Find

Find is a local-first AI image intelligence platform for uploading, indexing, searching, and clustering images on your own machine.

All image processing, vector generation, and search stay inside your local stack.

What it does

• Upload individual images or ZIP archives
• Extract captions, detected objects, OCR text, EXIF metadata, and dimensions
• Generate hybrid embeddings for semantic search
• Automatically cluster related images after indexing completes
• Browse gallery, inspect details, like/delete media, and review cluster members

Tech stack

• Frontend: Next.js 16, React 19, React Query, Tailwind CSS, Biome
• Backend: FastAPI, SQLAlchemy, PostgreSQL + pgvector, Redis, RQ, MinIO
• ML pipeline: YOLOv10, Florence-2, PaddleOCR, SigLIP (open-clip), HDBSCAN

Architecture

Next.js frontend
    |
    v
FastAPI API
    |
    +--> PostgreSQL + pgvector  (metadata, embeddings, clusters)
    +--> MinIO                  (image object storage)
    +--> Redis + RQ             (background analysis and clustering jobs)
            |
            v
        ML worker

GSSoC'26 contributors

This project is open for GSSoC'26 contributions.

• Start with issues labeled good first issue
• For medium/advanced work, check level 2 and level 3
• Look for priority queue items via help wanted
• Follow the contribution rules in CONTRIBUTING.md

Install and run

Option A: one-command demo stack (recommended)

From repository root:

docker compose up --build

Services:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• MinIO API: http://localhost:9000
• MinIO console: http://localhost:9001

Notes:

• Current Docker setup is GPU-oriented and expects NVIDIA GPU access.
• If no root .env is present, compose defaults support local demo startup.

Option B: local development without Docker

Prerequisites

• Node.js 18+ and pnpm
• Python 3.12 and uv
• PostgreSQL with pgvector
• Redis
• MinIO (or S3-compatible storage)

1. Clone and configure env

git clone https://github.com/Abhash-Chakraborty/Find.git
cd Find
cp .env.example .env

2. Backend API

cd backend
uv sync --group dev
uv run uvicorn find_api.main:app --reload

3. Worker (separate terminal)

cd backend
uv run rq worker --url redis://localhost:6379 high default low

4. Frontend (separate terminal)

cd frontend
pnpm install
pnpm dev

Local quality checks

Frontend

cd frontend
pnpm check
pnpm build

Backend

cd backend
uv run ruff check .
uv run ruff format --check .

Core flow

1. Frontend uploads images to /api/upload or /api/upload/bulk.
2. Backend stores files in MinIO and creates media rows in PostgreSQL.
3. Uploads are queued through RQ.
4. Worker extracts metadata and generates embeddings.
5. Backend queues clustering once indexing succeeds.
6. Frontend polls job status and updates gallery/search/cluster views.

Key endpoints

• POST /api/upload
• POST /api/upload/bulk
• GET /api/status/{job_id}
• GET /api/gallery
• GET /api/image/{media_id}
• POST /api/image/{media_id}/like
• DELETE /api/image/{media_id}
• GET /api/search?q=...
• GET /api/clusters
• GET /api/cluster/{cluster_id}
• POST /api/cluster/run

Configuration notes

.env.example reflects the current stack. Keep EMBEDDING_DIM aligned with the selected CLIP/SigLIP model and pgvector dimensions.

Contribution quick start

1. Pick an issue and comment to get assigned.
2. Fork and create a branch from main.
3. Make changes with focused commits.
4. Run quality checks from CONTRIBUTING.
5. Open a PR using the project template and link the issue.

Contact and support

• Use GitHub Issues for bugs/features/questions.
• For contributor context, tag maintainers in your issue or PR (@Abhash-Chakraborty).
• Follow Code of Conduct in all interactions.

License

MIT