Role-Playing RAG Architecture Implementation

Introduction

This document outlines the implementation of a Role-Playing Retrieval-Augmented Generation (RAG) architecture. The system enhances Large Language Models (LLMs) with role-specific memory, combining semantic relevance and emotional factors to generate contextually rich and emotionally aligned responses. The architecture leverages the Mood-Dependent Memory theory to improve role fidelity in conversational agents.

Architecture Overview

The Role-Playing RAG framework consists of four main components:

• Query Encoding ComponentExtracts semantic and emotional representations from user queries.
• Memory Encoding ComponentStores and retrieves historical role-related interactions.
• Emotional Retrieval ComponentRetrieves contextually and emotionally relevant memories.
• Response Generation ComponentConstructs responses using retrieved memory and role context.

Implementation Steps

1. Query Encoding

• Semantic Embedding: Convert the input query into a dense vector using a pretrained transformer-based embedding model.
• Emotional Encoding: Extract emotional signals using a classifier that maps the query into an 8-dimensional emotion space.

2. Memory Encoding

• Semantic Representation: Store role interactions in an embedding space.
• Emotional Representation: Annotate past interactions with emotional metadata.

3. Emotional Retrieval

• Similarity Search: Compute semantic and emotional distances to rank relevant memories.
• Retrieval Strategies: Apply combination (weighted sum of semantic and emotional scores) or sequential filtering (semantic-first, emotion-refined).

4. Response Generation

• Prompt Construction: Combine retrieved memory, character profile, and query.
• LLM Completion: Generate responses based on enriched context.

Deployment Considerations

Optimization Strategies

• Efficient Retrieval: Use vector databases such as FAISS for scalable retrieval.
• Fine-Tuning: Optimize LLMs with reinforcement learning based on personality evaluations.
• Memory Updates: Implement a memory consolidation mechanism to prioritize relevant past interactions.

Evaluation Metrics

• Personality Fidelity: Compare responses against known personality traits using MBTI or BFI scoring.
• Emotional Consistency: Measure alignment of retrieved emotions with user queries.
• User Engagement: Analyze conversation coherence and engagement using human evaluations.

Conclusion

This Role-Playing RAG architecture integrates emotional and semantic retrieval to generate highly immersive and engaging conversational agents. Future improvements may include multi-modal memory integration, reinforcement learning for retrieval refinement, and adaptive role-based persona modeling.