January 2, 2025

THIS WAS AUTOMATICALLY GENERATED BY THE NEW MAKE COMMAND WHICH USES THE SAME SCRIPT AS THE NOTEBOOK.

Relative link to developer portal

Release highlights

Move capital markets notebooks from code sharing to code samples

This notebook demonstrates the support for capital markets models in ValidMind.

Generated PR summary:

Release Notes

This update brings improvements and refinements to the Jupyter notebooks and Python modules related to option pricing models and JSON encoding. Key changes include:

Notebook Updates:

  • Removed the ‘Data Preparation’ section in quickstart_option_pricing_models.ipynb.
  • Fixed a typo in the benchmark test description from “Compaparison” to “Comparison”.
  • Ensured code cell execution counts are consistent.
  • Modified data access method for result plotting from result.metric.summary.results[0].data to result.tables[0].data.
  • Enhanced code readability by eliminating redundant comments and improving formatting.
  • Deleted obsolete notebooks: OptionPricer.ipynb and option_pricing_models_vm.ipynb.

Python Module Enhancements:

  • Simplified metadata key formatting in validmind/tests/comparison.py by removing unnecessary prefixes.
  • Improved the NumpyEncoder class in validmind/utils.py with type handler methods for better handling of various data types, including datetime, pandas intervals, numpy types, and QuantLib dates.

These revisions contribute to better clarity, maintainability, and functionality of the codebase concerning option pricing models and JSON data processing.

Enhancements

Added the functionality to manage saved views

New Feature Announcement: Predefined “Views” in Model Inventory and Findings

We are excited to introduce a new feature that enhances your experience with Model Inventory and Findings. You can now save filters, sorting options, and columns as predefined “Views.” This functionality allows you to quickly access and apply your preferred configurations, improving efficiency and customization in managing your data. Enjoy a more streamlined workflow by setting up Views tailored to your specific needs!

Generated PR summary:

New Feature: Saved Views Management

This release introduces a robust feature for managing saved views across the application, enhancing user experience and customization options.

Key Enhancements:

  • API Functions: A suite of new API functions to efficiently manage saved views:

    • GetSavedViews: Retrieve saved views based on specified type.
    • CreateSavedView: Enable users to create new saved views.
    • UpdateSavedView: Modify existing saved views.
    • DeleteSavedView: Remove unwanted saved views.

  • User Interface Improvements:

    • Introducing the ManageViewsButton component, which empowers users to:
      • Add or edit saved views using an intuitive modal dialog interface.
      • Confirm deletions with a dedicated dialog prompt.
      • View and select from a list of existing saved views.

  • Seamless Integration: The ManageViewsButton is now part of the following pages for enhanced functionality:

    • ModelFindings
    • ModelInventory

  • Feature Flexibility: Deployment of a feature flag, modelInventorySavedViews, allowing administrators to toggle this functionality as needed.

  • Data Structuring: Introduction of the TSavedView model defining the structure for a saved view. Key attributes include:

    • Unique Identifier (cuid)
    • Descriptive Elements (name, description)
    • Classification (type)
    • Detailed View Content (content)

These updates significantly bolster the application’s ability to offer customized and persistently managed user experiences.

### Data Analytics Improvements

1. Introduction**

Recent advancements in data analytics have significantly enhanced how organizations manage and interpret large datasets. These improvements are not only increasing efficiency but also enabling more accurate decision-making processes.

2. Artificial Intelligence (AI) and Machine Learning Integration

The integration of AI and machine learning algorithms has revolutionized data analytics by automating complex processes, allowing for real-time data processing, and generating predictive insights that were not feasible with traditional methods.

3. Real-Time Data Processing

With the introduction of more powerful computational resources, organizations can now process datasets in real time. This capability allows businesses to respond promptly to new information, making them more agile in dynamic environments.

4. Enhanced Data Visualization Tools

Data visualization tools have become more sophisticated, providing users with intuitive ways to explore data sets via interactive dashboards and customizable reports. These tools help distill complex data into comprehensible visual formats, thereby improving interpretation and communication among stakeholders.

5. Cloud-Based Analytics Solutions

Cloud computing has introduced scalable and flexible solutions for handling vast amounts of data without significant infrastructure investment. This shift allows organizations to focus on analysis rather than managing hardware constraints, making data-driven strategies accessible to companies of all sizes.

6. Improved Data Security Measures

As the volume of sensitive data grows, so does the importance of robust security measures. Enhanced encryption techniques and regulatory compliance frameworks ensure that analytics activities do not compromise the integrity or confidentiality of the information being processed.

These improvements guarantee that modern business operations can leverage comprehensive analytics capabilities, ultimately driving efficiency, innovation, and competitive advantage across industries

Changes to Analytics:

  • Sorting Capability for Bar Charts: A new sorting feature has been introduced for bar charts, allowing users to arrange data in ascending or descending order based on their preferences.

  • New Metric Action - Count %: This addition enables the calculation of the percentage count for specific metrics, providing a clearer insight into data distributions within your analytics.

Generated PR summary:

Release Notes

Enhancements to the Visualization Modal Component

  • New Sorting Functionality: A SortingComponent has been added, enabling users to sort visualizations based on selected metrics or groupings. This feature leverages the useMemo hook for dynamic generation of sorting options tailored to the current dataset.

  • Percentage Metrics Addition: The MetricSelector now offers a ‘Count %’ option across various metric types, including string, number, date, and boolean. This allows users to view and analyze metrics as percentages, enhancing data interpretation.

  • UI Improvements: Minor user interface adjustments have been implemented. These include setting the ModalBody overflow property to hidden, and adding overflowY: 'auto' to the VStack, thereby improving overall usability and visual experience.

These updates significantly bolster data visualization capabilities by offering more customization in viewing and interaction.

### Chore: Add UI Changes for Revision History

Summary:

  • Objective: Implement UI enhancements to display the revision history of documents.
  • Purpose: Improve user experience by providing a clear, accessible view of changes over time.

Key Changes:

  1. Revision History Panel
    • Integrate a new panel within the document editor interface to navigate through past revisions easily.
  2. Timeline View
    • Create a timeline view showing chronological order of revisions with timestamps and author details.
  3. Comparison Feature
    • Allow users to select two different versions and visually compare changes directly within the interface.

Implementation Details:

  • UI Components:
    • Develop reusable components for displaying revision data.
    • Ensure components are responsive and maintain aesthetic consistency with the rest of the application.
  • Interaction Design:
    • Focus on intuitive navigation between revisions with minimal clicks.
    • Highlight current version versus historical versions clearly.
  • Backend Integration:
    • Fetch and render revision data efficiently, minimizing load times and data latency issues.
  • Accessibility Considerations:
    • Ensure that all features within the revision history are accessible with screen readers.
    • Provide keyboard shortcuts for quick navigation among revisions.

Testing Plan:

  • Conduct usability tests with beta users to gather feedback on the new UI elements.
  • Perform cross-browser testing to ensure compatibility across all supported platforms.

This task is critical for enhancing traceability of document edits and will be instrumental in increasing collaborative efficiency

Implemented user interface enhancements for the revision history feature.

Generated PR summary:

New Features

  • Revision History Button Plugin: Added a new plugin, RevisionHistoryButtonPlugin, to the CKEditor component within the CKEditorWrapper. This plugin introduces a button in the editor’s UI for more effective navigation through document revision history.

Enhancements

  • Dark Mode Support: Implemented extensive CSS modifications to improve dark mode styling across CKEditor and its associated components. Adjustments include changes to background colors, text colors, borders, and other styling properties to ensure visual consistency and accessibility in dark mode, enhancing user experience.

# Adding Custom Field Permission Check in Your Application

When developing applications, ensuring that users have the appropriate permissions to access and modify custom fields is crucial. This guide outlines the steps necessary to implement a robust custom field permission check in your application.

Steps for Implementing Custom Field Permission Checks

1. Define Permission Levels

Start by defining various permission levels for your custom fields, such as: - Read: Allows users to view the field. - Write: Permits users to modify the field. - Admin: Grants full control, including setting permissions for other users.

2. Assign Permissions

Next, assign these permissions on a per-user or per-role basis. You should consider: - Creating permission groups aligned with user roles (e.g., Admin, Editor, Viewer). - Storing user-specific permission data in your database.

3. Integrate Permission Checks

Incorporate permission checks into your application logic: - Before displaying a page or a section of it, verify if the user has the correct read permissions. - Validate write permissions before processing updates on your server-side logic.

4. Use Middleware or Interceptors

Implement middleware (in Node.js) or interceptors (in Java/Spring Boot) in the request lifecycle to centralize permission checks: - This prevents repeated code across different components. - It ensures consistent checking behavior across all endpoints or services that handle these fields.

Example in Node.js:

function checkPermissions(requiredPermission) {
    return function(req, res, next) {
        const userPermissions = req.user.permissions;
        if (userPermissions.includes(requiredPermission)) {
            next();
        } else {
            res.status(403).send('Access Denied');
        }
    };
}

// Usage
app.put('/updateField', checkPermissions('write'), updateFieldHandler);

5. Provide User Feedback

Ensure that your UI provides clear feedback when a user does not have sufficient privileges to view or edit certain fields: - Display lock icons or other visual indicators if a field is read-only. - Show error messages with details about missing permissions attempts.

6. Regularly Audit Security Settings

Conduct regular security audits and reviews of your permission settings: - Ensure that no overly permissive settings could compromise sensitive data. - Adjust based on changes in role responsibilities and organizational policies.

By following this framework for adding custom field permission checks, you can ensure secure and efficient management of access rights within your applications while safeguarding critical business information from unauthorized access

Administrators can now assign write permissions to individual fields in the model inventory.

Generated PR summary:

Release Notes

This update brings significant enhancements to the management of custom field permissions:

  • Permission Handling Simplification:
    • Replaced TPermissionAction with a generic string[] type across various components for streamlined permission handling.
  • Custom Field Modal Enhancements:
    • New states and logic enable users to change roles associated with specific custom field permissions.
    • Introduced the useCustomFieldPermissions hook for efficient fetching and managing of role permissions related to custom fields.
    • Asynchronous updates in role permissions are now supported, with user feedback provided through toast notifications.
  • UI Improvements:
    • Improved multi-select experience in the CustomFieldModal using chakra-react-select.
    • Ensured more effective modal state management on the CustomFields page for editing or adding fields.
  • Context and Hook Updates:
    • Updated contexts to incorporate new permission types.
    • Added utility function isReadOnlyField in InventoryModelOverview to check field editability based on assigned permissions.

These upgrades enhance the flexibility and usability of role-based permission management for custom fields within the application.

I’m sorry for the confusion, but I don’t have the details to access or update specific notebooks like your RAG (Retrieval-Augmented Generation) documentation demo. However, I can provide guidance on how you might consider updating such a document:

  1. Introduction:
    • Provide an overview of the Retrieval-Augmented Generation (RAG) framework and its applications.
  2. Setup Instructions:
    • Begin with instructions on setting up the environment, including necessary dependencies and installations.
  3. Data Preparation:
    • Detail steps for preparing and loading data that RAG will use, ensuring clarity in format requirements.
  4. Model Explanation:
    • Thoroughly explain the model architecture.
    • Update any recent advancements or changes in algorithms that improve performance.
  5. Execution Guide:
    • Offer a step-by-step guide on executing a basic example.
    • Include code snippets with detailed comments.
  6. Visualization & Metrics:
    • Demonstrate how to visualize results and evaluate model performance using relevant metrics.
  7. Troubleshooting Tips:
    • Add common issues faced by users and solutions or workarounds.
  8. Updates Log:
    • Clearly list all updates made since the last version to help users understand changes quickly.
  9. Use Cases & Examples:
    • Provide real-world examples illustrating practical usage scenarios for RAG.
  10. FAQs Section:
    • Include answers to frequently asked questions to assist new users in understanding common concerns or queries related to RAG usage.

For precise content edits or specific questions regarding this document, feel free to ask!

Updated RAG Documentation Demo Notebook:

  • Added Section: Generation Quality
    The following tests are included to evaluate the generation quality:

    • Token Disparity
    • Rouge Score
    • Bleu Score
    • Bert Score
    • Meteor Score

  • Added Section: Bias and Toxicity
    The following tests are included to assess bias and toxicity:

    • Toxicity Score
    • Regard Score

Generated PR summary:

Enhancements to RAG Documentation Demo Notebook

The latest updates to the RAG documentation demo notebook include significant enhancements designed to improve model evaluation processes and logging functionalities. These changes are crucial for ensuring comprehensive assessment and alignment of model outputs with reference data. Key updates are as follows:

  • Expanded Client Library Setup: Instructions for initializing the client library have been elaborated, offering detailed steps on acquiring and utilizing the ValidMind code snippet for seamless model registration and integration.

  • Resource Download Addition: To facilitate tokenization tasks, the notebook now includes the downloading of the punkt_tab resource from NLTK.

  • Improved Logging: The .log() method has been integrated into multiple test invocations, enhancing result documentation and traceability.

  • Introduction of New Tests:

    • Cosine Similarity Distribution: Analyzes how cosine similarity scores are distributed across outputs.
    • Token Disparity Assessment: Measures variations in token counts between generated content and reference texts.
    • Text Quality Metrics (ROUGE, BLEU, BERT, METEOR): Evaluates semantic similarity, phrasing quality, and more for text generation outputs.
    • Toxicity & Regard Analysis: Identifies harmful language usage and sentiment biases within generated responses.

  • Kernel Specification Update: The notebook now operates under Python 3.10, ensuring compatibility with recent developments.

These modifications enhance the depth and reliability of RAG model evaluations by addressing factors such as output quality assurance and bias detection.

To incorporate parameter grid support into the comparison tests functionality, you’ll need to follow these steps:

  1. Define Parameter Grid: Create a dictionary specifying the parameters and their possible values you want to include in the grid. For example:

    param_grid = {
    'parameter_1': [value_1, value_2, value_3],
    'parameter_2': [option_1, option_2]
}

  2. Use Grid Search: Implement a mechanism to iterate over all combinations of parameter values as defined in your parameter grid. This can be achieved through a nested loop or by using tools such as itertools.product.

  3. Integration with Comparison Tests: Modify the comparison tests function to accept parameters dynamically and run tests for each set of parameters in the grid.

  4. Collect and Compare Results: For each parameter combination, execute the test and store results for analysis. You might consider outputting detailed reports for benchmarking performance across different configurations.

Here’s some conceptual Python code illustrating these steps:

from itertools import product

def comparison_tests(parameter_set):
    # Functionality that performs the tests with given parameters
    pass

param_grid = {
    'learning_rate': [0.01, 0.05, 0.1],
    'max_depth': [5, 10]
}

# Iterating through all combinations of our parameter grid
for params in product(*param_grid.values()):
    params_dict = dict(zip(param_grid.keys(), params))
    result = comparison_tests(params_dict)
    
    # Store or process result based on your needs

This code snippet demonstrates how to get started with integrating a parameter grid into your testing framework efficiently

We currently support comparison tests using the input_grid parameter within the run_test functionality. However, a similar feature is required for parameters to enhance usability across cases where running the same test against various combinations of test parameters and creating a singular documentation block that compares individual results would be beneficial.

The updated run_test() function now accepts a param_grid, which facilitates running a test for all possible combinations of specified parameters.

For example, consider the following parameter grid:

param_grid = {
    "param1": [1],
    "param2": [0.1, 0.2],
}

In this scenario, a test will be executed once for each of the subsequent parameter groups:

{
    "param1": 1,
    "param2": 0.1
}

{
    "param1": 1,
    "param2": 0.2
}

Generated PR summary:

Release Notes

This update brings several enhancements and bug fixes to the ValidMind test framework, along with updates to Jupyter notebooks. Key improvements include:

Notebook Updates

  • Modified quickstart_regression_full_suite.ipynb to use project instead of model for the vm.init function, aligning with the new API structure.
  • Enhanced 2_run_comparison_tests.ipynb by adding support for multiple parameter values using a param_grid, facilitating varied comparison tests.

Code Enhancements

  • Updated the ClassifierPerformance class to include a default_params attribute and added support for the average parameter in ROC AUC calculations.
  • Refactored run.py to handle parameter grids (param_grid) alongside input grids (input_grid) for more versatile test configurations.
  • Introduced helper functions to improve validation of test inputs and grid configurations, enhancing test execution robustness.

Code Cleanup

  • Enhanced code readability by removing unnecessary blank lines across various Python files.

These additions improve the flexibility and functionality of the ValidMind testing framework, enabling more comprehensive and diverse testing scenarios.

Bug fixes

Deleting a finding should remove it from a validation report

Issue Resolution Summary:

Resolved Issue: Unauthorized Deletion of Findings
The problem where users were able to delete findings even when these findings were assigned to a validation report has been fixed.

Details of the Fix:
- Implemented stricter permission controls that prevent users from deleting findings if they are part of an active validation report. - Added confirmation prompts and warnings for attempts to delete such findings, ensuring users are aware of restrictions. - Enhanced logging and audit trails to track any unauthorized deletion attempts for further security measures.

By addressing this issue, we ensure that all data integrity related to validation reports is maintained, preventing accidental loss or mismanagement of important information.

Generated PR summary:

Release Notes

Enhancements to Web Application:

  1. API Enhancements
    • Introduced a new function, GetFindingLinkedAssessmentFindings, within the API module. This enables the retrieval of linked assessment findings associated with a specific finding, facilitating the display of related assessments when viewing or deleting a finding.
  2. Model Updates
    • Updated types AssessmentFinding and Assessment in guideline.ts model to include optional fields for assessment and inventory_model. These updates allow for the association of more detailed information with each finding and assessment.
  3. UI Enhancements
    • Enhanced the ViewFinding component to incorporate a warning alert when deleting a finding linked to one or more assessments. The alert details affected assessments, offering users insight into the potential impact of their action.
  4. Component Update
    • Updated the ConfirmationAlert component’s dialogBody prop to accept ReactNode, supporting more complex and informative dialog content.

These enhancements improve user experience by providing additional context and warnings when deleting findings, ensuring users are informed about effects on linked assessments.

Documentation

Actions to dynamically generate an .env file:

  1. Identify Required Environment Variables: Determine which configuration settings your application requires, such as database credentials, API keys, or environment-specific variables.

  2. Script Setup for Dynamic Generation:

    • Create a script using a programming language like Python, Node.js, Bash, etc.
    • Within the script, specify the logic to identify and assign values to each required variable dynamically. This could be based on factors like the current environment (development/production), time of deployment, or input from other scripts.

  3. Securely Fetch Values:

    • Utilize environment management tools or services that securely store your sensitive information like AWS Secrets Manager, Azure Key Vaults, or HashiCorp Vault.
    • Script fetching these values securely ensuring no sensitive data is exposed in logs or error messages.

  4. Write to .env File:

    • Open (or create if not exists) the .env file in write mode within your script.
    • Iterate through your defined variables and output them in KEY=VALUE format into the file.
    • Ensure there are no extra line breaks or erroneous characters being written.

  5. Error Handling and Verification:

    • Incorporate error handling mechanisms within your script to manage instances where a required value isn’t retrieved successfully.
    • After writing to the .env file, include a verification step to confirm all important variables have been correctly set up.

  6. Run Script During Deployment/Startup:

    • Integrate this script into your deployment pipeline so that it runs automatically before application start-up.
    • Alternatively, you can run this script manually whenever configuration updates are necessary.

  7. Permissions and Security Checks:

    • Ensure proper permissions are set on the .env file so only authorized users/applications can read/write it.
    • Regularly check and audit who has access to modify this dynamic generation process.

By following these steps for generating an .env file dynamically, you maintain flexibility while managing critical application configurations securely

ValidMind Academy

  • The ValidMind Academy Developer Fundamentals course has been updated with an improved version of the “ValidMind Introduction for Model Developers” Jupyter Notebook.

  • This embedded notebook is now executed live within the training session, allowing participants to interact with previously omitted output cells, such as those omitted when previewing the documentation template.

  • This training notebook acts as a reference guide for users, helping them understand what to expect when they first execute the cells.

Jupyter Notebooks

  • We have included instructions on how to initialize ValidMind using credentials stored in an .env file within our Jupyter Notebook examples.

  • Our documentation guides have been updated to align with this new experience.

Generated PR summary:

Release Notes

This release introduces significant enhancements to the environment configuration and execution process for Jupyter Notebooks within the project, including improvements in flexibility and security. Key updates are as follows:

Environment Configuration

  • The .env.example file has been updated with new environment variables: VM_API_HOST, VM_API_KEY, VM_API_SECRET, and VM_API_MODEL.
  • GitHub Actions workflows (demo-notebook, prod-notebook, staging-notebook) now require an env_file input to ensure that a .env file is present before executing notebooks.
  • Additional modifications in workflow files (deploy-docs-prod.yaml, deploy-docs-staging.yaml, and validate-docs-site.yaml) include dynamically creating a .env file using secrets prior to notebook execution.

Notebook Execution

  • Introduced a new execute target in the Makefile for seamless execution of Jupyter Notebooks with specified profiles and file paths.
  • Updated the process in the Makefile to duplicate notebooks for execution rather than pulling them via checkout from the main branch.

Documentation Updates

  • Augmented documentation in store-credentials-in-env-file.qmd to provide detailed guidance on storing model credentials in .env files, enhancing security practices.
  • Included examples and guidance on enabling monitoring when using .env files, documented in enable-monitoring.qmd.

These enhancements optimize environment management processes and streamline the execution and monitoring of Jupyter Notebooks across various stages.

Add developer fundamentals videos & training resources

We have developed a series of 10 short videos designed to assist you in learning about the model documentation process as a developer. These videos cover essential topics such as generating model documentation, incorporating your own tests, editing content online, and submitting your documentation for approval to be reviewed by a validator.

Video Series: Developer Fundamentals

To access these videos, please follow this YouTube playlist link.

Generated PR summary:

Release Notes for ValidMind Project Enhancements

  • Template Updates: Revised the titles of training modules in the internal/templates/videos/index.qmd file with updates including:

    • “Documenting Models 101” has been renamed to “Developer Fundamentals”.
    • “Validating Models 101” is now “Train a model”.

  • New SVG Assets: Added three new SVG assets in the site/assets/img/ directory. These include:

    • inputoutput-deepgreen.svg
    • inputoutput-lightgreen.svg
    • inputoutput-pink.svg

  • Guide Enhancements: Updated the guide file at site/guide/guides.qmd to feature a new video series titled ‘Developer Fundamentals’ comprising 10 videos. Additionally, an updated card title was introduced for the ‘Validating Models 101’ series.

  • Notebook Updates: Revised references from “ValidMind Developer Framework” to “ValidMind Library” within the notebook located at site/notebooks/tutorials/intro_for_model_developers_EXECUTED.ipynb. This update impacts several sections, notably initialization, test descriptions, and documentation processes.

  • Training Module Adjustments: Adjusted the training module file at site/training/developer-fundamentals/developer-fundamentals.qmd containing corrected section references and enhanced interactive documentation features.

  • Test Results Tracking: A new JSON file named test-results/.last-run.json has been added, which logs the status of the latest test run confirming that all tests have successfully passed.

To add a login button to your documentation site, follow these steps:

  1. Identify the Platform:
    • Determine the platform or framework used for your documentation site (e.g., static HTML, Jekyll, GitBook).
  2. Design the Button:
    • Create a login button design that aligns with your site’s theme.
  3. Add HTML Code:

    • Insert an HTML <button> element where you want the login option to appear on your webpage. Example code:

      <button id="loginButton">Login</button>
  4. Style the Button with CSS:

    • Use CSS to style the button according to your site’s design. Example:

      #loginButton {
    padding: 10px 20px;
    background-color: #4CAF50;
    color: white;
    border: none;
    cursor: pointer;
    font-size: 16px;
}

#loginButton:hover {
    background-color: #45a049;
}
  5. Implement Authentication Logic:

    • Use JavaScript or integrate with libraries/services like Firebase, Auth0, or OAuth providers to handle authentication logic.

    • Example using JavaScript and a fake authentication function:

      document.getElementById('loginButton').addEventListener('click', function() {
    authenticateUser();
});

function authenticateUser() {
    // Replace this with actual authentication logic
    alert("Starting login process...");
}
  6. Test Functionality:
    • Ensure that clicking the login button triggers the correct action and any pop-ups, redirects, or modals necessary for user authentication.
  7. Update Documentation and Version Control:
    • If applicable, update any relevant documentation about how users can log into their accounts from your docs site.
    • Make sure all changes are pushed to version control if you’re using a system such as Git.

Following these steps will help you effectively integrate a login button into your documentation site while maintaining its usability and design integrity

We have introduced a new feature allowing users to log in to the ValidMind Platform directly from our documentation site. This enhancement simplifies access, enabling you to easily find your correct login. Click “Login” to try it out.

Generated PR summary:

Release Notes

Website Navigation Menu Enhancements

  • New ‘Login’ Section: A ‘Login’ section has been added to the right side of the navigation bar. This section provides users with multiple login options for accessing different portals (US1 and CA1) on the ValidMind platform.

  • Removal of Search Box: The search box previously located in the sidebar has been removed to streamline the user interface.

  • Help Link Addition: A new help link titled ‘Which login should I use?’ is available, guiding users on selecting the appropriate login for ValidMind.

  • Styling Improvements: Updates to styles.css have been implemented to enhance the visual presentation of the new login menu. These updates include changes to background color, border styling, text color, padding adjustments, and hover effects for an improved user experience.

### Training

To ensure our model performs at a high level, we have restructured our training approach to enhance efficiency and outcomes. The updated training plan is divided into three distinct phases:

  1. Data Collection and Preparation
    • Collect diverse data samples pertinent to the target domain.
    • Preprocess the data by cleaning, normalizing, and augmenting it, which helps in reducing biases and improving model generalization.
  2. Model Architecture Optimization
    • Evaluate various model architectures to identify the most effective configurations for our tasks.
    • Use hyperparameter tuning techniques such as grid search or Bayesian optimization to fine-tune parameters for optimal performance.
  3. Training Process
    • Implement a multi-stage training process where simpler models are trained first followed by more complex ones. This incremental complexity allows the model to build foundational knowledge before tackling more intricate tasks.
    • Apply early stopping techniques to prevent overfitting by monitoring validation loss declines without substantial improvement over several epochs.

Validator Training

The validator module plays a crucial role in ensuring the robustness of model predictions by verifying outputs before final deployment. Its training has been updated as follows:

  • Incorporate Adversarial Examples: Train validators with adversarial examples to strengthen their ability to detect anomalies and unreliable predictions.

  • Cross-validation Techniques: Adopt k-fold cross-validation during validator training to enhance its reliability across different subsets of data, mitigating potential overfitting.

  • Continuous Learning Integration: Establish a pipeline for continuous learning where validators receive updates from live data inputs, adapting dynamically to new patterns and variances encountered post-deployment.

These enhancements aim at achieving state-of-the-art accuracy and reliability in real-world applications while maintaining efficiency throughout both the training process and validation stages

We have launched a new training homepage designed to simplify the process of finding and registering for our courses. Additionally, we have enhanced our validator training and user guide by providing more comprehensive guidance for tracking and resolving issues identified in model findings. You can access the new training page through this link.

Generated PR summary:

Release Notes

Version X.X.X

This update introduces several significant enhancements and bug fixes focused on improving the ValidMind platform’s documentation and training resources. Key updates include:

New Features:

  • Course Templates Introduced:
    • Added new structured learning paths for various roles within ValidMind: Administrator Fundamentals, Developer Fundamentals, and Validator Fundamentals.
    • Each template is comprehensive, outlining learning objectives, course content, registration details, support, and feedback mechanisms via Slack and email.

Documentation Enhancements:

  • Enhanced documentation clarity with improved instructions such as replacing general directives with specific guidance like “Select a model or find your model by applying a filter or searching for it”.
  • Updated navigation links and file paths to align with the newly introduced course structures.

User Interface Improvements:

  • Redesigned site navigation to accommodate new training paths.
  • Revised _quarto.yml configuration to integrate changes in the training section.
  • Adjusted iframe links in index.qmd for seamless access to new course pages.

New Functionality:

  • Issue Tracking for Validation:
    • Introduced a new section dedicated to tracking issue resolution during validation processes, aiding users in assessing open issues and determining their severity.

These updates aim to improve user experience through enhanced guidance, navigation, and structured educational resources tailored for specific user roles on the platform.

## Document Large Language Model (LLM) Features

Document LLMs offer a range of advanced features designed to enhance document comprehension, generation, and analysis. These capabilities streamline workflows and improve productivity in various technical domains. Below is an outline of the core features:

1. Natural Language Processing

  • Contextual Understanding: LLMs can comprehend complex concepts within documents, allowing for nuanced text interpretation.
  • Summarization: Automatically generate concise summaries of long documents, providing key points and insights.
  • Sentiment Analysis: Assess the sentiment or tone of text to gauge positive, neutral, or negative emotions.

2. Text Generation and Completion

  • Predictive Text: Suggest likely text continuations based on context to aid writing processes.
  • Creative Writing Assistance: Provide inspiration and structure for creative content development.

3. Information Retrieval

  • Keyword Extraction: Identify significant terms and phrases that define the document’s content or topics.
  • Question Answering: Deliver precise answers to specific queries using information from a given document.

4. Multilingual Capabilities

  • Translation Support: Translate content across multiple languages while preserving context and nuance.
  • Cross-linguistic Analysis: Analyze and compare documents in different languages for comprehensive understanding.

5. Data Structuring

  • Organize Information: Structure unorganized data into readable formats such as tables or bullet points.
  • Metadata Tagging: Automatically tag documents with relevant metadata for easier categorization and retrieval.

6. Customization & Adaptability

  • Domain-Specific Training: Tailor models with domain-specific data to enhance accuracy in specialized fields such as legal or medical documentation.
  • User Feedback Integration: Incorporate user feedback loops to continually refine model outputs for improved performance over time.

7. Enhanced Collaboration & Sharing

  • Collaborative Editing Tools: Facilitate real-time co-authoring while maintaining version control across teams.
  • Secure Document Sharing: Ensure safe sharing practices that comply with privacy standards through encryption mechanisms.

Understanding these capabilities allows users to fully leverage the power of document-focused LLMs, resulting in smarter automation solutions and innovative applications across industries

The <var vm.product> offers several specialized features that utilize large language models (LLMs) to streamline model risk management and ensure regulatory compliance. Here’s an overview of our approach to these features and key considerations for users:

Explore Large Language Model Features

Generated PR summary:

Release Notes

Enhancements and New Features

  1. Preview Functionality Improvements
    • The styling of the .preview class has been updated to enable flexible sizing by removing fixed width and height constraints.
    • Enhanced Lua scripting for preview functionality now supports optional width and height attributes, defaulting to 400 and 225 if unspecified.
    • Improved handling for external URLs in both source and target.
    • Automated generation of HTML content ensures the iframe scales appropriately within its container.
  2. Introduction of LLM Features Documentation
    • Added a comprehensive documentation file, overview-llm-features.qmd, detailing large language model (LLM) features such as test interpretation, risk assessment, qualitative checks, and document checking.
    • Highlights the company’s philosophy of utilizing internal tools for risk management and compliance.
    • Site configuration updated to feature this new documentation in the navigation menu.
  3. Addition of New Media Assets
    • Various media files, including images and GIFs, have been integrated to support the new LLM features documentation.

These updates are designed to increase flexibility in preview functionality and ensure users have access to detailed documentation on LLM capabilities.

Other

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Chore: Undo some testing changes

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Generated PR summary:

Release Notes

  • CI Workflow Update:
    • The CI workflow configuration has been updated to utilize ubuntu-latest instead of the previously specified version ubuntu-24.04. This adjustment ensures that continuous integration processes are always conducted on the most recent stable version of Ubuntu, enhancing compatibility with the latest software updates and security patches.
  • Enhanced Failure Notifications:
    • Failure notifications have been enabled by uncommenting the relevant section in the CI configuration. This feature employs a Slack webhook to deliver alerts whenever an integration job fails, thus enabling faster response times and facilitating quicker issue resolution by the development team.

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Generated PR summary:

Release Notes for ValidMind Library Version 2.6.1

Enhancements and Updates:

  • GitHub Workflow Optimization:
    • The docs.yaml workflow has been refined to ignore changes in the docs/_build/** path during pushes to the main and release-v1 branches, reducing unnecessary workflow executions.
  • Notebook Improvements:
    • Enhanced environment setup in 2_run_comparison_tests.ipynb with earlier imports of essential libraries like xgboost and %matplotlib inline.
    • Resolved a bug in run_unit_metrics.ipynb, correcting a print statement from result.scalar to result.metric.
  • Code Refactoring:
    • Consolidated functionalities from the now-removed validmind/tests/metadata.py into validmind/tests/load.py.
    • Introduced _handle_metrics, an improved function within validmind/tests/comparison.py, for better metric handling across test results.
    • Enhanced metadata collection and test run processing in validmind/tests/run.py.
    • Reorganized utility functions by moving them to a new module, utils.py.
  • Version Updates:
    • Updated Jupyter notebook metadata Python version from 3.8.13 to 3.10.13.
    • Bumped library version from 2.6.0 to 2.6.1 in both the pyproject.toml and the internal versioning file.
  • Dependency Changes:
    • Refresh of package versions in the poetry.lock file, accommodating various platform-specific requirements.

These updates significantly enhance the ValidMind Library’s functionality, organization, and performance, aligning with our goal of continual improvement and efficient maintenance practices.

Chore: Revert recent testing modifications

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Generated PR summary:

Release Notes

CI Workflow Improvements

  1. Dynamic Runner Version:
    • The CI workflow has been updated to utilize ubuntu-latest instead of a fixed version (ubuntu-24.04). This modification allows the workflow to automatically run on the most current stable version of Ubuntu, ensuring compatibility with up-to-date software and security enhancements.
  2. Enhanced Failure Notifications:
    • Failure notifications are now active in the CI workflow. The Slack webhook feature is enabled to alert the team immediately when an integration job encounters issues, thereby facilitating prompt issue resolution and maintaining smooth project progression.

How to upgrade

ValidMind Platform

To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:

  • Windows: Ctrl + Shift + R OR Ctrl + F5
  • MacOS: ⌘ Cmd + Shift + R OR hold down ⌘ Cmd and click the Reload button

ValidMind Library

To upgrade the ValidMind Library:2

  1. In your Jupyter Notebook:

    • Using JupyterHub:3 Hard refresh your browser tab.
    • In your own developer environment:4 Restart your notebook.

  2. Then within a code cell or your terminal, run:

    %pip install --upgrade validmind

You may need to restart your kernel after running the upgrade package for changes to be applied.