fix(ai-import): adapt to models that reject an explicit temperature

[silasg]

Problem

AI Bean Import fails for OpenAI GPT‑5.x / reasoning models (e.g. gpt-5.6-terra) — they reject a non-default temperature with 400 Unsupported value: 'temperature'. See graphefruit#1151. The request body hard-coded temperature: 0.1 for every model.

Approach

Rather than maintain a model-name allowlist (which would break again on the next new model), send temperature best-effort and adapt:

1. Send temperature: 0.1 as before.
2. If a request is rejected specifically because of temperature (a 400 whose body mentions temperature), drop the parameter, retry once, and remember the provider+model so later requests skip it.

This keeps the deterministic low temperature on every model that honors it (Anthropic, Gemini, Mistral, gpt-4o, …) and self-heals for current and future models that don't. Callers that already know a model's capabilities can set supportsTemperature: false to skip even the first failed attempt (wired up in a follow-up PR).

Changes

• temperature is now an optional, per-protocol build option instead of a hard-coded literal.
• sendCloudLLMPrompt gains the send → drop/retry-once → remember flow, plus a session cache keyed by provider::baseUrl::model.
• New supportsTemperature? config flag and resetTemperatureSupportCache() export.

Testing

Added 7 specs covering: default temperature still sent, retry-without-temperature on rejection, remembering it, no retry on unrelated 400s, supportsTemperature:false short-circuit, keeping the value where accepted, and a failing retry surfacing its error.

Note: the full Karma suite could not be executed in the authoring environment — pnpm install is blocked by an egress-policy 403 on xlsx (sheetjs CDN), which the Angular build needs. The changed service is pure TypeScript, so its behavior was verified in isolation (esbuild bundle of the real module, 14/14 assertions incl. the added scenarios) and type-checked with tsc. Please run pnpm test in CI to confirm.

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