dsh-vision-bridge

Universal vision bridge for DeepSeek Harness (dsh) — a self-contained replacement for dsh-vision-router.

When the chat model has no vision (e.g. deepseek-v4-flash) and a message contains an image, the image never reaches the text-only model. Instead, the plugin picks how to handle it — depending on the configured mode:

hybrid (default) — auto-rewrite image blocks into text descriptions using a vision model; tools stay available for explicit follow-ups.
llm — auto-rewrite via vision model only (the text-only model never sees raw images); tools remain callable.
tools — auto-rewrite is off. The text model must call describe_image (or another tool) explicitly, otherwise the adapter fails on the raw image.

Plus, multi-channel endpoints: chain dsh-catalog, openai-compatible, ollama, custom, and webhook endpoints — sequential or parallel-race fallback, per-channel cooldown, optional placeholder on total failure, zero-config Ollama discovery, and LM Studio preset.

Install

dsh plugin --profile web add @goodandready/dsh-vision-bridge

Restart the Web UI, open Plugins → Settings → vision-bridge (collapsible card).

Tools (25)

Core

Tool	What it does
`describe_image`	Ask the vision model about an image (attachment id or local path)
`read_image`	Native-shape alias — read local files through the bridge when the current model can't accept images

Grounding / geometry

Tool	Result
`vision_ground(image, target)`	bbox `[x1,y1,x2,y2]` in 0–1000 coords
`vision_crop(image, region)`	crop bbox (phrase or coords)
`vision_detect(image, kind)`	numbered inventory `[{label,bbox}]`
`vision_compare(images[], q)`	joint multi-image deltas (all images sent simultaneously)
`vision_present(path)`	publish local file as chat attachment

OCR & analysis

Tool	Result
`vision_ocr(image)`	transcribe all visible text
`vision_ocr_local(image, psm)`	local Tesseract OCR (no network); PSM 3/4/6/11
`vision_long_ocr(image)`	long screenshot OCR, stitched Markdown
`vision_trace(image)`	SVG vectorization
`vision_colors(image, top)`	dominant colors palette
`vision_extract_foreground(image)`	foreground bbox (SAM3 upgrade path)

Structured / Q&A

Tool	Result
`vision_describe_structured(image)`	JSON: `{summary, ocr, layout[], entities[], uncertainty[]}`
`vision_vqa(image, question)`	short answer to a visual question (token-efficient, maxTokens=100)
`vision_ui_layout(image)`	structured UI breakdown: header/main/sidebar/footer with sizes and contents
`vision_translate_image(image)`	extract text verbatim (ready for translation by main model)

Pixel loop & rendering

Tool	Result
`vision_pixel_diff(A, B)`	diff ratio + worst regions
`vision_html_screenshot(html, w, h)`	render HTML → PNG (headless Chrome)
`vision_materialize(id, filename)`	copy attachment to workspace path
`vision_pdf_pages(path, pages[])`	PDF pages → PNG → vision per page (requires poppler-utils)

Video / browser

Tool	Result
`vision_video_describe(path, question, frames)`	ffmpeg frame extraction → vision per frame → summary
`vision_page_persist(url, width)`	URL screenshot → attachment (headless Chrome)
`vision_browser_snapshot(url)`	fetch URL → rendered text content
`vision_batch(images[], prompt)`	process N images in parallel

Binary requirements

html_screenshot and page_persist need Chrome (/usr/bin/google-chrome or CHROME_PATH). video_describe needs ffmpeg. pdf_pages needs pdftoppm. ocr_local needs tesseract. Each degrades to a clear note when absent.

Settings

Plugins → Settings → vision-bridge (collapsible card):

Mode — hybrid / llm / tools
Describe strategy — auto / llm / ocr-local / cache-only
Focus hint — pass latest user message as context to vision model
Task mode — glance / ocr / region / compare
Escalation — simple-only / auto-escalate
Vision provider / model — explicit override; empty = auto-pick
Channels editor — add/remove/reorder endpoints with status-dot per key
Presets — Local / Cloud / LM Studio (one click)
Bench — probe every channel, show latency
Test vision — single end-to-end call

In settings.yaml:

dsh-vision-bridge:
  mode: hybrid
  describeStrategy: auto
  focusHint: true                    # task-aware prompts
  taskMode: glance                   # glance | ocr | region | compare
  escalation: simple-only            # simple-only | auto-escalate
  nativePassthrough: prefer          # prefer | always | never
  visionProvider: ""
  visionModel: ""
  channels: []
  channelFallback: sequential        # sequential | parallel-race
  channelTimeoutMs: 30000
  channelCooldownMs: 60000
  channelFailureMode: placeholder    # placeholder | error
  autoLocalOllama: true
  keysFromEnv: [VISION_API_KEY, DASHSCOPE_API_KEY, OPENAI_API_KEY, ZHIPUAI_API_KEY]
  sanitizeImages: true
  cacheEnabled: true
  cacheMaxEntries: 256
  evidencePersist: false             # persist descriptions across restarts
  evidenceDir: ""                    # default = cwd
  evidenceMaxEntries: 2000
  allowedImageDirs: []               # empty = any path allowed
  auditLog: off                      # off | errors | all
  maskSecrets: true
  maxImageBytes: 20971520
  timeoutMs: 120000

Channel types

channels:
  - type: dsh-catalog         # DSH catalog model
    provider: <provider>
    model: <model>
  - type: openai-compatible   # any OpenAI-format endpoint
    baseURL: https://<HOST>/v1
    apiKey: ""
    model: <MODEL_ID>
    protocol: openai-chat     # openai-chat | openai-responses
  - type: ollama              # local Ollama
    baseURL: http://localhost:11434/v1
    model: <OLLAMA_MODEL>
  - type: lmstudio            # LM Studio (localhost:1234)
    baseURL: http://localhost:1234/v1
    model: <LMSTUDIO_MODEL>
  - type: webhook             # your own HTTP endpoint
    baseURL: https://<YOUR_SERVICE>/vision
    apiKey: ""
  - type: custom              # template-based
    baseURL: https://<CUSTOM_HOST>/vision
    requestTemplate: |
      {"model":{{model}},"messages":[{"role":"user","content":[{"type":"image_url","image_url":{"url":{{dataUrl}}}}, {"type":"text","text":{{prompt}}}]}]}
    responsePath: choices.0.message.content

Routes

Route	Method	Purpose
`/dsh-vision-bridge/config`	GET/POST	read/write plugin config
`/dsh-vision-bridge/channels`	GET/POST	list/edit channels
`/dsh-vision-bridge/models`	GET	list all models + vision flag
`/dsh-vision-bridge/test`	POST	single end-to-end call
`/dsh-vision-bridge/stats`	GET	per-channel usage stats
`/dsh-vision-bridge/bench`	POST	probe every channel latency
`/dsh-vision-bridge/costs`	GET	token estimate per channel
`/dsh-vision-bridge/cache`	GET/DELETE	cache inspector / clear

Skill

The bundled vision-skills Skill (5 playbooks: long-screenshot OCR, restore UI/graphic/structure, GUI ops) is registered via ctx.skills.registerProvider — the model loads the matching playbook when a visual task starts.

Structure

dsh-vision-bridge/
├── package.json
├── cordis.patch.yml
├── lib/index.js            # host: sanitizer + tools + channels + routes + skill
├── lib/channels.js         # multi-channel driver (6 types) — stdlib
├── lib/cache.js            # LRU cache + composite key
├── lib/evidence.js         # persistent description store
├── lib/client.js           # browser: Plugins-tab collapsible card
├── skills/vision-skills/   # bundled Skill (5 playbooks)
├── test/regression.test.js # 32 regression tests
├── test/eval.test.js       # 6 eval tests
├── README.md
└── LICENSE                 # MIT

Compatibility notes

Default behavior is identical to v0.1.x.
Settings live in a collapsible card on Plugins tab, fallback to sidebar if slot absent.
No new peer dependencies. Chrome/ffmpeg/pdftoppm/tesseract used only when present; each degrades gracefully.

License

MIT

dsh-vision-bridge

Install

Source

Tags

Readme

dsh-vision-bridge

Install

Tools (25)

Core

Grounding / geometry

OCR & analysis

Structured / Q&A

Pixel loop & rendering

Video / browser

Binary requirements

Settings

Channel types

Routes

Skill

Structure

Compatibility notes

License