extract(temperature=...) is silently ignored when config= or model= is passed

Summary

lx.extract() accepts a top-level temperature argument documented as "Set to 0.0 for deterministic output". That value is only ever merged into the provider kwargs inside the model_id branch of extraction.py. When the caller supplies a pre-built config= (a factory.ModelConfig) or model= (a language model instance), temperature is dropped: no merge, no error, no warning.

For the OpenAI provider the consequence is visible on the wire. The provider is constructed with temperature=None, and openai.py only sets the parameter when it is not None:

temp = normalized_config.get('temperature', self.temperature)
if temp is not None:
  api_params['temperature'] = temp

so the request omits temperature entirely and generation runs at whatever the server default is.

Versions affected

• Reproduced at HEAD 9853e4466c2a9c52c10280e0c6474d1c3b36dea1 (version 1.6.0).
• Also present in the released 1.x line installed from PyPI.
• Location: langextract/extraction.py, the if model: / elif config: / else: model-resolution block. Only the final else: branch builds base_lm_kwargs containing "temperature": temperature.

Repro

Self-contained, no API key and no network. A stub replaces openai.OpenAI and records the kwargs the provider passes to chat.completions.create, which is the wire payload.

import json, types
from unittest import mock

import langextract as lx
from langextract import factory
from langextract.core import data

CAPTURED = []

class _FakeCompletions:
  def create(self, **api_params):
    CAPTURED.append(api_params)
    payload = json.dumps({"extractions": [{"entity": "Alice"}]})
    message = types.SimpleNamespace(content=payload)
    return types.SimpleNamespace(choices=[types.SimpleNamespace(message=message)])

class _FakeOpenAI:
  def __init__(self, *a, **kw):
    self.chat = types.SimpleNamespace(completions=_FakeCompletions())

EXAMPLES = [
    data.ExampleData(
        text="Alice went to Berlin.",
        extractions=[data.Extraction(extraction_class="entity",
                                     extraction_text="Alice")],
    )
]

def run(label, **kw):
  CAPTURED.clear()
  with mock.patch("openai.OpenAI", _FakeOpenAI):
    lx.extract(text_or_documents="Bob went to Paris.",
               prompt_description="Extract entities.",
               examples=EXAMPLES, use_schema_constraints=False, **kw)
  print(label, "->", sorted(CAPTURED[0]))

run("A. model_id= + temperature=0.0",
    model_id="gpt-4o-mini", api_key="not-a-real-key", temperature=0.0)

run("B. config=   + temperature=0.0",
    config=factory.ModelConfig(model_id="gpt-4o-mini",
                               provider="OpenAILanguageModel",
                               provider_kwargs={"api_key": "not-a-real-key"}),
    temperature=0.0)

Actual output (HEAD 9853e44)

A. model_id= + temperature=0.0 -> ['messages', 'model', 'n', 'response_format', 'temperature']
B. config=   + temperature=0.0 -> ['messages', 'model', 'n', 'response_format']

Case B has no temperature key at all.

Expected

Both calls send temperature=0.0. At minimum, case B should tell the caller that the argument is being discarded rather than accepting it silently.

The workaround that does work today is putting the value inside the config:

factory.ModelConfig(model_id="gpt-4o-mini",
                    provider_kwargs={"api_key": ..., "temperature": 0.0})

but nothing in the extract() signature or docstring suggests the top-level argument stops working once config= is supplied.

Impact

This fails silently and in the direction that is hardest to notice: the run still succeeds and still returns plausible extractions, they are just sampled at the server default temperature instead of the requested one.

We hit this chasing a reproducibility discrepancy against a batch-invariant vLLM deployment. Raw API probes at temperature 0 returned byte-identical completions across runs, but the same prompts driven through langextract with a config= produced different extractions run to run. The missing wire parameter was the whole cause. Because temperature is the standard lever for determinism, anyone using config= or model= for evaluation harnesses, response caching, or reproducibility claims is affected without any signal that their setting never took effect.

config= is also the documented route for custom and OpenAI-compatible providers, so the paths most likely to be used for self-hosted deterministic setups are exactly the ones where the parameter is dropped.

Suggested fix

When config= is given, merge an explicit temperature into config.provider_kwargs, with the explicit argument taking precedence over any value already in the config, and without mutating the caller's ModelConfig.

When model= is given the instance is already constructed, so there is no provider-agnostic way to apply the value; a UserWarning there matches the existing treatment of use_schema_constraints in the same branch.

Happy to send a PR; a patch with tests is ready.