# MemeMeow-Studio/MemeMeow

智能管理表情包

Repository: https://github.com/MemeMeow-Studio/MemeMeow
Canonical: https://ross.abutalabs.com/products/mememeow
Homepage: https://zvv.quest
Language: Python
License: MIT
License Family: permissive
Topics: artificial-intelligence, embeddings-word2vec, python, streamlit, streamlit-webapp
Last push: 2026-09-02T15:43:36+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 35, longevity 40
- inputs: {"age_days": 568, "days_push": 0, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1260, forks 36 (observed 2026-09-03T02:15:06.646291+00:00)

## What it is
MemeMeow is a self-hosted meme/sticker management and retrieval application that lets users find images by describing the desired scene in natural language. It uses embedding models for Q&A-style search, DINOv2 visual vectors for near-duplicate detection, and ships a Vue 3 web UI with a unified API deployed via Docker Compose.

## Use cases
- find memes by describing a scene in natural language
- manage a personal sticker collection with semantic search
- search reaction images without remembering filenames or tags
- self-host a meme search service with a web interface
- detect visually similar images using embeddings
- serve meme search through an API

## When to choose
- you want natural-language search over a meme or sticker library
- you prefer a self-hosted, Docker-based deployment with a web UI and API
- you need both text-embedding and visual-similarity retrieval
- you want a free retrieval-only setup with no API cost

## When to avoid
- you need general-purpose image asset management beyond memes
- you cannot run Docker or a PostgreSQL + pgvector stack
- you need a lightweight tool without an agent/async processing pipeline
- you require guaranteed provenance of image content, since results are AI-contextualized

## Facets
- artifact type: application
- maturity: active
- function: search-engine, nlp, machine-learning, image-processing, web-framework, api-framework
- domain: artificial-intelligence, image-processing, web-development, self-hosted
- platform: python, self-hosted
- tags: meme-search, semantic-search, embeddings, pgvector, streamlit, vue3, sticker-management, natural-language-retrieval, dinov2, search, docker, web-server, linux

## Member repositories
- MemeMeow-Studio/MemeMeow (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:06.646291+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:04:21.934238+00:00, confidence not recorded.
  - readme: https://github.com/MemeMeow-Studio/MemeMeow (fetched 2026-09-03T02:15:06.646291+00:00, sha cf3ca4cbd15f)
- Data as of 2026-08-30T08:39:29.467469+00:00.