> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/JhonHander/obstetrics-rag-benchmark/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > A comprehensive research project comparing RAG techniques for medical question-answering in obstetrics # Obstetrics RAG Benchmark A systematic evaluation of Retrieval-Augmented Generation (RAG) architectures applied to medical question-answering in the obstetrics domain. This research project benchmarks multiple RAG strategies across various Large Language Models using the RAGAS evaluation framework. ## Overview This project investigates the effectiveness of different RAG retrieval strategies for medical Q\&A, specifically focusing on pregnancy and childbirth guidance. We implement and evaluate four distinct RAG architectures, comparing their performance across multiple state-of-the-art language models. Get up and running with your first evaluation in minutes Learn about the different RAG strategies we benchmark Understand RAGAS metrics and how we measure performance Explore the complete API documentation ## Key Features **6 RAG Strategies** Simple Semantic, Hybrid (BM25+Semantic), Hybrid-RRF, HyDE, Query Rewriter, and PageIndex **4 Core Metrics** Faithfulness, Answer Relevancy, Context Precision, and Context Recall **Multiple LLMs** Default models (GPT-4o, GPT-3.5-turbo) plus extensible registry supporting GPT-5, GPT-5.2, MediPhi, and MedGemma **ChromaDB + OpenAI** Persistent vector store with OpenAI text-embedding-3-small **Production-Ready** Built on LangChain for reliable retrieval and generation **Detailed Analytics** JSON output with timestamps and question-by-question breakdown ## Research Focus This project addresses several key questions in the RAG domain: * How do different retrieval strategies (semantic, hybrid, hypothetical embeddings, query reformulation) compare in medical Q\&A scenarios? * What is the impact of model selection on RAG performance in specialized domains? * How do we quantitatively assess retrieval quality and generation faithfulness without manual annotation? * Which RAG configuration produces the highest quality responses for obstetrics-related questions? ## Use Cases Benchmark RAG techniques for healthcare applications, compare retrieval strategies for domain-specific knowledge bases, and establish baseline performance metrics for medical Q\&A systems. Evaluate different RAG strategies side-by-side, identify optimal configurations for your use case, and understand trade-offs between retrieval approaches. Compare multiple language models on the same task, assess model-specific performance variations, and identify the best model for your requirements. Learn RAG implementation patterns, understand evaluation methodologies, and explore best practices for knowledge-augmented generation. ## Getting Started Clone the repository and install Python dependencies including LangChain, ChromaDB, and RAGAS. ```bash theme={null} git clone https://github.com/JhonHander/obstetrics-rag-benchmark.git cd obstetrics-rag-benchmark pip install -r requirements.txt ``` Set up your OpenAI API key for embeddings and LLM access. ```bash theme={null} echo "OPENAI_API_KEY=your_key_here" > .env ``` Generate vector embeddings from the medical text corpus. ```bash theme={null} python scripts/create_embeddings.py ``` Execute your first RAG evaluation and view results. ```bash theme={null} python scripts/run_evaluation.py hybrid ``` ## Research Contributions * **Systematic Evaluation**: RAGAS-based assessment of RAG architectures in the medical domain * **Multiple Architectures**: Comparison of 6 distinct RAG retrieval strategies * **Model Diversity**: Evaluation across general-purpose and specialized medical language models * **Reproducible Benchmark**: Complete pipeline from data processing to evaluation with detailed results ## Next Steps Complete installation guide Understand the fundamentals Run your first benchmark