External Database

Overview

The External Database Node in UPTIQ Workbench enables developers to connect their workflows to external databases of their choice for persistent storage, data retrieval, and data manipulation. This node offers an alternative to the Tables feature in UPTIQ by providing a generic interface for interacting with a variety of external databases, including MongoDB, SQL, PostgreSQL, Oracle, and BigQuery.

With the External Database Node, developers can: ✅ Perform CRUD operations (Create, Read, Update, Delete) across supported databases. ✅ Integrate existing external data sources into AI agent workflows. ✅ Store and retrieve data from custom databases for more flexibility.

Configurations

Database Type	Supported Operations	Configuration Fields
MongoDB	CRUD (Read, Write)	Database URI, Database Name, Collection Name, Filters, Projections, Data (for Write)
SQL	Query Execution	Database URI, Database Name, Query
PostgreSQL	Query Execution	Database URI, Database Name, Query
Oracle	Query Execution	Database URI, User, Password, Query
BigQuery	Query Execution	Project ID, Client Email, Private Key, Query

MongoDB Configuration Details

• Database URI: Specify the URI to connect to the MongoDB instance.

• Database Name: Name of the MongoDB database.

• Collection Name: Collection to read from or write to.

• Filters: JSON object specifying which documents to retrieve or modify. Example: { "_id": "123", "status": "active" }

• Projections: JSON object specifying fields to include or exclude in results. Example: { "_id": 0, "name": 1 }

• Data (for Write): JSON object or array for insert/update operations. Example: [ { "orderId": "1001", "totalAmount": 250 } ]

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SQL, PostgreSQL, and Oracle Configuration Details

• Database URI: URI to connect to the database.

• Database Name: Name of the database.

• Query: SQL query to execute. Example: SELECT * FROM orders WHERE status = 'completed';

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BigQuery Configuration Details

• Project ID: Google Cloud project ID.

• Client Email: Email associated with authentication.

• Private Key: Private key for authentication.

• Query: SQL query for BigQuery. Example: SELECT orderId, totalAmount FROM orders WHERE status = 'completed';

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Output Format

The output is always returned in the following format:

{
  "data": any[]
}

Example Use-Cases

Use-Case 1: Retrieving Completed Orders from MongoDB

A workflow retrieves a list of completed orders from a MongoDB collection for reporting purposes.

Configuration:

Field	Value
Operation	Read
Database Type	MongoDB
Database URI	mongodb://localhost:27017
Database Name	myDatabase
Collection Name	Orders
Filters	{ "status": "completed" }
Projections	{ "_id": 0, "orderId": 1, "totalAmount": 1 }

Output:

{
  "data": [
    { "orderId": "1001", "totalAmount": 250 },
    { "orderId": "1002", "totalAmount": 320 }
  ]
}

🔹 Why use this approach? ✔ Integrates existing order data into AI workflows. ✔ Supports dynamic reporting based on external data sources.

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Use-Case 2: Executing a SQL Query for Customer Insights

A workflow queries a PostgreSQL database to extract customer details for marketing purposes.

Configuration:

Field	Value
Operation	Read
Database Type	PostgreSQL
Database URI	postgresql://localhost:5432
Database Name	customerDB
Query	SELECT name, email FROM customers WHERE status = 'active';

Output:

{
  "data": [
    { "name": "John Doe", "email": "[e-mail]" },
    { "name": "Jane Smith", "email": "[e-mail]" }
  ]
}

🔹 Why use this approach? ✔ Supports real-time data retrieval for targeted marketing campaigns. ✔ Connects AI workflows to external customer databases seamlessly.

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Use-Case 3: Writing New Transactions to a MongoDB Collection

A workflow writes new transaction records into a MongoDB collection.

Configuration:

Field	Value
Operation	Write
Database Type	MongoDB
Database URI	mongodb://localhost:27017
Database Name	financialDB
Collection Name	Transactions
Data	[ { "transactionId": "TX1003", "amount": 500 } ]

Output:

{
  "data": [
    { "status": "success", "insertedCount": 1 }
  ]
}

🔹 Why use this approach? ✔ Supports flexible data storage in custom databases. ✔ Integrates transaction data into external systems seamlessly.

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Key Takeaways for Developers

✅ Flexible Database Support – Connects to a wide range of external databases, including MongoDB, SQL, PostgreSQL, Oracle, and BigQuery.

✅ CRUD and Query Operations – Supports read, write, update, and delete operations, enabling dynamic data management.

✅ Seamless Integration – Acts as a generic interface, allowing developers to use their preferred databases for persistent storage or AI processing.

✅ Alternative to Tables – Provides an alternative to the UPTIQ Tables feature, offering greater flexibility with external databases.

By leveraging the External Database Node, developers can integrate real-time data from external sources into AI workflows, enhancing decision-making, and enabling scalable, data-driven automation. 🚀