🔬Topic Classifier
Topic Classifier
Purpose: The Topic Classifier workflow categorizes content by topics, making it easier for teams to find relevant information and streamline knowledge management. This workflow enhances searchability and allows better organization of content by grouping it under relevant topics or themes.
Prerequisites:
A corpus of project documentation.
Configured Topic Classifier and Loader Agents for content ingestion.
How it Works:
The Loader Agents collect content from project sources.
The Topic Classifier scans the content and identifies topics or keywords.
The content is categorized into relevant topics, which can be used to filter or search for information more easily.
Inputs:
Documentation and content from sources like GitBook, GitHub, websites.
Keywords or training data for topic identification.
Processing:
The Topic Classifier applies machine learning algorithms to identify recurring themes or subjects within the content.
Content is sorted into categories or clusters based on identified topics.
Outputs:
Categorized content, grouped by relevant topics for easier navigation and discovery.
A list of keywords or topics used to classify the content.
Usage: Admins initiate the classification process via the admin panel, applying the Topic Classifier to the documentation to organize content.
Example: The Topic Classifier identifies topics such as "API Configuration," "User Authentication," and "Error Codes" in a software project’s documentation, grouping them accordingly.
Advanced Features:
Customizable keyword or topic settings to refine content categorization.
Troubleshooting:
Inaccurate Topic Detection: Review and adjust the classifier’s training data for improved topic recognition.
Logs:
Logs can be found under Topic Classification Logs, showing details on how content was classified.
Related Workflows:
Taxonomy Builder for creating a structured hierarchy for the topics.
Sentiment Agent for analyzing user feedback on specific topics.
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