patched the Copilot client to sanitize malformed usage metadata before langchain_openai processes it
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@ -8,6 +8,7 @@ No env var or separate auth module needed — run ``gh auth login`` once.
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"""
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import subprocess
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from copy import deepcopy
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from typing import Any, Optional
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import requests
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@ -117,10 +118,51 @@ def check_copilot_auth() -> bool:
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class NormalizedChatOpenAI(ChatOpenAI):
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"""ChatOpenAI with normalized content output."""
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def _create_chat_result(self, response, generation_info=None):
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return super()._create_chat_result(
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_sanitize_copilot_response(response), generation_info
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)
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def invoke(self, input, config=None, **kwargs):
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return normalize_content(super().invoke(input, config, **kwargs))
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def _sanitize_copilot_response(response: Any) -> Any:
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"""Normalize Copilot token usage fields for langchain_openai.
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Copilot can return ``service_tier`` along with ``None`` values in
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``cached_tokens`` or ``reasoning_tokens``. ``langchain_openai`` subtracts
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those fields from the prompt/completion totals, which raises ``TypeError``
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when the detail value is ``None``.
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"""
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if isinstance(response, dict):
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response_dict = deepcopy(response)
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elif hasattr(response, "model_dump"):
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response_dict = response.model_dump()
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else:
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return response
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usage = response_dict.get("usage")
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if not isinstance(usage, dict):
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return response_dict
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if response_dict.get("service_tier") not in {"priority", "flex"}:
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return response_dict
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prompt_details = usage.get("prompt_tokens_details")
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if isinstance(prompt_details, dict) and prompt_details.get("cached_tokens") is None:
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prompt_details["cached_tokens"] = 0
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completion_details = usage.get("completion_tokens_details")
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if (
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isinstance(completion_details, dict)
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and completion_details.get("reasoning_tokens") is None
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):
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completion_details["reasoning_tokens"] = 0
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return response_dict
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class CopilotClient(BaseLLMClient):
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"""Client for GitHub Copilot inference API.
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