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SHEETS RAG

This workflow aims to achieve automatic data synchronization between Google Sheets and a PostgreSQL database, supporting intelligent recognition of table structures and field types to avoid the tediousness of manual table creation and data cleaning. By monitoring file changes in real time, it automatically triggers data updates. Additionally, by integrating large language models, users can easily generate and execute SQL queries using natural language, reducing the complexity of database operations and enhancing data processing efficiency, making it suitable for various business scenarios.

Workflow Diagram
SHEETS RAG Workflow diagram

Workflow Name

SHEETS RAG

Key Features and Highlights

This workflow implements a complete process for automatically synchronizing data from Google Sheets to a PostgreSQL database. It supports intelligent detection of table structures and field types—including date formats and various currency symbols—automatic table creation, and data insertion. By integrating the powerful large language model Google Gemini with a custom SQL query execution tool, it can intelligently generate and execute efficient PostgreSQL queries based on natural language questions, providing a dynamic and interactive data querying experience.

Core Problems Addressed

  • Automatically map diverse data structures and formats (text, dates, currencies, etc.) from Google Sheets into database tables, eliminating the tedious manual table creation and data cleansing.
  • Real-time monitoring of specified Google Drive files to trigger automatic data synchronization.
  • Utilize an AI assistant to understand users’ natural language queries, construct safe and accurate SQL statements based on the database schema, and execute them, lowering the barrier to complex database querying.
  • Dynamically manage database table structures to avoid conflicts from duplicate table creation and ensure data consistency.

Use Cases

  • Enterprises or teams needing seamless migration of business data from Google Sheets to PostgreSQL for subsequent analysis and reporting.
  • Business users or data analysts who want to query databases directly using natural language without requiring SQL skills.
  • Scenarios requiring regular monitoring and synchronization of online spreadsheet data to build flexible data platforms or automated data pipelines.
  • Any context involving multi-currency amounts and date data management, ensuring accurate storage and processing of data formats.

Main Workflow Steps

  1. Google Drive Trigger: Monitor changes in specified Google Sheets files to trigger the workflow.
  2. Parameter Configuration: Set the target spreadsheet URL and worksheet name.
  3. Database Check: Verify whether the corresponding PostgreSQL table exists.
  4. Data Retrieval: Fetch spreadsheet data from Google Sheets.
  5. Schema Inference: Use custom code nodes to dynamically infer field types (text, date, currency, etc.) and generate table creation SQL.
  6. Table Management: If the table exists, drop it first, then create a new table.
  7. Data Insertion: Format and clean the data, construct insertion SQL statements, and batch write to PostgreSQL.
  8. AI Query Support: Combine Google Gemini large language model with custom tools to automatically parse natural language queries, invoke the database query tool, execute queries, and return results.
  9. Result Output: Format and output query results, supporting further interaction.

Involved Systems and Services

  • Google Drive Trigger: Monitors file changes on Google Drive.
  • Google Sheets: Retrieves online spreadsheet data.
  • PostgreSQL: Database storage and query execution.
  • Google Gemini Chat Model: AI model for natural language understanding and SQL construction.
  • n8n Code Node: Implements dynamic type inference and SQL generation logic.
  • LangChain Integration: AI agent combined with toolchains to perform query analysis and execution.

Target Users and Value

  • Data engineers and automation developers: Quickly build automated synchronization pipelines from spreadsheets to databases.
  • Business analysts and non-technical users: Query databases directly using natural language, reducing technical barriers.
  • Product managers and operations teams: Obtain and analyze multi-dimensional business data in real time to support decision-making.
  • Multi-currency financial management and cross-regional business scenarios with high adaptability requirements for complex data formats.

By providing an intelligent and automated data synchronization and interactive querying solution, this workflow significantly enhances data processing efficiency and user experience, empowering enterprises to achieve data-driven operations.