Data Product Query

The Data Product Query agent enables natural language querying of databases associated with data products. It can execute SQL queries, retrieve schemas, and generate visualizations using charts.

How it works

The agent follows this workflow:

1. Receives a natural language question from the user
2. Uses the Get Data Schema tool to understand available tables and columns
3. Generates and executes SQL queries using the SQL Execution tool
4. Analyzes results and may refine queries as needed
5. If visualization is requested, generates a chart using the Chart Generation tool
6. Returns the final consolidated results

Input parameters

Required:

• message (string): The natural language question or request to send to the agent
• data_product_id (string): The ID of the data product to query

Optional:

• auth_id (string): The ID of the authentication credentials to use for the query

Output format

The agent produces a series of thinking, tool call, tool return, and text blocks as it works through the user request. The final message, assuming no errors, is a string which addresses the user’s request.

Available tools

The agent has access to three tools:

SQL execution

Executes SQL queries against the data product’s database. The tool automatically handles result serialization and may limit rows/columns for very large results.

Key parameters:

• data_product_id: The data product to query
• sql: The SQL query to execute
• result_table_name: A semantic name for the result table

Get data schema

Retrieves the schema for tables in the data product. Optionally supports semantic search of sample values.

Key parameters:

• data_product_id: The data product to get schema for
• query: Optional semantic search query for sample values

Chart generation

Generates visualizations using Altair and Python. Takes a SQL query and Python code snippet to create interactive charts.

Key parameters:

• data_product_id: The data product to query
• sql: SQL query to generate data for the chart
• chart_code_snippet: Python function using Altair to create the chart
• image_title: Title for the chart

Behavior notes

• The agent always uses result tables from executed queries to answer questions (never synthesizes data)
• For complex questions, it executes a final consolidated query to return all results in a single table
• When generating charts, it returns the first reasonable visualization without excessive iteration
• Charts are generated using Altair and returned as JSON representations