A Beginner’s Guide to Semantic Search
summary
Compares classic TF-IDF and BM25 lexical search with dense neural embeddings and shows concrete tradeoffs: lexical can be tuned but fails on vocabulary mismatch (searching “God particle” returns 7 docs vs “Higgs boson” 12,000) and long queries (example: Uniqlo blue jeans with pockets yields no hits), while embeddings let text images and audio map into ~1k–4k dimensional vectors indexed into Elasticsearch/OpenSearch/Solr and retrieved by KNN or approximate methods for scale — next step covers handling 1 billion vectors.
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chapters (9)
- 0:00Introduction
- 1:00Lexical Search
- 2:42Limitations
- 4:10Query Handling
- 6:18Vectors
- 7:05Vector Space
- 8:07Query Time
- 10:09How to measure similarity
- 11:05Conclusion
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