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A vector database searches by meaning instead of by words. Once you see how that works, you will also spot why you might already own one without knowing it.
This article provides a complete guide on What Is a Vector Database, including its meaning, importance, history, working process, features, ... Read more The post What Is a Vector...
Most conversations about vector databases start and end with embeddings. Discussions typically center around how they're generated, which model produced them, how many dimensions t...
AlloyDB is a fully managed, PostgreSQL-compatible database service built for your most demanding enterprise workloads. It combines the best of open source PostgreSQL with Google’s...
FundamentalsContinue reading on Medium »
ChromaDB alternatives help teams move from a lightweight vector store to a retrieval stack that matches production RAG, semantic search, metadata filtering, hybrid search, multi-te...
The rapid growth of generative AI has introduced a concept I hadn’t paid much attention to before: vector databases. Initially, I assumed…Continue reading on Medium »
For years, search technology meant one thing: type in a keyword, and the system goes hunting for an exact match. That works fine for product SKUs or error codes, but it falls apart...
To satisfy the demands of enterprise-grade agentic AI applications, underlying vector databases often struggle to scale effectively as modern use cases can scale to billions of vec...
AWS offers a broad portfolio of vector search built directly into the databases and storage services you already use, with no standalone vector database or data migration required....
Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with pr...
Running pgvector on Amazon Aurora PostgreSQL gives you a production-grade vector store on a database you already know, backed by the operational tooling, high availability, and sca...
В распределённой СУБД YDB (читается вай‑ди‑би) векторный поиск по kmeans‑tree индексу раскрывался оптимизатором в цепочку из нескольких стадий StreamLookup. Это работало, но порожд...
Architecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN The post How to Optimize Vector Search When RAM Gets Too Exp...
Полгода назад я начал писать in-memory базу с векторным поиском на Go: RESP-протокол, HNSW-индекс, WAL, многопоточность. Рассказываю, что из этого вышло: как я мерил производительн...
VDBBench already tests vector databases under real production workloads; The new release lets any team measure what a vector database actually costs to run in production — across d...
Many applications that use Amazon DynamoDB for operational data also need vector similarity search, which until now meant running a separate vector database. DynamoDB now supports...
Vector RAG and Graph RAG solve different retrieval problems. Vector RAG is strongest when a system needs passages with similar meaning. Graph RAG becomes useful when an answer depe...
This world map may look like an interactive map but it is actually an image. It is an SVG map of the world that I created in a couple of seconds using Polygrid Map2SVG.One of the g...
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases. T...
Cosine, dot product, and Euclidean distance collapse into one ranking once vectors are normalized, and diverge dangerously when they aren’t.Continue reading on Medium В»
GPU-accelerated vector (k-NN) indexing on Amazon OpenSearch Service and OpenSearch Serverless lets you build billion-scale vector indexes in hours instead of days. This post goes d...
Free Map Tiles Providers:OpenFreeMapVersaTilesMaptoolkit.orgOne of the biggest barriers to building web maps has always been the cost and complexity of map tiles. Services such as...
The adoption of retrieval-augmented generation (RAG) from research papers to production systems has been rapid. Those who tried it in 2023 are now deploying it at scale for enterpr...
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