Query Google Sheets/CSV data through an AI Agent using PostgreSQL

Conversational AI for Google Sheets Data Querying This workflow transforms a Google Sheet into a fully conversational data source. It automatically ingests, cleans, and structures data from a specified Google Sheet into a PostgreSQL database. It then deploys a sophisticated AI Agent, powered by Google Gemini, that allows users to ask questions about the data in natural language. The AI translates these questions into precise SQL queries, executes them against the database, and provides answers in a user-friendly format. Technologies Used - Google Sheets: The primary data source. - Google Drive: Triggers the workflow when the source sheet is modified. - PostgreSQL: A reliable relational database used to store and structure the sheet data for efficient querying. - Google Gemini (AI): The Large Language Model (LLM) that powers the conversational agent. - n8n LangChain Agent: Provides the framework for the AI to use tools and reason through problems. Automation and AI Integration The workflow automates two key processes: 1. Data Pipeline (ETL): It automates the extraction of data from Google Sheets, its transformation (inferring data types, cleaning values), and loading it into a structured PostgreSQL database table. This entire data engineering pipeline is triggered automatically on file changes. 2. Conversational Querying (Text-to-SQL): The AI Agent automates the process of data analysis. Instead of writing complex SQL, a user can simply chat with the workflow. The AI integrates by: - Receiving a user's question in plain English. - Using a get_postgres_schema tool to understand the available tables and columns. - Using its pre-defined instructions (system prompt) and the schema information to construct an accurate PostgreSQL query. - Using an execute_query_tool to run the generated query against the database. - Interpreting the query results and presenting the final answer back to the user in a conversational manner. This provides immense value by enabling non-technical users to perform complex data analysis and gain insights from their data without needing any knowledge of SQL or database management.