fix(structured outputs): resolve memory leak in parse methods

[karpetrosyan]

This PR avoids using runtime generics with Pydantic, which can leak memory, by using a simpler implementation for parsing the response message.
It also fixes generic conflicts in the tests, which happen because of how Pydantic v1 and Pydantic v2 implement __repr__.

Leak results in the main

iter=0  RSS=68.2 MB
iter=20  RSS=71.5 MB
iter=40  RSS=74.6 MB
iter=60  RSS=77.8 MB
iter=80  RSS=81.0 MB
iter=100  RSS=84.2 MB
iter=120  RSS=87.4 MB
iter=140  RSS=89.2 MB
iter=160  RSS=90.0 MB
iter=180  RSS=88.2 MB
iter=200  RSS=88.8 MB
iter=220  RSS=89.3 MB
iter=240  RSS=89.6 MB
iter=260  RSS=90.1 MB
iter=280  RSS=90.5 MB
iter=300  RSS=91.0 MB
iter=320  RSS=91.5 MB
iter=340  RSS=92.2 MB
iter=360  RSS=92.5 MB
iter=380  RSS=92.9 MB
iter=400  RSS=93.3 MB
iter=420  RSS=93.8 MB
iter=440  RSS=94.1 MB
iter=460  RSS=94.4 MB
iter=480  RSS=94.7 MB
iter=500  RSS=95.2 MB
iter=520  RSS=95.8 MB
iter=540  RSS=96.3 MB
iter=560  RSS=96.8 MB
iter=580  RSS=97.5 MB
iter=600  RSS=97.8 MB
iter=620  RSS=100.5 MB
iter=640  RSS=100.8 MB
iter=660  RSS=101.1 MB
iter=680  RSS=101.4 MB
iter=700  RSS=101.7 MB
iter=720  RSS=102.2 MB
iter=740  RSS=102.6 MB
iter=760  RSS=102.9 MB
iter=780  RSS=103.3 MB
iter=800  RSS=103.9 MB
iter=820  RSS=104.3 MB
iter=840  RSS=104.7 MB
iter=860  RSS=105.1 MB
iter=880  RSS=105.4 MB
iter=900  RSS=105.8 MB
iter=920  RSS=106.1 MB
iter=940  RSS=104.9 MB
iter=960  RSS=105.8 MB
iter=980  RSS=106.0 MB

Leak results in #2860

iter=0  RSS=71.9 MB
iter=20  RSS=72.0 MB
iter=40  RSS=72.1 MB
iter=60  RSS=72.1 MB
iter=80  RSS=72.1 MB
iter=100  RSS=72.1 MB
iter=120  RSS=72.1 MB
iter=140  RSS=72.1 MB
iter=160  RSS=72.1 MB
iter=180  RSS=72.1 MB
iter=200  RSS=72.1 MB
iter=220  RSS=72.1 MB
iter=240  RSS=72.1 MB
iter=260  RSS=71.5 MB
iter=280  RSS=70.9 MB
iter=300  RSS=70.9 MB
iter=320  RSS=70.9 MB
iter=340  RSS=70.9 MB
iter=360  RSS=70.9 MB
iter=380  RSS=70.9 MB
iter=400  RSS=70.9 MB
iter=420  RSS=70.9 MB
iter=440  RSS=70.9 MB
iter=460  RSS=70.9 MB
iter=480  RSS=70.9 MB
iter=500  RSS=70.9 MB
iter=520  RSS=70.9 MB
iter=540  RSS=70.9 MB
iter=560  RSS=70.9 MB
iter=580  RSS=70.9 MB
iter=600  RSS=70.9 MB
iter=620  RSS=68.4 MB
iter=640  RSS=68.2 MB
iter=660  RSS=68.2 MB
iter=680  RSS=68.2 MB
iter=700  RSS=68.2 MB
iter=720  RSS=68.2 MB
iter=740  RSS=68.2 MB
iter=760  RSS=68.2 MB
iter=780  RSS=68.0 MB
iter=800  RSS=68.0 MB
iter=820  RSS=68.0 MB
iter=840  RSS=68.0 MB
iter=860  RSS=68.0 MB
iter=880  RSS=68.0 MB
iter=900  RSS=68.0 MB
iter=920  RSS=68.0 MB
iter=940  RSS=68.0 MB
iter=960  RSS=68.0 MB
iter=980  RSS=68.0 MB

Leak results in #2148

iter=0  RSS=67.8 MB
iter=20  RSS=71.2 MB
iter=40  RSS=74.4 MB
iter=60  RSS=77.7 MB
iter=80  RSS=81.0 MB
iter=100  RSS=84.2 MB
iter=120  RSS=87.4 MB
iter=140  RSS=89.3 MB
iter=160  RSS=90.1 MB
iter=180  RSS=90.8 MB
iter=200  RSS=91.2 MB
iter=220  RSS=91.5 MB
iter=240  RSS=92.1 MB
iter=260  RSS=92.9 MB
iter=280  RSS=93.4 MB
iter=300  RSS=93.8 MB
iter=320  RSS=90.5 MB
iter=340  RSS=90.9 MB
iter=360  RSS=91.6 MB
iter=380  RSS=92.0 MB
iter=400  RSS=92.7 MB
iter=420  RSS=93.2 MB
iter=440  RSS=93.7 MB
iter=460  RSS=94.1 MB
iter=480  RSS=94.6 MB
iter=500  RSS=94.9 MB
iter=520  RSS=95.3 MB
iter=540  RSS=95.6 MB
iter=560  RSS=96.0 MB
iter=580  RSS=96.5 MB
iter=600  RSS=97.0 MB
iter=620  RSS=99.9 MB
iter=640  RSS=100.3 MB
iter=660  RSS=100.7 MB
iter=680  RSS=101.6 MB
iter=700  RSS=102.2 MB
iter=720  RSS=102.6 MB
iter=740  RSS=102.9 MB
iter=760  RSS=103.4 MB
iter=780  RSS=103.9 MB
iter=800  RSS=104.8 MB
iter=820  RSS=105.4 MB
iter=840  RSS=105.7 MB
iter=860  RSS=106.0 MB
iter=880  RSS=106.4 MB
iter=900  RSS=106.8 MB
iter=920  RSS=107.3 MB
iter=940  RSS=108.0 MB
iter=960  RSS=108.2 MB
iter=980  RSS=108.6 MB

Running tests locally

To run the test locally, use the following PEP 723 compatible script:
Note that you should set the appropriate branch for the openai-python dependency in the script.

For the main branch, use: openai @ git+https://github.com/openai/openai-python.git@main,

# /// script
# requires-python = ">=3.8"
# dependencies = [
#   "hishel[httpx]",
#   "openai @ git+https://github.com/openai/openai-python.git@refs/pull/2860/head",
#   "psutil",
#   "pydantic>=2.0.0",
# ]
# ///

import gc
import os
import asyncio
from typing import List

import hishel
import psutil
from pydantic import Field, create_model
from hishel.httpx import AsyncCacheClient

from openai import AsyncOpenAI

proc = psutil.Process(os.getpid())

StepModel = create_model(
    "Step",
    explanation=(str, Field()),
    output=(str, Field()),
)


def create_new_model():
    return create_model(
        "MathResponse",
        steps=(List[StepModel], Field()),
        final_answer=(str, Field()),
    )


async def main():
    client = AsyncOpenAI(http_client=AsyncCacheClient(policy=hishel.FilterPolicy()))
    for i in range(1000):
        model = create_new_model()
        await client.beta.chat.completions.parse(
            model="gpt-4o-2024-08-06",
            messages=[
                {"role": "system", "content": "You are a helpful math tutor."},
                {"role": "user", "content": "solve 8x + 31 = 2"},
            ],
            response_format=model,
        )
        gc.collect()
        if i % 20 == 0:
            print(f"iter={i}  RSS={proc.memory_info().rss / 1024**2:.1f} MB")


asyncio.run(main())

[karpetrosyan]

memory leak tests are a bit flaky, tbh, that’s why I didn’t add it

[RobertCraigie]

How does pydantic know what class to instantiate now that we aren't actually passing it?

[RobertCraigie]

q: what does this do? can you add a comment

[karpetrosyan]

ahh right, I'll add a comment. It's stripping out the generic name

[karpetrosyan]

added a comment, also I've fixed the same leak for streaming too

[karpetrosyan]

How does pydantic know what class to instantiate now that we aren't actually passing it?

We’re already instantiating the appropriate class here

[RobertCraigie]

ahhh

[avilaton]

@karpetrosyan Would you please take a look at this other PR #3068 ? We experienced a memory leak after this change and would like your feedback on this