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PAL-866-collect-metrics #1230

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16 changes: 15 additions & 1 deletion langtest/modelhandler/llm_modelhandler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,13 @@ def load_model(cls: Type[_T], hub: str, path: str, *args, **kwargs) -> _T:
model_type = kwargs.get("model_type", None)
output_schema = kwargs.get("output_schema", None)

# Optional logical task identifier coming from ModelConfig.task_id.
# We pull it out early so it doesn't get passed as a raw keyword
# argument to the underlying LangChain model classes (which would
# raise on unexpected parameters). Instead, we convert it into
# provider-specific request metadata (e.g. OpenAI extra_body.metadata).
task_id = kwargs.pop("task_id", None)

exclude_args = [
"task",
"device",
Expand All @@ -100,6 +107,14 @@ def load_model(cls: Type[_T], hub: str, path: str, *args, **kwargs) -> _T:
try:
cls._update_model_parameters(hub, filtered_kwargs)

# Attach task_id as provider metadata where supported.
# For OpenAI-compatible hubs (openai, azure-openai, openrouter),
# we use the standard `extra_body={"metadata": {"task_id": ...}}`
# pattern so the identifier is available on every completion.
if task_id and hub in ("openai", "azure-openai", "openrouter"):
extra_body = {"metadata": {"tags": [f"task_id:{task_id}"]}}
filtered_kwargs["extra_body"] = extra_body

from .utils import MODEL_CLASSES

if model_type is None and hub in ("azure-openai", "openai"):
Expand Down Expand Up @@ -226,7 +241,6 @@ def predict(self, text: Union[str, dict], prompt: dict, *args, **kwargs):

# prompt configuration
prompt_manager = PromptManager()

prompt_template = prompt_manager.get_prompt()

if prompt_template is None:
Expand Down
3 changes: 3 additions & 0 deletions langtest/types.py
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Expand Up @@ -9,11 +9,14 @@ class ModelConfig(TypedDict):
model (str): The name of the model.
type (Literal['chat', 'completion']): The type of the model, either 'chat' or 'completion'.
hub (str): The hub where the model is located.
task_id (Optional[str]): Optional identifier used to track requests on LitellmProxy
by adding this as a toag to extra_body``).
"""

model: str
type: Literal["chat", "completion"]
hub: str
task_id: Optional[str]


class DatasetConfig(TypedDict):
Expand Down