MedGeroX Documentation

Overview

MedGeroX uses Retrieval-Augmented Generation (RAG) to connect a large language model with a domain-specific scientific knowledge base. Instead of relying only on the model's pre-existing knowledge, the system retrieves relevant information from indexed scientific documents and uses that context when generating a response.

The platform is designed around scientific topics including ageing biology, geroscience, longevity research, biomarkers, multimorbidity and precision geromedicine.

Research Scope

MedGeroX is intended to support exploration of scientific literature and concepts relevant to healthy ageing and precision approaches to age-related health.

Technology Stack

• Python — application logic, document processing and backend implementation.
• LangChain — orchestration of document retrieval and language-model generation.
• Embedding Model — converts scientific text and user queries into numerical vector representations for semantic similarity search.
• Vector Database — stores embeddings and enables efficient retrieval of semantically relevant scientific content.
• Large Language Model — synthesizes retrieved context into a natural-language response.
• Flask — web application and backend interface.
• Docker — application containerization and reproducible deployment.
• GitHub Actions — automated testing, build and deployment workflows where configured.

Retrieval-Augmented Generation Pipeline

1. Scientific document ingestion

Scientific documents are collected and loaded into the document-processing pipeline. Depending on the source, documents may include research papers, reports or other structured scientific material.

2. Text extraction and chunking

Documents are converted into machine-readable text and divided into smaller chunks. Chunking allows the retrieval system to identify specific passages rather than returning entire documents for every query.

3. Embedding generation

Each text chunk is transformed into a numerical vector using an embedding model. The resulting vectors capture semantic relationships between pieces of scientific text.

4. Vector indexing

The embeddings are indexed in a vector database. This allows the system to perform semantic similarity searches when a user submits a question.

5. Query embedding

When a user asks a question, the query is also converted into a vector representation using the same embedding model.

6. Semantic retrieval

The query vector is compared with indexed document vectors. The system retrieves the most relevant scientific passages from the knowledge base.

7. Context construction

Retrieved passages are assembled into contextual information that can be supplied to the language model.

8. Response generation

The language model receives the user's question together with the retrieved context and generates a response based on the available evidence.

Example Request Flow

User Question
      ↓
Query Embedding
      ↓
Semantic Similarity Search
      ↓
Relevant Scientific Passages
      ↓
Context Assembly
      ↓
LangChain Retrieval Pipeline
      ↓
Large Language Model
      ↓
Evidence-Grounded Response
    

Why Retrieval-Augmented Generation?

General-purpose language models can generate fluent answers, but their responses are not necessarily grounded in a specific scientific knowledge base.

RAG introduces an external retrieval layer. Relevant scientific information is retrieved at query time and supplied to the language model as context.

• Domain grounding — responses can be anchored to a curated scientific knowledge base.
• Semantic retrieval — relevant information can be found even when the user's wording differs from the source text.
• Knowledge-base flexibility — documents can be updated without retraining the language model.
• Research exploration — users can interact with a large collection of scientific material through natural-language questions.

Configuration

API credentials and configuration values should be stored as environment variables rather than embedded directly in application source code.

LLM_API_KEY=your_api_key
VECTOR_DATABASE_API_KEY=your_api_key
VECTOR_INDEX_NAME=your_index
    

Deployment

MedGeroX can be packaged as a Docker container to provide a reproducible application environment. The application backend serves the web interface and coordinates document retrieval, vector search and language-model generation.

A continuous integration and deployment workflow can be implemented using GitHub Actions to automate application testing, container builds and deployment.

Limitations & Responsible Use

MedGeroX is designed primarily as a research and educational knowledge-exploration platform. Retrieval quality depends on the coverage, quality and currency of the underlying scientific knowledge base.

Generated responses should therefore be interpreted in the context of the retrieved evidence and should not be treated as an independent substitute for expert scientific or clinical judgement.