✨ Why DSH Vision Analysis?
Your text-only agent can finally "see" — with a free vision source built in: install the plugin, paste an image, ask. No API key, no model swap, no local-file dance.
- 🆓 Built-in FREE vision source — ships pointed at OVHcloud AI Endpoints' anonymous tier (Qwen2.5-VL-72B). Zero cost, zero key, zero config.
- 🖼️ Image bridge for text-only models — paste or send images directly in conversation; the plugin routes them to vision automatically (native multimodal routes stay untouched).
- 🔁 Rate-limit failover — when one vision model is throttled, the next in the chain answers; if everything is exhausted you get clear recovery guidance instead of a failure.
- 🧾 Structured output —
chart-data and ocr return machine-readable JSON (rows, lines, …) your agent can consume directly.
- 8 analysis modes out of the box —
describe, ocr, ui-review, chart-data, object-detect, compare, code-gen, debug — each with a tuned instruction template.
- Any vision endpoint — OpenAI
chat/completions or Anthropic messages wire formats. MiMo, Step, SiliconFlow, OpenRouter, Gemini (OpenAI-compat), GPT-4o, Claude, Qwen-VL, or a local Ollama / LM Studio / vLLM.
- Any input — absolute local path,
http(s) URL, or base64 data: URL; up to 4 images per call with built-in comparison.
- Privacy-first by design — image bytes never enter the session log or reach your main model; only the vision model's text comes back. The
debug report never reveals your API key (fully masked).
- Production-grade plumbing — result caching, retry with exponential backoff, live configuration from
Settings → 插件配置.
- Dependency-light — just
@deepseek-ai/schemastery + @deepseek-ai/dsh-settings at runtime.
🖼️ Demo
Paste an image, ask a question, get a real answer — even on a text-only model. The image is routed to your configured vision endpoint and the analysis lands straight in the conversation:
dsh-vision-analysis in action: a pasted image of a DeepSeek fan-art character is identified with full reasoning in a DSH conversation
In the screenshot: a pasted image plus the question "这是谁?" — the vision endpoint identifies the DeepSeek fan-art character and walks through its reasoning, all without switching models or saving files locally.
More scenarios — real outputs from the free vision models
Three everyday capabilities, each answered by a different free vision model automatically (when one is rate limited, the plugin fails over to the next).
1. OCR — pull text out of documents and screenshots
A short ops-report document to transcribe
Weekly Ops Report — 2026-W33
Item 01 · Pending action: review queue / escalate blocker
Item 02 · Pending action: review queue / escalate blocker
… (all lines transcribed verbatim)
2. Charts → structured data your agent can use
A monthly revenue bar chart
{ "title": "Monthly Revenue — Q1–Q3", "rows": [["Jan","82"],["Feb","95"],…] }
3. UI review — a designer's eye on your interface
A simple e-commerce product page mockup
• Inconsistent button styling across "Add to cart" and "Checkout" (High)
• Product name and price lack visual hierarchy (Medium)
• Cart items unstructured; subtotal not visually distinct (Medium)
🚀 Quick start
# From GitHub (no npm needed)
dsh plugin --profile web add github:Harvey-Will/dsh-vision-analysis
# Or one-click from the plugin market inside the Harness
Restart the web profile and ask your agent to analyze an image by path or URL:
"Use analyze_image to OCR /tmp/screenshot.png and tell me what it says."
That works with zero configuration: the plugin ships pointed at a free anonymous vision endpoint (OVHcloud AI Endpoints, Qwen2.5-VL-72B) — no API key required.
Two ways to use it
1. analyze_image tool (zero config) — the agent reads a local path, an http(s) URL, or a data URL. Works immediately after install.
2. Paste images straight into the conversation (image bridge) — requires two setup steps:
- add the model to
bridgeModels in the plugin config;
- declare
image in that model's inputModalities in settings.yaml (this is what lets the Harness admit image prompts for it).
# ① ~/.dsh/settings.yaml — under llm-deepseek.models, for each text-only model:
# inputModalities: [text, image]
# ② plugin config:
bridgeModels: [deepseek-v4-flash]
Bring your own endpoint (optional)
config:
apiFormat: openai # or anthropic
baseURL: https://api.siliconflow.cn/v1
apiKey: your-key # leave empty for anonymous/local endpoints
model: Qwen/Qwen2.5-VL-72B-Instruct
fallbackModels: [Qwen3.5-9B] # same-endpoint alternates tried on HTTP 429
🧭 Choose the right mode
| Mode | What it does | Built-in tokens / temp |
|---|
describe | General understanding (default) | 4096 / 0.7 |
ocr | Exact text extraction | 4096 / 0.0 |
ui-review | Design review with score | 4096 / 0.5 |
chart-data | Tables + trend from charts | 4096 / 0.0 |
object-detect | Objects, people, activities | 4096 / 0.5 |
compare | Two+ images side by side | 4096 / 0.5 |
code-gen | HTML+CSS from a UI shot | 4096 / 0.3 |
debug | Endpoint connectivity report | 4096 / 0.7 |
🔧 The tool
analyze_image(image?, images?, mode?, prompt?)
image — absolute path, http(s) URL, or data:image/...;base64, URLimages — up to maxImages (default 2, max 4) for multi-image callsmode — one of the eight above; describe by defaultprompt — your precise instruction overrides the mode template
A targeted prompt beats a generic description: prompt: "Extract the table as CSV" >> prompt: "Describe this".
⚙️ Configuration
- id: vision-analysis
name: dsh-vision-analysis
config:
apiFormat: openai # openai | anthropic
baseURL: https://api.siliconflow.cn/v1
apiKey: '' # empty → UNIVERSAL_VISION_API_KEY → local model
model: Qwen/Qwen2.5-VL-72B-Instruct
defaultMode: describe
maxImages: 2 # 1-4
maxBytes: 10485760 # per-image cap (10 MB)
timeoutMs: 120000
maxTokens: 4096
temperature: 0.7
modes: # per-mode overrides
ocr:
temperature: 0.0
All fields are editable live from Settings → 插件配置 (API key field is masked).
🔒 Security & privacy
- Your images stay private: local files are read by the tool and sent base64-embedded only to your configured endpoint; the raw bytes never enter the session log or reach the main model.
- Your key stays secret: never embedded in requests to the main model; the
debug report only says configured / not configured — no prefix, no characters.
- Prefer the environment: keep keys out of
cordis.yml — use UNIVERSAL_VISION_API_KEY or the masked secret field in Settings.
- Endpoints are not sandboxed by tool approvals — only point the tool at endpoints you control, and only reference
http(s) image URLs you trust the endpoint to fetch.
- Installing a plugin runs its code with your permissions — review the source before installing.
🧩 Compatibility
| Supported |
|---|
| DeepSeek Harness | 0.1.0-rc.x (verified on rc.8) |
| Node.js | ^22.19 || >=24 |
| Vision wire formats | OpenAI chat/completions, Anthropic messages |
| Image formats | PNG, JPEG, GIF, WebP, BMP (local / URL / data URL) |
⚠️ Community plugin — not an official DeepSeek product. The Harness API is in developer preview and may break between versions.
