litellm-mirror/tests/logging_callback_tests/test_datadog_llm_obs.py
Ishaan Jaff 030ece8c3f
(Feat) New Logging integration - add Datadog LLM Observability support (#6449)
* add type for dd llm obs request ob

* working dd llm obs

* datadog use well defined type

* clean up

* unit test test_create_llm_obs_payload

* fix linting

* add datadog_llm_observability

* add datadog_llm_observability

* docs DD LLM obs

* run testing again

* document DD_ENV

* test_create_llm_obs_payload
2024-10-28 22:01:32 +05:30

141 lines
4.1 KiB
Python

"""
Test the DataDogLLMObsLogger
"""
import io
import os
import sys
sys.path.insert(0, os.path.abspath("../.."))
import asyncio
import gzip
import json
import logging
import time
from unittest.mock import AsyncMock, patch
import pytest
import litellm
from litellm import completion
from litellm._logging import verbose_logger
from litellm.integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger
from datetime import datetime, timedelta
from litellm.types.integrations.datadog_llm_obs import *
from litellm.types.utils import (
StandardLoggingPayload,
StandardLoggingModelInformation,
StandardLoggingMetadata,
StandardLoggingHiddenParams,
)
verbose_logger.setLevel(logging.DEBUG)
def create_standard_logging_payload() -> StandardLoggingPayload:
return StandardLoggingPayload(
id="test_id",
call_type="completion",
response_cost=0.1,
response_cost_failure_debug_info=None,
status="success",
total_tokens=30,
prompt_tokens=20,
completion_tokens=10,
startTime=1234567890.0,
endTime=1234567891.0,
completionStartTime=1234567890.5,
model_map_information=StandardLoggingModelInformation(
model_map_key="gpt-3.5-turbo", model_map_value=None
),
model="gpt-3.5-turbo",
model_id="model-123",
model_group="openai-gpt",
api_base="https://api.openai.com",
metadata=StandardLoggingMetadata(
user_api_key_hash="test_hash",
user_api_key_org_id=None,
user_api_key_alias="test_alias",
user_api_key_team_id="test_team",
user_api_key_user_id="test_user",
user_api_key_team_alias="test_team_alias",
spend_logs_metadata=None,
requester_ip_address="127.0.0.1",
requester_metadata=None,
),
cache_hit=False,
cache_key=None,
saved_cache_cost=0.0,
request_tags=[],
end_user=None,
requester_ip_address="127.0.0.1",
messages=[{"role": "user", "content": "Hello, world!"}],
response={"choices": [{"message": {"content": "Hi there!"}}]},
error_str=None,
model_parameters={"stream": True},
hidden_params=StandardLoggingHiddenParams(
model_id="model-123",
cache_key=None,
api_base="https://api.openai.com",
response_cost="0.1",
additional_headers=None,
),
)
@pytest.mark.asyncio
async def test_datadog_llm_obs_logging():
datadog_llm_obs_logger = DataDogLLMObsLogger()
litellm.callbacks = [datadog_llm_obs_logger]
litellm.set_verbose = True
for _ in range(2):
response = await litellm.acompletion(
model="gpt-4o", messages=["Hello testing dd llm obs!"], mock_response="hi"
)
print(response)
await asyncio.sleep(6)
@pytest.mark.asyncio
async def test_create_llm_obs_payload():
datadog_llm_obs_logger = DataDogLLMObsLogger()
standard_logging_payload = create_standard_logging_payload()
payload = datadog_llm_obs_logger.create_llm_obs_payload(
kwargs={
"model": "gpt-4",
"messages": [{"role": "user", "content": "Hello"}],
"standard_logging_object": standard_logging_payload,
},
response_obj=litellm.ModelResponse(
id="test_id",
choices=[{"message": {"content": "Hi there!"}}],
created=12,
model="gpt-4",
),
start_time=datetime.now(),
end_time=datetime.now() + timedelta(seconds=1),
)
print("dd created payload", payload)
assert payload["name"] == "litellm_llm_call"
assert payload["meta"]["kind"] == "llm"
assert payload["meta"]["input"]["messages"] == [
{"role": "user", "content": "Hello, world!"}
]
assert payload["meta"]["output"]["messages"] == [
{
"content": "Hi there!",
"role": "assistant",
"tool_calls": None,
"function_call": None,
}
]
assert payload["metrics"]["input_tokens"] == 20
assert payload["metrics"]["output_tokens"] == 10
assert payload["metrics"]["total_tokens"] == 30