🤖 LLM for Healthcare (LRCH) 🚀

🏥 LRCH: LLM using RAG & CAG for Healthcare Applications

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🧠 Overview

LRCH (Language Reasoning for Clinical Help) is a healthcare-focused large language model pipeline that intelligently switches between Cache-Augmented Generation (CAG) and Retrieval-Augmented Generation (RAG) based on the confidence of domain classification. It combines curated domain knowledge and dynamic retrieval for producing reliable, semantically accurate answers in medical contexts.

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⚙️ Architecture Summary

• 🗂️ Domain Specialization via CAG

  • Curated Q&A pairs across 10 medical specialties are fed into the LLaMA 3.1 3B model.
  • Precomputed KV caches are stored offline for each specialty to support faster, context-rich generation.
  • On receiving a query, if confidently classified into a domain, the corresponding static cache is loaded into the model for answer generation (Cache-Augmented Generation).

• 📚 Fallback with RAG

  • If a query cannot be confidently mapped to a known specialty, RAG is triggered.
  • Relevant medical documents are retrieved from a domain-specific database.
  • The retrieved context is appended to the prompt for Retrieval-Augmented Generation.

• 🧪 Domain Classification

  • A lightweight DistilBERT classifier maps each query to one of the 10 medical domains.
  • Low-confidence classifications trigger the RAG pathway instead of CAG.

• ⚡ Model & Optimization

  • All generations are powered by an quantized LLaMA-3.1 3B model for efficient performance on edge devices or resource-constrained environments.

• 🔎 Answer Evaluation

  • Each generated answer is evaluated using semantic similarity against reference answers using MiniLM-L6-v2 embeddings.

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📘 Technologies Used

🔄 Retrieval-Augmented Generation (RAG)

A method where a model retrieves external documents relevant to a query and then generates a response using both the query and the retrieved content. It boosts factual accuracy by grounding answers in real data sources.

🧠 Cache-Augmented Generation (CAG)

Instead of always relying on dynamic retrieval, CAG leverages precomputed Key-Value caches of domain-specific knowledge. When a query falls under a known domain, cached memory is used, making responses faster and more domain-aware.

🦙 LLaMA 3.1 3B (Quantized)

A state-of-the-art open LLM developed by Meta. In this project, it's quantized to int 4 format to ensure low memory usage and faster inference without compromising quality.

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🏁 Future Scope

• Add support for multi-domain fusion queries.
• Incorporate user feedback loop for continuous learning.
• Explore on-device deployment for offline clinical use-cases.

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🧑‍⚕️ Built for Smarter, Safer Healthcare AI

With LRCH, we move one step closer to intelligent, context-aware, and resource-efficient AI that can support real-world healthcare professionals and systems. 🌐💊

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📫 Contact
Satyam Solanki | satyamsolanki.official@gmail.com | https://www.linkedin.com/in/satyam-solanki/