README.md

metadata

title: Medical Chatbot
emoji: 🤖🩺
colorFrom: blue
colorTo: purple
sdk: docker
sdk_version: latest
pinned: false
license: apache-2.0
short_description: MedicalChatbot, FAISS, Gemini, MongoDB vDB, LRU

Medical Chatbot Backend

Project Structure

The backend is organized into logical modules for better maintainability:

📁 api/

• app.py - Main FastAPI application with endpoints
• init.py - API package initialization

📁 models/

• llama.py - NVIDIA Llama model integration for search processing
• summarizer.py - Text summarization using NVIDIA Llama
• download_model.py - Model download utilities
• warmup.py - Model warmup scripts

📁 memory/

• memory_updated.py - Enhanced memory management with NVIDIA Llama summarization
• memory.py - Legacy memory implementation

📁 search/

• search.py - Web search and content extraction functionality

📁 utils/

• translation.py - Multi-language translation utilities
• vlm.py - Vision Language Model for medical image processing
• diagnosis.py - Symptom-based diagnosis utilities
• connect_mongo.py - MongoDB connection utilities
• clear_mongo.py - Database cleanup utilities
• migrate.py - Database migration scripts

Key Features

🔍 Search Integration

• Web search with up to 10 resources
• NVIDIA Llama model for keyword generation and document summarization
• Citation system with URL mapping
• Smart content filtering and validation

🧠 Enhanced Memory Management

• NVIDIA Llama-powered summarization for all text processing
• Optimized chunking and context retrieval
• Smart deduplication and merging
• Conversation continuity with concise summaries

📝 Summarization System

• Text Cleaning: Removes conversational fillers and normalizes text
• Key Phrase Extraction: Identifies medical terms and concepts
• Concise Summaries: Preserves key ideas without fluff
• NVIDIA Llama Integration: All summarization uses NVIDIA model instead of Gemini

Usage

Running the Application

# Using main entry point
python main.py

# Or directly
python api/app.py

Environment Variables

• NVIDIA_URI - NVIDIA API key for Llama model
• FlashAPI - Gemini API key
• MONGO_URI - MongoDB connection string
• INDEX_URI - FAISS index database URI

API Endpoints

POST /chat

Main chat endpoint with search mode support.

Request Body:

{
  "query": "User's medical question",
  "lang": "EN",
  "search": true,
  "user_id": "unique_user_id",
  "image_base64": "optional_base64_image",
  "img_desc": "image_description"
}

Response:

{
  "response": "Medical response with citations <URL>",
  "response_time": "2.34s"
}

Search Mode Features

When search: true:

1. Web Search: Fetches up to 10 relevant medical resources
2. Llama Processing: Generates keywords and summarizes content
3. Citation System: Replaces <#ID> tags with actual URLs
4. UI Integration: Frontend displays magnifier icons for source links

Summarization Features

All summarization tasks use NVIDIA Llama model:

• get_contextual_chunks: Summarizes conversation history and RAG chunks
• chunk_response: Chunks and summarizes bot responses
• summarize_documents: Summarizes web search results

Text Processing Pipeline

1. Clean Text: Remove conversational elements and normalize
2. Extract Key Phrases: Identify medical terms and concepts
3. Summarize: Create concise, focused summaries
4. Validate: Ensure quality and relevance

Dependencies

See requirements.txt for complete list. Key additions:

• requests - Web search functionality
• beautifulsoup4 - HTML content extraction
• NVIDIA API integration for Llama model