Composer-attached images are auto-described by an OpenAI-compatible vision model and handed to text-only models (DeepSeek) as text. ???????????
Pending human review — the facts below come from the source repository.
Composer-attached images are auto-described by an OpenAI-compatible vision model and handed to text-only models (DeepSeek) as text. ???????????
Users who want this capability in DSH; check the source and docs before installing.
dsh plugin --profile web add github:ximengxiaolan/dsh-vision-bridge No supported platforms were declared.
MODEL_DOES_NOT_SUPPORT_IMAGES）。本插件打通两个官方扩展点： 1. 准入放行：给 ctx.llm.resolveModelInfo 打补丁，声明当前模型支持 image 输入，让图片附件能进入会话。 2. 发模型前转换：包装 llm.streamWithRegistration，每次请求发往模型前扫描所有消息中的图片块，调用配置的 VLM 生成文字描述，替换成： [用户附上的图片已由视觉模型自动识别（qwen-vl-max）] <描述内容> 如果路由到的模型原生支持图片，则放行原图、不做转换。 ## 安装 sh # 前置：dsh CLI 与 pnpm dsh plugin --profile web add dsh-vision-bridge # 或从 git 安装： dsh plugin --profile web add https://github.com/<your-name>/dsh-vision-bridge 安装后重启 profile（dsh web）。 ## 配置 唯一需要的是一个 OpenAI 兼容多模态模型（/chat/completions + image_url），例如阿里云百炼 qwen-vl-max、智谱 GLM-4V 等。 环境变量： powershell $env:VISION_API_KEY = "sk-..." $env:VISION_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1" $env:VISION_MODEL = "qwen-vl-max" $env:VISION_LANG = "zh" # zh | en 或在 profile 的 cordis.patch.yml 中配置（优先于环境变量）： yaml - id: dsh-vision-bridge config: apiKey: 'sk-...' baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1' model: 'qwen-vl-max' lang: zh timeoutMs: 180000 enabled: true ## 使用 重启后直接在输入框粘贴图片并发送即可。DeepSeek 会基于视觉模型的描述继续处理。 ## 说明 - 同一张图片（按 attachmentId）在同一进程内缓存描述，重复发送不重复计费。 - VLM 调用失败时降级为 [图片识别失败: 原因]，对话不会中断。 - 子智能体会话（subagent）仍受 DSH 内核限制，不支持贴图。 ## License MITNot by stars — picked by hand, one at a time.
A visual settings dashboard for DeepSeek Harness: every registered settings namespace — including third-party plugin ones the official UI never covers — rendered as editable, schema-driven forms. Early, but the right direction: stop hand-editing YAML.
A DIY trajectory visualizer for DeepSeek Harness: watch the agent’s plan and execution unfold as a graph instead of scrolling raw logs. Early, but it fills a real gap — understanding at a glance what the agent is doing.
Dispatch DSH work from Claude Code / Codex: spin up in-host DSH agent sessions with per-tier presets, watch native subagent progress, and borrow a multimodal bridge that lends the text-only harness vision and image generation. An orchestration plugin that turns DSH into a backend for other coding agents.