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fix: pass **kwargs through to ChatOpenAI in init_chat_model #673

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fix: pass **kwargs through to ChatOpenAI in init_chat_model #673

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38 changes: 28 additions & 10 deletions src/art/langgraph/llm_wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,31 +108,43 @@ async def wrapper(*args, **kwargs):


def init_chat_model(
model: Literal[None] = None,
model: str | None = None,
*,
model_provider: str | None = None,
configurable_fields: Literal[None] = None,
config_prefix: str | None = None,
**kwargs: Any,
):
config = CURRENT_CONFIG.get()
timeout = kwargs.pop("timeout", 10 * 60)
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@chatgpt-codex-connector chatgpt-codex-connector Bot May 8, 2026

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P2 Badge Pass timeout through to ChatOpenAI

init_chat_model currently strips timeout out of **kwargs before constructing ChatOpenAI, so that value is never applied to the underlying model client. This means callers using init_chat_model(..., timeout=...) still cannot configure request timeout behavior at the provider layer (and non-float timeout objects expected by ChatOpenAI are instead routed into asyncio.wait_for). In practice, this reintroduces silent misconfiguration for a common kwarg even though the function now claims to forward kwargs.

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chat_model_kwargs: dict[str, Any] = {
"base_url": config["base_url"],
"api_key": config["api_key"],
"model": model or config["model"],
"temperature": 1.0,
}
chat_model_kwargs.update(kwargs)
Comment on lines 118 to +126
return LoggingLLM(
ChatOpenAI(
base_url=config["base_url"], # ty:ignore[unknown-argument]
api_key=config["api_key"], # ty:ignore[unknown-argument]
model=config["model"], # ty:ignore[unknown-argument]
temperature=1.0,
),
ChatOpenAI(**chat_model_kwargs),
config["logger"],
timeout=timeout,
)


class LoggingLLM(Runnable):
def __init__(self, llm, logger, structured_output=None, tools=None):
def __init__(
self,
llm,
logger,
structured_output=None,
tools=None,
timeout: float = 10 * 60,
):
self.llm = llm
self.logger = logger
self.structured_output = structured_output
self.tools = [convert_to_openai_tool(t) for t in tools] if tools else None
self.timeout = timeout

def _log(self, completion_id, input, output):
if self.logger:
Expand Down Expand Up @@ -167,7 +179,7 @@ async def ainvoke(self, input, config=None, **kwargs):
async def execute():
try:
result = await asyncio.wait_for(
self.llm.ainvoke(input, config=config), timeout=10 * 60
self.llm.ainvoke(input, config=config), timeout=self.timeout
)
self._log(completion_id, input, result)
except asyncio.TimeoutError as e:
Expand All @@ -194,10 +206,16 @@ def with_structured_output(self, tools):
self.logger,
structured_output=tools,
tools=[tools],
timeout=self.timeout,
)

def bind_tools(self, tools):
return LoggingLLM(self.llm.bind_tools(tools), self.logger, tools=tools)
return LoggingLLM(
self.llm.bind_tools(tools),
self.logger,
tools=tools,
timeout=self.timeout,
)

def with_retry(
self,
Expand Down
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