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vision tool for DeepSeek Harness: analyze images via any OpenAI-compatible vision API.","maintainers":[{"name":"aalongaa","email":"3986567044@qq.com"}],"readme":"# 🖼️ dsh-tool-vision\n\n> 给 DeepSeek Harness 装上\"眼睛\"的 OpenAI 兼容视觉理解插件。\n> 智能体终于不用再靠猜了——虽然它猜得也不差。\n\n`@aalongaa/dsh-tool-vision` 为 DeepSeek Harness 注册一个 `vision` 工具：把本地图片或远程图片丢给任意 OpenAI 兼容的视觉模型，拿回一句人话描述。\n\n## 它帮你做三件事\n\n1. **看** —— 单图 / 多图一次分析，本地文件或 URL 都行。\n2. **省** —— 图片自动缩放压缩（默认 1024px、JPEG 85%），token 省下来，钱包谢谢你。\n3. **记** —— 结果缓存，同一张图问第二遍不花钱。AI 也怕重复劳动。\n\n## 安装\n\n```bash\n# 装进 dsh 的 web profile（装之前记得先停掉正在跑的 dsh）\nnpx @deepseek-ai/dsh plugin --profile web add @aalongaa/dsh-tool-vision\n\n# 启动\nnpx @deepseek-ai/dsh web\n```\n\n装完打开 Web 设置 → 插件 → **视觉理解**，就能看到配置卡片和智能检测到的配置文件路径。\n\n不想用了？先停掉 dsh，然后卸载：\n\n```bash\nnpx @deepseek-ai/dsh plugin --profile web remove @aalongaa/dsh-tool-vision\n```\n\n## 配置\n\n### 配置从哪里来？\n\n插件会从三个地方读取配置，**优先级从低到高**（高优先级的会覆盖低优先级的同名项）：\n\n| 优先级 | 来源 | 说明 |\n| --- | --- | --- |\n| 1（最低） | `cordis.yml` | 插件的 schema 默认值。一般不用管，放着就行 |\n| 2 | `.dsh-tool-vision.json` | 项目级配置文件，放在项目根目录（可用环境变量 `DSH_TOOL_VISION_CONFIG` 指定路径） |\n| 3（最高） | `~/.dsh/settings.yaml` | dsh 的全局设置文件，**Web 设置页里改的就是它** |\n\n> 推荐做法：日常在 Web 设置页里改（第 3 层），想跟着项目走就写 `.dsh-tool-vision.json`（第 2 层）。两种都不配时，插件用默认值也能跑，只是会指向 OpenAI 官方接口。\n\n### 完整配置项\n\n| 字段 | 类型 | 默认值 | 说明 |\n| --- | --- | --- | --- |\n| `apiKey` | string | 无 | 视觉接口的 API 密钥（如 `sk-xxx`） |\n| `baseUrl` | string | `https://api.openai.com/v1` | OpenAI 兼容的接口地址。用本地推理服务（如 LM Studio / Ollama / vLLM）就填 `http://127.0.0.1:1234/v1` |\n| `model` | string | `gpt-4o-mini` | 视觉模型名称，如 `minicpm-v-4_6` |\n| `maxTokens` | number | `1024` | 每次请求的最大输出 token 数 |\n| `preprocess.enabled` | boolean | `true` | 是否自动缩放压缩图片 |\n| `preprocess.maxWidth` / `preprocess.maxHeight` | number | `1024` | 缩放上限（像素） |\n| `preprocess.quality` | number | `85` | JPEG 压缩质量（1–100） |\n| `preprocess.format` | string | `jpeg` | 压缩格式：`jpeg` / `png` / `webp` / `avif` |\n| `cache.enabled` | boolean | `true` | 是否缓存分析结果 |\n| `cache.dir` | string | `.cache/dsh-tool-vision` | 缓存目录 |\n| `cache.ttl` | number | 无（不过期） | 缓存有效期（毫秒） |\n| `artifactsDir` | string | `.dsh-tool-vision/artifacts` | 视觉工具产物目录（标注图 / 裁剪图 / 热力图） |\n| `liftProviders` | string[] | `[\"deepseek-official\"]` | 哪些 provider 被\"声明支持图片\"（解除发图限制）。只放行列出的 provider，不污染第三方；`[\"*\"]` 恢复全局放行 |\n| `rewriteImages` | boolean | `true` | 图片块在进模型前被改写成\"调用 vision_describe\"的文本标记——**图片字节永不发给文本模型**。用原生多模态模型做会话模型时建议关掉 |\n| `providers` | object[] | `[]` | `vision_describe` 的**降级链**：按顺序尝试，前面的挂了自动换下一个。每项 `{ baseUrl, apiKey?, model?, maxTokens? }`，`apiKey` 缺省继承顶层 |\n\n### 配置示例\n\n在 `~/.dsh/settings.yaml` 或 `.dsh-tool-vision.json` 里写：\n\n```yaml\ntool-vision:\n  apiKey: sk-xxx\n  baseUrl: http://127.0.0.1:1234/v1\n  model: minicpm-v-4_6\n  maxTokens: 8192\n  # 主端点挂了自动切到这个（本地 → 云端兜底）\n  providers:\n    - baseUrl: https://api.siliconflow.cn/v1\n      apiKey: sk-cloud\n      model: Qwen/Qwen2.5-VL-72B-Instruct\n```\n\n`.dsh-tool-vision.json` 支持 **JSONC**（带注释）。保存后每个字段上方自动带一行中英双语注释，不用猜\"这行是干嘛的\"：\n\n```jsonc\n{\n  // apiKey —— 在这里填写 API 密钥（例如 sk-xxx） / put your API key here (e.g. sk-xxx)\n  // baseUrl —— 在这里填写接口地址（OpenAI 兼容） / OpenAI-compatible endpoint base URL\n  \"baseUrl\": \"http://127.0.0.1:1234/v1\",\n  // model —— 在这里填写视觉模型名称 / vision model name\n  \"model\": \"minicpm-v-4_6\"\n}\n```\n\n### 常见坑\n\n- **请求一直失败？** 检查 `baseUrl` 是不是默认的 `https://api.openai.com/v1`——如果你的网络访问不了 OpenAI，就把地址换成本地/可达的 OpenAI 兼容服务。插件启动时会打印一行诊断日志（`[tool-vision] config: ...`），一眼就能看到最终生效的配置。\n- **`apiKey` 是占位符？** 插件会警告配置里的 key 疑似是 `test` / `placeholder` / `example` 之类的测试值，说明真实密钥没配上去。\n- **配置改完没生效？** 第 3 层（settings.yaml）是实时读取的，改完立即生效；第 2 层（JSON 文件）在插件启动时加载，改完需要重启 dsh。\n\n## 使用\n\n装好插件后，即使底层模型本身不支持图片输入，**聊天框也可以直接粘贴/上传图片**——插件自动放行图片附件，并遵守一条铁律：**图片字节永不发给 DeepSeek**。发图的那一轮，图片会被改写成一段\"这里有一张图，请调用 `vision_describe` 查看\"的标记，视觉工具链会自动挂载，DeepSeek 只负责基于工具读回来的文字继续干活，不会因为\"模型不支持图片\"而翻车。\n\n插件默认只暴露一个零参工具 `vision_activate`，发图或首次调用后挂载完整的视觉工具链（保持工具面最小）：\n\n| 工具 | 作用 |\n| --- | --- |\n| `vision_describe` | 看图问答 / 多图对比，返回文字描述（带缓存 + 多端点降级） |\n| `vision_ground` | 定位目标 → 返回**原图像素坐标框** + 标注 PNG |\n| `vision_crop` | 按坐标裁剪出图，返回 PNG 路径 |\n| `vision_diff` | 两图逐像素对比：差异率 + 最差区域 + 热力图 PNG |\n\n它们组成一个**可验证的闭环**：\n\n```text\nvision_ground   source=ref.png target=\"发送按钮\"     → {x1,y1,x2,y2} + 标注图\nvision_crop     source=ref.png x1=.. y1=.. x2=.. y2=..  → 裁剪 PNG\nvision_describe source=crop.png prompt=\"里面是什么？\"    → 描述\nvision_diff     original=ref.png candidate=impl.png      → 差异率 + 热力图\n```\n\n三个值得知道的细节：\n\n1. **上传的图片可以直接用附件 id 喂工具**：聊天框里发的图，各工具的 `source` 参数可以直接传 `sha256:...` 形式的附件 id，插件会从会话里把图读出来——不用先把它落到磁盘再找路径。\n2. **主端点挂了自动降级**：配置了 `providers` 后，`vision_describe` 会按顺序尝试，前面的失败自动换下一个，全部失败时返回每一家的原因。\n3. **产物自动落盘**：标注图、裁剪图、热力图默认保存在 `.dsh-tool-vision/artifacts/`，返回绝对路径，可继续喂给下一个工具。\n\n## 提示：想完全离线，本地部署一个视觉模型\n\n如果不想把图片交给云端 API，可以在自己电脑上跑一个 OpenAI 兼容的视觉模型，把 `baseUrl` 指到本地即可，零成本、不泄露图片、断网也能用。\n\n**部署方式二选一：**\n\n| 工具 | 特点 | 地址 |\n| --- | --- | --- |\n| **LM Studio** | 图形界面，下载模型一键启动，最省心 | https://lmstudio.ai |\n| **llama.cpp** | 命令行 / 服务端，轻量高效，适合折腾 | https://github.com/ggml-org/llama.cpp |\n\n**推荐模型：MiniCPM-V 4.6 Thinking**（OpenBMB，Apache 2.0）\n\n面壁智能联合清华 NLP 实验室开源的端侧多模态模型——**专门以手机配置为标准训练**，能同时理解**图像和视频**，给 DeepSeek 充当\"眼睛\"再合适不过：\n\n- 仅 **1.3B 参数**，约 6GB 内存即可流畅运行，普通笔记本无压力\n- 基于 SigLIP2-400M 视觉编码器 + Qwen3.5-0.8B 语言模型，支持 4x/16x 混合视觉 token 压缩\n- 长思维链推理变体，复杂多模态推理、OCR、数学题表现更稳\n- 官方适配 llama.cpp / Ollama / vLLM / SGLang 等推理框架\n\n**模型下载：** [Hugging Face](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking) · [项目主页](https://github.com/OpenBMB/MiniCPM-V)\n\n> 本地服务起好后，把 `baseUrl` 指向本地 OpenAI 兼容地址即可：**LM Studio 用 `http://127.0.0.1:1234/v1`，llama.cpp 用 `http://127.0.0.1:8080/v1`**，`model` 填模型名，其他照常。\n\n## 开发\n\n```bash\nnpm install\nnpm test          # 跑测试，全绿就对了\nnpm run build     # tsup 打包\nnpm publish       # 发版（记得先改 version）\n```\n\n## License\n\nMIT —— 随便用。但如果你的智能体靠它写出了论文，记得在致谢里提一嘴。\n（开玩笑的，不提也行，它不会记仇。）\n","readmeFilename":"readme.md"}