Chart SQL Agent Results with OpenAI and QuickChart

### Overview - This workflow aims to provide data visualization capabilities to a native SQL Agent. - Together, they can help foster data analysis and data visualization within a team. - It uses the native SQL Agent that works well and adds visualization capabilities thanks to OpenAI’s Structured Output and Quickchart.io. ### How it works 1. Information Extraction: - The Information Extractor identifies and extracts the user's question. - If the question includes a visualization aspect, the SQL Agent alone may not respond accurately. 2. SQL Querying: - It leverages a regular SQL Agent: it connects to a database, queries it, and translates the response into a human-readable format. 3. Chart Decision: - The Text Classifier determines whether the user would benefit from a chart to support the SQL Agent's response. 4. Chart Generation: - If a chart is needed, the sub-workflow dynamically generates a chart and appends it to the SQL Agent’s response. - If not, the SQL Agent’s response is output as is. 5. Calling OpenAI for Chart Definition: - The sub-workflow calls OpenAI via the HTTP Request node to retrieve a chart definition. 6. Building and Returning the Chart: - In the "Set Response" node, the chart definition is appended to a Quickchart.io URL, generating the final chart image. - The AI Agent returns the response along with the chart. ### How to use it - Use an existing database or create a new one. - For example, I've used this Kaggle dataset and uploaded it to a Supabase DB. - Add the PostgreSQL or MySQL credentials. - Alternatively, you can use SQLite binary files (check this template). - Activate the workflow. - Start chatting with the AI SQL Agent. - If the Text Classifier determines a chart would be useful, it will generate one in addition to the SQL Agent's response. ### Notes - The full Quickchart.io specifications have not been fully integrated, so there may be some glitches (e.g., radar graphs may not display properly due to size limitations).