Using Database tools in an Agent

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Prerequisites

• Database tools already configured for your database type
• Project with agent building permissions
• Database server accessible via public IP

💡 Setup Required: Ensure you’ve configured your database tools first by following the setup guide for your specific database type.

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Supported Database Types

• MySQL - Standard metadata + query pattern
• PostgreSQL - Standard metadata + query pattern
• MS SQL Server - Standard metadata + query pattern
• Snowflake - Standard metadata + query pattern
• MongoDB - Standard pattern with Find + Aggregate tools
• Databricks - Simplified query-only pattern

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Agent Patterns

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Agent Pattern (MySQL, PostgreSQL, MS SQL, Snowflake, MongoDB)

1. Metadata Retrieval Agent - Scheduled agent that extracts database schema
2. Query Agent - Interactive agent that answers user questions

• Database-specific SQL Query Tool
• Database-specific Metadata Retrieval Tool
• Memory object (for metadata storage)
• LLM model

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Simplified Pattern (Databricks)

1. Query Agent - Direct SQL query execution with LLM processing

• Databricks SQL Query Tool
• LLM model

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Setting Up Standard Database Agents

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Step 1: Create Metadata Retrieval Agent

1. Create a new agent in Agent Canvas
2. Add the following components:
   • LLM model
   • [Database] Metadata Retrieval Tool (e.g., “MySQL Metadata Retrieval Tool”)
   • Memory object
3. Configure connections:
   • Input → LLM
   • LLM → Output
   • Memory → Output
4. Add the Metadata Retrieval Tool to the LLM
5. Schedule the agent:
   • Click the gear icon on the Input block
   • Toggle Schedule Execution ON
   • Set repeat interval based on how often your database schema changes

💡 Scheduling Tip: Run metadata retrieval daily for frequently changing databases, or weekly for stable schemas.

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Step 2: Create Query Agent

1. Create a new agent in Agent Canvas
2. Add the following components:
   • LLM model
   • [Database] SQL Query Tool (e.g., “MySQL SQL Query Tool”)
   • Memory object (same as metadata agent)

3. Configure connections:
   • Input → LLM
   • LLM → Output
   • Memory → LLM

4. Add the SQL Query Tool(s) to the LLM

⚠️ Important: Run the Metadata Retrieval Agent at least once before using the Query Agent to populate database schema information.

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Setting Up Databricks Agents

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Single Query Agent Setup

1. Create a new agent in Agent Canvas
2. Add the following components:
   • Databricks SQL Query Tool
   • LLM model
3. Configure connections:
   • Input → Databricks SQL Query Tool
   • Tool → LLM
   • LLM → Output
4. Configure the SQL Query Tool:
   • Click on the Databricks SQL Query Tool
   • Set it to receive value from Input Step Result

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Using Parameterized Queries

1. Click on the [Database] SQL Query Tool (e.g. Databricks SQL Query Tool)
2. Enter your SQL query with variables: SELECT * FROM {{table_name}} WHERE date > '{{start_date}}'
3. Save the configuration

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Using Your Database Agents

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Query Examples

"Show me all customers from the last month"
"What are the top 10 products by sales?"
"Find all orders with status 'pending'"
"Summarize revenue by region for Q4"

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MongoDB-Specific Queries

"Find all documents in the users collection where age > 25"
"Aggregate sales data by product category"
"Show me the most recent orders from each customer"

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Direct SQL Queries

SELECT customer_id, SUM(order_value) 
FROM orders 
WHERE order_date >= '2024-01-01' 
GROUP BY customer_id

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Best Practices

💡 Tip: Add custom prompts to your LLM with:

• Database schema context
• Common query patterns for your domain
• Output formatting preferences
• Business logic explanations

💡 Memory Management: Use the same Memory object across both agents to ensure consistent metadata access.

⚠️ Connection Requirements: All database servers must be accessible via public IP. Contact support@airia.com for IP whitelisting assistance.