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65 lines
2.9 KiB
Markdown
65 lines
2.9 KiB
Markdown
---
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title: Logging and Monitoring with Instructor - Debug Guide
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description: Implement comprehensive logging for Instructor LLM calls. Track API usage, debug issues, and monitor performance with DEBUG level logging.
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---
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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.
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```python
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import instructor
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import logging
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from pydantic import BaseModel
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# Set logging to DEBUG
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logging.basicConfig(level=logging.DEBUG)
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client = instructor.from_provider("openai/gpt-4.1-mini")
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class UserDetail(BaseModel):
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name: str
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age: int
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user = client.create(
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response_model=UserDetail,
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messages=[
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{"role": "user", "content": "Extract Jason is 25 years old"},
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],
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) # type: ignore
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"""
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...
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DEBUG:instructor:Patching `client.chat.completions.create` with mode=<Mode.TOOLS: 'tool_call'>
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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'}}}
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DEBUG:instructor:max_retries: 1
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...
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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))
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DEBUG:httpcore.connection:close.started
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DEBUG:httpcore.connection:close.complete
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"""
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```
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## Provider initialization logs
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`from_provider()` now emits structured logs at the `INFO` level when a provider
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is initialized. Enable logging to see which provider and model are being used.
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```python
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import logging
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import instructor
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logging.basicConfig(level=logging.INFO)
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instructor.from_provider("openai/gpt-4.1-mini")
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```
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Example output:
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```
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INFO:instructor.auto_client:Initializing openai provider with model gpt-4.1-mini
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INFO:instructor.auto_client:Client initialized
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```
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