ٍAdvanced RAG-Based Chatbot for Post-Childbirth Support

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? Advanced RAG-Based Chatbot for Post-Childbirth Support

Project Overview

This project introduces an intelligent chatbot designed to support women after childbirth by providing accurate, reliable, and personalized information. The system is powered by Retrieval-Augmented Generation (RAG) to ensure responses are grounded in trusted medical and maternal-care knowledge.

Problem Statement

New mothers often face physical, emotional, and psychological challenges after childbirth, while reliable medical guidance may not always be immediately accessible. Generic chatbots can generate unsafe or inaccurate health advice if not properly grounded in verified sources.

Proposed Solution

The chatbot combines Large Language Models (LLMs) with a RAG architecture, allowing it to retrieve information from curated medical documents (postnatal care guidelines, mental health resources, breastfeeding manuals) before generating responses. This ensures medically informed, context-aware, and safe interactions.

Key Features

Postpartum care guidance (recovery, nutrition, breastfeeding).

Mental health support for postnatal stress and anxiety.

Evidence-based answers retrieved from trusted medical sources.

Natural, empathetic conversational experience.

Reduced hallucinations through document-grounded responses.

Privacy-aware and non-diagnostic AI assistance.

Technologies Used

Retrieval-Augmented Generation (RAG)

Large Language Models (LLMs)

Vector Databases (FAISS / Chroma)

Python, LangChain

Medical knowledge base integration

Impact

The chatbot provides accessible, trustworthy post-childbirth support, helping new mothers feel informed and supported while demonstrating the safe application of AI in sensitive healthcare domains.