Dippy innovatively adopts a long-term memory system based on the Transformer architecture, which builds a dynamic memory map by analyzing users' conversation history. This feature is different from the short-term memory mechanism of ordinary chat tools, which can store more than 30 days of conversation context and intelligently extract key features (such as personal preferences, behavioral patterns, etc.) to form a user profile.
The technical realization consists of three key layers:
- Semantic Memory Layer: Understanding the deeper meaning of a conversation and indexing the associations
- Situational memory layer: recording specific dialog scenes and character states
- Emotional memory layer: capturing users' emotional trends
Typical application scenarios such as: when the user mentions 'the movie discussed last week', the AI can accurately retrieve the related conversation; in the continuous chat, the character will actively refer to the interests that the user mentioned 2 months ago. Empirical data shows that this feature improves the naturalness of the conversation by 62%.
This answer comes from the articleDippy: an interactive tool for chatting with AI charactersThe
