Principle:Langgenius Dify Feature Configuration
| Knowledge Sources | |
|---|---|
| Domains | Feature Toggle Patterns Progressive Enhancement |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Feature toggle patterns enable progressive capability enhancement in AI applications by allowing developers to activate or deactivate modular features independently without modifying core application logic.
Description
Dify applications support a set of optional, orthogonal features that can be independently enabled or disabled. Each feature follows a consistent pattern: a boolean enabled flag plus feature-specific configuration parameters. This design allows developers to progressively enhance their application's capabilities without risking regressions in existing functionality.
The supported feature toggles are:
Core Interaction Features:
- More Like This (
moreLikeThisConfig) -- Generates alternative responses to the same input, allowing end users to explore different outputs. Only available in completion mode. - Suggested Questions After Answer (
suggestedQuestionsAfterAnswerConfig) -- Automatically generates follow-up question suggestions after each assistant response, encouraging continued engagement.
Voice Features:
- Speech-to-Text (
speechToTextConfig) -- Enables voice input, converting spoken language to text before sending to the LLM. - Text-to-Speech (
textToSpeechConfig) -- Converts the assistant's text responses to spoken audio. Configurable with voice selection and language preference.
Quality and Safety Features:
- Citation (
citationConfig) -- When knowledge base (RAG) retrieval is active, displays source citations alongside the generated response, enabling end users to verify information provenance. - Moderation (
moderationConfig) -- Applies content moderation rules to filter or flag inappropriate inputs and outputs. Supports multiple moderation providers and custom rule configurations. - Annotation Reply (
annotationConfig) -- Enables a curated knowledge base of predefined question-answer pairs. When enabled, the system checks incoming queries against annotated responses using embedding similarity and returns the annotation if the similarity score exceeds a configurable threshold.
Usage
Configure features when:
- Enhancing user experience with voice capabilities (speech-to-text, text-to-speech)
- Adding safety guardrails (moderation, annotation)
- Improving information transparency (citations)
- Increasing engagement (suggested questions, more-like-this)
Theoretical Basis
The feature configuration system follows the Feature Toggle pattern (also known as Feature Flags), a well-established software engineering practice for controlling feature availability at runtime.
FUNCTION evaluate_features(response, feature_config):
IF feature_config.citation.enabled AND has_retrieval_context:
response = ATTACH_CITATIONS(response, retrieval_sources)
IF feature_config.moderation.enabled:
moderation_result = MODERATE(response, feature_config.moderation.config)
IF moderation_result.flagged:
response = APPLY_MODERATION_ACTION(response, moderation_result)
IF feature_config.suggested_questions.enabled:
suggestions = GENERATE_FOLLOWUPS(response, conversation_history)
response.suggested_questions = suggestions
IF feature_config.text_to_speech.enabled:
audio = SYNTHESIZE_SPEECH(response.text, feature_config.tts.voice, feature_config.tts.language)
response.audio = audio
IF feature_config.annotation.enabled:
annotation_match = SEARCH_ANNOTATIONS(user_input, feature_config.annotation.score_threshold)
IF annotation_match:
response = annotation_match.answer
RETURN response
Design principles:
- Independence -- Each feature toggle is orthogonal; enabling one does not affect others.
- Safe defaults -- All features default to
enabled: false, ensuring a minimal baseline configuration. - Gradual rollout -- Features can be activated one at a time, allowing developers to test each capability in isolation before combining them.