litellm-mirror/litellm/tests/test_custom_logger.py
2023-11-29 12:11:08 -08:00

146 lines
4.4 KiB
Python

### What this tests ####
import sys, os, time
import pytest
sys.path.insert(0, os.path.abspath('../..'))
from litellm import completion, embedding
import litellm
from litellm.integrations.custom_logger import CustomLogger
class MyCustomHandler(CustomLogger):
success: bool = False
failure: bool = False
def log_pre_api_call(self, model, messages, kwargs):
print(f"Pre-API Call")
def log_post_api_call(self, kwargs, response_obj, start_time, end_time):
print(f"Post-API Call")
def log_stream_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Stream")
def log_success_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Success")
self.success = True
def log_failure_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Failure")
self.failure = True
def test_chat_openai():
try:
customHandler = MyCustomHandler()
litellm.callbacks = [customHandler]
response = completion(model="gpt-3.5-turbo",
messages=[{
"role": "user",
"content": "Hi 👋 - i'm openai"
}],
stream=True)
time.sleep(1)
assert customHandler.success == True
except Exception as e:
pytest.fail(f"An error occurred - {str(e)}")
pass
test_chat_openai()
def test_completion_azure_stream_moderation_failure():
try:
customHandler = MyCustomHandler()
litellm.callbacks = [customHandler]
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": "how do i kill someone",
},
]
try:
response = completion(
model="azure/chatgpt-v-2", messages=messages, stream=True
)
for chunk in response:
print(f"chunk: {chunk}")
continue
except Exception as e:
print(e)
time.sleep(1)
assert customHandler.failure == True
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_azure_stream_moderation_failure()
# def custom_callback(
# kwargs,
# completion_response,
# start_time,
# end_time,
# ):
# print(
# "in custom callback func"
# )
# print("kwargs", kwargs)
# print(completion_response)
# print(start_time)
# print(end_time)
# if "complete_streaming_response" in kwargs:
# print("\n\n complete response\n\n")
# complete_streaming_response = kwargs["complete_streaming_response"]
# print(kwargs["complete_streaming_response"])
# usage = complete_streaming_response["usage"]
# print("usage", usage)
# def send_slack_alert(
# kwargs,
# completion_response,
# start_time,
# end_time,
# ):
# print(
# "in custom slack callback func"
# )
# import requests
# import json
# # Define the Slack webhook URL
# slack_webhook_url = os.environ['SLACK_WEBHOOK_URL'] # "https://hooks.slack.com/services/<>/<>/<>"
# # Define the text payload, send data available in litellm custom_callbacks
# text_payload = f"""LiteLLM Logging: kwargs: {str(kwargs)}\n\n, response: {str(completion_response)}\n\n, start time{str(start_time)} end time: {str(end_time)}
# """
# payload = {
# "text": text_payload
# }
# # Set the headers
# headers = {
# "Content-type": "application/json"
# }
# # Make the POST request
# response = requests.post(slack_webhook_url, json=payload, headers=headers)
# # Check the response status
# if response.status_code == 200:
# print("Message sent successfully to Slack!")
# else:
# print(f"Failed to send message to Slack. Status code: {response.status_code}")
# print(response.json())
# def get_transformed_inputs(
# kwargs,
# ):
# params_to_model = kwargs["additional_args"]["complete_input_dict"]
# print("params to model", params_to_model)
# litellm.success_callback = [custom_callback, send_slack_alert]
# litellm.failure_callback = [send_slack_alert]
# litellm.set_verbose = False
# # litellm.input_callback = [get_transformed_inputs]