Darlyne AI: A Deterministic, Structural Retrieval Engine for PGx Exploration

The exponential growth of pharmacogenomic (PGx) data has created a heavily siloed landscape, forcing researchers to manually synthesize findings across disparate databases. While artificial intelligence and Retrieval-Augmented Generation (RAG) offer potential solutions to this fragmentation, current probabilistic models suffer from severe limitations in biomedical contexts, primarily "hallucinations" and inaccurate source retrieval. We propose a solution to the PGx data fragmentation problem using a deterministic, structural retrieval architecture. By aggregating proven clinical topologies into a unified PostgreSQL matrix and restricting the Large Language Model (LLM) strictly to these verified structural edges, we eliminate probabilistic guessing. The resulting platform, Darlyne, serves as a centralized, hallucination-free knowledge graph that democratizes precision medicine research.