Contextualization Quality Dashboard

Overview

The Contextualization Quality Dashboard module provides a comprehensive solution for measuring, monitoring, and visualizing the contextualization quality of your data in Cognite Data Fusion (CDF). It consists of two main components:

1. Contextualization Quality Metrics Function (external_id: context_quality_handler) - Computes all quality metrics and saves them as a JSON file in CDF
2. Streamlit Dashboard (context_quality_dashboard) - Visualizes the pre-computed metrics with interactive gauges, charts, and tables

This module helps data engineers and operations teams understand how well their data is contextualized across seven key dimensions:

• Asset Hierarchy Quality - Structural integrity of the asset tree
• Equipment-Asset Relationships - Quality of equipment-to-asset mappings
• Time Series Contextualization - How well time series are linked to assets
• Maintenance Workflow Quality - Quality of maintenance data from RMDM v1 (notifications, work orders, failure documentation)
• File Annotation Quality - Quality of P&ID diagram annotations linking files to assets/equipment
• 3D Model Contextualization - Quality of 3D object linking to assets
• File Contextualization - Quality of file-to-asset linking and file metadata completeness

Module Components

context_quality/
├── auth/                                    # Authentication configurations
├── data_sets/
│   └── context_quality_dashboard.DataSet.yaml  # Dataset for storing function code and files
├── functions/
│   ├── context_quality_handler/
│   │   ├── handler.py                       # Main Cognite Function orchestration
│   │   └── metrics/                         # Modular metric computation
│   │       ├── __init__.py                  # Exports all metric functions
│   │       ├── common.py                    # Shared utilities and data classes
│   │       ├── asset_hierarchy.py           # Asset hierarchy metrics
│   │       ├── equipment.py                 # Equipment-asset metrics
│   │       ├── timeseries.py                # Time series metrics
│   │       ├── maintenance.py               # Maintenance workflow metrics (RMDM v1)
│   │       ├── file_annotation.py           # File annotation metrics (CDM)
│   │       ├── model_3d.py                  # 3D model contextualization metrics
│   │       ├── files.py                     # File contextualization metrics
│   │       └── storage.py                   # File storage utilities
│   └── context_quality.Function.yaml        # Function configuration
├── streamlit/
│   ├── context_quality_dashboard/
│   │   ├── main.py                          # Streamlit dashboard entry point
│   │   ├── requirements.txt                 # Python dependencies
│   │   └── dashboards/                      # Modular dashboard components
│   │       ├── __init__.py                  # Exports all dashboard functions
│   │       ├── common.py                    # Shared UI components & color functions
│   │       ├── sidebar.py                   # Sidebar with metadata
│   │       ├── configuration.py             # Configuration & Run tab
│   │       ├── asset_hierarchy.py           # Asset Hierarchy tab
│   │       ├── equipment.py                 # Equipment-Asset tab
│   │       ├── timeseries.py                # Time Series tab
│   │       ├── maintenance.py               # Maintenance Workflow tab (RMDM v1)
│   │       ├── file_annotation.py           # File Annotation tab (CDM)
│   │       ├── model_3d.py                  # 3D Model Contextualization tab
│   │       ├── files.py                     # File Contextualization tab
│   │       ├── reports.py                   # PDF report generation
│   │       └── ai_summary.py                # AI-powered insights generator
│   └── context_quality_dashboard.Streamlit.yaml  # Streamlit app configuration
├── scripts/                                 # Local script execution (alternative to Cognite Functions)
│   ├── client.py                            # CDF authentication for local runs
│   ├── run_metrics.py                       # Main local execution script
│   ├── requirements.txt                     # Python dependencies for local script
│   └── README.md                            # Local script documentation
├── module.toml                              # Module metadata
└── README.md                                # This file

Deployment

Prerequisites

Before you start, ensure you have:

• A Cognite Toolkit project set up locally
• Your project contains the standard cdf.toml file
• Valid authentication to your target CDF environment

Step 1: Enable External Libraries

Edit your project's cdf.toml and add:

[alpha_flags]
external-libraries = true

[library.cognite]
url = "https://github.com/cognitedata/library/releases/download/latest/packages.zip"
checksum = "sha256:795a1d303af6994cff10656057238e7634ebbe1cac1a5962a5c654038a88b078"

This allows the Toolkit to retrieve official library packages.

Note: Replacing the Default Library

By default, a Cognite Toolkit project contains a [library.toolkit-data] section pointing to https://github.com/cognitedata/toolkit-data/.... This provides core modules like Quickstart, SourceSystem, Common, etc.

These two library sections cannot coexist. To use this Deployment Pack, you must replace the toolkit-data section with library.cognite:

Replace This	With This
[library.toolkit-data]	[library.cognite]
github.com/cognitedata/toolkit-data/...	github.com/cognitedata/library/...

The library.cognite package includes all Deployment Packs developed by the Value Delivery Accelerator team (RMDM, RCA agents, Context Quality Dashboard, etc.).

Checksum Warning

When running cdf modules add, you may see a warning like:

WARNING [HIGH]: The provided checksum sha256:... does not match downloaded file hash sha256:...
Please verify the checksum with the source and update cdf.toml if needed.
This may indicate that the package content has changed.

This is expected behavior. The checksum in this documentation may be outdated because it gets updated with every release. The package will still download successfully despite the warning.

To resolve the warning: Copy the new checksum value shown in the warning message and update your cdf.toml with it. For example, if the warning shows sha256:da2b33d60c66700f..., update your config to:

[library.cognite]
url = "https://github.com/cognitedata/library/releases/download/latest/packages.zip"
checksum = "sha256:da2b33d60c66700f..."

Step 2: Add the Module

Run:

cdf modules init .

Disclaimer: This command will overwrite existing modules. Commit changes before running, or use a fresh directory.

Step 3: Select the Dashboards Package

From the menu, select:

Dashboards: Streamlit dashboards and visualization modules

Then select Contextualization Quality Dashboard.

Step 4: Verify Folder Structure

After installation, your project should contain:

modules/
    └── dashboards/
        └── context_quality/

Step 5: Deploy to CDF

Build and deploy:

cdf build

cdf deploy --dry-run

cdf deploy

Note: Function Deployment Time

After running cdf deploy, the Cognite Function (context_quality_handler) may take 2-5 minutes to fully deploy. The Streamlit dashboard will be available immediately, but the function needs time to initialize before you can run it.

If you see "Function not available" when running a batch in the dashboard, wait a few minutes and try again. You can verify deployment status in CDF:

• Navigate to Data management → Build solutions → Functions
• Look for context_quality_handler and check its status

Running the Dashboard

Support

For troubleshooting or deployment issues:

• Refer to the Cognite Documentation
• Contact your Cognite support team
• Join the Slack channel #topic-deployment-packs for community support and discussions