Skip to content
NEWMarketplace and the trig8n Agent: sell what you build, and get plans and changes that are tested before you see them. See what is new

Library / RAG & Knowledge Bases

RAG & Knowledge Bases

Chat with Supabase Storage Files Using an OpenAI Agent

When you run it, it embeds PDFs from Supabase Storage into a vector store so an AI agent can answer questions about them.

Import it free Sign in to open it

What it does

![5min Logo]({{ $env.WEBHOOK_URL }} ## AI Agent To Chat With Files In Supabase Storage Made by [Mark Shcherbakov]({{ $env.WEBHOOK_URL }} from community [5minAI]({{ $env.WEBHOOK_URL }} Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: - Fetching and comparing files to avoid duplicate processing. - Handling file downloads and extracting content based on the file type. - Converting documents into vectorized data for contextual information retrieval. - Storing and querying vectorized data from a Supabase vector store.

The steps

  1. Get All files
  2. Default Data Loader
  3. Recursive Character Text Splitter
  4. Extract Document PDF
  5. Embeddings OpenAI
  6. Create File record2
  7. If Node
  8. Get All Files
  9. Download
  10. Loop Over Items
  11. When clicking ‘Test workflow’
  12. Aggregate
  13. When chat message received
  14. OpenAI Chat Model1

46 steps in total.

Similar automations

RAG & Knowledge BasesChat with Files in Supabase Storage Using OpenAI22 stepsRAG & Knowledge BasesChat with Supabase Storage Documents Using OpenAI49 stepsRAG & Knowledge BasesChat with Supabase Vector Store and Postgres Memory12 stepsRAG & Knowledge BasesChat with Supabase Storage Files Using OpenAI RAG22 steps