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role",
usage.get("total_tokens"), choice.get("message").get("role"),
) )
span.set_attribute( span.set_attribute(
SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
usage.get("completion_tokens"), choice.get("message").get("content"),
) )
span.set_attribute(
SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
usage.get("prompt_tokens"),
)
for idx, choice in enumerate(response_obj.get("choices")):
span.set_attribute(
f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason",
choice.get("finish_reason"),
)
span.set_attribute(
f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role",
choice.get("message").get("role"),
)
span.set_attribute(
f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
choice.get("message").get("content"),
)
if (
level == "ERROR"
and status_message is not None
and isinstance(status_message, str)
):
span.record_exception(Exception(status_message))
span.set_status(Status(StatusCode.ERROR, status_message))
span.end(end_time)
except Exception as e: except Exception as e:
print_verbose(f"Traceloop Layer Error - {e}") print_verbose(f"Traceloop Layer Error - {e}")

74
litellm/tests/test_traceloop.py
View file

@ -1,35 +1,49 @@
import sys # Commented out for now - since traceloop break ci/cd
import os # import sys
import time # import os
import pytest # import io, asyncio
import litellm
from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
from traceloop.sdk import Traceloop
sys.path.insert(0, os.path.abspath("../..")) # sys.path.insert(0, os.path.abspath('../..'))
# from litellm import completion
# import litellm
# litellm.num_retries = 3
# litellm.success_callback = [""]
# import time
# import pytest
# from traceloop.sdk import Traceloop
# Traceloop.init(app_name="test-litellm", disable_batch=True)
@pytest.fixture() # def test_traceloop_logging():
def exporter(): # try:
exporter = InMemorySpanExporter() # litellm.set_verbose = True
Traceloop.init( # response = litellm.completion(
app_name="test_litellm", # model="gpt-3.5-turbo",
disable_batch=True, # messages=[{"role": "user", "content":"This is a test"}],
exporter=exporter, # max_tokens=1000,
) # temperature=0.7,
litellm.success_callback = ["traceloop"] # timeout=5,
litellm.set_verbose = True # )
# print(f"response: {response}")
return exporter # except Exception as e:
# pytest.fail(f"An exception occurred - {e}")
# # test_traceloop_logging()
@pytest.mark.parametrize("model", ["claude-instant-1.2", "gpt-3.5-turbo"]) # # def test_traceloop_logging_async():
def test_traceloop_logging(exporter, model): # # try:
# # litellm.set_verbose = True
litellm.completion( # # async def test_acompletion():
model=model, # # return await litellm.acompletion(
messages=[{"role": "user", "content": "This is a test"}], # # model="gpt-3.5-turbo",
max_tokens=1000, # # messages=[{"role": "user", "content":"This is a test"}],
temperature=0.7, # # max_tokens=1000,
timeout=5, # # temperature=0.7,
) # # timeout=5,
# # )
# # response = asyncio.run(test_acompletion())
# # print(f"response: {response}")
# # except Exception as e:
# # pytest.fail(f"An exception occurred - {e}")
# # test_traceloop_logging_async()

12
litellm/utils.py
View file

@ -2027,7 +2027,6 @@ class Logging:
response_obj=result, response_obj=result,
start_time=start_time, start_time=start_time,
end_time=end_time, end_time=end_time,
user_id=kwargs.get("user", None),
print_verbose=print_verbose, print_verbose=print_verbose,
) )
if callback == "s3": if callback == "s3":
@ -2599,17 +2598,6 @@ class Logging:
level="ERROR", level="ERROR",
kwargs=self.model_call_details, kwargs=self.model_call_details,
) )
if callback == "traceloop":
traceloopLogger.log_event(
start_time=start_time,
end_time=end_time,
response_obj=None,
user_id=kwargs.get("user", None),
print_verbose=print_verbose,
status_message=str(exception),
level="ERROR",
kwargs=self.model_call_details,
)
if callback == "prometheus": if callback == "prometheus":
global prometheusLogger global prometheusLogger
verbose_logger.debug("reaches prometheus for success logging!") verbose_logger.debug("reaches prometheus for success logging!")