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