A company is building a conversational AI assistant on Amazon Bedrock.

Amazon Kendra Experience Builder provides a fully managed, low-code solution for building conversational search and question-answering applications. AWS documentation states that Kendra natively supports semantic search, vector embeddings, and vector-based retrieval, making it well suited for RAG-style applications with minimal development effort.

When integrated with Amazon Bedrock, Kendra can act as the retrieval layer, handling document ingestion, indexing, embedding generation, and relevance ranking automatically. This eliminates the need to manually manage embedding models, vector databases, and search logic.

Options B and C require custom schema design, vector indexing, query logic, and operational management of PostgreSQL instances. Although pgvector supports vector search, it significantly increases development and maintenance effort. Option D is unrelated to vector search and is used only for metadata cataloging.

AWS explicitly positions Amazon Kendra as the fastest way to build enterprise-grade conversational assistants that integrate with foundation models.

Therefore, Option A is the correct and most AWS-aligned solution.