Snowflake User-Level Connector with RBAC Integration

Overview

This guide provides step-by-step instructions for setting up a Snowflake connector with Role-Based Access Control (RBAC) and integrating it with Abacus.AI's ChatLLM platform. This configuration enables secure, user-level access to Snowflake data through a conversational AI interface.

Prerequisites

Architecture Overview

The integration follows a secure RBAC flow where each user authenticates individually with Snowflake, ensuring data access is governed by their specific permissions.

Setup Process

Step 1: Create Snowflake Connector

Create a Snowflake connector at the organization level following the RBAC Flow for Snowflake documentation. Ensure you include additional details for:

Step 2: Navigate to Projects

After creating the connector, navigate to the projects page by clicking on the Abacus.AI logo.

Abacus.AI logo

Step 3: Create New Project

Create a new project to house your ChatLLM implementation.

New project

Step 4: Select ChatLLM Option

Select ChatLLM - Custom LLM Chat option from the available project types.

Options

Step 5: Configure Project Name

Enter a descriptive name for your project that reflects its purpose.

Step 6: Skip to Dashboard

Select Skip to project dashboard to proceed directly to the main project interface.

Model Training Configuration

Step 7: Access Model Training

Click on the Model option in the left toolbar and select Train Model in the top right corner of the page.

![Train model]](/static/imgs/connectors/snowflake_user_images/model_selection.webp){width=900px }

Step 8: Configure Data Source

From the dropdown in Structured data source, select External service.

External Service Dropdown

Step 9: Select Snowflake Connector

Select the connector you created in Step 1 from the dropdown menu.

Step 10: Initiate Training

Click on Train Model to begin the model training process.

Model Deployment

Step 11: Access Trained Model

Once training is complete, click on Models and select your trained model.

Step 12: Create Deployment

Click on Create a new deployment to prepare your model for production use.

Step 13: Configure Deployment Type

Select Offline Batch + Realtime deployment type and click Next.

Step 14: Name Your Deployment

Enter a user-friendly name that your end users will see and click Deploy.

Step 15: Monitor Deployment Status

Return to your model by clicking on Model and then your model name. Wait for the deployment to reach Active state, then click on the deployment name.

Testing and Usage

Step 16: Access Prediction Dashboard

Click on the Prediction Dash option under Deployment.

Step 17: Launch Chat Interface

Click on Go to Abacus.AI chat to test your bot.

Step 18: User Authentication Flow

When users enter their first question/prompt, they will be prompted to log in to Snowflake. Each user must authenticate using their individual Snowflake credentials.

The system will present the Snowflake login screen:

Step 19: Interactive Chat Experience

Once authenticated, users can interact with the chat interface to query their Snowflake data:

The system will process queries and return results based on the user's RBAC permissions:

Security Considerations

RBAC Implementation

Best Practices

  1. Role Assignment: Ensure users have appropriate roles assigned in Snowflake before accessing the chat interface
  2. Database Permissions: Verify that the connector has access to the necessary databases and schemas
  3. Monitoring: Regularly monitor usage patterns and access logs for security compliance
  4. Updates: Keep connector configurations updated as organizational roles and permissions change

Troubleshooting

Common Issues

Support Resources

Conclusion

This setup provides a secure, scalable solution for enabling conversational access to Snowflake data while maintaining strict RBAC controls. Users can interact naturally with their data through the chat interface while the system ensures they only access information according to their assigned permissions.

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