Project Completed. Now I’m Going to Break It Down.

Project Completed. Now I’m Going to Break It Down. I recently finished building a full-stack semantic search engine — not just keyword search, but real context-aware search✨ Before I start sharing how each part works, here’s the big picture: 🔎 What it does Parses any website into semantic HTML chunks (≤500 tokens) Uses AI embeddings to understand meaning, not keywords Returns top-10 relevant results with 0–100% match scores Keeps the original HTML structure intact on output ⚙️ Tech behind it Backend: Django 5 + Django REST Framework Frontend: React 18 + Vite AI/ML: Sentence-Transformers + BERT tokenizer Vector DB: Milvus Lite (with smart fallback) 💡 Favorite part If Milvus isn’t available, the system doesn’t break — it automatically switches to in-process cosine similarity without changing the output. No failures, just graceful fallback. 🎥 In the next posts, I’ll share: 2: Technical Deep Dive — NLP Pipeline 3: React Architecture Excellence 4: Django REST API Best Practices Stay tuned. This one’s going to be fun. 🔥 hashtag#SemanticSearch hashtag#AI hashtag#Python hashtag#Django hashtag#React hashtag#MachineLearning hashtag#FullStackDevelopment