AI/ML
by @ops Python + pgvector: Semantic Document Search
Embeddings-backed search over a document corpus — distinct from the AI SaaS Starter (that's a RAG chat product) and Search-as-a-Service (Meilisearch keyword search): this is specifically finding documents by meaning, not exact terms.
Loads this recipe straight into the wizard — every setting stays editable before you generate anything.
The stack
Install
# pulls this exact recipe straight from the Vault $ npx stacktora install python-semantic-document-search --remote $ stacktora sync # generate the files
What you get
More from the Vault
AI Content Classification & Moderation
Auto-classification via an LLM (toxicity, spam, category tagging) with a worker to process items async and Redis to avoid re-classifying the same content twice. Distinct from the Content Moderation Pipeline recipe: that's the human-review queue; this is the AI doing the first pass.
Podcast/Video Transcription & Captioning Service
Audio/video files land in MinIO, a Celery worker runs speech-to-text and generates captions/transcripts stored in Postgres. Distinct from AI Content Classification: that classifies existing text, this creates text from audio in the first place.
Python + pgvector + MinIO: Duplicate Image/Media Detection Backend
Perceptual-hash and embeddings-based detection of duplicate or near-duplicate images, a worker processing uploads and comparing against stored vectors — a distinct application of pgvector from document search or recommendations: finding near-identical media, not similar meaning.
Python + pgvector + Redis: AI SaaS Starter
A production-ready backend for LLM-powered products: FastAPI-style Python API, Postgres with pgvector for embeddings and semantic search, Redis for response caching and rate limiting, and a background worker for async completions and embedding jobs.