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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)

JSON Logs​

If you need to store the logs as JSON, just set the litellm.json_logs = True.

We currently just log the raw POST request from litellm as a JSON - [See Code].

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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)

Still Seeing Issues?​

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