In the realm of medicine, incorporating advanced technologies is essential to enhance patient care and improve research methodologies. Retrieval-augmented generation (RAG) is one of these pioneering innovations, blending the power of large language models (LLMs) with external knowledge retrieval. By pulling relevant information from databases, scientific literature, and patient records, RAG systems provide a more accurate and contextually enriched response foundation, addressing limitations like outdated information and hallucinations often observed in pure LLMs.

In this overview, we’ll explore RAG’s growing role in healthcare, focusing on its potential to transform applications like drug discovery and clinical trials. We'll also dive into the methods and tools necessary to evaluate the unique demands of medical RAG systems, such as NVIDIA’s LangChain endpoints and the Ragas framework, along with the MACCROBAT dataset, a collection of patient reports from PubMed Central.

Key Challenges of Medical RAG

1. Scalability: With medical data expanding at over 35% CAGR, RAG systems need to manage and retrieve information efficiently without compromising speed, especially in scenarios where timely insights can impact patient care.

2. Specialized Language and Knowledge Requirements: Medical RAG systems require domain-specific tuning since the medical lexicon and content differ substantially from other domains like finance or law.

3. Absence of Tailored Evaluation Metrics: Unlike general-purpose RAG applications, medical RAG lacks well-suited benchmarks. Conventional metrics (like BLEU or ROUGE) emphasize text similarity rather than the factual accuracy critical in medical contexts.

4. Component-wise Evaluation: Effective evaluation requires independent scrutiny of both the retrieval and generation components. Retrieval must pull relevant, current data, and the generation component must ensure faithfulness to retrieved content.

Introducing Ragas for RAG Evaluation

Ragas, an open-source evaluation framework, offers an automated approach for assessing RAG pipelines. Its toolkit focuses on context relevancy, recall, faithfulness, and answer relevancy. Utilizing an LLM-as-a-judge model, Ragas minimizes the need for manually annotated data, making the process efficient and cost-effective.

Evaluation Strategies for RAG Systems

For robust RAG evaluation, consider these steps:

1. Synthetic Data Generation: Generate triplet data (question, answer, context) based on the vector store documents to create synthetic test data.
2. Metric-Based Evaluation: Evaluate the RAG system on metrics like precision and recall, comparing its responses to the generated synthetic data as ground truth.
3. Independent Component Evaluation: For each question, assess retrieval context relevance and the generation’s answer accuracy.

Here’s an example pipeline: given a question like “What are typical BP measurements in congestive heart failure?” the system first retrieves relevant context and then evaluates if the response addresses the question accurately.

Setting Up RAG with NVIDIA API and LangChain

To follow along, create an NVIDIA account and obtain an API key. Install the necessary packages with:

pip install langchain
pip install langchain_nvidia_ai_endpoints
pip install ragas

Download the MACCROBAT dataset, which offers comprehensive medical records that can be loaded and processed via LangChain.

from langchain_community.document_loaders import HuggingFaceDatasetLoader
from datasets import load_dataset

dataset_name = "singh-aditya/MACCROBAT_biomedical_ner"
page_content_column = "full_text"

loader = HuggingFaceDatasetLoader(dataset_name, page_content_column)
dataset = loader.load()

Using NVIDIA endpoints and LangChain, we can now build a robust test set generator and create synthetic data based on the dataset:

from ragas.testset.generator import TestsetGenerator
from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings

critic_llm = ChatNVIDIA(model="meta/llama3.1-8b-instruct")
generator_llm = ChatNVIDIA(model="mistralai/mixtral-8x7b-instruct-v0.1")
embeddings = NVIDIAEmbeddings(model="nv-embedqa-e5-v5", truncate="END")

generator = TestsetGenerator.from_langchain(
    generator_llm, critic_llm, embeddings, chunk_size=512
)
testset = generator.generate_with_langchain_docs(dataset, test_size=10)

Deploying and Evaluating the Pipeline

Deploy your RAG system on a vector store, generating sample questions from actual medical reports:

# Sample questions
["What are typical BP measurements in the case of congestive heart failure?",
 "What can scans reveal in patients with severe acute pain?",
 "Is surgical intervention necessary for liver metastasis?"]

Each question links with a retrieved context and a generated ground truth answer, which can then be used to evaluate the performance of both retrieval and generation components.

Custom Metrics with Ragas

Medical RAG systems may need custom metrics to assess retrieval precision. For instance, a metric could determine if a retrieved document is relevant enough for a search query:

from dataclasses import dataclass, field
from ragas.evaluation.metrics import MetricWithLLM, Prompt

RETRIEVAL_PRECISION = Prompt(
    name="retrieval_precision",
    instruction="Is this result relevant enough for the first page of search results? Answer '1' for yes and '0' for no.",
    input_keys=["question", "context"]
)

@dataclass
class RetrievalPrecision(MetricWithLLM):
    name: str = "retrieval_precision"
    evaluation_mode = EvaluationMode.qc
    context_relevancy_prompt: Prompt = field(default_factory=lambda: RETRIEVAL_PRECISION)

# Use this custom metric in evaluation
score = evaluate(dataset["eval"], metrics=[RetrievalPrecision()])

Structured Output for Precision and Reliability

For an efficient and reliable evaluation, structured output simplifies processing. With NVIDIA's LangChain endpoints, structure your LLM response into predefined categories (e.g., yes/no).

import enum

class Choices(enum.Enum):
    Y = "Y"
    N = "N"

structured_llm = nvidia_llm.with_structured_output(Choices)
structured_llm.invoke("Is this search result relevant to the query?")

Conclusion

RAG bridges LLMs and dense vector retrieval for highly efficient, scalable applications across medical, multilingual, and code generation domains. In healthcare, its potential to bring accurate, contextually aware responses is evident, but evaluation must prioritize accuracy, domain specificity, and cost-efficiency.

The outlined evaluation pipeline, employing synthetic test data, NVIDIA endpoints, and Ragas, offers a robust method to meet these demands. For a deeper dive, you can explore Ragas and NVIDIA Generative AI examples on GitHub.