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    Ultimate Local RAG-Pipeline

    by Martin Gunderman

    Deploy a local, private RAG pipeline using Supabase, n8n, and Ollama in minutes.

    Updated Jul 2026
    Security scanned
    Claude Code

    $19

    · or 95 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Perform hybrid search queries against a self-hosted PGVector instance.
    • Process sensitive files without sending data to external cloud APIs.
    • terminal automation included
    • Ready for Claude Code
    • Instant install

    Media gallery

    See it in action

    You say

    I want to set up a local RAG system. Can you help me initialize the database and import the workflows?

    Your agent does

    I will initialize Supabase and prepare the migration files.

    1. Running supabase init.
    2. Moving schema.sql to migrations folder.
    3. Starting local containers.

    You can now import rag-indexing-workflow.json into n8n at http://localhost:5678. Use host.docker.internal for the DB connection.

    About This Skill

    The problem

    Building a RAG pipeline from scratch involves manually configuring vector databases, embedding models, and orchestration flows. Developers often waste hours debugging local Docker networking or writing complex SQL for hybrid search instead of building features.

    What it does

    • Deploys a local Postgres environment with PGVector and pre-configured schemas.
    • Provides ready-to-import n8n workflows for document indexing and query retrieval.
    • Integrates Ollama for local embedding generation and LLM inference.
    • Sets up hybrid search functions to combine vector similarity with keyword matching.
    • Automates the connection between n8n, Supabase, and local AI models.

    Frameworks & tools

    Supabase, PGVector, n8n, Ollama, Docker, and SQL.

    Why this beats prompting it yourself

    Getting local Docker services like n8n and Supabase to communicate with local Ollama instances requires specific networking configurations and SQL search functions that general LLMs often hallucinate. This kit provides tested JSON workflow exports and localized SQL schemas that work out of the box.

    Use cases

    • Building a private local knowledge base for proprietary documentation.
    • Prototyping RAG applications without incurring OpenAI or Pinecone API costs.
    • Processing sensitive data that must remain on-premises for GDPR compliance.
    • Testing different embedding models and retrieval strategies locally.

    Known limitations

    Requires Docker and sufficient local RAM to run LLMs via Ollama. Performance depends on host hardware rather than cloud scaling.

    Use Cases

    • Automate document chunking and embedding generation locally.
    • Perform hybrid search queries against a self-hosted PGVector instance.
    • Process sensitive files without sending data to external cloud APIs.
    • Sync local Markdown notes or Obsidian vaults into a searchable vector store.

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell

    Allowed Hosts

    ollama
    host.docker.internal

    File Scopes

    workflows/**
    skill/**
    sql/**

    Tags

    rag
    supabase
    pgvector
    n8n
    embeddings
    ollama
    hybrid-search
    starter-kit

    Claude Code, Hermes, Openclaw usw.

    Creator

    Martin Gunderman

    I use Agent Skills to increase my Work Output by a factor of 35 % ore more.

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