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Skeleton ok
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9 files changed

+571
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‎pyproject.toml‎

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@@ -17,6 +17,10 @@ classifiers = [
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"Programming Language :: Python :: 3.13",
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"Topic :: Software Development :: Libraries :: Python Modules",
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]
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dependencies = [
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"openai>=1.58.1",
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"pydantic>=2.10.4",
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]
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[project.urls]
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Homepage = "https://sarus-tech.github.io/structured-logprobs/"
@@ -35,6 +39,7 @@ dev = [
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"mkdocs>=1.4.2",
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"mkdocs-material>=8.5.10",
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"mkdocstrings[python]>=0.26.1",
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"python-dotenv>=1.0.1",
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]
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[build-system]

‎structured_logprobs/__init__.py‎

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from .main import add_logprobs as add_logprobs

‎structured_logprobs/foo.py‎

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‎structured_logprobs/main.py‎

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from typing import TypeVar
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from openai.types.chat.chat_completion import ChatCompletion
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from openai.types.chat.parsed_chat_completion import ParsedChatCompletion
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CC = TypeVar("CC", bound=ChatCompletion)
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def add_logprobs(chat_completion_response: CC) -> CC:
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"""Summary line.
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Extended description of function.
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Args:
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bar: Description of input argument.
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Returns:
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Description of return value
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"""
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# TODO NG: add the useful code there
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if isinstance(chat_completion_response, ParsedChatCompletion):
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# Process the Parsed case
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pass
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else:
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# The default case
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pass
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return chat_completion_response
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if __name__ == "__main__": # pragma: no cover
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pass

‎tests/conftest.py‎

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from pathlib import Path
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import pytest
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from openai.types.chat.parsed_chat_completion import ParsedChatCompletion
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from pydantic import BaseModel
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class CalendarEvent(BaseModel):
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name: str
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date: str
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participants: list[str]
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@pytest.fixture
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def simple_parsed_completion(pytestconfig) -> ParsedChatCompletion[CalendarEvent] | None:
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base_path = Path(pytestconfig.rootdir) # Base directory where pytest was run
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with open(base_path / "tests" / "simple_parsed_completion.json") as f:
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return ParsedChatCompletion[CalendarEvent].model_validate_json(f.read())
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return None

‎tests/simple_parsed_completion.json‎

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{
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"id": "chatcmpl-AigSM81aLFRN07IUezlC3zlZ9zdaU",
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"logprobs": {
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"content": [
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{
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"token": "{\"",
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"bytes": [123, 34],
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"logprob": -0.000012590794,
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"top_logprobs": []
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},
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{
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"token": "name",
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"bytes": [110, 97, 109, 101],
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"logprob": 0.0,
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"top_logprobs": []
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},
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{
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"token": "\":\"",
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"bytes": [34, 58, 34],
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"logprob": -6.704273e-7,
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"top_logprobs": []
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},
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{
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"token": "Science",
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"bytes": [83, 99, 105, 101, 110, 99, 101],
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"logprob": -0.00012964146,
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"top_logprobs": []
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},
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{
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"token": " Fair",
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"bytes": [32, 70, 97, 105, 114],
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"logprob": -0.000058603408,
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"top_logprobs": []
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},
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{
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"token": "\",\"",
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"bytes": [34, 44, 34],
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"logprob": -0.0018461747,
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"top_logprobs": []
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},
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{
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"token": "date",
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"bytes": [100, 97, 116, 101],
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"logprob": 0.0,
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"top_logprobs": []
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},
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{
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"token": "\":\"",
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"bytes": [34, 58, 34],
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"logprob": -4.9617593e-6,
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"top_logprobs": []
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},
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{
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"token": "Friday",
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"bytes": [70, 114, 105, 100, 97, 121],
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"logprob": -0.09504829,
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"top_logprobs": []
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},
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{
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"token": "\",\"",
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"bytes": [34, 44, 34],
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"logprob": -0.0026011032,
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"top_logprobs": []
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},
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{
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"token": "participants",
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"bytes": [
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112, 97, 114, 116, 105, 99, 105, 112, 97, 110, 116,
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115
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],
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"logprob": -1.9361265e-7,
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"top_logprobs": []
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},
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{
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"token": "\":[\"",
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"bytes": [34, 58, 91, 34],
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"logprob": 0.0,
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"top_logprobs": []
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},
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{
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"token": "Alice",
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"bytes": [65, 108, 105, 99, 101],
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"logprob": 0.0,
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"top_logprobs": []
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},
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{
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"token": "\",\"",
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"bytes": [34, 44, 34],
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"logprob": -1.2664457e-6,
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"top_logprobs": []
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},
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{
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"token": "Bob",
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"bytes": [66, 111, 98],
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"logprob": -7.89631e-7,
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"top_logprobs": []
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},
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{
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"token": "\"]",
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"bytes": [34, 93],
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"logprob": -1.9361265e-7,
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"top_logprobs": []
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},
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{
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"token": "}",
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"bytes": [125],
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"logprob": 0.0,
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"top_logprobs": []
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}
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],
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"refusal": null
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},
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"message": {
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"content": "{\"name\":\"Science Fair\",\"date\":\"Friday\",\"participants\":[\"Alice\",\"Bob\"]}",
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"refusal": null,
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"role": "assistant",
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"audio": null,
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"function_call": null,
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"tool_calls": [],
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"parsed": {
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"name": "Science Fair",
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"date": "Friday",
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"participants": ["Alice", "Bob"]
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}
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}
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}
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],
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"created": 1735212998,
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion",
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"service_tier": null,
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"system_fingerprint": "fp_5f20662549",
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"usage": {
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"completion_tokens": 18,
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"prompt_tokens": 92,
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"total_tokens": 110,
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"completion_tokens_details": {
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"accepted_prediction_tokens": 0,
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"audio_tokens": 0,
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"reasoning_tokens": 0,
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"rejected_prediction_tokens": 0
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},
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"prompt_tokens_details": {
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"audio_tokens": 0,
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"cached_tokens": 0
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}
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}
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}

‎tests/test_foo.py‎

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‎tests/test_main.py‎

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import pytest
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from dotenv import load_dotenv
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from openai import OpenAI
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from openai.types.chat.parsed_chat_completion import ParsedChatCompletion
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from pydantic import BaseModel
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from structured_logprobs import add_logprobs
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# Your token should be in a .env file OPENAI_API_KEY="..."
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load_dotenv()
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@pytest.mark.skip(reason="We do not want to automate this as no OPENAI_API_KEY is on github yet")
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def test_simple_parsed_completion_with_openai():
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client = OpenAI()
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# A simple data model
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class CalendarEvent(BaseModel):
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name: str
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date: str
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participants: list[str]
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# A request with structured output
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completion = client.beta.chat.completions.parse(
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model="gpt-4o-2024-08-06",
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messages=[
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{"role": "system", "content": "Extract the event information."},
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{"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
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],
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logprobs=True,
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response_format=CalendarEvent,
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)
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completion = add_logprobs(completion)
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event = completion.choices[0].message.parsed
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print(event)
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# Test if logprobs are there in the expected way (when they will be)
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assert event.name == "Science Fair"
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def test_simple_parsed_completion(simple_parsed_completion):
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assert isinstance(simple_parsed_completion, ParsedChatCompletion)
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completion = add_logprobs(simple_parsed_completion)
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event = completion.choices[0].message.parsed
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print(event)
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# Test if logprobs are there in the expected way (when they will be)
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assert event.name == "Science Fair"

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