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2.9 KiB
2.9 KiB
title, description
| title | description |
|---|---|
| Logging and Monitoring with Instructor - Debug Guide | Implement comprehensive logging for Instructor LLM calls. Track API usage, debug issues, and monitor performance with DEBUG level logging. |
In order to see the requests made to OpenAI and the responses, you can set logging to DEBUG. This will show the requests and responses made to OpenAI. This can be useful for debugging and understanding the requests and responses made to OpenAI. I would love some contributions that make this a lot cleaner, but for now this is the fastest way to see the prompts.
import instructor
import logging
from pydantic import BaseModel
# Set logging to DEBUG
logging.basicConfig(level=logging.DEBUG)
client = instructor.from_provider("openai/gpt-4.1-mini")
class UserDetail(BaseModel):
name: str
age: int
user = client.create(
response_model=UserDetail,
messages=[
{"role": "user", "content": "Extract Jason is 25 years old"},
],
) # type: ignore
"""
...
DEBUG:instructor:Patching `client.chat.completions.create` with mode=<Mode.TOOLS: 'tool_call'>
DEBUG:instructor:Instructor Request: mode.value='tool_call', response_model=<class '__main__.UserDetail'>, new_kwargs={'model': 'gpt-4.1-mini', 'messages': [{'role': 'user', 'content': 'Extract Jason is 25 years old'}], 'tools': [{'type': 'function', 'function': {'name': 'UserDetail', 'description': 'Correctly extracted `UserDetail` with all the required parameters with correct types', 'parameters': {'properties': {'name': {'title': 'Name', 'type': 'string'}, 'age': {'title': 'Age', 'type': 'integer'}}, 'required': ['age', 'name'], 'type': 'object'}}}], 'tool_choice': {'type': 'function', 'function': {'name': 'UserDetail'}}}
DEBUG:instructor:max_retries: 1
...
DEBUG:instructor:Instructor Pre-Response: ChatCompletion(id='chatcmpl-8zBxMxsOqm5Sj6yeEI38PnU2r6ncC', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_E1cftF5U0zEjzIbWt3q0ZLbN', function=Function(arguments='{"name":"Jason","age":25}', name='UserDetail'), type='function')]))], created=1709594660, model='gpt-4.1-mini-0125', object='chat.completion', system_fingerprint='fp_2b778c6b35', usage=CompletionUsage(completion_tokens=9, prompt_tokens=81, total_tokens=90))
DEBUG:httpcore.connection:close.started
DEBUG:httpcore.connection:close.complete
"""
Provider initialization logs
from_provider() now emits structured logs at the INFO level when a provider
is initialized. Enable logging to see which provider and model are being used.
import logging
import instructor
logging.basicConfig(level=logging.INFO)
instructor.from_provider("openai/gpt-4.1-mini")
Example output:
INFO:instructor.auto_client:Initializing openai provider with model gpt-4.1-mini
INFO:instructor.auto_client:Client initialized