TradChem - Traditional Medicine Database for LLM Integration

Status: ✅ READY FOR LLM INTEGRATION

A comprehensive database for traditional medicine data, optimized for integration with LLM chatbots and AI applications.

🎯 Project Overview

TradChem is an open-source traditional medicine database project designed specifically for: - LLM Chatbot Integration: Seamless integration with AI chatbots - Research Applications: Academic and scientific research - Open Contribution: Community-driven database growth - Chemical Analysis: SMILES notations and molecular data

Author: Anu Gamage
Organization: Institute of Scientific Informatics

🚀 Quick Start

Installation

```bash

Clone the repository

git clone https://github.com/INSTITUTE-OF-SCIENTIFIC-INFORMATICS/Trad-Chem.git cd Trad-Chem

Install dependencies

pip install -r requirements.txt ```

Basic Usage

```python import tradchem

Get database statistics

stats = tradchem.getdatabasestats() print(f"Database contains {stats['total_medicines']} medicines")

LLM-optimized query

result = tradchem.llmquery("immunity boost", contextlimit=3) print(f"Found {result['total_found']} relevant medicines")

Search by specific criteria

immunitymedicines = tradchem.searchbybenefits("immunity") fatiguemedicines = tradchem.searchbydisease("fatigue") ```

🤖 LLM Integration

Integration with Trad-Chem LLM Chatbot

TradChem is designed to work seamlessly with the Trad-Chem LLM Chatbot:

```python

Example LLM integration

import tradchem

def getmedicinecontext(userquery): """Get relevant medicine data for LLM context""" result = tradchem.llmquery( query=userquery, contextlimit=5, include_smiles=False # Include chemical data if needed )

return result['context_data']

Usage in LLM prompt

context = getmedicinecontext("What helps with stress and anxiety?")

Use context in your LLM prompt...

```

LLM-Friendly Features

• Structured JSON Response: Ready for LLM context injection
• Relevance Scoring: Intelligent ranking of search results
• Multi-field Search: Product names, benefits, diseases, chemical composition
• Configurable Output: Include/exclude SMILES chemical notations
• Context Limiting: Control response size for token efficiency

📊 Database Statistics

Current database contains: - 4 Traditional Medicines: Comprehensive entries with benefits and diseases - Multiple Traditional Systems: Ayurveda, Traditional Chinese Medicine - Chemical Compositions: SMILES notations for molecular analysis
- Geographic Data: Origins and regional information - English Names: Clear English translations for all traditional names

Sample Medicines:

• Kameshwari Rasayana (Vitality Enhancement Tonic)
• Ginseng Root Extract (American Ginseng Root Extract)
• Turmeric Curcumin Complex (Anti-inflammatory Complex)
• Ashwagandha Root Extract (Winter Cherry Adaptogenic Extract)

🔧 API Reference

Core Functions

llm_query(query, context_limit=5, include_smiles=False)

Main LLM query function with intelligent search and relevance scoring.

get_database_stats()

Get comprehensive database statistics for LLM context.

search_by_benefits(query, limit=10)

Search medicines by therapeutic benefits.

search_by_disease(query, limit=10)

Search medicines by treatable diseases.

get_all_medicines(include_smiles=False)

Retrieve all medicines with optional chemical data.

🤝 Contributing

We welcome contributions to expand the traditional medicine database!

How to Contribute

1. Add New Medicines: Use the provided templates in contributions/templates/
2. Improve Data Quality: Verify and enhance existing entries
3. Submit Pull Requests: Follow our contribution guidelines

Data Format

json { "product_name": "Traditional Name", "english_name": "English Translation", "description": "Detailed description", "benefits": ["Benefit 1", "Benefit 2"], "diseases": ["Disease 1", "Disease 2"], "chemical_composition": { "ingredients": { "Ingredient Name": { "compound_name": "SMILES_notation" } } }, "traditional_system": "System Name", "geographic_origin": "Region" }

🔗 Related Projects

• Trad-Chem LLM: AI chatbot for traditional medicine queries
• Research Applications: Academic papers and studies using TradChem data

📋 Recent Updates

v1.0.0 - Production Ready ✅

• FIXED: Complete resolution of null bytes corruption in Python files
• UPDATED: Author changed to Anu Gamage throughout codebase
• TRANSLATED: All Chinese comments converted to English
• IMPROVED: Enhanced database with real traditional medicines
• OPTIMIZED: Enhanced LLM query functionality with better English content
• TESTED: Comprehensive integration testing completed
• READY: Fully prepared for Trad-Chem LLM integration

Key Improvements:

• ✅ Clean UTF-8 encoding throughout all files
• ✅ Author updated to Anu Gamage in all files
• ✅ Complete English localization (no Chinese text remaining)
• ✅ Improved database content with 4 real traditional medicines
• ✅ Enhanced LLM-friendly JSON responses
• ✅ Improved search relevance scoring
• ✅ Comprehensive error handling
• ✅ All functionality tested and verified

📞 Support

• Issues: Report bugs via GitHub Issues
• Features: Request features via GitHub Discussions
• Contact: Institute of Scientific Informatics

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Traditional medicine practitioners and researchers
• Open-source community contributors
• Chemical database providers
• Academic institutions supporting this research

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Author: Anu Gamage
Organization: Institute of Scientific Informatics
Last Updated: 2024-01-01
Status: Production Ready for LLM Integration