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3 production projects and 0 rule sets built with pgvector.
Production projects
- LLM Knowledge Base & RAG Platform Classic WeKnora Retrieval, agents and the API sit in one Go service, and parsing is delegated to a Python gRPC service. Storage backends are pluggable, so the same codebase runs as a lite SQLite binary or a full Kubernetes deployment on existing Postgres, Elasticsearch or vector databases.
- Multi-Model AI Agent & Chat Workspace Hybrid LibreChat A Node/Express API with a Mongoose-modeled MongoDB store handles chat state, while Vite builds the React client separately; optional Docker sidecars for pgvector, Meilisearch, and a Go OpenTelemetry relay keep RAG, search, and observability decoupled from the core API.
- No-Code Database Platform Hybrid Baserow A Django/PostgreSQL core with Nuxt/Vue frontend layers lets Baserow add premium and enterprise features as separate installable packages on top of one open-source base, while Channels and Celery keep realtime sync and background work inside the same deployable unit.
Often used with pgvector
Questions about pgvector
How many production projects use pgvector?
3 verified production projects in the STACK IT FAST directory use pgvector, each with a full architecture breakdown.
What is pgvector most often used with?
Across projects in the directory, pgvector is most commonly paired with Redis, Docker, Go.