LLM powered chatbot for nutrition science information


Project: Nutrition Science Chatbot

Situation

The project needed a way to make a large body of nutrition research easier to access, answer user questions instantly, reduce manual research effort for common topics, and maintain scientific grounding in the responses.

Task

Build an AI-powered chatbot that could ingest nutrition content, retrieve relevant source material, generate useful answers, and keep the experience reliable enough for ongoing user research and discovery.

Action

  • Built an automated content pipeline for scraping, cleaning, chunking, and storing nutrition research in a vector database
  • Developed a chat interface for natural-language question answering with source citations and conversation history
  • Added knowledge management workflows for regular content updates, quality control, and feedback collection
  • Integrated OpenRouter for LLM access alongside custom retrieval endpoints, monitoring, and analytics

Result

  • 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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