RAG consulting & development
Service Description
Overview
This service provides a complete development of a Retrieval Augmented Generation (RAG) model for your SME’s needs. RAG is a cutting-edge AI methodology that has been gaining traction in recent years. RAG models combine the capabilities of classical large language models (LLMs) with the ability to retrieve relevant information from external knowledge sources, such as databases or document collections. This approach allows RAG models to provide more accurate and context-specific responses, stating the precise source of the information they generate. RAG models can be more resource-effective by focusing on relevant information retrieval rather than relying solely on extensive pre-training. RAG models have a wide range of potential applications, from specialized clinical tools, such as those used in molecular tumor boards, to more general applications, like serving as powerful search engines when connected to cloud storage.
We provide expertise and technical support in the following areas:
- Project design & management
- Legal & ethical support
SciLifeLab Data Centre has extensive experience in AI development and offers this comprehensive RAG service. Our service covers the entire AI lifecycle, including:
Design
- Understanding the problem
- Requirements engineering
- Data fitting
Development
- Modeling
- Evaluation
- Fine-tuning and optimization
Deployment
- Moving to production
Please note that long-term maintenance and hosting responsibilities are not included in this service and will need to be managed by the SME. However, our team can provide preparation and advisory support before the service is finalized.
How can the service help you?
This service helps SMEs use advanced AI technology by creating Retrieval Augmented Generation (RAG) models designed to meet their specific needs. With expert support through every step of the process, the service ensures the model is effective, easy to use, and ready to deploy, helping SMEs make better decisions, save time and build confidence in their AI tools.
How the service will be delivered?
The service will be delivered according to established ethical agreements and guidelines, in collaboration with the SME and researchers from the Swedish TEF-Health node.
Additional information
Provider description
The Swedish TEF-Health node is a collaboration between Karolinska Institutet, SciLifeLab and RISE, and is led by Karolinska Institutet. Together, we offer world-leading services with our unique collection of core facilities. We can grant services in expert consulting, virtual- and physical testing in the range of in vivo imaging, ex vivo OMICS, pharmaceutical development, simulated healthcare environments, AI-system validation and development, advanced data analysis and other data-driven life science.
Technical description
The service develops RAG models, leveraging advanced AI frameworks and retrieval systems. Model development involves fine-tuning LLMs using frameworks such as PyTorch and TensorFlow and integrating them with external knowledge sources, such as databases or document collections, using retrieval mechanisms like ElasticSearch. Data preprocessing includes tokenization, vectorization, and indexing to ensure efficient information retrieval. The models are optimized for accuracy, relevance, and transparency, with quality assurance protocols applied to validate performance and compliance with ethical AI guidelines. All operations adhere to established standards for AI development and incorporate best practices for interoperability and scalability.
Service customization
The service can be customized according to your specific needs. It may be required to combine this service with other services on offer.
Use case example
Context
A life science SME specializing in clinical decision support tools aims to develop an AI-driven application to assist molecular tumor boards in identifying personalized treatment options for cancer patients. The SME has access to large document collections, including scientific literature, clinical guidelines and patient case studies. However, the SME lacks the technical expertise to build a system that can retrieve relevant, context-specific information from these sources while integrating it with a large language model for natural language responses. This limitation hinders their ability to create an efficient tool for supporting oncologists, slowing product development and reducing stakeholder interest.
Objective
To develop a RAG model tailored to the SME’s needs, enabling seamless integration of large language models with their document collections. Ensure the model provides accurate, context-specific responses with transparent source references, supporting oncologists in making personalized treatment decisions. Deliver a fully optimized, production-ready model to enhance the SME's product development and scalability.
Solution
The SME collaborates with the Swedish TEF-Health node to develop a RAG model using frameworks like TensorFlow. Document collections are indexed with FAISS for efficient retrieval, and a pre-trained large language model is fine-tuned for clinical applications. The solution integrates scientific literature and clinical guidelines into the retrieval system, ensuring context-specific outputs. The final model is validated with TensorBoard for accuracy and relevance, with clear source references, and is prepared for deployment using Docker for scalability and secure handling.
Implementation
Ethical Agreement
The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring that all data handling and model development comply with GDPR and Swedish national regulations, particularly regarding the use of sensitive clinical and scientific data.
Data Preparation and Knowledge Integration
The SME’s document collections, including clinical guidelines and scientific literature, are preprocessed for integration with the RAG model. This includes text cleaning, tokenization, and embedding generation using Hugging Face Transformers. The documents are indexed with FAISS to enable efficient and accurate information retrieval.
Model Development and Fine-Tuning
A pre-trained large language model is fine-tuned on the SME’s specific domain data using TensorFlow. The retrieval system is connected to the language model to ensure context-specific outputs. Recall@K, BLEU, and precision are used as metrics to validate the model’s accuracy and relevance during development.
Validation and Optimization
The integrated RAG model is validated using the evaluation frameworks TensorBoard, enabling real-time tracking of performance metrics and optimization of hyperparameters. This ensures the model is robust.
Deployment Preparation
The final RAG model is containerized using Docker to ensure scalability and ease of deployment. Advisory support is provided to the SME, including documentation and best practices for hosting and maintaining the model post-deployment.
Benefits
- Insight Optimization: Enhancing SMEs’ ability to deliver accurate, context-specific insights for molecular tumor boards.
- Credibility: Fine-tuned and validated models build trust with stakeholders, including clinicians and regulators.
- Transparency & Compliance: Clear source references foster trust and support ethical and legal standards.
Impact
The SME’s RAG model establishes itself as a reliable and cutting-edge tool in clinical decision support, gaining recognition within the oncology and AI research communities. The model’s ability to deliver accurate, context-aware insights with transparent references enhances trust among clinicians and regulatory bodies. This innovation fosters new collaborations with academic researchers and industry stakeholders, accelerating the adoption of AI-driven personalized treatment solutions and positioning the SME as a leader in advanced healthcare technologies.
Provider & Contact
Pricing is available to registered users. SMEs receive significant state-aid reductions (GBER) — or, depending on the call, free services during the funded project. Sign in or register to see the price for your organisation.
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