LLM powered chatbot for nutrition science information

Problem:

1. Make extensive research content more accessible
2. Provide instant answers to nutrition questions
3. Reduce manual research time for common queries
4. Maintain scientific accuracy in responses

Solution:

End-to-end development of an AI chatbot featuring:

• Content Pipeline:
  • Automated scraping of articles (transcripts) about nutrition facts
  • Content cleaning and chunking
  • Vector database storage
• AI Chat Interface:
  • Natural language question answering
  • Source citation for all responses
  • Conversation history
• Knowledge Management:
  • Regular content updates
  • Quality control mechanisms
  • Feedback system for improvements
• API Integration:
  • OpenRouter API for LLM access
  • Custom retrieval endpoints
  • Monitoring and analytics

Impact:

• Instant access to nutrition research
• 24/7 availability for questions
• Reduced research time for common topics
• Increased content discoverability

Key Innovations:

• Hybrid retrieval-augmented generation
• Context-aware responses
• Cost-effective LLM usage

Technologies

Backend Technologies:

• Python
• Scrapy (web scraping)
• Zyte API (proxy rotation)
• LangChain (AI orchestration)
• ChromaDB (vector database)
• FastAPI (API server)
• Redis (caching)
• Sentry (error tracking)
• Pytest (testing)

Frontend Technologies:

• Next.js
• Tailwind CSS
• React Hook Form
• Vercel (hosting)

AI/ML Components:

• HuggingFace embeddings
• OpenRouter API
• Custom prompt engineering
• Retrieval-augmented generation

APIs & External Services:

• OpenRouter (LLM access)
• Vercel (deployment)
• AWS S3 (document storage)
• GitHub Actions (CI/CD)

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