Tutorialraglangchainweb scrapingchromadb
RAG Builds Real-Time Coffee Recommendation Bot
|
7.2
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.
Scoring Rationale
Practical, actionable RAG tutorial with concrete scraping and embedding details, but limited novelty and single-source coverage.
Practice with real FinTech & Trading data
90 SQL & Python problems · 15 industry datasets
Used by DS/ML engineers at top companies
Active Verified Users by Income TierEasyTechnology Stocks with High BetaMediumPortfolio Performance ScorecardHard
250 free problems · No credit card
See all FinTech & Trading problems
