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¶ ObjLearning
The ObjLearning class provides a comprehensive framework for managing
learning resources within the system. It enables teams to catalog,
organize, and retrieve educational materials, documentation, tutorials,
and reference materials related to various modules and packages.
¶ Table of Contents
- Overview
- Key Features
- Database Schema
- Configuration
- CLI Commands
- Usage Examples
- Resource Types
- Best Practices
¶ Overview
ObjLearning manages a centralized repository of learning resources,
including:
- External websites and tutorials
- YouTube videos and playlists
- Academic papers and research
- Documentation files
- Books and reference materials
- Internal markdown documentation
The system supports automatic import of markdown files and manual
curation through YAML configuration files.
¶ Key Features
¶ Resource Management
- CRUD Operations: Create, read, update, and delete learning
resources - Rich Metadata: Store titles, descriptions, authors, tags, and
URLs - Module Organization: Organize resources by module (e.g., ObjML,
ObjData) - Package Scoping: Scope resources to specific packages
¶ Automatic Documentation Import
- Markdown Scanning: Recursively scans directories for .md files
- Metadata Extraction: Automatically extracts titles and
descriptions - Module Categorization: Intelligently categorizes based on file
paths - Batch Import: Import entire documentation directories at once
¶ Search and Filtering
- Multi-Criteria Search: Filter by module, resource type, or tags
- Result Limiting: Control the number of results returned
- Combined Filters: Use multiple filters simultaneously
¶ CLI Interface
- User-Friendly: Rich table formatting with color-coded output
- Type-Based Filtering: Filter by resource type (Website, YouTube,
etc.) - Tag-Based Search: Find resources by specific tags
¶ Database Schema
¶ Table: def_learning
CREATE TABLE def_learning (
guid CHAR(36) NOT NULL,
package VARCHAR(255) NOT NULL,
module VARCHAR(255) NOT NULL,
title VARCHAR(500) NOT NULL,
description TEXT,
resource_type VARCHAR(50) NOT NULL,
url TEXT NOT NULL,
author VARCHAR(255),
tags VARCHAR(500),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE
CURRENT_TIMESTAMP,
PRIMARY KEY (guid, package, module)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
¶ Schema Details
- guid: Unique identifier (UUID v4)
- package: Package name (e.g.,
factory.core) - module: Module name (e.g.,
ObjML,HowTo,Architecture) - title: Resource title (max 500 characters)
- description: Detailed description (TEXT field)
- resource_type: Type of resource (see Resource Types)
- url: URL or file path to the resource
- author: Resource author or creator
- tags: Comma-separated tags for searching
- created_at: Timestamp when resource was added
- updated_at: Timestamp of last update
¶ Configuration
¶ YAML Configuration File
Resources are defined in factory.core/ObjLearning.yaml:
database:
schema:
def_learning: |
CREATE TABLE def_learning (
guid CHAR(36) NOT NULL,
package VARCHAR(255) NOT NULL,
module VARCHAR(255) NOT NULL,
...
);
learning_resources:
ObjML:
- title: "Weight of Evidence (WOE) and Information Value (IV)"
description: "Comprehensive tutorial with examples"
resource_type: "Website"
url: "https://www.listendata.com/2015/03/woe-iv.html"
author: "Listendata"
tags: "WOE,IV,feature-engineering,credit-scoring"
- title: "Credit Risk Modeling in Python"
description: "Interactive tutorial with hands-on exercises"
resource_type: "Tutorial"
url: "https://www.datacamp.com/courses/credit-risk"
author: "DataCamp"
tags: "python,credit-risk,tutorial,interactive"
¶ Module Categorization
When importing markdown files, ObjLearning automatically categorizes
them by module based on file paths and names:
| Pattern | Module |
|---|---|
howto* |
HowTo |
architecture* |
Architecture |
config* |
Configuration |
workflow* |
Workflow |
scorecard*, objml* |
ObjML |
policies/* |
Policies |
package.* |
Package. |
| Other | General |
¶ CLI Commands
¶ Create Table
Creates the def_learning table if it doesn't exist:
python factory.core/ObjLearning.py create-table
¶ Load Resources from YAML
Load all resources defined in ObjLearning.yaml:
# Load all modules
python factory.core/ObjLearning.py load-resources
# Load specific module only
python factory.core/ObjLearning.py load-resources --module ObjML
¶ List Resources
Display learning resources with optional filters:
# List all resources
python factory.core/ObjLearning.py list-resources
# Filter by module
python factory.core/ObjLearning.py list-resources --module ObjML
# Filter by resource type
python factory.core/ObjLearning.py list-resources \
--resource-type YouTube
# Filter by tag
python factory.core/ObjLearning.py list-resources --tag WOE
# Limit results
python factory.core/ObjLearning.py list-resources --limit 10
# Combine filters
python factory.core/ObjLearning.py list-resources \
--module ObjML \
--resource-type Website \
--limit 5
¶ Show Resource Details
Display detailed information about a specific resource:
python factory.core/ObjLearning.py show-resource <guid>
¶ Import Markdown Documentation
Scan and import markdown files from a directory:
# Import from resource.notes
python factory.core/ObjLearning.py import-markdown resource.notes
# Import with custom package
python factory.core/ObjLearning.py import-markdown \
/path/to/docs \
--package custom.package
¶ Clear Module Resources
Remove all resources for a specific module:
python factory.core/ObjLearning.py clear-module ObjML
Warning: This action requires confirmation and cannot be undone.
¶ Usage Examples
¶ Python API
¶ Basic Resource Insertion
from ObjLearning import ObjLearning
obj = ObjLearning(0)
# Insert a learning resource
guid = obj.insert_learning_resource(
module="ObjML",
title="Scikit-learn Documentation",
description="Official scikit-learn machine learning library docs",
resource_type="Documentation",
url="https://scikit-learn.org/stable/",
author="Scikit-learn Team",
tags="python,machine-learning,scikit-learn,official",
package="factory.core"
)
print(f"Inserted resource with GUID: {guid}")
¶ Querying Resources
# Get all ObjML resources
resources = obj.list_learning_resources(module="ObjML")
# Get YouTube videos only
videos = obj.list_learning_resources(resource_type="YouTube")
# Search by tag
woe_resources = obj.list_learning_resources(tag="WOE")
# Combined search
results = obj.list_learning_resources(
module="ObjML",
resource_type="Website",
tag="credit-scoring",
limit=10
)
for resource in results:
print(f"{resource['title']} - {resource['url']}")
¶ Loading from YAML
# Load all modules from YAML
obj.load_resources_from_yaml()
# Load specific module
obj.load_resources_from_yaml(module="ObjML")
¶ Importing Markdown Files
# Scan and import markdown documentation
obj.import_markdown_resources(
directory="resource.notes",
package="factory.core"
)
¶ Deleting Resources
# Delete a specific resource
obj.delete_learning_resource(
guid="abc-123-def-456",
package="factory.core",
module="ObjML"
)
# Clear all resources for a module
obj.clear_module_resources("ObjML")
¶ Resource Types
The following resource types are supported:
| Type | Description | Example |
|---|---|---|
| Website | Web pages and online articles | Blog posts, tutorials |
| YouTube | YouTube videos and playlists | Video tutorials, lectures |
| Paper | Academic papers and research | ArXiv, journal articles |
| Tutorial | Interactive tutorials | DataCamp, Coursera |
| Documentation | Official documentation | API docs, user guides |
| Book | Books and e-books | Technical books, references |
¶ Best Practices
¶ Organizing Resources
- Use Descriptive Titles: Keep titles concise but descriptive
- Add Meaningful Tags: Use comma-separated tags for easy searching
- Include Authors: Always credit the original author or source
- Write Good Descriptions: Provide context and key takeaways
¶ Tagging Strategy
Recommended tag categories:
- Topic:
WOE,IV,credit-scoring,machine-learning - Technology:
python,scikit-learn,tensorflow - Level:
beginner,intermediate,advanced - Type:
tutorial,reference,example,theory - Domain:
finance,healthcare,retail
¶ Markdown Documentation
When creating markdown files for import:
- Start with H1 Heading: Use
# Titlefor the main heading - Add Description: First paragraph becomes the description
- Use Descriptive Names: File names help with categorization
- Organize by Topic: Use subdirectories for different modules
Example markdown structure:
# How to Configure Machine Learning Models
This guide explains the configuration options for ML models in the
system. It covers data preprocessing, model selection, and hyperparameter
tuning.
## Prerequisites
...
¶ Security Considerations
- URL Validation: Always validate URLs before adding resources
- SQL Injection Prevention: The system uses parameterized queries
and SQL escaping - Access Control: Limit write access to the def_learning table
- Content Review: Review imported markdown before loading
¶ API Reference
¶ Class Methods
¶ __init__(db=0)
Initializes the ObjLearning instance.
Parameters:
db(int): Database instance to use (default: 0)
¶ create_table_if_not_exists()
Creates the def_learning table if it doesn't exist.
Returns: None
¶ insert_learning_resource(...)
Inserts a learning resource into the database.
Parameters:
module(str): Module name (e.g., 'ObjML')title(str): Resource titledescription(str): Resource descriptionresource_type(str): Type of resourceurl(str): URL to the resourceauthor(str, optional): Resource authortags(str, optional): Comma-separated tagspackage(str): Package name (default: 'factory.core')
Returns: str (GUID of inserted resource)
¶ list_learning_resources(...)
Lists learning resources from the database.
Parameters:
module(str, optional): Module filterresource_type(str, optional): Resource type filtertag(str, optional): Tag filterlimit(int, optional): Result limit
Returns: List[Dict] (List of resources)
¶ delete_learning_resource(guid, package, module=None)
Deletes a learning resource from the database.
Parameters:
guid(str): Resource GUIDpackage(str): Package namemodule(str, optional): Module name
Returns: None
¶ clear_module_resources(module)
Clears all learning resources for a specific module.
Parameters:
module(str): Module name
Returns: None
¶ scan_markdown_files(directory)
Scans markdown files in a directory and extracts metadata.
Parameters:
directory(str): Directory path to scan
Returns: List[Dict] (List of markdown file metadata)
¶ import_markdown_resources(directory, package='factory.core')
Scans markdown files and imports them as learning resources.
Parameters:
directory(str): Directory to scanpackage(str): Package name (default: 'factory.core')
Returns: None
¶ Testing
Comprehensive tests are available in:
resource.test/pytests/factory.core/test_objlearning.py
Run tests with:
# Run all ObjLearning tests
pytest resource.test/pytests/factory.core/test_objlearning.py -v
# Run specific test category
pytest resource.test/pytests/factory.core/test_objlearning.py::TestSQLSecurity -v
Test coverage includes:
- Table creation and schema validation
- CRUD operations
- YAML resource loading
- Markdown scanning and import
- Filtering and search
- SQL injection prevention
- Unicode support
- Edge cases and error handling
¶ Troubleshooting
¶ Common Issues
¶ Table Already Exists
If you see "Table already exists", this is normal. The system checks
and creates the table only if needed.
¶ Import Errors
If markdown import fails:
- Check directory path exists
- Verify read permissions
- Ensure markdown files have valid UTF-8 encoding
- Check for very long lines or special characters
¶ Duplicate Resources
The system allows duplicate titles but assigns unique GUIDs. To avoid
duplicates:
- Clear module before reloading:
clear-module <module> - Use unique titles when possible
- Check existing resources before adding
¶ Search Returns No Results
If searches return empty:
- Verify the module name is correct (case-sensitive)
- Check resource_type spelling (e.g., "YouTube" not "youtube")
- Ensure resources exist:
list-resources --limit 5
¶ Related Modules
- ObjML: Machine learning module that uses learning resources
- ObjMLDatasets: Dataset management with documentation links
- ObjData: Base data access object class
¶ Version History
- v1.0.0 (2025-12-26): Initial release
- Learning resource management
- YAML configuration support
- Markdown auto-import
- CLI commands
- Comprehensive testing
¶ Support
For issues or questions:
- Check this documentation
- Review test cases for examples
- Check the learning_resources section in ObjLearning.yaml
- Contact the development team
Last Updated: 2025-12-26
Author: TechnoCore Development Team