✨📊Multi-AI Agent Chatbot for Postgres/Supabase DB and QuickCharts + Tool Router

Multi-AI Agent Chatbot for Postgres/Supabase DB and QuickCharts enables users to interact with their database through natural language queries, execute SQL commands, and generate visual charts effortlessly. This automated workflow streamlines data retrieval and visualization, allowing for quick insights and decision-making without technical expertise. Users can easily create dynamic charts based on their data, enhancing data analysis and presentation.

7/8/2025
40 nodes
Complex
manualcomplexlangchainpostgrestoolexecuteworkflowtriggersticky noteadvanceddatabasedataapiintegrationlogicrouting
Categories:
Complex WorkflowManual TriggeredData Processing & AnalysisBusiness Process Automation
Integrations:
LangChainPostgresToolExecuteWorkflowTriggerSticky Note

Target Audience

Target Audience


- Data Analysts: Those who need to extract insights from databases and visualize them quickly.
- Business Intelligence Professionals: Users who require automated reporting and chart generation for decision-making.
- Developers: Individuals looking to integrate AI capabilities into their applications for database interactions and data visualization.
- Researchers: Those needing to analyze large datasets and present findings in a clear, visual format.
- Educators: Teachers and trainers who want to utilize data in their curriculum and demonstrate data analysis techniques.

Problem Solved

Problem Solved


This workflow addresses the challenge of efficiently querying databases and visualizing the results without requiring extensive coding knowledge. It automates the process of generating SQL queries based on user prompts and creating dynamic charts using QuickChart, thereby saving time and reducing the complexity of data analysis tasks.

Workflow Steps

Workflow Steps


1. Trigger: The workflow is initiated when a chat message is received from a user, allowing for a manual start.
2. User Input Processing: The primary AI agent captures the user's prompt and determines the action required (querying the database or generating a chart).
3. Database Query Execution: If the user requests data, the workflow executes an SQL query against a PostgreSQL database using the Execute SQL Query tool, returning the relevant records.
4. Chart Generation: If the user requests a chart, the workflow processes the database records and user prompt through the generate_chart_tool, which utilizes AI to create a Chart.js configuration object.
5. URL Construction: The workflow constructs a URL for QuickChart using the generated chart configuration, facilitating the creation of the chart image.
6. Final Output: The results of the database query and the chart URL are returned to the user in the chat, providing immediate insights and visualizations.

Customization Guide

Customization Guide


- Adjust SQL Queries: Users can modify the SQL queries in the Execute SQL Query node to fit their specific database schema and requirements.
- Customize Chart Types: Users can change the chart type and styling in the generate_chart_tool to match their visualization needs by editing the JSON schema example provided.
- Integrate Additional Tools: The workflow can be expanded by adding more tools or nodes for additional functionality, such as integrating other APIs or data sources.
- Change AI Model: Users can switch the AI model used for generating queries and chart configurations by modifying the model settings in the gpt-4o-mini nodes.
- Modify User Prompts: Users can adjust the prompts sent to the AI agents to refine the output and tailor the responses to specific use cases.