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The original cap held a process-global slot across the WHOLE vision analysis (image load + encode + LLM call) with a default of min(CPUs, 4). That serialized legitimate multi-image workflows — "compare these 6 screenshots", "read this 10-page scan", "analyze every frame" — behind a 4-wide gate, and on the native fast path it even throttled calls that make no LLM request at all. Excess calls queued (blocking acquire, nothing dropped), but the latency hit on real fan-out was the wrong tradeoff. The incident was CPU exhaustion, not call count: concurrent base64/resize bursts saturated every core and left none to service the shared event loop serving /api/status. So cap ONLY that: - A dedicated, bounded ThreadPoolExecutor (_vision_cpu_executor) runs the encode/resize/dimension-check off the caller's loop, sized to the host's usable core count with NO fixed ceiling — the cap tracks the actual exhausted resource (cores), not a magic number. Excess encodes queue on the executor; cores stay free for the loop. - The LLM call is deliberately OUTSIDE the executor, so multi-image workflows keep full request concurrency. - Override via auxiliary.vision.max_concurrency / HERMES_VISION_MAX_CONCURRENCY (honored verbatim, including above core count); sub-1 ignored. - _vision_concurrency_slot() is now a no-op shim for back-compat. Tests assert: resolver defaults to host cores with no ceiling; env/config override (incl. above cores); sub-1 rejection; the executor is dedicated and core-sized; encode runs on a vision-encode thread; and crucially that encode bursts are bounded to the cap while the analyses themselves stay fully concurrent (calls_peak > cap). |
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| .. | ||
| _category_.json | ||
| automation-blueprints-catalog.mdx | ||
| cli-commands.md | ||
| environment-variables.md | ||
| faq.md | ||
| mcp-config-reference.md | ||
| model-catalog.md | ||
| optional-skills-catalog.md | ||
| profile-commands.md | ||
| skills-catalog.md | ||
| slash-commands.md | ||
| tools-reference.md | ||
| toolsets-reference.md | ||