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Intelligent Document Q&A and Vector Database Management Workflow

This workflow automatically downloads eBooks from Google Drive, splits the text, and generates vectors, which are stored in the Supabase vector database. Users can ask questions in real-time through a chat interface, and the system quickly provides intelligent answers using vector retrieval and question-answering chain technology. Additionally, it supports operations for adding, deleting, modifying, and querying documents, enhancing the flexibility of knowledge base management. This makes it suitable for enterprise knowledge management, educational tutoring, and content extraction needs in research institutions.

Workflow Diagram
Intelligent Document Q&A and Vector Database Management Workflow Workflow diagram

Workflow Name

Intelligent Document Q&A and Vector Database Management Workflow

Key Features and Highlights

This workflow automates the entire process of downloading eBooks (in epub format) from Google Drive, splitting the text content, generating embeddings using OpenAI’s text embedding model, and storing the vectors in a Supabase vector database (with pgvector extension enabled). Users can interact via a chat interface to ask questions in real-time. The system leverages vector retrieval and QA chain techniques to quickly fetch relevant document content from the database and generate intelligent answers. It supports document insertion, upsert (update or insert), querying, and provides guidance for deletion operations.

Core Problems Addressed

  • Automated processing and structured import of document content into a vector database
  • Efficient semantic search and question answering based on vector retrieval technology
  • Simplified user interaction with the knowledge base through a chatbot interface
  • End-to-end intelligent document management by integrating multiple technologies (Google Drive, OpenAI, Supabase)
  • Full lifecycle support for vector database operations (CRUD), enhancing data management flexibility

Application Scenarios

  • Enterprise knowledge base management and intelligent Q&A
  • Intelligent tutoring for eBooks in education and training
  • Semantic search and content extraction for research institutions
  • Intelligent FAQ systems in customer service
  • Any scenario requiring transformation of unstructured documents into a queryable knowledge base

Main Workflow Steps

  1. Download Document: Download the target eBook file via the Google Drive node.
  2. Load and Split: Load the epub file using the default data loader and split the text into vectorizable chunks using a recursive character text splitter.
  3. Generate Vectors: Generate text embeddings using OpenAI’s text-embedding-3-small model.
  4. Insert/Update Data: Insert or update the generated vectors and corresponding text content into the Supabase vector database table.
  5. Vector Retrieval: Receive user chat messages and invoke the vector retrieval node to find the most relevant document fragments from the Supabase database.
  6. Answer Generation: Use OpenAI’s chat model to generate answers based on the retrieved results.
  7. Response Output: Customize and return the final Q&A text to the user.
  8. Deletion Operation: Demonstrate how to call the Supabase API via an HTTP request node to delete records from the vector database.

Involved Systems or Services

  • Google Drive: Storage and provision of eBook file downloads.
  • OpenAI: Text embedding generation (text-embedding-3-small model) and chat-based Q&A (ChatGPT model).
  • Supabase: Hosting the vector database with pgvector extension for vector storage and similarity search.
  • n8n Built-in Nodes: Workflow construction tools including text splitting, data setting, HTTP requests, etc.

Target Users and Value

  • Product managers and technical staff aiming to quickly build intelligent document Q&A systems.
  • Data scientists and AI engineers seeking reference examples for knowledge base and semantic search development.
  • Enterprise IT teams needing to integrate multi-platform content for automated intelligent customer service or internal knowledge management.
  • Educational and training institutions looking to provide intelligent tutoring and query services based on eBooks.
  • Any users who want to lower the barrier to document search and achieve efficient knowledge acquisition through natural language interaction.

This workflow centers on the n8n automation platform, skillfully integrating cloud storage, AI embedding vectors, and database technologies to deliver a comprehensive intelligent document management and Q&A solution, significantly enhancing information retrieval efficiency and user experience.