SImS - Semantic Image Search

The Challenge

File-based image organization doesn't scale. You can be diligent about naming conventions and folder structures, but eventually you're looking for "that screenshot with the architecture diagram" and no amount of folder drilling helps.

Cloud services exist for this (Google Photos, Apple Photos), but they require uploading your images to someone else's servers. For personal photos, maybe that's fine. For screenshots of work projects, client documents, or anything sensitive? Not an option.

My Approach

The solution combines two AI capabilities: vision-language models that can describe images, and vector embeddings that enable semantic search. Process every image once to generate descriptions and embeddings, then search by meaning forever after.

Design requirements:

• Completely local: No image data leaves the machine, ever
• Natural language queries: Search the way you think about images
• One-time processing: Index once, search many times
• Incremental updates: Adding new images shouldn't require reprocessing everything

The Solution

SImS consists of an indexing pipeline that processes images and a search interface that queries the index. Both run entirely locally.

Vision-Language Processing

Each image passes through a vision-language model that generates a detailed description. Not just "a photo" but "a photo of a wooden desk with a laptop, coffee mug, and scattered papers. Natural lighting from a window on the left. The laptop screen shows a code editor with dark theme."

The descriptions are surprisingly detailed and accurate. They capture not just objects but relationships, colors, moods, and context. This richness makes search much more powerful than simple object detection.

Embedding and Indexing

Descriptions get embedded into vector representations that capture semantic meaning. These vectors go into a local vector database optimized for similarity search. When you search for "laptop on desk," the system finds images whose descriptions are semantically similar—even if they don't contain those exact words.

Search Interface

The search interface accepts natural language queries and returns ranked results. You can search for concepts ("birthday party"), visual elements ("red dress"), combinations ("person presenting to a group"), or even moods ("cozy evening scene"). The semantic matching finds relevant images even when your query words don't appear in the description.

Automatic Tagging

As a bonus, the vision model generates tags for each image—objects, people, scenes, activities. These tags enable faceted browsing: show me all images with dogs, all screenshots, all diagrams. It's like having a meticulous librarian organize your collection.

Lessons Learned

• Vision models have gotten impressive. The quality of automatic image descriptions surprised me. They notice details I'd miss on casual viewing.
• Semantic search beats keyword search. Finding images by meaning rather than exact words is dramatically more useful for visual content.
• Index once, search forever. The upfront processing time is worth it. Once indexed, the collection becomes instantly navigable.
• Privacy and capability aren't tradeoffs. Local models have reached the point where you don't have to choose between functionality and keeping your data private.