Philipp Krenn

Information Retrieval from the Ground Up - Philipp Krenn, Elastic

Jul 27, 2025 · 1:48:07

Philipp Krenn of Elastic demonstrates that vector search is only a feature of information retrieval, not its foundation, in this hands-on workshop on retrieval for RAG. He contrasts classic keyword search—using tokenization, stemming, stopword removal, and BM25 scoring with the inverted index—with sparse (Elser/Splade) and dense (OpenAI text-embedding-small) vector embeddings. Krenn explains why hybrid search, combining lexical and semantic methods via reciprocal rank fusion (RRF), outperforms any single approach, especially for brand or exact-match queries where keyword search remains superior. He also covers scoring normalization, chunking strategies, and the new `text_similarity_reranker` retriever, showing how Elasticsearch's retrievers API enables two-stage retrieval with a cross-encoder for re-ranking. The session emphasizes that retrieval quality depends on evaluating against a golden dataset or LLM-as-judge, and that 'it depends' is the correct answer for choosing between PgVector and dedicated search engines.

Vector Search Benchmark[eting] - Philipp Krenn, Elastic

Jun 27, 2025 · 14:10

Philipp Krenn from Elastic dissects 'benchmarketing' and explains why most vector search benchmarks are unreliable due to selective scenarios, outdated competitor versions, and omitted quality metrics like precision-recall. He highlights that read-only benchmarks don't reflect real workloads, filtering can slow HNSW-based search, and implicit biases favor the benchmarker's own system. Krenn advises building automated, reproducible benchmarks (like Elastic's nightly Rally tool) to avoid the 'boiling frog' problem of gradual performance degradation. He concludes that only running your own tailored benchmarks yields trustworthy results, urging listeners to learn from even flawed benchmarks rather than dismissing them entirely.