MindsEye

Let DeepSeek see images natively — model-driven vision tools for DeepSeek Harness

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Current version: 0.2.3

MindsEye is a vision plugin for DeepSeek Harness (dsh). Pasted images stay visible in the conversation while DeepSeek keeps reasoning and the vision model does the seeing. The plugin exposes task-specific vision tools that the model selects by intent; each tool maps to a fixed intent and model route, returns structured JSON, and reduces repeated calls through caching and evidence reuse.

Core Experience

• Paste and see: takes over the deepseek-official route so images enter the conversation natively; when takeover is unavailable it automatically falls back to path-based paste, so new images always get through
• Model picks the intent, plugin routes the model: mindseye_read_image takes an intent (visual-qa / ocr / layout / chart / color / pixel-diff / general) plus optional extract for combined structured evidence in one call; mindseye_ground stays separate for coordinates
• Generated images appear in the conversation: mindseye_generate_image delegates to a dedicated image generation model and returns the result as a dsh attachment, without auto-saving to the project or running automatic verification
• Automatic mounting on image turns: vision tools are registered when an image message arrives; text-only turns keep only one activation entry so tools do not occupy model context permanently
• Batch reads in one call: multiple images are read together, with exponential split fallback on batch 4xx so a failure affects only the failed image
• Old sessions stay clean: image-bearing history remains usable in fallback mode, with image blocks rewritten into attachment markers
• Every call is transparent: provider, model, latency, token usage, and fallback markers are returned for auditability

Implemented Features

Image Input

• Native paste/drag (takeover mode, no duplicate model selector entry)
• paste-to-path fallback: pasted images are converted to path text in text-only model scenarios
• mindseye_read_image general vision, supporting local paths, single attachment ids, and batch attachment ids

Tools and Routing

• understand / extract / locate model routes are independently configurable and fall back to the general understanding model when unset
• Vision tools auto-mount on image turns; text-only turns keep only mindseye_vision_activate so tools do not permanently consume model context
• Structured JSON: images / evidence / answer / meta; meta includes real token usage, call attempts, and fallback markers
• Exact cache: image sha256 + normalized query + region + baseUrl + model + prompt version; a hit skips the vision model call

Image Generation

• mindseye_generate_image(intentId): text-to-image through image.routes
• mindseye_edit_image(intentId, attachmentId): image-to-image through image.edits, sending the reference image to the provider
• Generated results are displayed as dsh attachments with a (token_usage=..., widthxheight, size) audit line

Providers

• OpenAI-compatible Chat Completions and Responses protocols
• image.routes drives mindseye_generate_image (text-to-image); image.edits drives mindseye_edit_image (image-to-image with a reference attachment)
• Image routes expose configurable endpoint, bodyMode (json or multipart), and imageField for provider compatibility
• Multi-image batch calls with exponential fallback (batch 4xx retries by halving; locate does not support batch)

Memory

• Image-level hard facts are persisted by sha256, with evidence evicted by capacity LRU (default 1000 entries)
• Soft memory uses BM25 retrieval of historical Q&A injected as context, evicted by capacity (default 1000 entries)
• mindseye_memory_put / get / search / diff are exposed as dsh tools; calls are visible in the session and audited

Data Handling and Security

• Native attachments first: image-capable models keep native dsh attachments; MindsEye associates images by attachment id and does not ask the user to choose local files manually
• Automatic temporary path fallback: when the current model is confirmed text-only and paste-to-path is enabled, freshly pasted PNG, JPEG, WebP, or GIF files (up to 25 MiB each) are validated, stored in an isolated system temp directory, and returned as a path. Temp files use 0600 mode
• External vision calls: image bytes and question text are sent only to the vision provider's Base URL when a MindsEye tool executes; configure only services you trust
• Credentials and cache: API keys resolve from environment variables, dsh Credentials, or plugin settings and are sent as Bearer auth to the matching provider only. The exact cache lives only in the current dsh process memory (max 500 entries), is never persisted, and clears on process exit
• Execution boundary: the plugin never starts shells, child processes, or executes downloaded code. The normal web paste fallback only reads the temporary image just created by the plugin; tools also accept dsh attachment ids

dsh Web Settings Card

• understand / extract / locate routes can be added as needed; unset routes fall back to the default model
• Base URL, API key (masked with eye toggle), model id, protocol (explicit), and common Max Tokens values
• Model takeover is enabled by default: changes take effect after restart, and a failed startup restores the official adapter with path-paste fallback

Installation

npx @deepseek-ai/dsh plugin --profile web add dsh-mindseye

Restart dsh web to paste images natively. "Model takeover" is enabled by default and can be adjusted under Settings → Plugins → MindsEye.

On first use, configure one general vision model (Base URL, API key, model id) in the MindsEye settings card; unconfigured OCR / locate routes fall back to the general model automatically.

Development

pnpm install
pnpm test
pnpm typecheck
pnpm build