Challenges of Medical RAG
One primary challenge is scalability. As the volume of medical data grows at a CAGR of >35%, RAG systems must efficiently process and retrieve relevant information without compromising speed or accuracy. This is crucial in real-time applications where timely access to information can directly impact patient care.
What is Ragas?
Ragas (retrieval-augmented generation assessment) is a popular, open-source, automated evaluation framework designed to evaluate RAG pipelines. The Ragas framework provides tools and metrics to assess the performance of these pipelines, focusing on aspects such as context relevancy, context recall, faithfulness, and answer relevancy. It employs LLM-as-a-judge for reference-free evaluations, which minimizes the need for human-annotated data and provides human-like feedback. This makes the evaluation process more efficient and cost-effective.
Strategies for Evaluating RAG
A typical strategy for robust evaluation of RAG involves the following process:
- Generate a set of synthetically generated triplets (question-answer-context) based on the documents in the vector store.
- Run evaluation precision/recall metrics for each sample question by running it through the RAG and comparing the response and context to ground truth.
- Filter out low-quality synthetic samples.
- Run the sample queries on the actual RAG and evaluate using the metrics using synthetic context and response as ground truth.
Set up
To get started, create a free account with the NVIDIA API Catalog and follow these steps:
- Select any model.
- Choose Python, Get API Key, and collect context and results.
- Store all data in a HF dataset for RAGAS.
- Override OpenAI LLM and embedding with NVIDIA AI endpoints.
Apply to Semantic Search
You can further modify the system to evaluate semantic search based on keywords, as opposed to question/answer pairs. In this case, you extract the keyphrases from Ragas and ignore the generated testset of question/answer data. This is often useful in medical systems where a full RAG pipeline is not yet deployed.
Customizing for Semantic Search
As mentioned earlier, default evaluation metrics are not always sufficient for medical systems, and often must be customized to support domain-specific challenges.
Conclusion
RAG has emerged as a powerful approach, combining the strengths of LLMs and dense vector representations. By using dense vector representations, RAG models can scale efficiently, making them well-suited for large-scale enterprise applications, such as multilingual customer service chatbots and code generation agents. As LLMs continue to evolve, it is clear that RAG will play an increasingly important role in driving innovation and delivering high-quality, intelligent systems in medicine.
FAQs
Q: What is the main challenge in medical RAG?
A: Scalability, as the volume of medical data grows at a CAGR of >35%.
Q: What is Ragas?
A: Ragas is a popular, open-source, automated evaluation framework designed to evaluate RAG pipelines.
Q: What are the key aspects evaluated in RAG pipelines?
A: Context relevancy, context recall, faithfulness, and answer relevancy.
Q: What is the advantage of using LLM-as-a-judge in evaluation?
A: It minimizes the need for human-annotated data and provides human-like feedback, making the evaluation process more efficient and cost-effective.
Q: What is the importance of proper evaluation in medical RAG systems?
A: It ensures the system provides accurate, relevant, and up-to-date information and remains faithful to the retrieved context.
