A cool step by step guide! Thanks for sharing Isaac Flath
In this new blog post I cover: 1. Why traditional search fails 2. Vector embeddings with LanceDB 3. Why that's not enough 4. Chunking, hybrid search, and re-ranking 90% of users never scroll past the first page of search results, and most only scan the top 3-5 entries before giving up. For content creators, this is a nightmare - your valuable tutorials and explanations are essentially invisible. The problem? Traditional search relies on exact keyword matching, which fails miserably with specialized technical vocabulary. Here's the fundamental issue: semantic relationships between technical concepts don't translate to keyword searches. My tutorial on custom FastHTML tags is highly relevant to someone looking for web components, but that term never appears in my post! So how do we make technical content discoverable by meaning rather than just keywords? The answer lies in implementing a proper semantic search system. After experimenting with various approaches, there's a three-layer solution that dramatically improves content discovery: 1. Vector embeddings: Convert your content into numerical representations that capture meaning, not just keywords. This allows users to find conceptually related content even when terminology differs. 2. Chunking strategy: Don't embed entire documents. Break content into meaningful sections that preserve context while remaining focused enough to be useful in search results. 3. Hybrid search + re-ranking: Combine vector similarity with keyword matching (BM25), then apply a cross-encoder to re-rank results for maximum relevance. That's your MVP 🔥 Key principles I've learned: • Semantic search isn't magic - it's about transforming text into numbers that capture meaning • Domain knowledge is crucial - understand your content and actually test your search system • Hybrid approaches outperform single methods - vector search + keyword matching + re-ranking works better than any single approach • Chunking matters - how you divide content dramatically impacts retrieval quality What's your experience with content discovery? Have you implemented semantic search for your technical content? I'd love to hear what's working (or not working) for you! Read it now! https://lnkd.in/eyiRTe6n