n8n template · RAG Assistant Pack

Ingest PDFs into Supabase pgvector with n8n

First link of a RAG pipeline: this workflow exposes a webhook that accepts your PDFs, extracts the text, splits it into chunks, computes embeddings and inserts everything into a Supabase pgvector table. One curl command indexes a document.

By Clément Lacaille (Tech-Bharat) · updated 11 July 2026 · how we build and test

{
  "name": "FlowKit — Ingestion de PDF dans Supabase pgvector",
  "nodes": [
    {"name": "Dépôt de PDF (Webhook)", "type": "n8n-nodes-base.webhook", "typeVersion": 2, "position": [-220,0]},
    {"name": "Insérer dans la base vectorielle", "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase", "typeVersion": 1.1, "position": [0,0]},
    {"name": "Confirmer l'ingestion", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1.1, "position": [220,0]},
    {"name": "Chargeur de PDF", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "typeVersion": 1, "position": [-80,200]},
    {"name": "Découpage du texte", "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter", "typeVersion": 1, "position": [-80,400]},
    {"name": "Embeddings OpenAI", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "typeVersion": 1.2, "position": [140,200]}
  ]
}

The problem it solves

A documentation chatbot is only as good as its index: without a reliable ingestion pipeline, the vector store gets filled by hand, badly, then not at all. Ingestion is the unglamorous part of RAG — extraction, chunking, vectorization, metadata — and exactly the part worth standardizing.

By exposing it as a simple HTTP endpoint, this template makes indexing trivial: from a terminal, an internal form or another workflow, anything that can upload a file can feed your assistant.

How the workflow works, node by node

Node names are in French, exactly as they appear in the imported workflow — the logic is language-agnostic.

1. 01

   Dépôt de PDF (Webhook)

   Webhook

   Receives the PDF as multipart (curl -F) on the flowkit-ingest path. The file arrives as binary data, ready for extraction.

2. 02

   Insérer dans la base vectorielle

   Supabase vector store

   Root node in insert mode targeting the documents table: it orchestrates loading, chunking and embeddings from its three sub-nodes, then writes chunks, metadata and vectors to Supabase.

3. 03

   Chargeur de PDF

   Document loader

   ai_document sub-node: extracts text from the PDF binary page by page — no prior conversion needed.

4. 04

   Découpage du texte

   Text splitter

   ai_textSplitter sub-node: 1,000-character chunks with 150 overlap, the best precision/recall trade-off for business documents.

5. 05

   Embeddings OpenAI

   OpenAI embeddings

   ai_embedding sub-node: text-embedding-3-small (1,536 dimensions), matching the table's vector(1536) column — dimensions must match on both sides.

6. 06

   Confirmer l'ingestion

   Webhook response

   Returns a JSON confirmation to the caller once indexing completes: easy to integrate in a bulk-load script.

What you need to run it

• ✔n8n ≥ 1.60 with the LangChain nodes
• ✔A Supabase project with pgvector enabled and the match_documents function (full SQL in the pack guide)
• ✔An OpenAI API key (embeddings)
• ✔Text-based PDFs (for scans, add an OCR step upstream)

Customization ideas

• →Add metadata (client, version, confidentiality) to the loader to filter searches later
• →Tune chunkSize and chunkOverlap to your documents: shorter for FAQs, longer for contracts
• →Chain ingestion from Google Drive: a Drive trigger replaces the webhook, the rest stays put