# hybrid-search Published articles for hybrid-search. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How we selected the next vector database at Booking.com DevFeed: [How we selected the next vector database at Booking.com]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2026-08-11T10:31:50Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Database](), [Retrieval Augmented Generation (RAG)](), [Embeddings](), [Back end](), [opensearch](), [AI search]() Tags: [ai](), [database](), [embeddings](), [featured](), [genai](), [hybrid-search](), [retrieval-augmented-generation](), [retrieval-augmented-generation-rag](), [semantic](), [vector-database]() ### AI overview Booking.com explains why selecting a vector database became an infrastructure decision as embeddings and vector search expanded across its machine learning and GenAI systems. The article describes diverse functional and operational requirements, including hybrid search, multi-vector support, capacity, request rates, metadata filtering, and concurrency, and introduces OpenSearch as the initial choice. ### Source excerpt This work was done in collaboration with Klaus Schaefers. Over the past few years, embeddings and vector search have become an important capability in many of our machine learning and GenAI systems at Booking.com. We initially started with a handful of use cases and experiments, and later this capability has grown into shared infrastructure that powers similarity search, semantic filtering, and retrieval-augmented generation (RAG). We used to treat vector search as a backend implementation detail, but today it directly drives the user experience. The real win isn't only speed but also the context. Expanding the variety of domain data we can retrieve efficiently gives our system the depth of context it needs to deliver accurate, and personalized experiences across the platform. This makes selecting the underlying vector database an infrastructure decision similar to choosing a primary datastore or message queue. It has to be predictable and scalable. As more teams started using our vector store, we began seeing highly diverse functional and operational requirements across different use cases. Some teams needed advanced capabilities like hybrid search or multi-vector support, while others demanded larger vector capacities and higher RPS metrics. These architectural needs ultimately brought us to a point where we needed to reassess whether our current setup could support this next phase of growth. Context: how embeddings fit into our stack Embeddings are vectors: fixed-length arrays of numbers produced by a model to represent an item (text, image, etc.). Each vector can be seen as a point in a high-dimensional space, where distance (or similarity) between points approximates semantic relatedness. By searching for the nearest vectors to a query vector, we retrieve items that are semantically "similar". This simple mechanism enables a wide range of use cases for us due its ability to do semantic similarity search. RAG-based use cases are the most well known examples. Ano ## Hybrid Search Patterns with Postgres and pgvector DevFeed: [Hybrid Search Patterns with Postgres and pgvector]() Original publisher: [Read original article]() Author: Christopher Winslett Published: 2026-07-30T15:00:00Z Content type: article Language: en Sources: [CrunchyData Blog]() Topics: [Embeddings](), [Query (disambiguation)](), [data]() Tags: [ann](), [caching](), [hybrid-search](), [performance](), [pgvector](), [postgres](), [search](), [vector](), [vector-search](), [vectors-llms]() ### AI overview This article explains why filtered vector search in Postgres creates a tradeoff between recall and performance. It discusses how ANN indexes such as HNSW and IVFFlat differ from B-tree indexes, and introduces approaches including pgvector iterative scans, partial indexes, oversampling, and caching. ### Source excerpt Filtered vector search in Postgres forces a tradeoff between recall and speed because ANN indexes and WHERE clauses do not intersect like B-trees. Read about pgvector iterative scans, partial HNSW indexes, oversampling with pg_stats, caching, and more. ## Your agent wants to search like a 2010 quant DevFeed: [Your agent wants to search like a 2010 quant]() Original publisher: [Read original article]() Author: Jon Bratseth Published: 2026-07-07T00:00:00Z Content type: opinion Language: en Sources: [Vespa Blog]() Topics: [AI search](), [information retrieval](), [Language models](), [Google Search]() Tags: [agents](), [ai](), [ai-agents](), [bm25](), [genai](), [google-search](), [hybrid-search](), [information-retrieval](), [rag](), [ranking](), [search]() ### AI overview The article argues that AI agents should retrieve information with more control and sophistication than ordinary human search users. It describes a progression from vector retrieval to hybrid search using methods such as BM25 and machine-learned ranking, and presents search as code as a possible next stage. ### Source excerpt The idea of empowering AI agents to retrieve information like a professional is going mainstream. ## Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery DevFeed: [Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery]() Original publisher: [Read original article]() Author: Inês Soveral Published: 2025-09-09T15:01:44Z Content type: article Language: en Sources: [OLX]() Topics: [AI search](), [Query (disambiguation)]() Tags: [article](), [classifieds](), [discovery](), [hybrid-search](), [retrieval](), [search](), [search-engines](), [vector-search]() ### AI overview OLX describes its transition from keyword matching to hybrid search, combining keyword and vector retrieval for classifieds discovery. The article explains the motivations, implementation challenges, solutions, and observed benefits, including improved handling of vague, misspelled, or differently phrased queries. ### Source excerpt Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery In the midst of a fast-paced technological revolution, where user expectations grow increasingly sophisticated, the quality of search in the classifieds space has never been more critical. In fact, users expect the search box to understand what they are looking for and produce relevant search results; if this is not the case, they will easily move on to any competitor who provides this experience. At OLX, our search system traditionally relied on keyword matching between user queries and ad titles and descriptions -- a straightforward but rigid approach. While functional, it often led to low recall or even zero results pages (ZRPs). To mitigate this, we gradually developed an extension chain logic -- which will be explained in detail in later sections -- to address specific edge cases and expand recall. Over time, however, this logic became increasingly complex and difficult to improve upon, calling for a disruptive solution to further evolve our search system. In this article, we share why and how we transitioned to Hybrid Search as a retrieval strategy for our double-sided marketplace. We walk through the key changes required to support semantic search, highlight the challenges we faced, and detail the solutions put in place to overcome them. Finally, we reflect on the tangible improvements and practical benefits observed after this shift. What is Hybrid Search and what value does it bring? Despite its lack of flexibility, keyword matching remains highly effective in e-commerce. It returns results that exactly match the user's query terms, offering clear traceability and helping users understand why specific ads appear. This is particularly useful when users know precisely what they're looking for, and when combined with structured filters, a feature of the OLX marketplace. However, keyword matching falls short when queries are vague, misspelled, or phrased differently from how sellers descri ## Supabase Beta March 2023 DevFeed: [Supabase Beta March 2023]() Original publisher: [Read original article]() Author: Ant Wilson Published: 2023-04-08T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [Artificial Intelligence](), [Next.js](), [OpenAI](), [Databases](), [Deno](), [Framer Motion](), [API](), [LangChain](), [MDX](), [Nuxt.js]() Tags: [ai](), [announcement](), [auth](), [devrel](), [embeddings](), [framer](), [github](), [hackathon](), [hybrid-search](), [infinite-scroll](), [langchain](), [launch](), [next-js](), [nuxt](), [open-source](), [openai](), [postgres](), [vercel]() ### AI overview Supabase's March 2023 beta update previews Launch Week 7, announces a Web3 AI hackathon, and releases a Next.js OpenAI documentation-search template using Vercel, GPT-3, Postgres with pgvector, and Deno Edge Functions. It also highlights an AI-powered ticket system, community projects, and upcoming events. ### Source excerpt We are in full shipping mode 🛥... Launch Week 7 can't come quickly enough!