HongMing-Huang/dsh-file-upload

File-message plugin for DeepSeek Harness (dsh). Claude/Codex-style uploads — drag-and-drop (files and folders), paperclip picker, paste-to-attach, multi-file support; content sniffing; fully bundled document → Markdown conversion (MarkItDown engine, 20+ formats, image OCR); Codex-style @relative/path references; automatic image explanations for text-only models; and a read_document tool for agents.

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Zero-config, install-and-use. Every feature works out of the box with sensible defaults — no Python, no downloads, no picking backends. Image explanations auto-discover a vision endpoint (local Ollama → OpenAI-compatible key from the dsh credentials seam).

Features

• Upload — composer paperclip button plus a global drag-and-drop overlay ("release to attach"), multi-file support.
• Attachment cards — color-coded type badges (PDF red / DOC blue / XLS green / TXT gray / ZIP purple / JSON gold) with name and size; removable.
• Codex-style file references — uploaded files appear in the message as @relative/path references (like OpenAI Codex), never as raw content dumped into the composer; the agent reads the file with read_document (converted to Markdown on demand).
• Codex-style @ mentions — type @ in the composer to pick any uploaded file by its relative path; the reference inserts as a mention.
• Document → Markdown, fully bundled — the MarkItDown engine ships inside the plugin (Microsoft MarkItDown TypeScript port, markitdown-node): PDF / DOCX / PPTX / XLSX / HTML / CSV / JSON / XML / RSS / Atom / ZIP / Jupyter / image OCR / audio transcription. No Python, no downloads, no setup.
• Image explanation for text-only models — upload an image and the plugin automatically generates a description ("讲解图片") through a vision discovery chain, so the DeepSeek API (text-only) can reason about the image: explicit visionEndpoint → local Ollama with a VL model (e.g. DeepSeek-VL2, zero-config) → OpenAI-compatible endpoint with a dsh-credentials key. Multimodal routes / vision bridges keep the official read_image path.
• read_document tool for agents — line-numbered paging (offset/limit), byte-budgeted LRU cache (invalidated on file change), size pre-checks, reads through ctx.fs (inherits sandbox and fs-observation policy).
• Security — loopback-only uploads, sanitized file names, session-isolated storage (.dsh-uploads/<sessionId>), sha256 content dedup, bounded concurrency, TTL sweep.

Install

dsh plugin --profile web add dsh-file-upload
# restart dsh web

Usage

1. Click the paperclip in the composer toolbar, or drag files anywhere over the window;
2. Small text files land directly in the composer; documents appear as attachment cards and their path is sent with the message;
3. The agent reads documents with read_document <path> — converted to Markdown on demand, pageable with offset/limit.

MarkItDown (fully bundled — no downloads, no setup)

The MarkItDown capability ships inside the plugin. Works out of the box: no Python, no pip, no downloads, no build-script approval.

• Bundled engine — the Microsoft MarkItDown TypeScript port (markitdown-node) is a regular dependency covering 20+ formats: PDF, DOCX, PPTX, XLSX, HTML, CSV, JSON, XML, RSS, Atom, ZIP, Jupyter notebooks, images (OCR via Tesseract, 110+ languages), and audio transcription (via LLM, needs model credentials).
• Images — OCR to text by default through the bundled engine.
• Offline — all parsing runs locally, no network calls.

Optional upgrade: if an official MarkItDown CLI already exists on your machine (or is set via markitdownBin), the plugin prefers it (adds EPUB and more); without one the bundled engine is always available.

- id: file-upload
  config:
    markitdownBin: /path/to/your/markitdown   # optional; empty = bundled engine only

Startup log (bundled mode):

[dsh-file-upload] Document → Markdown ready: bundled MarkItDown engine (20+ formats, image OCR) — fully packaged, no downloads, no Python.

How images are handled (auto-explained)

The plugin detects your session's model capability at upload time:

Detected route	What happens
Multimodal model (declares image input, e.g. GPT-4o / Qwen-VL / Claude / Gemini)	imageMode: native — the agent uses the official read_image tool; the image enters model context directly
Vision bridge installed (dsh-vision-proxy and similar)	detected automatically (they declare image input on their route) — same native path
Text-only model (the DeepSeek API is text-only)	an automatic image description is generated via the vision discovery chain and inserted with the message — the model reasons about the image content immediately

Vision discovery chain (zero-config): ① explicit visionEndpoint/visionModel → ② local Ollama at http://localhost:11434 (picks a VL model such as DeepSeek-VL2 — images never leave the machine) → ③ OpenAI standard endpoint using a key from the dsh credentials seam.

Note: DeepSeek's official API does not offer vision input (the multimodal line — DeepSeek-VL2/Janus — is open-source and self-hostable); deploy DeepSeek-VL2 via Ollama for a fully local "official DeepSeek vision" experience.

The route detection mirrors the official read_image gate (ctx.llm.resolveModelInfo + inputModalities).

Configuration

All fields have sensible defaults — you can install and use the plugin without touching any of them. Tune only what you need.

Field	Default	Description
uploadMaxBytes	25165824 (24 MB)	Max bytes per uploaded file
allowedExtensions	[]	Extension allowlist; empty = all allowed
uploadTtlMs	604800000 (7 days)	Unreferenced upload lifetime
sweepIntervalMs	3600000 (1 h)	Sweep period; 0 = disabled
maxConcurrentUploads	4	Concurrent upload limit
inlineTextLimit	8192 (8 KB)	Text inlined into the composer up to this size
previewTextLimit	2048 (2 KB)	Preview length for larger text files
maxFileBytes	25165824	Byte cap for one document read
readLimit	2000	Max lines returned by one read_document call
sheetRowLimit	200	Rows kept per XLSX sheet
maxSheets	5	Sheets read per workbook
cacheEntries	16	Parse-cache entry count
cacheMaxBytes	67108864 (64 MB)	Parse-cache byte budget
markitdownBin	''	Optional MarkItDown CLI path; empty = auto-detect PATH
markitdownTimeoutMs	120000	Timeout for one CLI invocation
visionEndpoint	''	Vision endpoint for image explanations; empty = auto (local Ollama → OpenAI standard)
visionModel	''	Vision model id; empty = auto
visionApiKeyEnv	OPENAI_API_KEY	Credential reference for the vision key (dsh credentials seam)
visionMaxBytes	10485760 (10 MB)	Max image bytes sent to the vision endpoint

Development

pnpm install
pnpm build     # tsc (host) + esbuild (client bundle)
pnpm test      # node --test

Architecture

src/
├── index.ts        # entry: apply + Config schema + assembly
├── detect.ts       # content sniffing (never trusts extensions)
├── convert.ts      # MarkItDown engine + optional CLI backend
├── vision.ts       # image explanations (vision discovery chain)
├── upload.ts       # upload route: loopback/session/size/dedup/TTL
├── tool.ts         # read_document: ctx.fs reads + paging + LRU cache
└── client/
    └── index.tsx   # paperclip + drag (files/folders) + paste + cards

Dual-face plugin: dsh.bundle (host) + dsh.client (web UI). No official patches — everything uses official seams (ctx.webServer, ctx.tools, ctx.systemPrompt, ctx.sessions, slash/input-insert-text, slash/input-insert-reference).

Security

• Uploads are loopback-only and same-origin checked.
• File names are sanitized (control chars, path separators, dot segments, leading dots stripped).
• Storage is session-isolated under the session's own workspace; unknown sessions get 403.
• sha256 content dedup, bounded concurrency (429 on overload), TTL sweep.
• Text extraction parses bytes, never trusts extensions; binaries are handed to the agent by path only.

License

MIT