Softwareproject

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👨‍💻 Contributors

• A. Doni Adithya (23bds007)
• L. Sahith Akash Manikanta (23bds031)
• Prasanna Kote (23bds045)
• C. Pothan Sai Reddy (23bds017)
• K. Ram Charan (23bds029)
• S. Sasi Rekha (23bds049)

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🧠 MediBot: AI-Based Medical Chatbot for Document-Driven Question Answering

MediBot is an intelligent chatbot built to simplify access to medical information through natural language queries. Instead of using traditional keyword searches, MediBot leverages cutting-edge AI — transformers, semantic embeddings, and vector databases — to fetch contextually accurate responses from static medical PDFs.

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🩺 Purpose

The primary goal of MediBot is to provide a fast, efficient, and context-aware interface for interacting with medical literature. This chatbot is best suited for:

• 🧑‍⚕️ Healthcare professionals
• 🎓 Medical students
• 🧠 Curious individuals exploring symptoms or medical knowledge

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🚀 Project Highlights

• 🔍 Semantic Search: Uses MiniLM sentence embeddings for contextual understanding.
• 📄 Document-Based QA: Answers are retrieved from actual medical documents.
• ⚡ Fast & Lightweight: Responds in under 50s even on CPU-based setups.
• 🧱 Modular Pipeline: Easily extendable for other document types or use cases.

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🧰 Tech Stack

Category	Tools/Libraries
Language Model	all-MiniLM-L6-v2 (SentenceTransformers)
Embeddings & Retrieval	ChromaDB
PDF Parsing	LangChain, PyPDFLoader
Text Chunking	RecursiveCharacterTextSplitter
Interface	Gradio
Deployment	Gradio

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🛠️ System Workflow

                ┌────────────────────────┐
                │   Medical PDF Input    │
                └─────────┬──────────────┘
                          ↓
     ┌──────────────────────────────┐
     │ Parse & Chunk Text (LangChain│
     │ + RecursiveCharacterSplitter)│
     └─────────┬────────────────────┘
               ↓
     ┌─────────────────────────────┐
     │ Generate Embeddings         │
     │ (MiniLM Transformer)        │
     └─────────┬───────────────────┘
               ↓
     ┌─────────────────────────────┐
     │ Store in Vector DB (ChromaDB)│
     └─────────┬───────────────────┘
               ↓
     ┌─────────────────────────────┐
     │ Query Processing via Gradio │
     └─────────────────────────────┘

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📊 Performance Summary

Metric	MediBot (MiniLM + ChromaDB)	Keyword Search	Fine-tuned BioGPT
Accuracy (%)	88	60	95
Contextual Relevance	92	55	96
Latency (s, CPU)	30–50	N/A	High

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🧪 How It Works

1. Input Source: Trusted PDF medical encyclopedia.
2. Parsing: Text extracted using LangChain's PyPDFLoader.
3. Chunking: Splits text into semantically coherent segments.
4. Embedding: Uses all-MiniLM-L6-v2 model for both chunks and queries.
5. Search: ChromaDB performs cosine similarity search.
6. Output: Most relevant answer displayed via Gradio interface.

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3. Hugging Face Spaces / Railway / Render / AWS EC2

Ensure Python 3.10+, torch, transformers, gradio, accelerate, sentencepiece are installed.

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🔐 Security & Compliance

• HTTPS, token-based authentication
• Log anonymization
• HIPAA compliance (for sensitive data scenarios)

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🧠 Key Takeaways

• ⚡ Embedding + semantic search outperforms traditional search.
• 🧩 Modular pipeline = easy customization.
• 🧑‍🎓 Excellent for educational and support use-cases.
• 🌍 Supports scalable expansion to more documents and languages.

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🧪 Experimental Setup

• Environment: Google Collab
• Libraries: HuggingFace Transformers, LangChain, SentenceTransformers
• Platform: CPU (GPU supported for acceleration)
• Interface: Gradio

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