A Memory-Augmented Conversational AI Framework Using Vector Databases and Real-Time API Integration
Abstract
Abstract In the current state-of-the-art conversational AI systems, issues related to sustaining context and personalization continue to persist due to the problem inherent to using fixed-size context windows in big language models. In this research work, a Memory-Augmented Conversational AI Framework has been presented which combines the vector databases with real-time API services for the purpose of enabling long-term memory retention as well as dynamic knowledge acquisition capabilities. User interactions are saved in the form of vector embeddings within the database and can be retrieved for semantic search purposes. The proposed model is an improvement on contextual understanding, personalization, and adaptation of conversational agents by applying retrieval-augmented generation (RAG) methods in combination with memory-augmented architectures. Experimental evaluation indicates that the proposed system performs significantly better than previous stateless chatbots with regard to response accuracy and consistency. Also, explainable retrieval techniques contribute to the transparent nature of the response generation process. It is shown that memory-augmented AI solutions have great potential in the development of intelligent assistants that can be used in smart cities, recommendation engines, etc.
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