Spaces:
Configuration error
Configuration error
File size: 4,164 Bytes
447ebeb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
"""
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=[{"role": "user", "content": "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"][0]["content"] == "Hi there!"
assert payload["metrics"]["input_tokens"] == 20
assert payload["metrics"]["output_tokens"] == 10
assert payload["metrics"]["total_tokens"] == 30
|