PgVector

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.

AI Templates: Gabriela and Aishwarya

Feb 6, 2025 · 1:03:20

Gabriela de Queiroz, Aishwarya, and Pamela from Microsoft present AI templates for rapidly prototyping and deploying generative AI applications, demonstrating how startups can leverage Microsoft for Startups Founders Hub, including up to $150,000 in Azure credits and expert guidance. The workshop covers three templates: a simple chat app using GPT-3.5 Turbo, a Retrieval Augmented Generation (RAG) app on Postgres with hybrid vector and text search, and a RAG app on unstructured documents using Azure AI Search and Document Intelligence. Key insights include the importance of query rewriting, hybrid search over pure vector search, and streaming responses for better user experience. The templates are open source on GitHub and can be deployed via Codespaces, with a proxy provided to bypass Azure OpenAI approval delays.

Supabase Vector: The Postgres Vector database: Paul Copplestone

Nov 3, 2023 · 16:05

Paul Copplestone, CEO of Supabase, makes the case for pgvector as an embedded vector database within Postgres, arguing it offers production-grade performance and unique advantages for AI applications. He recounts how pgvector was contributed by a single developer, Andrew Kane, and how Supabase integrated it, leading to 12,000 new databases launched weekly with 10-15% using pgvector. Addressing benchmarks that claimed pgvector was 20x slower, Copplestone shows that adding HNSW indexing brought accuracy to 0.99, matching specialized vector databases, and a cost comparison vs. Pinecone ($410 vs. $480 for comparable queries/sec) demonstrates Postgres competitiveness. He demonstrates Postgres's extensibility with a cat image filtering example using partitions and triggers to separate good cats (similarity >0.8) from bad, all in 14 lines of SQL. Finally, he outlines future work on sharding with Citus to handle billions of vectors, and invites design partners for enterprise use cases.