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This project presents a Retrieval-Augmented Generation (RAG) based Healthcare AI Assistant designed to answer user queries using a custom healthcare knowledge base. Unlike traditional chatbots that rely on general-purpose knowledge, this assistant retrieves relevant information from domain-specific documents and generates responses strictly grounded in those documents.
The project demonstrates how information retrieval and large language models (LLMs) can be combined to build reliable, context-aware AI systems, especially for sensitive domains such as healthcare.
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances language models by integrating an external knowledge source.
A RAG system works in two stages:
Retrieval – Relevant information is fetched from a document store using semantic similarity.
Generation – The retrieved information is provided as context to a language model to generate an answer.
This approach significantly reduces hallucinations and ensures that responses are fact-based and explainable.
🏥 Why RAG is Important in Healthcare
Healthcare information must be:
Accurate
Ethical
Transparent
Non-hallucinatory
RAG is particularly suitable for healthcare because:
Responses are grounded in verified documents
The system avoids making unsupported medical claims
Missing information is handled safely
Users receive informational (not diagnostic) answers
This project focuses on educational healthcare content, such as diseases, precautions, home remedies, public health, and ethics.
🏗️ System Architecture
The Healthcare RAG Assistant consists of the following components:
1️⃣ Document Loader
Reads healthcare-related .txt files from the data/ directory
Loads content into memory for processing
2️⃣ Text Chunking Module
Splits large documents into smaller chunks
Improves retrieval accuracy
Ensures efficient embedding and storage
3️⃣ Embedding Model
Uses Sentence Transformers
Converts text chunks into numerical vectors
Enables semantic similarity search
4️⃣ Vector Database (ChromaDB)
Stores document embeddings
Performs similarity-based retrieval
Returns the most relevant healthcare information
5️⃣ Prompt Engineering Layer
Combines retrieved context with user queries
Enforces strict grounding rules
Prevents hallucinations
6️⃣ Large Language Model (LLM)
Generates natural language responses
Uses OpenAI / Groq / Google Gemini APIs
Produces answers only from retrieved context
🔄 End-to-End Workflow
Healthcare documents are added to the system
Documents are chunked and embedded
Embeddings are stored in the vector database
User submits a question
The query is embedded
Relevant document chunks are retrieved
Context + question are passed to the LLM
A grounded response is generated
✂️ Text Chunking Strategy
Text chunking is a crucial step in RAG systems.
In this project:
Documents are split into small, manageable chunks
Each chunk retains meaningful context
Chunking improves:
Retrieval precision
Response relevance
System performance
Proper chunking ensures that only the most relevant healthcare information is retrieved.
🔍 Semantic Search Using Vector Embeddings
Instead of keyword matching, this project uses semantic search:
Both documents and queries are converted into embeddings
Similarity is calculated using vector distance
The system retrieves context based on meaning, not exact words
This allows the assistant to understand variations in medical terminology and user phrasing.
🧾 Prompt Design and Hallucination Control
A carefully designed prompt ensures:
The AI answers only from retrieved context
No external knowledge is used
Unknown queries are handled safely
Prompt Behavior:
If information exists → generate a detailed answer
If information is missing → return a friendly fallback message
Example fallback:
“I’m sorry, I couldn’t find relevant information about this in the provided documents.”
This design is essential for responsible AI usage in healthcare.
🏥 Healthcare Ethics and Safety Considerations
This assistant:
Does not provide medical diagnosis
Does not suggest treatments or prescriptions
Is intended for educational and informational purposes only
Ethical principles followed:
Transparency
Safety
Fairness
Non-maleficence
🧪 Testing and Validation
The system was tested using:
Healthcare-related questions
Disease symptoms and precautions
Public health concepts
Out-of-scope questions
The assistant:
Provides accurate answers when data is present
Responds politely when data is missing
Demonstrates controlled and reliable behavior
🎓 Learning Outcomes
Through this project, the following concepts were learned and applied:
Retrieval-Augmented Generation architecture
Text chunking techniques
Vector embeddings and similarity search
Vector database usage (ChromaDB)
Prompt engineering for safe AI
Responsible AI design for healthcare applications
🏁 Conclusion
This Healthcare RAG-Based AI Assistant demonstrates how modern AI systems can be built responsibly by combining document retrieval with language models. The project highlights the importance of grounding AI responses in trusted data, especially in sensitive domains like healthcare.
The system serves as a strong foundation for future enhancements such as web interfaces, APIs, or expanded medical knowledge bases.
✅ Module 1 Project Successfully Completed
🔚 End of Publication