Local Hybrid Retrieval-Augmented Document QA
This work addresses the privacy-performance trade-off for organizations like banks, hospitals, and law firms, enabling them to adopt conversational AI without compromising data security, though it is incremental in combining existing retrieval methods.
The paper tackles the problem of balancing data privacy and accuracy in document question-answering systems for sensitive domains by developing a local system that combines semantic and keyword retrieval strategies, achieving competitive accuracy on complex queries across legal, scientific, and conversational documents without internet access.
Organizations handling sensitive documents face a critical dilemma: adopt cloud-based AI systems that offer powerful question-answering capabilities but compromise data privacy, or maintain local processing that ensures security but delivers poor accuracy. We present a question-answering system that resolves this trade-off by combining semantic understanding with keyword precision, operating entirely on local infrastructure without internet access. Our approach demonstrates that organizations can achieve competitive accuracy on complex queries across legal, scientific, and conversational documents while keeping all data on their machines. By balancing two complementary retrieval strategies and using consumer-grade hardware acceleration, the system delivers reliable answers with minimal errors, letting banks, hospitals, and law firms adopt conversational document AI without transmitting proprietary information to external providers. This work establishes that privacy and performance need not be mutually exclusive in enterprise AI deployment.