Intel Labs Introduces fastRAG: Enhancing Efficient Generative AI

Efficient, Privacy-Preserving, and Factual Generative AI

Intel Labs has developed fastRAG, an extension to the open-source Haystack project. This framework serves as an efficient generative AI tool, providing several advantages. By utilizing a retriever model, fastRAG can efficiently extract up-to-date information from external knowledge bases, such as databases containing doctor-patient interaction transcripts. Additionally, it employs a generative model that produces answers based on the retrieved documents. Rather than relying on a large generative model with an extensive parameter count to store all knowledge, fastRAG’s generative model focuses on conversational question answering within a given language, leveraging the retrieved information.

The benefits of this approach for medical institutions are as follows:

Access to the Latest Knowledge without Training Investments

With faster, medical institutions can benefit from the latest knowledge without the need to invest in or possess the expertise to train a language model from scratch. The framework enables ongoing access to up-to-date information, eliminating the necessity for frequent re-training.

Smaller Models with Scalable Architecture

fastRAG employs significantly smaller models that possess an architecture composed of scalable, CPU-friendly components. This reduction in model size lowers the compute requirements for deployment, making it more efficient and accessible for medical institutions.

Knowledge Base Scaling and Collaboration

Medical institutions can scale their knowledge base across all participating members by collaborating with other institutions and pooling their knowledge databases. This collaborative approach ensures more comprehensive access to valuable insights and can be extended to locales where professional expertise is scarce.

To illustrate the capabilities of faster, consider the following patient-doctor interaction:

Patient: Hi doctor, I am experiencing a one-sided headache. Although infrequent, the pain occurs behind my right eye and temple area. Presently, I am also dealing with a severe cold and cough. The headache began late this morning and intensifies when I cough, shake my head, or bend over. Approximately four months ago, I encountered a similar headache. At that time, it appeared in the midafternoon and lasted until the following morning. Prior to that, I had a headache on the right side, which persisted for an entire day. I am currently taking CCB and Protonix. I am worried that this may indicate a brain tumor. Please assist me.

Here’s the response generated by fastRAG to address the headache inquiry:

fastRAG Response: Based on your symptoms, it is highly unlikely that you have a brain tumor. Your headache is most likely a tension headache or a migraine. Although the cough and cold symptoms may be related, they could also be a separate issue. We recommend taking painkillers and anti-inflammatory medication for the headache, along with cough syrup for the cough. Additionally, consider obtaining an X-ray of your sinuses to rule out any sinus infections. If your symptoms persist or worsen, we strongly advise consulting your doctor for further evaluation.

One significant advantage of fastRAG is its ability to generate specific answers with supporting document links, derived from an institution’s actual interaction transcripts. This facilitates fact-checking while minimizing generic disclaimers. Moreover, fastRAG allows institutions to keep their knowledge bases private and easily update them with the latest information, surpassing the challenges associated with retraining AI models. Institutions can effectively supplement and pool physicians’ knowledge by limiting interactions to physicians and the AI assistant. However, it is crucial to follow responsible AI best practices to ensure responses are accurate, unbiased, and devoid of potential issues—a task best left to domain experts.

fastRAG: Advancing Efficient Retrieval-Augmented Generation

fastRAG represents a research framework designed to streamline the development of retrieval-augmented generative pipelines. Its primary objective is to maximize the efficiency of recovery augmented generation by leveraging state-of-the-art retrieval and generative models. The framework encompasses various sparse and dense retrieval models, along with different extractive and generative information processing models. fastRAG aims to provide researchers and developers with a comprehensive toolset for exploring and advancing the retrieval augmented generation field.

Key Features of fastRAG:

Retrieval Augmented X: Efficient and Fast Generative Applications

fastRAG serves as a framework for developing efficient and fast retrieval augmented generative applications. It harnesses the power of the latest transformer-based NLP models, enabling rapid and accurate generation.

Optimized Models for Enhanced Compute Efficiency

fastRAG includes optimized models that significantly enhance compute efficiency within supported pipelines. These optimizations ensure maximum hardware utilization, reduced latency, and increased throughput. Leveraging Intel’s advancements in PyTorch (IPEX) and Transformers, fastRAG delivers superior performance.

Intel Optimizations (TBA): Harnessing Cutting-Edge Intel Technologies

Utilizing Intel’s latest optimizations, fastRAG maximizes hardware utilization, reduces latency, and improves overall pipeline throughput. Leveraging Intel extensions for PyTorch (IPEX) and Intel extension for Transformers, fastRAG ensures an optimized experience.

Customizable Framework Compatible with Haystack

Built upon Haystack and HuggingFace, fastRAG offers complete compatibility with the Haystack framework. All components of fastRAG are fully compatible, facilitating seamless integration and customization.

In conclusion, fastRAG empowers medical institutions with an efficient and privacy-preserving generative AI framework. By leveraging retrieval augmented generation, institutions gain access to up-to-date knowledge, reduced training investments, and scalable models. Furthermore, the collaborative potential of fastRAG allows for the pooling of knowledge databases, expanding the reach of valuable insights. With Intel’s optimizations, fastRAG paves the way for efficient and high-performance retrieval augmented generation, propelling advancements in the field of generative AI.

Source: Intel and Github