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27b2b67680
In tool-enabled requests MindIEChatModel._astream falls back to awaiting the full _agenerate response and re-emitting it as simulated AIMessageChunks. The full response carries usage_metadata, but none of the simulated chunks copied it, so chunk aggregation (add_ai_message_chunks) produced a final message with usage_metadata=None. Token usage therefore vanished from token accounting, run stats, persistence and the UI for every tool-enabled streamed turn. Mirror OpenAI's terminal-usage-frame convention: attach msg.usage_metadata to exactly the last simulated chunk (the trailing tool-call chunk when present, else the last text chunk / the single tool-only chunk) so the aggregated message carries it exactly once. add_usage() is per-chunk additive, so attaching usage to every chunk would multiply the totals. Scope: MindIEChatModel only; other providers keep native streaming and ainvoke/non-tool astream were already correct. Tests: regression guard asserting exactly one carrier chunk equals the last one and that merged usage equals the original across all three simulated-stream branches, plus chain-level tests driving the public astream() wrapper and asserting the persisted model_dump() shape. Closes #5192