RAG Builds Real-Time Coffee Recommendation Bot

An independent developer builds a real-time coffee recommendation chatbot using retrieval-augmented generation (RAG), scraping JavaScript-heavy roaster sites with Zyte API and Scrapy, storing content in ChromaDB, and querying via LangChain with OpenAI (GPT-4 for generation, text-embedding-3-large for 3,072-dimension embeddings). The pipeline retrieves up to k=50 semantically similar items per query, enabling fresh, inventory-aware recommendations from Dak Coffee Roasters' catalog.