Building an Agentic Healthcare Retrieval System Using QQL and Qdrant
Researchers have developed an agentic healthcare retrieval system that semantically understands patient-doctor conversations. This system utilizes Qdrant for vector database storage and QQL, a SQL-like language, for declarative retrieval. The architecture integrates with Hugging Face datasets and employs an Agno agent for orchestration, aiming to provide more accurate and contextually grounded responses than traditional keyword search. AI
IMPACT This system demonstrates a novel approach to semantic retrieval in healthcare, potentially improving the accuracy and contextuality of responses derived from patient-doctor conversations.
