An Optimized Context-Aware Conversational AI Architecture with Hybrid Vector–Semantic Retrieval, Redis Caching, and Adaptive Memory Prioritization

Abstract

Large Language Models (LLMs) have significantly advanced conversational artificial intelligence by generating fluent and contextually appropriate responses. However, conventional LLM-based chatbots are inherently stateless, making it difficult to preserve conversational continuity, user preferences, and personalized interactions across multiple sessions. This paper presents an optimized context-aware conversational AI architecture that enhances dialogue consistency through a hybrid memory framework integrating semantic vector retrieval, adaptive memory prioritization, and Redis-based caching. The proposed system combines short-term conversational context with long-term semantic memory by storing user interactions as vector embeddings and retrieving contextually relevant information using similarity search. Structured user profiles are continuously updated to capture behavioral patterns, preferences, goals, and emotional characteristics, enabling highly personalized responses. A Redis caching layer minimizes retrieval latency by storing frequently accessed conversation history and user profile data, thereby improving system responsiveness. Adaptive memory prioritization further refines retrieved information by considering semantic similarity, recency, interaction frequency, and contextual relevance before constructing prompts for the language model. The architecture employs LangChain for prompt orchestration, Pinecone for vector storage, Firebase Firestore for persistent session management, and GPT-based language models for response generation. Experimental evaluation demonstrates improved conversational coherence, enhanced personalization, reduced response latency, and greater user engagement compared with conventional stateless chatbot systems. The proposed framework provides a scalable and efficient solution for long-term conversational AI applications, particularly in personalized education, mental health assistance, neurodivergent support, digital companionship, and intelligent virtual assistants.