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|
import traceback
from litellm._logging import verbose_logger
class TraceloopLogger:
"""
WARNING: DEPRECATED
Use the OpenTelemetry standard integration instead
"""
def __init__(self):
try:
from traceloop.sdk import Traceloop
from traceloop.sdk.tracing.tracing import TracerWrapper
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()}"
)
raise e
Traceloop.init(
app_name="Litellm-Server",
disable_batch=True,
)
self.tracer_wrapper = TracerWrapper()
def log_event(
self,
kwargs,
response_obj,
start_time,
end_time,
user_id,
print_verbose,
level="DEFAULT",
status_message=None,
):
from opentelemetry.semconv.ai import SpanAttributes
from opentelemetry.trace import SpanKind, Status, StatusCode
try:
print_verbose(
f"Traceloop Logging - Enters logging function for model {kwargs}"
)
tracer = self.tracer_wrapper.get_tracer()
optional_params = kwargs.get("optional_params", {})
start_time = int(start_time.timestamp())
end_time = int(end_time.timestamp())
span = tracer.start_span(
"litellm.completion", kind=SpanKind.CLIENT, start_time=start_time
)
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_REQUEST_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, # type: ignore
kwargs.get("temperature"),
)
for idx, prompt in enumerate(kwargs.get("messages")):
span.set_attribute(
f"{SpanAttributes.LLM_PROMPTS}.{idx}.role",
prompt.get("role"),
)
span.set_attribute(
f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
prompt.get("content"),
)
span.set_attribute(
SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model")
)
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"),
)
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:
print_verbose(f"Traceloop Layer Error - {e}")
|
