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n8n_pinecone_qdrant_supabase

Orchestrate n8n workflows with Pinecone, Qdrant, and Supabase for RAG pipelines.

Updated Aug 23, 2026
Search Tags:#embeddings#rag#supabase#n8n#qdrant#pinecone#vector-search
One-click install
npx skills add https://github.com/denish12/codex-ai-agent-and-skills --skill n8n-pinecone-qdrant-supabase
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: n8n_pinecone_qdrant_supabase
Source: https://github.com/denish12/codex-ai-agent-and-skills/tree/main/locales/en/.agents/skills/n8n_pinecone_qdrant_supabase
Command: npx skills add https://github.com/denish12/codex-ai-agent-and-skills --skill n8n-pinecone-qdrant-supabase

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Integration patterns for n8n (workflow automation) with Vector DBs Pinecone and Qdrant and with Supabase (PostgreSQL + Auth + Storage + Realtime). Use this skill when building AI pipelines, RAG systems, data automation, working with embeddings, semantic search, or any orchestration between these tools. Activate for questions about n8n workflows, vector search, embedding upsert, Supabase RLS, or building RAG on top of this stack.

Core Features & Use Cases

  • Orchestrate end-to-end AI pipelines in n8n for embedding generation, vector upsert, and retrieval-augmented generation.
  • Integrate with Pinecone and Qdrant for semantic search and scalable vector storage, including common filtering scenarios.
  • Connect to Supabase for Postgres-backed storage with vector support, RLS, and realtime updates to drive downstream actions.
  • Real-world use case: build a RAG workflow that ingests documents, embeds them, stores vectors, queries for relevant chunks, and feeds results to an LLM.

Quick Start

Set up an n8n project and import this skill to start building a retrieval-augmented workflow with Pinecone, Qdrant, and Supabase.

Frequently Asked Questions about n8n_pinecone_qdrant_supabase

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG pipeline using n8n with Pinecone or Qdrant?▼

You can build a RAG pipeline in n8n by orchestrating document ingestion, embedding generation, and vector upserts into Pinecone or Qdrant, then querying relevant chunks to feed an LLM. This skill coordinates the end-to-end AI workflow.

Can I use Supabase with n8n for vector search and realtime updates?▼

Yes, n8n workflows can integrate Supabase for Postgres-backed vector storage with RLS and realtime updates, driving downstream actions. This connects vector search capabilities directly with your Postgres data layer.

What's the best way to manage embedding upserts across multiple vector stores in n8n?▼

Orchestrate batch upserts and semantic searches across Pinecone and Qdrant using n8n workflows. This skill handles embedding management and coordinates scalable vector storage with common filtering scenarios.

Does this n8n vector database integration support Row Level Security with Supabase?▼

Yes, the integration connects to Supabase for Postgres-backed storage with vector support and Row Level Security (RLS). This ensures your vector search workflows respect data access policies.

How do I automate semantic search across vector stores integrated with Postgres?▼

Automate semantic search by orchestrating n8n workflows that query Pinecone or Qdrant and integrate with Supabase Postgres storage. This enables retrieval-augmented generation pipelines with realtime features.

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