AI Agent To Chat With Files In Supabase Storage
This workflow achieves content-based intelligent querying by automatically retrieving and processing files stored in Supabase, combined with OpenAI's text embedding technology. It effectively deduplicates, extracts PDF and text content, and stores it in a vectorized format, supporting fast and accurate information retrieval. It is suitable for scenarios such as enterprise knowledge base management, customer support, and professional document querying, significantly enhancing document management efficiency and user interaction experience.
Tags
Workflow Name
AI Agent To Chat With Files In Supabase Storage
Key Features and Highlights
This workflow automates the retrieval, deduplication, downloading, and processing of files from Supabase Storage. It leverages OpenAI’s text embedding technology to vectorize document content for storage, enabling intelligent, context-aware document querying through an AI chatbot. It supports content extraction from PDF and text files and combines vector databases for efficient and accurate information retrieval.
Core Problems Addressed
- Time-consuming and labor-intensive manual retrieval and analysis of large volumes of documents;
- Resource waste caused by handling duplicate files;
- Difficulty in quickly locating key information within documents using traditional methods;
- Lack of integrated AI-powered document management and intelligent Q&A solutions.
Application Scenarios
- Internal enterprise knowledge base management and intelligent Q&A;
- Rapid document retrieval in customer support systems;
- Intelligent understanding and querying of large volumes of professional documents in industries such as legal and healthcare;
- Any scenario requiring automated file processing and content-based intelligent interaction with stored files.
Main Workflow Steps
- Batch retrieve file lists from Supabase private storage buckets, excluding empty folder placeholders;
- Compare with records of already processed files in the Supabase database to filter out duplicates;
- Download new, unprocessed files;
- Extract document content based on file type (PDF or text);
- Recursively split text content into chunks to maintain contextual continuity;
- Generate vector embeddings of the text content via OpenAI API;
- Insert the vectorized data into Supabase’s vector storage table to support efficient retrieval;
- Use an AI Agent node to listen for chat messages and combine results from the vector database to return relevant document snippets, enabling intelligent Q&A.
Involved Systems and Services
- Supabase (object storage, database, vector storage)
- OpenAI (text embedding models, conversational language models)
- n8n (workflow automation platform, including HTTP requests, file processing, text splitting, conditional logic, data merging nodes)
Target Users and Value
- Developers and data engineers looking to quickly build AI-driven intelligent document query systems;
- Enterprise knowledge management teams aiming to improve document retrieval efficiency and user interaction experience;
- AI enthusiasts and automation workflow designers leveraging open-source tools to implement complex scenario automation;
- Industry users needing to process large volumes of unstructured files using vector search and natural language understanding technologies.
By seamlessly integrating Supabase Storage with OpenAI intelligent services, this workflow automates file processing and enables intelligent Q&A, significantly reducing document management complexity and enhancing information access efficiency. It is a vital component of modern intelligent knowledge management platforms.
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