✨📊 Multi-AI Agent Chatbot for Postgres/Supabase DB and QuickCharts + Tool Router
This workflow integrates multiple intelligent chatbots, allowing users to directly query Postgres or Supabase databases using natural language and automatically generate intuitive charts. It employs an intelligent routing mechanism for efficient tool scheduling, supporting dynamic SQL queries and the automatic generation of chart configurations, thereby simplifying the data analysis and visualization process. Additionally, the integrated memory feature enhances contextual understanding, making it suitable for various application scenarios such as data analysts, business decision-makers, and educational training.
Tags
Workflow Name
✨📊 Multi-AI Agent Chatbot for Postgres/Supabase DB and QuickCharts + Tool Router
Key Features and Highlights
This workflow integrates a Multi-AI Agent chatbot that supports natural language interaction for querying Postgres or Supabase databases and automatically generates intuitive QuickChart visualizations based on query results. Core highlights include an intelligent routing mechanism (Tool Router) that automatically dispatches different tool agents to perform database queries or chart generation, enhancing response efficiency and accuracy; integrated memory functionality to support context-aware continuous conversations; and automatic generation of dynamic SQL queries and chart JSON configurations, significantly simplifying complex data analysis and visualization tasks.
Core Problems Addressed
- Enables non-technical users to directly query structured databases using natural language without writing SQL.
- Automatically converts database query results into chart visualizations, improving data comprehension and decision-making efficiency.
- Implements intelligent tool routing to facilitate multitasking and ensure efficient collaboration between chatbot querying and chart generation.
- Supports persistent conversational memory to enhance interaction experience and contextual understanding.
Application Scenarios
- Data analysts and business personnel quickly obtain database information and generate chart reports via chatbot.
- Management accesses real-time business data through natural language interaction to support decision-making.
- Developers and operations staff rapidly debug and query Postgres/Supabase databases.
- Educational and training environments demonstrate automated workflows for data visualization and database querying.
Main Workflow Steps
- Chat Message Trigger: The workflow listens for chat inputs as the starting point of user query requests.
- Primary AI Agent Command Parsing: Based on user input, the tool router determines whether to invoke the database query tool or the chart generation tool.
- Secondary Postgres Agent Executes SQL Query: Converts natural language into SQL statements, executes the database query, and retrieves results.
- Secondary QuickChart Agent Generates Chart Configuration: Creates Chart.js-compatible JSON chart configurations based on query results and user requirements.
- QuickChart Service Invocation: Sends the chart configuration to QuickChart.io via HTTP request to generate a chart URL.
- Result Return and Display: Returns both the database query results and corresponding chart links to complete the interaction.
- Chat History Persistence: Stores all conversation data in Postgres to enable session memory and context management.
Involved Systems and Services
- Postgres / Supabase: Relational databases used for data storage and querying.
- OpenAI GPT-4o-mini Model: Utilized as the Multi-AI Agent for natural language understanding and generation.
- QuickChart.io: Online service providing chart generation capabilities.
- n8n Nodes: Including LangChain chat trigger, Postgres tool node, HTTP request node, structured output parser, and tool workflow nodes.
Target Users and Value Proposition
- Data Analysts and Business Decision Makers: Obtain data insights and graphical presentations via natural language without complex SQL.
- Product Managers and Operations Staff: Quickly access database information and generate real-time business reports.
- Developers and DBAs: Simplify database interaction workflows and improve query efficiency.
- Educational and Training Institutions: Demonstrate the integration of AI with databases and data visualization.
In summary, this workflow seamlessly combines Multi-AI Agent dialogue, database querying, and automatic chart generation to create an efficient, intelligent, and user-friendly data interaction and visualization solution suitable for diverse industries and scenarios.
Strava Activity Data Synchronization and Deduplication Workflow
This workflow automatically retrieves the latest cycling activity data from the Strava platform at scheduled intervals, filtering out any existing records to ensure data uniqueness. Subsequently, the new cycling data is efficiently written into Google Sheets, allowing users to manage and analyze the data centrally. This process significantly reduces the workload of manual maintenance and is suitable for cycling enthusiasts, sports analysts, and coaches who need to regularly manage and analyze sports data.
ETL Pipeline
This workflow automates the extraction of tweets on specific topics from Twitter, conducts sentiment analysis using natural language processing, and stores the results in MongoDB and Postgres databases. It is triggered on a schedule to ensure real-time data updates, while intelligently pushing important tweets to a Slack channel based on sentiment scores. This process not only enhances data processing efficiency but also helps the team respond quickly to changes in user sentiment, optimize content strategies, and improve brand reputation management. It is suitable for social media operators, marketing teams, and data analysts.
Automated Detection and Tagging of Processing Status for New Data in Google Sheets
This workflow can automatically detect and mark the processing status of new data in Google Sheets. It reads the spreadsheet every 5 minutes to identify unprocessed new entries and performs custom actions to avoid duplicate processing. It supports manual triggering, allowing for flexible responses to different needs. By marking the processing status, it enhances the efficiency and accuracy of data processing, making it suitable for businesses that regularly collect information or manage tasks. It ensures that the system only processes the latest data, making it ideal for users who require dynamic data management.
Automated RSS Subscription Content Collection and Management Workflow
This workflow automates the management of RSS subscription content by regularly reading links from Google Sheets, fetching the latest news, and extracting key information. It filters content from the last three days and saves it while deleting outdated information to maintain data relevance and cleanliness. By controlling access frequency appropriately, it avoids API request overload, enhancing user efficiency in media monitoring, market research, and other areas, helping users easily grasp industry trends.
Very Quick Quickstart
This workflow demonstrates how to quickly obtain and process customer data through a manual trigger. Users can simulate batch reading of customer information from a data source and flexibly assign values and transform fields, making it suitable for beginners to quickly get started and understand the data processing process. This process not only facilitates testing and validation but also provides a foundational template for building automated operations related to customer data.
Update the Properties by Object Workflow
This workflow is primarily used for batch importing and updating various object properties in HubSpot CRM, such as companies, contacts, and deals. Users can upload CSV files, and the system automatically matches and verifies the fields, allowing for flexible configuration of relationships to ensure data accuracy. Additionally, the workflow supports data synchronization between HubSpot and Google Sheets, facilitating property management and backup, which greatly enhances the efficiency and accuracy of data imports. It is suitable for marketing teams, sales teams, and data administrators.
Pipedrive and HubSpot Contact Data Synchronization Workflow
This workflow implements automatic synchronization of contact data between the two major CRM systems, Pipedrive and HubSpot. It regularly fetches and compares contact information from both systems to eliminate duplicates and existing email addresses, ensuring data accuracy and consistency. Through this automated process, sales and marketing teams can obtain a unified view of customers, reduce the tediousness of manual maintenance, and enhance the efficiency and quality of customer data management.
LinkedIn Profile Enrichment Workflow
This workflow automatically extracts LinkedIn profile links from Google Sheets, retrieves detailed personal and company information by calling an API, and updates the data back into the sheet. It effectively filters enriched data to avoid duplicate requests, thereby enhancing work efficiency. This process addresses the cumbersome and error-prone nature of manual data updates and is suitable for various scenarios such as recruitment, sales, and market analysis, helping users quickly obtain high-quality LinkedIn data and optimize their workflows.