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dsh-official-vision

Model Inference Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install wilianyichen/dsh-official-vision

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install wilianyichen/dsh-official-vision in your DeepSeek Harness CLI; the full source is at https://github.com/wilianyichen/dsh-official-vision

About this plugin

Vision inference in DeepSeek Harness has long relied on third-party bridges — GLM, Gemini, InternVL, and others. They work, but every image request detours through an external model, adding latency and muddying the billing and privacy boundary. dsh-official-vision solves that in one move: it registers the official multimodal model deepseek-v4-flash-vision-exp as a native provider route so that encoded images are sent straight to api.deepseek.com with no third-party model in the path.

Under the hood the plugin supports two image-transport modes. The default inline base64 path is ideal for local images up to 32 MiB per file. When preferFilesApi is enabled, images are first uploaded via the official Files API, cached by a digest-based file_id for deduplication, and then referenced in subsequent requests — a cleaner fit for large files or the same image reused across turns. Plain-text conversations are transparently forwarded to the standard deepseek route, so there is zero overhead when no image is attached.

It is best suited for developers running a Harness build that has not yet shipped a built-in vision route and want to skip the third-party detour entirely, as well as teams that need a single, auditable, official entry point for rate-limiting, logging, and cost tracking. Once installed, you simply pick DeepSeek-V4-Flash-Vision-Exp from the model selector, paste an image, and the entire inference chain stays on DeepSeek's own infrastructure.

Use Cases

  • Access official vision routing on Harness builds that lack it out of the box
  • Replace third-party bridge plugins to keep every image request on DeepSeek's own endpoint
  • Upload large images via the Files API and reuse a cached file_id across conversation turns

Best For

  • Developers on older Harness releases who need official vision capability today
  • AI engineering teams that require a single auditable entry point for rate-limiting and billing
  • Inference-pipeline users who prefer a direct official link over third-party model detours