Skip to main content

Slack - Logging LLM Input/Output, Exceptions

info

We want to learn how we can make the callbacks better! Meet the LiteLLM founders or join our discord

Pre-Requisites

Step 1

pip install litellm

Step 2

Get a slack webhook url from https://api.slack.com/messaging/webhooks

Quick Start

Create a custom Callback to log to slack

We create a custom callback, to log to slack webhooks, see custom callbacks on litellm

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
# get it from https://api.slack.com/messaging/webhooks
slack_webhook_url = os.environ['SLACK_WEBHOOK_URL'] # "https://hooks.slack.com/services/<>/<>/<>"

# Remove api_key from kwargs under litellm_params
if kwargs.get('litellm_params'):
kwargs['litellm_params'].pop('api_key', None)
if kwargs['litellm_params'].get('metadata'):
kwargs['litellm_params']['metadata'].pop('deployment', None)
# Remove deployment under metadata
if kwargs.get('metadata'):
kwargs['metadata'].pop('deployment', None)
# Prevent api_key from being logged
if kwargs.get('api_key'):
kwargs.pop('api_key', None)

# 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())

Pass callback to LiteLLM

litellm.success_callback = [send_slack_alert]
import litellm
litellm.success_callback = [send_slack_alert] # log success
litellm.failure_callback = [send_slack_alert] # log exceptions

# this will raise an exception
response = litellm.completion(
model="gpt-2",
messages=[
{
"role": "user",
"content": "Hi 👋 - i'm openai"
}
]
)

Support & Talk to Founders