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
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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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