Local Debugging

There's 2 ways to do local debugging - litellm.set_verbose=True and by passing in a custom function completion(...logger_fn=<your_local_function>). Warning: Make sure to not use set_verbose in production. It logs API keys, which might end up in log files.

Set Verbose

This is good for getting print statements for everything litellm is doing.

import litellm
from litellm import completion

litellm.set_verbose=True # 👈 this is the 1-line change you need to make

## set ENV variables
os.environ["OPENAI_API_KEY"] = "openai key"
os.environ["COHERE_API_KEY"] = "cohere key"

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion(model="gpt-3.5-turbo", messages=messages)

# cohere call
response = completion("command-nightly", messages)

Logger Function

But sometimes all you care about is seeing exactly what's getting sent to your api call and what's being returned - e.g. if the api call is failing, why is that happening? what are the exact params being set?

In that case, LiteLLM allows you to pass in a custom logging function to see / modify the model call Input/Outputs.

Note: We expect you to accept a dict object.

Your custom function

def my_custom_logging_fn(model_call_dict):
    print(f"model call details: {model_call_dict}")

Complete Example

from litellm import completion

def my_custom_logging_fn(model_call_dict):
    print(f"model call details: {model_call_dict}")

## set ENV variables
os.environ["OPENAI_API_KEY"] = "openai key"
os.environ["COHERE_API_KEY"] = "cohere key"

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=my_custom_logging_fn)

# cohere call
response = completion("command-nightly", messages, logger_fn=my_custom_logging_fn)

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