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Kimi was trying to use Python tools to analyze images rather than just
describing them. Updated both vision system prompts (consult_vision and
image compaction) to explicitly instruct: do not use any tools unless
specifically asked to — just use your vision to see the image and describe
it directly.
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When a non-vision model (e.g. GLM) calls consult_vision, the new Kimi
consultation tab now shows 'IMAGE - <question>' instead of the bare
question-derived title, making image-consultation tabs visually
distinguishable from normal conversation tabs.
- Add formatConsultationTitle(question) pure helper (pure.ts): prefixes
'IMAGE - ' and truncates the question to 80 chars (matching the
conversation store's TITLE_MAX) with an ellipsis.
- Add setConversationTitle dep to VisionHandoffDeps, wired in the
extension to the conversation store's setConversationTitle.
- Call it in consultVision BEFORE the turn starts so the title is
correct from the first moment (the store keeps a non-'Untitled'
title on first message append). Best-effort: a title-write failure
logs a warning but does not break the consultation.
- Tests: 4 pure + 2 service (title set + optional-dep graceful).
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purge on compaction/close
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settings
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auto-transcription
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rejects temperature: 0)
The vision handoff hardcoded temperature: 0 for the transcription sub-call,
but the Moonshot/Kimi vision model only allows temperature: 1 (or omitted),
causing an HTTP 400 "invalid temperature: only 1 is allowed for this model"
that blocked the entire image analysis for non-vision models like GLM 5.2.
Fix: omit temperature entirely so each vision provider uses its own default —
the truly universal, provider-agnostic approach (different providers have
different temperature constraints).
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vision handoff
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