Ai Agent To Chat With Files In Supabase Storage 2621 – n8n Workflows – Free Template

What This Workflow Does

This n8n workflow enables you to build an AI-powered chatbot that can intelligently interact with files stored in Supabase Storage. The system processes documents, converts them into searchable embeddings, and uses OpenAI’s language models to answer questions about your file contents in natural conversation.

How It Works

The workflow follows a multi-stage process to enable intelligent file conversation:

• Files are retrieved from Supabase Storage using HTTP requests
• Documents are loaded and parsed using the default data loader
• Text content is split into manageable chunks using recursive character splitting for optimal processing
• OpenAI embeddings are generated for each text chunk to create numerical representations
• Embeddings are stored in a Supabase vector database for efficient semantic search
• User messages trigger the chat interface and are processed through decision nodes
• The AI agent searches relevant file chunks using vector similarity matching
• OpenAI’s language model generates contextual responses based on file content
• Responses are delivered back through the chat interface

Use Cases

• Customer support chatbots that answer questions based on company documentation, manuals, and knowledge bases
• Legal document analysis tools where AI summarizes and explains complex contracts and agreements
• Educational platforms enabling students to ask questions about course materials and textbooks
• Research assistants that help analyze and extract information from academic papers and reports
• Internal knowledge management systems where employees can query organizational policies and procedures

Nodes Used

• Manual Trigger – Initiates the workflow execution
• HTTP Request – Retrieves files from external sources and Supabase
• Document Default Data Loader – Parses and loads document content
• Text Splitter Recursive Character – Breaks documents into optimal chunks
• Extract From File – Pulls structured data from documents
• Embeddings OpenAI – Converts text into vector embeddings
• Supabase – Manages database operations and file storage
• Split In Batches – Processes data in manageable groups
• Vector Store Supabase – Stores and retrieves embeddings
• Chat Trigger – Captures user messages from chat interface
• LM Chat OpenAI – Generates AI-powered responses
• Tool Vector Store – Enables semantic search functionality
• Agent – Orchestrates the conversation flow and decision-making
• If/Switch – Controls conditional logic and workflow paths
• Aggregate/Merge – Combines data from multiple sources
• Sticky Note – Documents workflow purposes and instructions

Prerequisites

• Active n8n instance or n8n Cloud account
• Supabase project with storage bucket and vector-enabled database
• OpenAI API key with access to GPT models and embedding models
• Files or documents ready to be uploaded to Supabase Storage
• Basic understanding of API credentials and authentication tokens
• Knowledge of workflow configuration and node setup

Difficulty Level

Intermediate to Advanced. This workflow requires configuration of multiple API credentials, understanding of vector databases and embeddings, and familiarity with the n8n platform. While the template provides structure, proper setup of Supabase credentials, OpenAI authentication, and storage configuration requires technical knowledge. Refer to the setup video for detailed configuration guidance.

This workflow template is shared under the n8n fair-code license. Free to use and modify.