# vector-database Published articles for vector-database. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs DevFeed: [Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [data](), [Large Language Model](), [Code](), [Retrieval-Augmented Generation](), [Access Control](), [Graphs](), [GitHub]() Tags: [agents](), [classification](), [code](), [data](), [documentation](), [governance](), [production](), [provenance](), [retrieval](), [vector-database]() ### AI overview Glyph is a production system for generating column descriptions and assigning sensitivity-ontology labels in enterprise data catalogs. It uses cooperating LLM agents, source-code-grounded retrieval, parallel tagging strategies, vector-based metadata matching, and ranked-output fusion to support auditable cataloging. ### Source excerpt Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs. The Descriptor grounds generation in the pipeline source code that produces each column, retrieved on demand from an... ## 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 ## How to Consolidate Your Database Stack for Production AI DevFeed: [How to Consolidate Your Database Stack for Production AI]() Original publisher: [Read original article]() Author: David Weiss Published: 2026-07-28T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Databases](), [Artificial Intelligence](), [Architecture & Design]() Tags: [ai](), [cockroachdb](), [complexity](), [consistency](), [databases](), [guide](), [how-to](), [latency](), [postgresql](), [redis](), [resilience](), [scale](), [vector-database](), [vector-search]() ### AI overview This guide examines consolidating PostgreSQL, Redis, and a dedicated vector database into CockroachDB for production AI applications. It discusses operational complexity, consistency, latency, resilience, scale, and the tradeoffs of using a single operational database. ### Source excerpt When builders ship AI-powered applications, the data layer quietly becomes the hardest part of the stack. ## Vector Databases Clearly Explained DevFeed: [Vector Databases Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-07-25T13:32:58Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Databases](), [Embeddings](), [AI search]() Tags: [databases](), [embeddings](), [search](), [technical](), [vector](), [vector-database](), [vector-search]() ### AI overview This tutorial explains how vector databases differ from traditional databases by searching for semantic similarity rather than exact keyword matches. It describes embeddings, vectors, metadata, filtering, permissions, ranking, and distance metrics as parts of vector search. ### Source excerpt The mental model that makes vector databases click. ## How BetterTracker Replaced Its Vector Store with CockroachDB DevFeed: [How BetterTracker Replaced Its Vector Store with CockroachDB]() Original publisher: [Read original article]() Author: Yohan Shirazi Published: 2026-07-15T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [AI Platform](), [Databases](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Availability](), [Software as a service](), [Transactions](), [data]() Tags: [ai](), [ai-platform](), [availability](), [cockroachdb](), [compliance](), [consistency](), [data](), [database](), [rag](), [saas](), [search](), [soc2](), [transactions](), [vector-database](), [vector-search]() ### AI overview BetterTracker replaced a standalone vector database by running transactional workloads and vector search together on CockroachDB. The article describes how this supports the company's AI-powered platform while reducing infrastructure complexity and compliance risk. ### Source excerpt BetterTracker eliminated a standalone vector database by running OLTP and vector search together on CockroachDB--cutting costs, complexity, and compliance risk in one move. ## Building the AI Retrieval Infrastructure Behind 20 Billion+ Vectors at HubSpot DevFeed: [Building the AI Retrieval Infrastructure Behind 20 Billion+ Vectors at HubSpot]() Original publisher: [Read original article]() Author: Oleg Tereshin & Xin Liu Published: 2026-06-25T18:17:44Z Content type: article Language: en Sources: [HubSpot]() Topics: [Qdrant](), [AI search](), [Artificial Intelligence](), [Embeddings](), [Retrieval-Augmented Generation](), [Open Source](), [systems](), [quantization]() Tags: [ai](), [embeddings](), [engineering](), [latency](), [open-source](), [quantization](), [rag](), [retrieval](), [search](), [vector-database](), [vectors]() ### AI overview HubSpot describes how it built VaaS, a centralized vector storage and search platform using Qdrant, to support semantic search across tens of billions of vectors and many use cases. ### Source excerpt Discover how HubSpot built a scalable AI retrieval infrastructure, managing over 20 billion vectors with Qdrant, to enhance semantic search and support diverse applications. ## Weave CLI: A Case Study in Shipping Retrieval-Augmented Generation Systems DevFeed: [Weave CLI: A Case Study in Shipping Retrieval-Augmented Generation Systems]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-04-29T11:04:33Z Content type: article Language: en Sources: [Decoding ML]() Topics: [Retrieval Augmented Generation (RAG)](), [Command-line interface](), [Databases](), [Open Source]() Tags: [case-study](), [cli](), [databases](), [open-source](), [rag](), [vector-database]() ### AI overview An interview with Michael Maximilien examines the practical difficulties of building and evaluating RAG systems, including vector-database selection, embedding models, chunking, ingestion failures, and unreliable comparisons. Maximilien describes Weave CLI, an open-source command-line tool that unifies 11 vector databases into one workflow. ### Source excerpt You iterate. You evaluate. Weave CLI unifies 11 vector databases into one workflow. ## How to Charge for RAG-as-a-Service DevFeed: [How to Charge for RAG-as-a-Service]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-26T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [API](), [Database](), [Embeddings](), [Large Language Model](), [OpenAI](), [Qdrant](), [voyage-ai]() Tags: [ai](), [api](), [billing](), [cost](), [embeddings](), [how-to](), [openai](), [payments](), [rag](), [retrieval](), [vector-database](), [voyage-ai]() ### AI overview A tutorial on monetizing RAG-as-a-Service platforms. It explains how embedding generation, vector database retrieval, and LLM generation contribute to per-query costs, and discusses usage limits, credit-based pricing, per-query billing, and entitlement management using Dodo Payments. ### Source excerpt Learn how to monetize Retrieval-Augmented Generation (RAG) services using per-query billing, credit packs, and usage-based models with Dodo Payments. ## Getting Started with GenAI Using CockroachDB DevFeed: [Getting Started with GenAI Using CockroachDB]() Original publisher: [Read original article]() Author: Amine El Kouhen, Ph.D. Published: 2025-06-05T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Embeddings](), [genai](), [Generative AI](), [AI search](), [Data Management](), [Databases](), [data]() Tags: [ai](), [cockroachdb](), [data](), [data-management](), [databases](), [embeddings](), [genai](), [generative-ai](), [retrieval](), [vector-database]() ### AI overview This introductory article explains how generative AI relates to vector embeddings and vector databases, and previews how CockroachDB can support vector search, data consistency, search, classification, and recommendations. ### Source excerpt Information today is generated and consumed in unprecedented magnitudes. With every click, swipe, and transaction, massive amounts of data are collected, waiting to be harnessed for insights, decision-making, and innovation. Today, more than 80% of the data that organizations generate is unstructured - and the amount of this data type will only grow in the coming decades. ## Building a Semantic Search Engine with Qdrant & Encore.ts DevFeed: [Building a Semantic Search Engine with Qdrant & Encore.ts]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2025-05-28T00:00:00Z Content type: tutorial Language: en Sources: [Encore Updates]() Topics: [Qdrant](), [AI search](), [Embeddings](), [OpenAI](), [Back end](), [Databases]() Tags: [backend](), [code](), [embeddings](), [openai](), [search](), [sql](), [sql-database](), [tutorial](), [vector](), [vector-database]() ### AI overview A tutorial for building a semantic search engine with OpenAI embeddings and the Qdrant vector database. It explains how natural-language queries can retrieve conceptually related documents and describes using Encore services with a SQL database for document metadata. ### Source excerpt Create powerful semantic search using OpenAI embeddings and Qdrant vector database ## A Developer's Journey To the AI and GraphQL Galaxy DevFeed: [A Developer's Journey To the AI and GraphQL Galaxy]() Original publisher: [Read original article]() Author: Yonatan (Yoni) Levin Published: 2023-10-03T03:50:29Z Content type: opinion Language: en Sources: [Stories by Yonatan V. Levin on Medium]() Topics: [Artificial Intelligence](), [GraphQL](), [API](), [AI Chat]() Tags: [ai](), [api](), [generative-ai](), [graphql](), [large-language-models](), [llm](), [makersuite](), [prompt-engineering](), [vector-database]() ### AI overview The article describes a personal exploration of using generative AI and large language models to generate GraphQL queries and mutations for the monday.com API. It explains the challenge of producing valid API-specific GraphQL and discusses an Implementation Consultant concept that creates tailored boards, while noting that the explored solution is not production-ready. ### Source excerpt It's all starts with simple drawingsThe article that helps tame an LLM to produce a GraphQL query for a specific API Generative AI has stirred debates. While some might regard it as a fleeting trend, I view it as a game-changer. AI and Large Language Models (LLM) combined give way to AI-powered chatbots that hold great potential for improving operations and customer services. Such AI bots are designed to communicate with an entire system via an API. Sounds okay... unless your API is built using GraphQL. What's the trick, you ask? GraphQL stands out as a potent query language due to its flexibility, versatility, and extensibility. Getting LLM to produce a perfect and valid GraphQL query for a specific API is challenging. Some even say it's impossible... At monday.com, innovation is our mantra. We set off on the AI journey some time ago, and very soon I realized that mastering AI feels like discovering a new galaxy far, far away -- there are no ready answers, so you need to trust you gut feeling and push the boundaries. And this thrill of making the impossible possible makes my determination surge like the Force within a Jedi. We started at monday.com with foundational tasks such as "Summary Update" and "Generate tasks". These tasks, powered by basic prompts, enabled app developers to create first bespoke AI solutions within the monday.com framework. https://medium.com/media/5f9f975664482bc1fa9a03a95b7481bf/hrefhttps://medium.com/media/2236f6215c3b2977349c18c538b95ca1/href Our ambition grew when we decided to create the Implementation Consultant. Think of it as a "droid" that estimates monday.com users' needs and offers bespoke boards to them. Once a user gives the go-ahead, this "droid" seamlessly crafts a GraphQL mutation over the monday.com API and creates the individually tailored solution. https://medium.com/media/36cb4871ab825aaf89514e37628b4c18/href However, using our LLM to generate these GraphQL API calls was like navigating the asteroid field -- so tricky! Our qui