Hybrid Memory Architectures for Conversational AI: Integrating Vector Databases and Retrieval-Augmented Generation for Long-Term Context Management

With the rise of large language models, conversational AI systems have come a long way. However, they still have trouble keeping up with long-term context and having conversations that make sense. This study centers on the creation of a Retrieval-Augmented Generation (RAG)-based hybrid memory architecture aimed at improving conversational continuity and the generation of intelligent responses. The suggested system combines different types of memory, such as semantic, episodic, procedural, and emotional memory, so that it can store and retrieve a wide range of contextual information quickly and easily. Knowledge graphs give structured reasoning abilities, and vector databases are used for embedding-based retrieval. The system also has privacy and governance features to make sure that data is handled ethically and that users have control over the information that is stored. Experimental observations demonstrate that the amalgamation of RAG with hybrid memory markedly enhances response precision, personalization, and the retention of long-term context. The proposed framework offers a scalable and effective solution for domain-specific conversational applications, especially in the sharing of agricultural knowledge, where ongoing interaction and contextual awareness are crucial.