# vector-search Published articles for vector-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. ## AI DevFeed: [AI]() Original publisher: [Read original article]() Published: 2026-09-15T02:59:12.316196Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Google](), [Protocol (disambiguation)](), [Databases](), [CockroachDB]() Tags: [2026](), [agents](), [ai](), [cockroachdb](), [databases](), [google](), [security](), [vector-search]() ### AI overview An AI-focused Cockroach Labs blog category page listing articles about agent-to-agent communication, AI agent security, database isolation, compliance, resilience, identity, vector search, operations automation, and durable execution with CockroachDB. ### Source excerpt Blog ai A2A Is Now an Open Standard. The Data Layer Underneath It Isn't. In April 2025, Google released a protocol for agent-to-agent communication. Within three months, Google had donated it to the Linux Foundation... Quentin Packard August 19, 2026 Multi-Tenant AI Agents: Why Data Isolation Starts at the Database Most SaaS teams shipping agentic features focus on prompt safety and API-layer filtering. But effective AI agent security also depends on... ## Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack DevFeed: [Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack]() Original publisher: [Read original article]() Author: Jonas Kim Published: 2026-09-14T16:55:58Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Graphs](), [Amazon Bedrock](), [Amazon OpenSearch Service](), [Amazon Web Services]() Tags: [amazon-bedrock](), [amazon-opensearch-service](), [aws](), [knowledge-graph](), [open-source](), [rag](), [retrieval](), [search](), [vector-search]() ### AI overview This article presents a unified knowledge-graph RAG stack that brings Microsoft GraphRAG and HKUDS LightRAG together on Amazon Bedrock, Amazon Neptune, and Amazon OpenSearch Service. The shared stack supports common ingestion, indexing, caching, and multilingual handling while allowing the retrieval methodology to be selected per query. ### Source excerpt Picture a compliance analyst staring at a few thousand contracts, amendments, and internal memos, trying to answer one question that sounds straightforward: "Which of our obligations are exposed if this one milestone slips?" The answer isn't written in any single document. It's stitched across three -- a master agreement that ties a payment to a [...] ## Three principles for building a vector platform at Thumbtack DevFeed: [Three principles for building a vector platform at Thumbtack]() Original publisher: [Read original article]() Author: John Zhu Published: 2026-09-10T15:45:00Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [Embeddings](), [AI search](), [Database](), [data](), [Machine learning](), [API]() Tags: [api](), [data](), [embeddings](), [engineering](), [etl](), [infrastructure](), [machine-learning](), [ml-platform](), [pgvector](), [retrieval](), [search](), [vector](), [vector-search]() ### AI overview This article explains how Thumbtack built a vector platform that lets ML engineers deploy production vector search without managing database access, custom ETL, or query services. It describes three guiding principles: reuse existing infrastructure, treat embeddings as data, and reduce adoption costs for future teams. ### Source excerpt Reusing what we already had, treating embeddings as data, and lowering the next team's cost Today, an ML engineer at Thumbtack can stand up production vector search without negotiating database access, building a custom ETL, or writing a query service. The team brings their choice of embedding model, the data, and the query; the platform handles what connects them. It took several iterations to get to this point. In this post we'll walk through how we got there and the three principles that shaped what we built. A vector database stores high-dimensional numeric arrays (embeddings) and serves nearest-neighbor queries against them. It's how an ML system asks "what's most similar to this?" instead of "what matches this exact key?" The shift from exact lookup to semantic retrieval is what makes vectors useful: a search can return results that mean the same thing, not just results that spell the same. At Thumbtack, embeddings sit between the models that produce them and the services that consume them: language models for text, multimodal models for images, retrieval models for ranking. The platform we describe here is where those embeddings live and how teams reach for them when they need to. Three principles shaped what we built. Reuse what we have: extend the infrastructure we already run rather than stand up a new system. Treat embeddings as data: flow them through the same pipelines that move every other dataset at the company. Lower the next team's cost: make the platform easier to adopt than to work around. Each principle shaped one layer of the system, and together they took vector search from a one-off project to a platform that any team can build on. Architecture at a glance The platform has four moving parts: where embeddings come from, how they reach the database, where they live, and how consumers query them. Each is a layer, and together they form a pipeline that produces vectors and serves similarity searches as a typed API call. The diagram below traces a ## Build a help center with semantic search using Appwrite VectorsDB DevFeed: [Build a help center with semantic search using Appwrite VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI search](), [Embeddings](), [Front end](), [Node.js]() Tags: [api](), [app](), [build](), [embeddings](), [front-end](), [node-js](), [search](), [tutorial](), [tutorials](), [vector-search]() ### AI overview This tutorial shows how to build a help center with semantic search using Appwrite VectorsDB and a TanStack Start front end. It covers creating a vector collection, embedding help articles, and ranking answers by meaning. ### Source excerpt Create a vector collection in the Appwrite Console, embed help articles as they are published, and rank answers by meaning in a TanStack Start app. ## Four things you can build with Appwrite VectorsDB DevFeed: [Four things you can build with Appwrite VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI search](), [Databases]() Tags: [ai](), [cache](), [database](), [databases](), [embedding](), [history](), [search](), [vector-search]() ### AI overview This tutorial explains how Appwrite VectorsDB uses vector embeddings to find documents by meaning rather than exact keywords. It demonstrates help-article search, filtered search, recommendations from user history, and an answer cache, including a 256-number vector approach used in the examples. ### Source excerpt Appwrite VectorsDB finds documents by meaning, not by words. This post builds four features with it: a help article search, a filtered search, a recommendation from user history, and an answer cache. ## Benchmarking vector indexes DevFeed: [Benchmarking vector indexes]() Original publisher: [Read original article]() Author: Evgeniy Patlan Published: 2026-08-27T13:35:32Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [benchmarking](), [Ground truth / benchmark quality](), [Databases](), [ann](), [datasets](), [Sorting]() Tags: [ai-vector](), [ann](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [blog-post](), [data](), [database-performance](), [database-trends](), [databases](), [datasets](), [embeddings](), [index](), [measurements](), [model](), [open-source](), [percona](), [points](), [report](), [run](), [search](), [sorting](), [vector-search](), [vectorsearch]() ### AI overview This article explains how Percona's vector-bench measures vector indexes by building database engines from pinned versions, running them in identical containers with the same data and hardware, and comparing consistent measurements. It describes embeddings, exact search, approximate nearest neighbour search, and ground truth as the brute-force reference needed to evaluate index accuracy. ### Source excerpt Nearly every database has vector search now, and every one of them has a blog post with a big number in it. Almost none of those numbers can be checked, because the thing that makes them meaningful is usually missing. We built a vector-bench to stop guessing. You name the engines you want, build them ... Continued The post Benchmarking vector indexes appeared first on Percona. ## 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. ## 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. ## RAG Clearly Explained DevFeed: [RAG Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-07-15T14:15:13Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Retrieval Augmented Generation (RAG)](), [Large Language Model](), [AI search]() Tags: [model](), [rag](), [retrieval](), [retrieval-augmented-generation-rag](), [vector-search]() ### AI overview A tutorial explaining Retrieval-Augmented Generation (RAG), a system pattern that retrieves relevant external information at query time and provides it to an LLM as context. It distinguishes RAG from fine-tuning, plain search, and vector search, and outlines knowledge preparation using chunks and searchable indexes. ### Source excerpt The mental model every engineer should have. ## 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. ## From Harness Lock-In to Portable Context Layer DevFeed: [From Harness Lock-In to Portable Context Layer]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-06-30T08:20:17Z Content type: article Language: en Sources: [Decoding ML]() Topics: [Artificial Intelligence](), [MCP Server](), [Graphs](), [AI search](), [Notion](), [Obsidian](), [Google]() Tags: [ai](), [claude-code](), [code](), [knowledge-graph](), [mcp](), [memory](), [open-source](), [vector-search]() ### AI overview The article argues that users should keep their context layer separate from any AI harness. It proposes unified memory combining a filesystem, BM25 keyword search, semantic vector search, and a typed knowledge graph, exposed through an MCP server or skills so different models and harnesses can use it. ### Source excerpt Build a unified memory (knowledge graph or an LLM wiki) and serve it over MCP servers or skills, so any agent, open or closed, plugs in within minutes. ## DigitalOcean's unified data and retrieval layer for AI applications DevFeed: [DigitalOcean's unified data and retrieval layer for AI applications]() Original publisher: [Read original article]() Author: Spoorthi Rao Nimmala Published: 2026-06-03T19:23:28Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Artificial Intelligence](), [data](), [Inference](), [PostgreSQL](), [MySQL](), [Latency](), [AI search]() Tags: [ai](), [data](), [digitalocean](), [inference](), [latency](), [multimodal](), [mysql](), [postgresql](), [product-updates](), [real-time](), [search](), [vector-search]() ### AI overview DigitalOcean describes a Data & Learning layer that combines structured transactional data, vector search, and retrieval tools for AI applications. The platform includes Managed PostgreSQL Advanced, MySQL Advanced Edition, Knowledge Bases, and Managed Weaviate, with integrations intended to support real-time, multimodal pipelines and grounded inference. ### Source excerpt Building an AI-native application requires a data layer that can do two things at once: handle the structured, transactional queries your application runs on, and understand meaning well enough to power semantic search across unstructured content. An AI application needs both -- precise SQL for account balances and transaction records, and vector search to surface conceptually related patterns, anomalies, or past cases that a keyword query would never find. Most teams end up stitching these together across different environments, where every query crosses a boundary. Latency compounds and costs grow with the complexity of the glue, not the value of the data. What holds together in a prototype starts to fracture under production load. The DigitalOcean Data & Learning layer is designed to close that gap by giving you structured, vector, and retrieval layers that work together in the same ecosystem. Real-Time Inference and Learning At the heart of any sophisticated AI application is the need for grounded, context-aware inference. DigitalOcean now supports a unified set of tools across the data layer: Managed PostgreSQL Advanced and MySQL Advanced Edition (Public Preview) for the structured, transactional data your application runs on Knowledge Bases (General Availability) to handle the full retrieval pipeline from ingestion to answer Managed Weaviate (Public Preview) for vector search on unstructured data Together, this unified platform allows developers to build real-time multimodal pipelines and manage enterprise knowledge bases with ease. Every retrieval your application or agent makes flows through this layer. When the data and retrieval layer is fully managed and scales with the application, your agent's answers stay grounded and your service stays available. These services run on the same platform as DigitalOcean's Inference Engine and Managed Agent infrastructure. This means zero egress between the data layer and inference, one billing relationship instead of thr ## Vespa Newsletter, May 2026 DevFeed: [Vespa Newsletter, May 2026]() Original publisher: [Read original article]() Author: Bonnie Chase Published: 2026-05-27T00:00:00Z Content type: news Language: en Sources: [Vespa Blog]() Topics: [AI search](), [Artificial Intelligence](), [Cloud](), [code productivity](), [configuration]() Tags: [ai](), [cloud](), [configuration](), [developer-productivity](), [embedding](), [newsletter](), [ranking](), [search](), [vector-search]() ### AI overview The May 2026 Vespa newsletter announces updates for retrieval and ranking systems, including improved ranking flexibility, embedding integrations with Voyage AI, OpenAI, and Mistral AI, Vespa Cloud dashboards and backups, maintenance controls, custom resource tags, agent skills, and new query and array-field capabilities. ### Source excerpt Advances in Vespa include finer control over deployments, smarter ranking, richer embedding integrations, and more scalable vector search. ## From 48 Seconds to 130 Milliseconds: Vector Search in Tinybird DevFeed: [From 48 Seconds to 130 Milliseconds: Vector Search in Tinybird]() Original publisher: [Read original article]() Author: Daniel Sangorrín Published: 2026-05-12T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [Embeddings](), [AI search]() Tags: [customer](), [embeddings](), [engineering-excellence](), [search](), [vector](), [vector-search]() ### AI overview A customer needed semantic search over 20 million embeddings, but the initial attempt timed out. The article reports that three changes reduced query latency to under 200 milliseconds. ### Source excerpt A customer needed semantic search over 20 million embeddings. Their first attempt timed out. Three changes turned it into sub-200ms queries. Here's what we learned. ## Six RAG strategies, explained simply (with code). DevFeed: [Six RAG strategies, explained simply (with code).]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-04-03T08:36:55Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [Retrieval Augmented Generation (RAG)](), [Database](), [Large Language Model]() Tags: [database](), [embeddings](), [full-text-search](), [rag](), [retrieval](), [tutorials](), [vector-search]() ### AI overview This tutorial explains retrieval-augmented generation and compares retrieval strategies, including loading complete documents, full-text search, and vector search. It presents Postgres as a practical option for implementing these approaches and discusses their trade-offs. ### Source excerpt Six Ways to Retrieve Data for an LLM ## How PostgreSQL and pgvector Support Vector Search and RAG DevFeed: [How PostgreSQL and pgvector Support Vector Search and RAG]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-03-09T00:00:00Z Content type: tutorial Language: en Sources: [Encore Updates]() Topics: [PostgreSQL](), [Retrieval Augmented Generation (RAG)](), [Embeddings](), [AI search](), [Database]() Tags: [ai](), [embeddings](), [pgvector](), [postgresql](), [rag](), [search](), [vector-search]() ### AI overview This tutorial explains embeddings, vector similarity search, and retrieval-augmented generation, then shows how to implement them with PostgreSQL and the pgvector extension instead of a separate vector database. It describes how embeddings represent semantic meaning and how vector search can find related content beyond exact keyword matches. ### Source excerpt What vectors, similarity search, and RAG actually do under the hood, and why PostgreSQL handles most of it. ## Building AI Search on Heroku DevFeed: [Building AI Search on Heroku]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2026-01-29T17:50:08Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Heroku](), [AI search](), [Retrieval-Augmented Generation](), [Claude]() Tags: [ai](), [ai-search](), [building](), [engineering](), [heroku](), [heroku-ai](), [managed-inference-and-agents](), [news](), [pgvector](), [rag](), [retrieval](), [retrieval-augmented-generation](), [search](), [vector-search]() ### AI overview This article presents a Heroku reference architecture for production-grade enterprise AI search using two-stage retrieval. It combines vector search with semantic reranking so documents are scored for whether they answer the query, then uses Claude to generate an answer. ### Source excerpt If you've built a RAG (Retrieval Augmented Generation) system, you've probably hit this wall: your vector search returns 20 documents that are semantically similar to the query, but half of them don't actually answer it. A user asks "how do I handle authentication errors?" and gets back documentation about authentication, errors, and error handling in [...] The post Building AI Search on Heroku appeared first on Heroku. ## Vehicle Search with SQL and Vector Embeddings DevFeed: [Vehicle Search with SQL and Vector Embeddings]() Original publisher: [Read original article]() Author: Alejandro Infanzon Published: 2026-01-27T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [AI search](), [SQL](), [Python](), [data]() Tags: [cockroachdb](), [embeddings](), [how-to](), [image-to-image](), [python](), [search](), [sql](), [vector](), [vector-search]() ### AI overview A tutorial demonstrating a vehicle-search application that uses Python, SQL, CockroachDB, vector embeddings, and image-to-image similarity search. Users can search for visually similar cars from an image while applying price filters. ### Source excerpt "I'll know it when I see it" is the classic car buyer's line, but it's also the one thing old-school search bars totally fail to deliver. Traditional keyword search fails when a customer wants a car that "looks like this photo from Fast and Furious." This is where vector search comes in, transforming unstructured data into mathematical vectors to find semantically similar items. ## ClickHouse Release 25.10 DevFeed: [ClickHouse Release 25.10]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-11-07T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Optimization](), [Replication](), [AI search](), [data]() Tags: [bug-fixes](), [clickhouse](), [cpu](), [improvements](), [memory](), [new-features](), [optimization](), [performance](), [release](), [replication](), [search](), [vector-search]() ### AI overview ClickHouse 25.10 introduces 20 new features, 30 performance optimizations, and 103 bug fixes. The release includes join improvements, a new data type for vector search, and late materialization of secondary indices. One highlighted optimization, lazy columns replication, reduces CPU and memory usage by avoiding repeated physical copying of duplicated join values. ### Source excerpt ClickHouse 25.10 is available. In this post, you will learn about join improvements, a new data type for vector search, late materialization of secondary indices, and more! ## 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 ## Full-text search and other searches with Data Connect DevFeed: [Full-text search and other searches with Data Connect]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2025-09-02T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Databases](), [Query (disambiguation)](), [AI search](), [Embeddings]() Tags: [database](), [embeddings](), [firebase](), [full-text-search](), [graphql](), [postgresql](), [search](), [sql-connect](), [vector](), [vector-search](), [vertex-ai]() ### AI overview Firebase Data Connect now supports PostgreSQL-powered full-text search. The article explains how it differs from string pattern filters and vector similarity search, using a movie app to show example use cases and query approaches. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB DevFeed: [Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB]() Original publisher: [Read original article]() Author: Tim Kelly Published: 2025-04-21T15:05:48Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Tutorial](), [Kafka](), [Embeddings](), [real-time](), [Development]() Tags: [ai](), [anomaly-detection](), [artificial-intelligence](), [building](), [embeddings](), [financial](), [fraud-detection](), [java](), [kafka](), [mongodb](), [pipeline](), [real-time](), [scalable-architecture](), [synthetic](), [tutorial](), [vector-search]() ### AI overview This tutorial builds a real-time fraud detection pipeline with MongoDB Atlas Vector Search, Apache Kafka, AI-generated embeddings, and MongoDB Change Streams. It compares new financial transactions with a user's historical transactions and flags potential fraud when no similar transactions exist or similar transactions are already marked as fraudulent. ### Source excerpt In this tutorial, we'll build a real-time fraud detection system using MongoDB Atlas Vector Search, Apache Kafka, and AI-generated embeddings. We'll demonstrate how MongoDB Atlas Vector Search can be ... The post Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB appeared first on DEV. ## Local first AI assistant Kin leverages Turso's libSQL for on-device native Vector Search DevFeed: [Local first AI assistant Kin leverages Turso's libSQL for on-device native Vector Search]() Original publisher: [Read original article]() Author: Simon Westh Henriksen Published: 2024-10-21T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [libSQL](), [Turso](), [Local-First](), [Artificial Intelligence](), [AI search](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [SQLite](), [Mobile](), [Latency]() Tags: [ai](), [embeddings](), [libsql](), [local](), [local-first](), [mobile](), [on-device](), [rag](), [react](), [sqlite](), [vector-search]() ### AI overview Kin is a privacy-focused, local-first AI assistant that keeps user data on-device. The article explains why its developers chose Turso's libSQL, a SQLite-compatible database with native vector embeddings, to support on-device vector search and retrieval-augmented generation while reducing dependence on cloud servers and network latency. ### Source excerpt When Kin decided to build a privace focused, local first AI assistant, they settled on Turso's libSQL to power it. [Next page]()