Sticky Note Automate

For Sticky Note, this automated workflow enables users to interact with a Supabase/PostgreSQL database through an AI agent. It simplifies data retrieval by dynamically generating SQL queries based on user requests, allowing for conversational access to database information. Users can easily extract, analyze, and summarize data without needing SQL expertise, enhancing productivity and decision-making efficiency.

7/8/2025
11 nodes
Medium
manualmediumsticky notelangchainpostgrestooladvanceddatabasedata
Categories:
Manual TriggeredData Processing & AnalysisMedium Workflow
Integrations:
Sticky NoteLangChainPostgresTool

Target Audience

This workflow is designed for:
- Data Analysts: Who need to analyze and retrieve data from PostgreSQL databases without deep SQL knowledge.
- Developers: Looking to automate interactions with databases through natural language queries.
- Business Analysts: Who want to generate insights from data quickly and efficiently.
- Startups: That require rapid development and testing of database interactions without extensive backend setup.
- Educators: Teaching database management and AI integration in a practical manner.

Problem Solved

This workflow addresses the challenge of accessing and analyzing database data, which often requires SQL expertise or dedicated reporting tools. It enables users to interact with their PostgreSQL database conversationally through an AI-powered agent, reducing the time and effort needed to retrieve and analyze data.

Workflow Steps

  • Trigger: The workflow begins when a chat message is received, indicating a user query.
    2. AI Agent Initialization: The AI agent processes the input from the user and determines the appropriate SQL query to execute based on the request.
    3. Database Schema Retrieval: The workflow retrieves the database schema, providing a list of tables available in the PostgreSQL database.
    4. Table Definition Query: If required, it fetches the definition of specific tables, including columns and their types, to inform the AI agent about the database structure.
    5. Run SQL Query: The AI agent formulates and executes the SQL query relevant to the user's request, utilizing the knowledge of the database schema.
    6. Response Generation: The results from the SQL query are processed, and a response is generated for the user, allowing them to receive insights or data based on their query.

    • Customization Guide

    To customize this workflow:
    - Adjust Database Credentials: Update the PostgreSQL connection settings to match your database credentials.
    - Modify SQL Queries: Change the SQL queries in the DB Schema and Get table definition nodes to suit your specific data retrieval needs.
    - Enhance AI Prompt: Modify the systemMessage in the AI agent to refine how the agent interacts with users or to change its response style.
    - Add More Nodes: Integrate additional nodes for further processing or to connect to other services as needed.
    - Change Trigger Method: Alter the trigger type if you want to automate the workflow based on different events or data inputs.