forked from phoenix/litellm-mirror
63 lines
No EOL
1.9 KiB
Markdown
63 lines
No EOL
1.9 KiB
Markdown
# Local Debugging
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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>)`
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## Set Verbose
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This is good for getting print statements for everything litellm is doing.
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```
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from litellm import completion
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litellm.set_verbose=True # 👈 this is the 1-line change you need to make
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## set ENV variables
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os.environ["OPENAI_API_KEY"] = "openai key"
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os.environ["COHERE_API_KEY"] = "cohere key"
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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# openai call
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response = completion(model="gpt-3.5-turbo", messages=messages)
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# cohere call
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response = completion("command-nightly", messages)
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```
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## Logger Function
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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?
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In that case, LiteLLM allows you to pass in a custom logging function to see / modify the model call Input/Outputs.
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**Note**: We expect you to accept a dict object.
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Your custom function
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```
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def my_custom_logging_fn(model_call_dict):
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print(f"model call details: {model_call_dict}")
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```
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### Complete Example
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```
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from litellm import completion
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def my_custom_logging_fn(model_call_dict):
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print(f"model call details: {model_call_dict}")
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## set ENV variables
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os.environ["OPENAI_API_KEY"] = "openai key"
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os.environ["COHERE_API_KEY"] = "cohere key"
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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# openai call
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response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=my_custom_logging_fn)
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# cohere call
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response = completion("command-nightly", messages, logger_fn=my_custom_logging_fn)
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```
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## Still Seeing Issues?
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Text us @ +17708783106 or Join the [Discord](https://discord.com/invite/wuPM9dRgDw).
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We promise to help you in `lite`ning speed ❤️ |