GXX182/dsh-vision-bridge

dsh-vision-bridge is an installable DeepSeek Harness bundle that adds image understanding to text-first model routes. It preserves the Harness model list, adds a small glasses control to eligible models, and delegates image analysis to a separately configured vision provider.

The plugin supports Gemini-native, OpenAI-compatible Chat Completions/Responses, and Anthropic-compatible Messages APIs. The selected vision provider returns bounded text analysis; image blocks are never forwarded directly to the active upstream model.

Highlights

• One model list: Vision Bridge no longer appears as a duplicated provider group.
• Per-model glasses toggle: Gray means the bridge preference is off; blue means it is on.
• Selection stays explicit: Clicking the glasses only changes the preference. Clicking the model name selects the model and applies that preference.
• Persistent without model churn: Glasses preferences are remembered in the Harness web client without triggering a model switch or model-directory refresh.
• Clear service provenance: Hover the glasses to see the active vision provider and model.
• Multiple vision providers: Add, switch, and delete isolated provider profiles from Settings.
• Native-vision aware: Models that already advertise image input do not receive a bridge toggle.
• Reasoning UI preserved: The original Harness reasoning-effort menu remains unchanged.

Model selector behavior

Control	Result
Gray glasses	Vision Bridge is disabled for that model.
Blue glasses	Vision Bridge is enabled for that model.
Click glasses	Toggle and remember the preference only; the selected model does not change.
Click model name/row	Select the model. Blue routes through Vision Bridge; gray uses the normal upstream route.
Hover glasses	Show the vision provider and model that will perform image understanding.

The glasses control appears only when a matching bridge route exists and the upstream model is text-only or has unknown image capability. The preference is stored per upstream model in the local Harness client.

How it works

1. Enable the glasses for an eligible text-first model, then click the model name to select it.
2. Attach an image and ask a visual question normally.
3. Harness validates and stores the attachment in the session.
4. The bridge route replaces image blocks only in the provider-bound request copy with controlled attachment markers. The original session and transcript keep the images.
5. The active upstream model calls vision_bridge; the tool reads the latest session image through Harness attachment services.
6. The plugin sends a bounded request to the configured vision provider and returns only its text analysis to the active agent.

Explicit workspace paths remain supported through image_paths; they are resolved with Harness filesystem policy.

Requirements

• DeepSeek Harness 0.1.0-rc.5 or a compatible 0.1.x release
• Node.js ^22.19 or >=24
• An upstream model route that supports Harness tool calls
• An API key for at least one image-capable endpoint

The backward-compatible default profile uses GOOGLE_API_KEY, the Gemini native endpoint, and gemini-3.6-flash.

Install

From GitHub

Install the highest semantic-version release tag:

dsh plugin --profile web add "github:GXX182/dsh-vision-bridge#semver:*"

#semver:* selects the newest matching GitHub version tag. Pin an exact tag such as #v0.2.0 when reproducible installs are required.

If Harness is started with npx:

npx @deepseek-ai/dsh plugin --profile web add "github:GXX182/dsh-vision-bridge#semver:*"
npx @deepseek-ai/dsh plugin --profile web list
npx @deepseek-ai/dsh web

The persistent web profile is stored under ~/.dsh/profiles/web unless DSH_HOME is changed.

From a checkout

npm install
npm run build
dsh plugin --profile web add .
dsh --profile web --dump-config
dsh --profile web

The config dump should contain a dsh-vision-bridge layer and a vision-bridge row.

Configure vision providers

Open Settings → Plugins → Plugin configuration → Image understanding.

Each provider profile contains:

• a display name;
• an HTTPS Base URL;
• an API format (auto, Gemini, OpenAI compatible, or Anthropic compatible);
• its own credential;
• a selected image-capable model.

Adding a provider first verifies its model-list endpoint. The credential is stored through Harness credential services; the complete key is never returned to the browser. Switching providers immediately updates the glasses tooltip, including the selected provider name and model.

Provider actions

• Add: Enter the provider details. The profile is saved only after model discovery succeeds.
• Switch: Choose another provider from the provider picker; its model list is loaded automatically.
• Choose a model: Select from the discovered model list—there is no free-form model field.
• Delete: Hover or focus a provider option and use its delete control. Its managed credential is removed with it.

Protocol detection

With apiFormat: auto, complete endpoint paths take priority, followed by official hosts and version paths:

• :generateContent, /v1beta, or generativelanguage.googleapis.com → Gemini native
• /v1/messages or api.anthropic.com → Anthropic compatible
• /chat/completions or /responses → OpenAI compatible
• any other relay URL → OpenAI compatible

Set the format explicitly when an ambiguous relay uses Gemini or Anthropic semantics. The plugin never probes several protocols by resending the same image.

Advanced bundle configuration

The schema defaults work without editing the patch. To override them, replace the inserted row's complete config in the profile cordis.patch.yml:

- id: vision-bridge
  config:
    bridgeProvider: deepseek-vision-bridge
    upstreamProvider: deepseek-official
    apiKeyEnv: GOOGLE_API_KEY
    apiFormat: auto
    baseURL: https://generativelanguage.googleapis.com/v1beta
    model: gemini-3.6-flash
    maxImages: 8
    maxImageBytes: 8388608
    maxTotalImageBytes: 12582912
    maxQuestionChars: 8000
    maxOutputTokens: 4096
    maxResponseBytes: 524288
    maxAnswerBytes: 131072
    timeoutMs: 90000

Use the tool directly

Conversation attachments normally require no explicit tool instruction. For a workspace file, ask the agent:

Use vision_bridge to inspect screens/settings.png. List the visible controls and validation errors.

Code Mode can call await tools.vision_bridge(...). Omit image arguments for the latest conversation attachment, use attachment_ids for specific session images, or use image_paths for workspace files.

Security

• Images are sent to the configured vision endpoint. Do not use an endpoint that is not allowed to receive them.
• Only HTTPS provider endpoints are accepted.
• File formats are detected from bytes instead of trusted extensions.
• Per-image, aggregate-image, question, response, answer, token, and time limits are enforced.
• API keys are resolved Host-side and are never included in tool results or browser responses.
• Text inside images is treated as untrusted evidence, not as an instruction.

Known limitations

• Provider requests send image bytes inline; remote image URLs and file/video upload APIs are not supported.
• Unknown Base URLs default to OpenAI compatibility unless apiFormat is explicit.
• The bridge returns the provider's text analysis; it does not independently verify OCR, measurements, or safety-critical conclusions.
• Custom llm/stream middleware observes both the bridge request and its delegated upstream request.

Development

npm install
npm run verify
npm pack --dry-run

Built lib/ artifacts are intentionally committed for direct GitHub installation.

License

MIT