dsh-vision-fallback

Vision Fallback

DSH 静默视觉增强：主模型照常选择，图片自动交给固定视觉模型后以隐藏上下文返回主模型。

目标 Profile

插件会安装到这里；不确定时保持 web。

npx -y @deepseek-ai/dsh plugin --profile web add dsh-vision-fallback@0.10.0

兼容性与来源证明

Vision Fallback 以 dsh-vision-fallback 发布，当前版本为 0.10.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。

DSH 兼容范围: *
运行环境: web
发布来源: npm
Registry 更新时间: 2026/9/20

版本

0.10.0stable

2026/9/3

0.9.1stable

2026/8/18

0.9.0stable

2026/8/15

查看其余 3 个版本收起版本

0.7.0stable

2026/8/14

0.6.0stable

2026/8/14

0.5.0stable

2026/8/14

README

dsh-vision-fallback

English | 中文

Silent vision enhancement for DeepSeek Harness (dsh): keep your real text-only main model (e.g. deepseek-v4-flash), and let chat images "just work" — every image you drop, paste, or reference in the chat box is automatically sent to a fixed vision model, converted into a factual text observation, and handed to your main model as hidden context. The UI keeps showing your original image; no model groups, no model switching, no extra tools.

Compatible with the current DSH Store window: 0.1.2-alpha.3, 0.1.2-alpha.4, and 0.1.2-alpha.5. Durable MCP / ACP image attachments and nested images forwarded by PTC Mode use the same vision bridge. When enabled, the plugin intentionally keeps advertising image input; otherwise the chat composer rejects the image before it can reach the bridge.

Why

DeepSeek V4 Flash / Pro and other strong coding models are text-only: dropping an image into the chat box fails with "model does not support image input".
Existing "vision tool" plugins require saving images as files and invoking a see_image(path) tool — clunky, and the main model still can't see chat attachments.
This plugin bridges the gap at the request layer, so chat-box images work exactly like you expect, regardless of which main model you pick in the model picker.

How it works

You drop/paste an image ──► chat attachment (kept visible in UI)
        │
        ▼
agent/pre-step ──► image + current question + recent context
        │                    │
        │                    ▼
        │            fixed vision model (OpenAI-compatible /chat/completions)
        │                    │  factual text observation
        │                    ▼
        └──► model-only surface replacement ──► main model (text only)

The plugin overrides the pre-send capability check, so a text-only model can receive image-bearing messages.
agent/pre-step detects images in the incoming turn, and sends the image, the latest user question, and recent conversation context to the configured vision model.
Your original image stays in the UI as a normal chat attachment.
A model-only surface replacement swaps the image for the vision observation before the request reaches the main model.
Switching the main model (DeepSeek, Kimi, MiniMax, ...) never changes the fixed vision model.

MCP / ACP / PTC images

DSH 0.1.0-rc.7 persists images produced by MCP, ACP, and PTC as durable attachments, then exposes them as core image content blocks. This plugin recursively handles images in ordinary messages, tool-result, and nested tool-result content while preserving the original text/image order:

MCP / ACP durable attachments are read by attachmentId, not temporary file paths;
nested PTC subtool images are converted into vision observations;
multiple images are sent to the vision model in their original order;
only the model-visible surface is replaced, so the original UI image remains intact.

The image-capability override is an intentional entry-point compatibility layer. It does not claim that the main model has native visual reasoning; it lets the image reach DSH so the bridge can convert it into text context.

DSH compatibility evidence

Verified on 2026-09-03 with Node.js 24.16.0 and a separate disposable DSH_HOME for each release:

DSH release	Local-path install	`--dump-config`	Authenticated cold start	Uninstall
`0.1.2-alpha.3`	passed	passed	HTTP 200	passed
`0.1.2-alpha.4`	passed	passed	HTTP 200	passed
`0.1.2-alpha.5`	passed	passed	HTTP 200	passed

The Profile operations use the official CLI with plugin --profile web add -w <local-path> and remove -w dsh-vision-fallback. The runtime fix does not mutate the deep-frozen llm/stream request introduced by current DSH builds; compaction creates a copied request and performs one guarded nested dispatch.

Install

From npm / local checkout

# npm (if published) or a local checkout directory
dsh plugin --profile web add dsh-vision-fallback
# or: dsh plugin --profile web add /path/to/dsh-vision-fallback

From source

git clone https://github.com/1HelloMan1/dsh-vision-fallback.git
cd dsh-vision-fallback
pnpm install --config.minimumReleaseAge=0   # pre-release peers may need the release-age flag bypassed
pnpm test                                    # node --test test/*.test.mjs
dsh plugin --profile web add "$PWD"

Then verify and restart:

dsh --profile web --dump-config   # expect a "# == dsh-vision-fallback" layer
# restart `dsh web` (patch/bundle layers are not hot-reloaded)

The plugin is also compatible with any profile (headless, TUI) — it registers on the host plane.

Configuration

Two ways, both live (no restart needed after saving):

Web settings page

Open Settings → 视觉增强 / Vision Enhancement in the DSH web UI. It exposes only:

Field	Default	Meaning
Enabled	`true`	Master switch
Vision model	`mimo-v2.5`	OpenAI-compatible `model`
Base URL	`https://opencode.ai/zen/go/v1`	The plugin appends `/chat/completions`
Credential ref	`OPENCODE_GO_API_KEY`	Key resolved from the DSH credential store (never written to env files)
Max tokens / Timeout / Max bytes	`1536` / `60000` / `15MB`	Vision request limits; oversized images fail
Recent context	`includeRecentContext: true`, `contextMessages: 6`, `contextMaxChars: 6000`	How much recent chat to attach for the vision model
Prompt	(Chinese detailed-analysis prompt)	Analysis instruction; the user's question is appended automatically
Tag result	`true`	Prepend `【视觉观察：<model>】` to the observation

settings.yaml

vision-fallback:
  enabled: true
  model: mimo-v2.5
  baseURL: https://opencode.ai/zen/go/v1
  apiKeyRef: OPENCODE_GO_API_KEY
  maxTokens: 1536
  timeoutMs: 60000
  maxBytes: 15728640
  includeRecentContext: true
  contextMessages: 6
  contextMaxChars: 6000
  prompt: "请分析这张图片..."
  tagResult: true

The API key is resolved through the DSH credentials system (~/.dsh/.credentials.yaml), with process.env[apiKeyRef] as a fallback — it is never materialized into shell environment files by the plugin.

Security & privacy

The config route is loopback-only and same-origin checked; request bodies are size-limited and schema-validated.
The vision model gets no tools, no system prompt, no execution permission — only the image, the question, and recent text context.
Observations are delivered as model-only surface replacements; your original image is never altered in the UI.
Image reads go through the DSH attachment service (sandbox/observation-policy aware); the vision request carries the official attributionHeaders().

Usage records (usage.jsonl)

With recordUsage enabled, every real vision call (success or failure) appends one JSON line to <dshHome>/vision-fallback/usage.jsonl (override via usageLogPath in the settings page), consumed by usage-dashboard. Fields:

Field	Meaning
`ts`	Call start time (epoch ms)
`durationMs`	Response latency of this call (ms)
`kind`	Always `"vision"`
`status`	`"ok"` success / `"error"` failure
`model`	Vision model name
`inputTokens` / `outputTokens`	Input / output tokens
`cacheReadTokens`	Cached input tokens served
`error`	Error message (failure entries only)
`imageName` / `mediaType` / `imageBytes`	Image filename / media type / byte size
`imageIndex` / `imageTotal`	This image's position / total images in the request

Reusing a remembered observation (observations.json) does not append a line — this file counts real external vision calls only.

Observation cache semantics

Observations are keyed by the image's occurrence in a session (session id + message id):

The same image at the same message position processed again (restart recovery, replay, compaction) → reused, no re-recognition;
The same image at a new position in the session (new turn, main model asking to "look again") → re-recognized with fresh context;
Observations are not shared across sessions.

The cache is capped at 256 entries (LRU eviction); failed results are never cached.

About image compression

In the current version, “compression” means reusing an existing vision observation during conversation-context compaction; it does not re-run vision analysis. The plugin does not yet resize, transcode, or quality-compress image files. Images above maxBytes fail explicitly instead of being silently changed. A future real image-compression feature should report the before/after byte counts and media type.

Default vision route

Model: mimo-v2.5 · Endpoint: https://opencode.ai/zen/go/v1/chat/completions · Credential: OPENCODE_GO_API_KEY

Any OpenAI-compatible vision endpoint works (Zhipu GLM-4V-Flash, SiliconFlow Qwen-VL, vLLM, Ollama, ...) — just change model, baseURL, and apiKeyRef in the settings page.

Relationship to the OpenCode ecosystem

The OpenCode community opencode-see-image hands a filePath + task question to a fixed vision model and returns text to the main model. DSH additionally performs a pre-send image-capability check, which this plugin also overrides, using DSH's official agent/pre-step and model-only surface replacement to keep the UI silent.

Development

pnpm test    # node --test test/*.test.mjs

Structure:

dsh-vision-fallback/
├── package.json        # dsh.bundle + dsh.client manifests
├── cordis.patch.yml    # inserts the vision-fallback row
├── lib/index.js        # host plugin (pre-step bridge, config route, controller)
├── lib/client.js       # Web settings page ("视觉增强")
└── test/               # unit tests

License

MIT

README

dsh-vision-fallback

English | 中文

Why

DeepSeek V4 Flash / Pro and other strong coding models are text-only: dropping an image into the chat box fails with "model does not support image input".
Existing "vision tool" plugins require saving images as files and invoking a see_image(path) tool — clunky, and the main model still can't see chat attachments.
This plugin bridges the gap at the request layer, so chat-box images work exactly like you expect, regardless of which main model you pick in the model picker.

How it works

You drop/paste an image ──► chat attachment (kept visible in UI)
        │
        ▼
agent/pre-step ──► image + current question + recent context
        │                    │
        │                    ▼
        │            fixed vision model (OpenAI-compatible /chat/completions)
        │                    │  factual text observation
        │                    ▼
        └──► model-only surface replacement ──► main model (text only)

The plugin overrides the pre-send capability check, so a text-only model can receive image-bearing messages.
agent/pre-step detects images in the incoming turn, and sends the image, the latest user question, and recent conversation context to the configured vision model.
Your original image stays in the UI as a normal chat attachment.
A model-only surface replacement swaps the image for the vision observation before the request reaches the main model.
Switching the main model (DeepSeek, Kimi, MiniMax, ...) never changes the fixed vision model.

MCP / ACP / PTC images

MCP / ACP durable attachments are read by attachmentId, not temporary file paths;
nested PTC subtool images are converted into vision observations;
multiple images are sent to the vision model in their original order;
only the model-visible surface is replaced, so the original UI image remains intact.

DSH compatibility evidence

Verified on 2026-09-03 with Node.js 24.16.0 and a separate disposable DSH_HOME for each release:

DSH release	Local-path install	`--dump-config`	Authenticated cold start	Uninstall
`0.1.2-alpha.3`	passed	passed	HTTP 200	passed
`0.1.2-alpha.4`	passed	passed	HTTP 200	passed
`0.1.2-alpha.5`	passed	passed	HTTP 200	passed

Install

From npm / local checkout

# npm (if published) or a local checkout directory
dsh plugin --profile web add dsh-vision-fallback
# or: dsh plugin --profile web add /path/to/dsh-vision-fallback

From source

git clone https://github.com/1HelloMan1/dsh-vision-fallback.git
cd dsh-vision-fallback
pnpm install --config.minimumReleaseAge=0   # pre-release peers may need the release-age flag bypassed
pnpm test                                    # node --test test/*.test.mjs
dsh plugin --profile web add "$PWD"

Then verify and restart:

dsh --profile web --dump-config   # expect a "# == dsh-vision-fallback" layer
# restart `dsh web` (patch/bundle layers are not hot-reloaded)

The plugin is also compatible with any profile (headless, TUI) — it registers on the host plane.

Configuration

Two ways, both live (no restart needed after saving):

Web settings page

Open Settings → 视觉增强 / Vision Enhancement in the DSH web UI. It exposes only:

Field	Default	Meaning
Enabled	`true`	Master switch
Vision model	`mimo-v2.5`	OpenAI-compatible `model`
Base URL	`https://opencode.ai/zen/go/v1`	The plugin appends `/chat/completions`
Credential ref	`OPENCODE_GO_API_KEY`	Key resolved from the DSH credential store (never written to env files)
Max tokens / Timeout / Max bytes	`1536` / `60000` / `15MB`	Vision request limits; oversized images fail
Recent context	`includeRecentContext: true`, `contextMessages: 6`, `contextMaxChars: 6000`	How much recent chat to attach for the vision model
Prompt	(Chinese detailed-analysis prompt)	Analysis instruction; the user's question is appended automatically
Tag result	`true`	Prepend `【视觉观察：<model>】` to the observation

settings.yaml

vision-fallback:
  enabled: true
  model: mimo-v2.5
  baseURL: https://opencode.ai/zen/go/v1
  apiKeyRef: OPENCODE_GO_API_KEY
  maxTokens: 1536
  timeoutMs: 60000
  maxBytes: 15728640
  includeRecentContext: true
  contextMessages: 6
  contextMaxChars: 6000
  prompt: "请分析这张图片..."
  tagResult: true

Security & privacy

The config route is loopback-only and same-origin checked; request bodies are size-limited and schema-validated.
The vision model gets no tools, no system prompt, no execution permission — only the image, the question, and recent text context.
Observations are delivered as model-only surface replacements; your original image is never altered in the UI.
Image reads go through the DSH attachment service (sandbox/observation-policy aware); the vision request carries the official attributionHeaders().

Usage records (usage.jsonl)

Field	Meaning
`ts`	Call start time (epoch ms)
`durationMs`	Response latency of this call (ms)
`kind`	Always `"vision"`
`status`	`"ok"` success / `"error"` failure
`model`	Vision model name
`inputTokens` / `outputTokens`	Input / output tokens
`cacheReadTokens`	Cached input tokens served
`error`	Error message (failure entries only)
`imageName` / `mediaType` / `imageBytes`	Image filename / media type / byte size
`imageIndex` / `imageTotal`	This image's position / total images in the request

Reusing a remembered observation (observations.json) does not append a line — this file counts real external vision calls only.

Observation cache semantics

Observations are keyed by the image's occurrence in a session (session id + message id):

The same image at the same message position processed again (restart recovery, replay, compaction) → reused, no re-recognition;
The same image at a new position in the session (new turn, main model asking to "look again") → re-recognized with fresh context;
Observations are not shared across sessions.

The cache is capped at 256 entries (LRU eviction); failed results are never cached.

About image compression

Default vision route

Model: mimo-v2.5 · Endpoint: https://opencode.ai/zen/go/v1/chat/completions · Credential: OPENCODE_GO_API_KEY

Any OpenAI-compatible vision endpoint works (Zhipu GLM-4V-Flash, SiliconFlow Qwen-VL, vLLM, Ollama, ...) — just change model, baseURL, and apiKeyRef in the settings page.

Relationship to the OpenCode ecosystem

Development

pnpm test    # node --test test/*.test.mjs

Structure:

dsh-vision-fallback/
├── package.json        # dsh.bundle + dsh.client manifests
├── cordis.patch.yml    # inserts the vision-fallback row
├── lib/index.js        # host plugin (pre-step bridge, config route, controller)
├── lib/client.js       # Web settings page ("视觉增强")
└── test/               # unit tests

License

MIT

Vision Fallback

兼容性与来源证明

版本

相关插件

Vision Fallback

README

dsh-vision-fallback

Why

How it works

MCP / ACP / PTC images

DSH compatibility evidence

Install

From npm / local checkout

From source

Configuration

Web settings page

settings.yaml

Security & privacy

Usage records (usage.jsonl)

Observation cache semantics

About image compression

Default vision route

Relationship to the OpenCode ecosystem

Development

License

兼容性与来源证明

版本

相关插件

相关插件

相关插件

README

dsh-vision-fallback

Why

How it works

MCP / ACP / PTC images

DSH compatibility evidence

Install

From npm / local checkout

From source

Configuration

Web settings page

settings.yaml

Security & privacy

Usage records (usage.jsonl)

Observation cache semantics

About image compression

Default vision route

Relationship to the OpenCode ecosystem

Development

License