dsh-vision-plugin

Give DeepSeek Harness text-only models a pair of eyes.

English | 中文

v1.3.0 · MIT License

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The deepseek-v4 family is text-only: you can paste screenshots and photos into the chat, but the model can't see them. This plugin sits in between — images are first sent to a vision model (qwen3.7-plus, kimi-k3, …) which transcribes them into text, and that text is what the main model reads. Paste an image, switch to any text-only model, and it just works.

What it looks like

You: What's this?                    (pasted a Clash Verge logo)

Assistant: That's the Clash Verge logo. A black cat silhouette on the left,
           the text "Clash Verge" on the right. It's a GUI client for the
           Clash proxy core…

The image is transcribed by a vision model; the main model answers from the description, as naturally as if it could see.

Three things it does

1. Paste an image and ask — paste or drop an image into the chat and any text-only model can answer about it. No special command needed.
2. vision_analyze tool — the model can call it directly to analyze a local image file, with an optional question ("What's the value in row 3 of this table?") and an optional vision model override.
3. Settings page for the vision model — a "Vision Model" page in the DSH settings panel: pick the default vision model, see the active route. Bilingual, follows the UI language.

Quick start

Permanent install — loads at every dsh start

The repo root is a dsh bundle package (dsh-vision-plugin): the host half lib/index.js, the browser half lib/client.js, and the plugin row cordis.patch.yml. Register the bundle in your profile and dsh mounts the plugin at boot — no cordis_define/cordis_run, survives restarts.

① Install with dsh plugin. The package is published on npm, so one command is enough — dsh plugin installs it with pnpm, then automatically appends it to dsh.profile.bundles (the package declares dsh.bundle):

dsh plugin --profile web add dsh-vision-plugin

Developing on the plugin itself? Install your local checkout instead — edits apply on the next dsh restart, no registry round-trip:

dsh plugin --profile web add /path/to/deepseek-harness-plug
# or run from inside the checkout directory:
dsh plugin --profile web add .

Then confirm the row composes:

dsh --profile web --dump-config        # the composed tree shows the "vision" row

② Declare image capability. Before a message enters the session, the host checks the current model's input modalities. Declare image capability for your text-only models in ~/.dsh/settings.yaml so they accept images (the plugin transcribes them afterwards):

llm-pi-ai:
  providers:
    opencode-go:
      apiKeyEnv: OPENCODE_GO_API_KEY
      modelOverrides:
        deepseek-v4-flash:
          input: [text, image]
        deepseek-v4-pro:
          input: [text, image]
        # add every other text-only model you may switch to;
        # native vision models need no override

The file is hot-reloaded — no restart. Declare every text-only model you might switch to: otherwise pasting an image after switching to an undeclared model is rejected before the plugin can transcribe it.

③ Restart dsh. On boot the loader mounts the vision row: vision_analyze is registered, the llm/stream image-translation waterfall is live, /vision/api/state answers the settings page, and the browser half is served at /plugins/dsh-vision-plugin/client.js. Verify: Settings → "Vision Model" page, vision_analyze in the tool list, then paste an image and ask.

Settings page

Open DSH settings (bottom-left ⚙️) → "Vision Model" page:

• Status badge with run state and version
• Dropdown to pick the default vision model (auto-select or a specific model) — saved changes take effect immediately and are remembered (stored in the browser), restored automatically on the next dsh start
• Notes showing the active model and route

The UI language follows DSH (Settings → General → Language), switching between Chinese and English automatically.

FAQ

Pasting an image fails after switching to another text-only model? That model isn't declared in settings.yaml. Add it as in step ② above — hot-reloaded, effective immediately.

What if opencode-go isn't configured? No problem. Since v1.1.0 the plugin scans all configured providers and picks the first route with a vision model. Only when no provider has one does it degrade (the conversation continues, with a note that the image is unavailable).

My provider rejects pasted images with unknown variant \image_url`, expected `text``? That error means an image block was sent to a model that only accepts text. Auto-selection never does that: only models on the native-vision whitelist (NATIVE_VISION_MODELS in lib/engine.js) are picked automatically. But the settings page lists every catalog image-capable model on every configured provider — whitelisted ones and any you declared image-capable via modelOverrides in settings.yaml alike — and an explicit choice (settings page or vision_analyze model override) is honored. If the chosen model turns out to reject image input upstream, the call automatically falls back to a whitelisted vision model (or degrades to a placeholder when none is available). If you use a genuinely vision-capable model that isn't whitelisted yet, add it to the whitelist so auto-selection prefers it.

Does it survive a DSH restart? Yes. The bundle is registered in the profile's dsh.profile.bundles and loads at every dsh boot — that's the whole point of the permanent install.

Does it cost extra? Each transcription is one vision-model call; the same image with the same question is cached, so follow-up turns reuse it. Vision model priority: explicit tool parameters (model/provider) > settings config > auto-select.

Is my vision-model choice remembered across restarts? Yes — since v1.2.0 the settings-page choice is saved in the browser (localStorage) and restored automatically on the next dsh start; the auto-translation waterfall uses the same route. The host-side route itself is process-local.

What happens when the vision model errors? The conversation never breaks: partial output is kept; a total failure retries once with a fallback model; a hung vision call is cut off after two minutes and counted as a failure (so the fallback retry kicks in); if that fails too, the model is told the image is unavailable and the chat continues.

Bundle layout

• lib/engine.js — the shared engine, single source of truth: vision_analyze tool, the llm/stream translation waterfall, route discovery, caches, timeouts. Zero imports on purpose (pnpm does not install the dependencies of link: profile plugins).
• lib/index.js — bundle host adapter (composition plugin row vision): registers the tool, the waterfall, and the /vision/api/state + /vision/api/model JSON API on ctx.webServer.
• lib/client.js — bundle browser half (dsh.client roster entry): the "Vision Model" settings page, served by the web shell at /plugins/dsh-vision-plugin/client.js.
• cordis.patch.yml — the loader patch row mounting the bundle (dsh.bundle.patch).
• scripts/check.js — the consistency check (npm run check): verifies the version number is in sync everywhere and lib/engine.js stays import-free.
• test/engine.test.js — the engine test suite (npm test): route discovery, override precedence, caching/dedup, timeouts, waterfall, tool.

Developing on the plugin itself: edit lib/engine.js (host logic) or lib/client.js (UI), then npm run check && npm test.

Under the hood (optional)

Flow: an image enters the session → the llm/stream listener checks the target model — native vision models (whitelist) see the image directly; text-only models get a vision-model transcription dispatched in its place. Image detection is recursive, mirroring the harness: images nested inside tool-result blocks and images in assistant messages (from a vision-model session) are translated or replaced too, so no image block ever reaches a text-only model. Translation requests carry a Symbol marker to prevent recursion; results are cached by image + question (TTL + FIFO cap, concurrent turns share one in-flight call).

The settings page lists every catalog image-capable model on every configured provider — not just opencode routes: whitelisted native vision models plus any model you declared image-capable via modelOverrides. Auto-selection is the only whitelist-gated path: resolveVisionRoute only ever picks NATIVE_VISION_MODELS entries, because catalog-only "image capability" is indistinguishable from a modelOverrides advertisement, and a text-only model would reject the image blocks upstream (unknown variant \image_url``). Explicit user choices are trusted — your configuration decides; if the chosen model fails upstream, the existing retry falls back to a whitelisted vision model.

Tunable constants live at the top of lib/engine.js: DEFAULT_MODEL (fallback model), NATIVE_VISION_MODELS (native-vision whitelist for auto-selection), MODEL_PRIORITY (auto-select order), STREAM_TIMEOUT_MS (vision-call hang timeout), CACHE_TTL_MS / CACHE_MAX (translation cache), CATALOG_TTL_MS (model-catalog cache).

License

MIT