Building a Retrieval-Augmented Conversational Agent with Vector Databases Conversational AI is rapidly advancing, and vector databases play a crucial role in enhancing these systems. In this post, we explore how to build a retrieval-augmented conversational agent using vector databases for more dynamic and context-aware interactions. Introduction to Vector Databases in AI Vector databases, like Pinecone, are adept at handling complex data in the form of vector embeddings. They are particularly useful in AI applications for efficient data handling and quick retrieval of relevant information.