TwistedRiCen/dsh-vision-bridge1

@liangdacheng/dsh-vision-bridge

DSH-native vision bridge for text-only models with native image attachments, multi-image evidence batching, and session-scoped validated Evidence caching.

AI 분석

核心用途是为不支持图片输入的纯文本推理模型桥接视觉能力,通过多模态模型处理图片并转化为文本证据。适合需要让纯文本模型理解图片内容的用户。

패키지
@liangdacheng/dsh-vision-bridge
버전
0.2.4
라이선스
NOASSERTION
최근 업데이트
2026. 8. 17.

설치

$npx -p @deepseek-ai/dsh dsh plugin --profile web add github:TwistedRiCen/dsh-vision-bridge

Configuration

All configuration is plugin row configuration for the dsh-vision-bridge row. The package's own bundle layer (cordis.patch.yml) inserts the row; your profile's cordis.patch.yml supplies the row's config.

The file to edit is $DSH_HOME/profiles/ /cordis.patch.yml — a YAML array of loader patch entries. Add (or extend) an entry with id: dsh-vision-bridge:

Minimal configuration

- id: dsh-vision-bridge
  config:
    upstreamProvider:    # your text-only reasoning route
    visionProvider:    # route serving an image-capable model
    visionModel:          # image-capable model id on that route

Annotated configuration

- id: dsh-vision-bridge
  config:
    upstreamProvider: deepseek-official # example: a text-only reasoning route
    visionProvider: deepseek-official   # example: the route serving the vision model
    visionModel: deepseek-vl            # example: the image-capable model id
    # providerId: my-bridge             # optional synthetic provider id

Configuration keys

KeyRequiredMeaning
upstreamProvideryesThe DSH provider route to wrap. Its models must be positively text-only (declare text and not image input).
visionProvideryesThe DSH provider route that serves the image-capable Vision model.
visionModelyesThe image-capable model id on the Vision route.
providerIdnoThe synthetic wrapper's provider id. Defaults to -vision-bridge. It must differ from both upstreamProvider and visionProvider (it may otherwise only wrap itself).

Notes:

  • upstreamProvider and visionProvider may be the same route — one DSH route can serve both a text-only reasoning model and an image-capable Vision model.
  • Vision capability is detected positively. The Vision route must positively declare image input (inputModalities contains image), and the upstream model must positively declare text-only input. Models with unknown or ambiguous modalities are refused.
  • Vision credentials belong to the configured DSH provider (DSH credentials layer). The bridge has no secret store of its own.
  • The Evidence cache is not configurable. Its scope is fixed (session-scoped, in-memory — see Caching behavior).
  • If the bridge is enabled but its config is missing or incomplete, the profile fails to boot with an error naming the missing key.

Minimal configuration

- id: dsh-vision-bridge
  config:
    upstreamProvider:    # your text-only reasoning route
    visionProvider:    # route serving an image-capable model
    visionModel:          # image-capable model id on that route

Annotated configuration

- id: dsh-vision-bridge
  config:
    upstreamProvider: deepseek-official # example: a text-only reasoning route
    visionProvider: deepseek-official   # example: the route serving the vision model
    visionModel: deepseek-vl            # example: the image-capable model id
    # providerId: my-bridge             # optional synthetic provider id

Configuration keys

KeyRequiredMeaning
upstreamProvideryesThe DSH provider route to wrap. Its models must be positively text-only (declare text and not image input).
visionProvideryesThe DSH provider route that serves the image-capable Vision model.
visionModelyesThe image-capable model id on the Vision route.
providerIdnoThe synthetic wrapper's provider id. Defaults to -vision-bridge. It must differ from both upstreamProvider and visionProvider (it may otherwise only wrap itself).

Notes:

  • upstreamProvider and visionProvider may be the same route — one DSH route can serve both a text-only reasoning model and an image-capable Vision model.
  • Vision capability is detected positively. The Vision route must positively declare image input (inputModalities contains image), and the upstream model must positively declare text-only input. Models with unknown or ambiguous modalities are refused.
  • Vision credentials belong to the configured DSH provider (DSH credentials layer). The bridge has no secret store of its own.
  • The Evidence cache is not configurable. Its scope is fixed (session-scoped, in-memory — see Caching behavior).
  • If the bridge is enabled but its config is missing or incomplete, the profile fails to boot with an error naming the missing key.

Usage examples

The examples below show what to ask and what the bridge does. Replies are produced by your models — treat them as illustrative, not guaranteed output.

Single-image request

Attach one image (a screenshot, a diagram, a receipt) and ask:

Describe only what can be verified from this image.

What happens:

  1. The bridge detects the image block and makes one Vision call with the image.
  2. The Vision output is parsed and validated into a single Evidence object (summary, ocr, layout, semantics, visual, uncertainty).
  3. The image is replaced by the rendered Evidence text, and the downstream text-only model answers from that Evidence.

Two-image comparison

Attach two images and ask:

Compare Image 1 and Image 2. Describe each independently, then state only
relationships that can be verified across the two images.

What happens:

  1. The two images form one multi-image work unit: a single Vision call carries both images in attachment order.
  2. The batch Evidence contains exactly two entries — Image 1 and Image 2 — each with its own summary/ocr/uncertainty, plus any verified cross-image relationships in a separate relations list.
  3. The downstream model receives [Image 1] and [Image 2] anchors at the original positions plus one Evidence block, so it can answer about each image and about their relationship without the images themselves.

Multi-image relationship analysis

Attach several related images (for example three screenshots of a workflow) and ask:

Walk through the steps visible across these screenshots and note any
sequence that can be verified from the images.

Each attachment stays an independent source image. The batch Evidence keeps one entry per image and records objective cross-image relations separately; the downstream model reasons over the Evidence, not over merged image data.