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Vector DB Loader from Google Drive

This workflow is designed to automatically download and process PDF, plain text, and JSON files from Google Drive. It converts these files into vector data using OpenAI's text embedding model and stores them in the PGVector vector database within a Postgres database. This process enables efficient management and retrieval of documents, while automatically archiving processed files, thereby enhancing work efficiency and automation. It is suitable for data engineers, knowledge management teams, and research institutions.

Workflow Diagram
Vector DB Loader from Google Drive Workflow diagram

Workflow Name

Vector DB Loader from Google Drive

Key Features and Highlights

This workflow automatically searches for and downloads files from a specified Google Drive folder. It supports content extraction from PDF, plain text, and JSON formats. Leveraging OpenAI’s text embedding model, it converts textual content into vector representations, which are then stored in a PGVector vector database within Postgres for efficient document vectorization management. Processed files are automatically moved to another designated folder to ensure clear and orderly file management. The workflow supports both manual triggering and scheduled automatic execution, significantly enhancing automation and operational efficiency.

Core Problems Addressed

  • Automated batch processing of multiple file formats (PDF, text, JSON)
  • Transforming unstructured document content into structured vector data for subsequent similarity search and knowledge base construction
  • Automated file downloading, processing, and archiving to reduce manual operations and minimize the risk of omissions
  • Integration of OpenAI’s powerful text embedding capabilities to achieve high-quality text vectorization

Application Scenarios

  • Enterprise knowledge base construction and maintenance
  • Vectorized storage and rapid retrieval of research materials
  • Intelligent document analysis and content recommendation systems
  • Automated document processing and archival management
  • Scenarios requiring conversion of large volumes of Google Drive documents into a vector database

Main Workflow Steps

  1. Trigger the workflow manually or via scheduled timing
  2. Search for target files in the specified Google Drive folder
  3. Download files one by one in a loop
  4. Route processing based on file type via a Switch node:
    • Extract text content from PDF files
    • Directly extract content from plain text files
    • Parse content from JSON files
  5. Generate text vectors using OpenAI’s text-embedding-3-small model
  6. Insert vector data into the designated table and collection in the Postgres PGVector database
  7. After processing, automatically move files to the “vectorized” archive folder in Google Drive
  8. Await the next trigger to repeat the process

Involved Systems or Services

  • Google Drive (file search, download, move)
  • OpenAI (text embedding model)
  • Postgres database (PGVector vector storage)

Target Users and Value

  • Data engineers and automation operators: enable automated processing and management of document data
  • Knowledge management and information retrieval teams: build efficient vectorized knowledge bases
  • Research institutions and enterprises: rapidly convert large volumes of documents into structured vector data to support intelligent search and analysis
  • Developers and product managers: quickly develop intelligent applications and services based on vector databases

This workflow is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), allowing free use, adaptation, and sharing to empower more users in building intelligent document vectorization solutions.