Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍

One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usage

https://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/

#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

Agents are now favoring vector search over classic RAG, as memory frameworks shift to vector storage for rapid similarity lookup. Learn how this changes retrieval infrastructure for LLM‑powered agents and what it means for future AI memory design. #VectorSearch #RAG #AIMemory #AgentSystems

🔗 https://aidailypost.com/news/agents-favor-vector-search-over-rag-noting-memory-frameworks-use

Avi Chawla (@_avichawla)

AWS에서 RAG 앱 구축 방법을 설명하는 게시물로, RAG(검색 보강 생성)는 지식 준비(ingestion)과 질의(querying)의 두 단계로 작동하며, 각 단계를 AWS의 기존 서비스로 구현하는 구체적 흐름을 시각적으로 제시한다.

https://x.com/_avichawla/status/2031994916667363580

#aws #rag #retrievalaugmentedgeneration #nlp #vectorsearch

Why choose between keyword search and semantic understanding? With Hybrid Search in #OpenSearch, you get the best of both worlds. 🧠

Improve your user experience by making your search ""smarter"" without sacrificing performance. 🔗 https://opensearch.org/platform/vector-engine

#Hybridsearch #Vectorsearch #searchinnovation

【OCI】10分でかんたん構築！すぐ試せるマルチモーダルAI資料検索アプリを動かしてみよう
https://qiita.com/yushibats/items/70bcc09733ae1c674e8c?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #AI #oci #rag #生成AI #VectorSearch

🕐 2026-03-02 06:00 UTC

📰 Skillsで実現する軽量パーソナルRAG (👍 59)

🇬🇧 Build a lightweight personal RAG using Skills instead of PostgreSQL+Docker. Simpler setup with vector search without heavy infrastructure.
🇰🇷 PostgreSQL+Docker 대신 Skills로 경량 개인 RAG 구축. 무거운 인프라 없이 벡터 검색 구현하는 간단한 방법.

🔗 https://zenn.dev/karaage0703/articles/d7eaf62437185d

#RAG #VectorSearch #Skills #Zenn

Avi Chawla (@_avichawla)

AI 엔지니어를 위한 8가지 RAG(검색 보강 생성) 아키텍처 시리즈를 소개하는 글의 시작으로, 첫 번째 예시인 'Naive RAG'는 쿼리 임베딩과 저장된 임베딩 간의 벡터 유사도로 문서를 검색하여 단순 사실 기반 질의에 적합하다고 설명합니다.

https://x.com/_avichawla/status/2027270775494082936

#rag #retrievalaugmentedgeneration #embeddings #vectorsearch

SurrealDB 3.0 now packs agent memory, business logic, and multimodal data into a single Rust‑powered engine. It blends graph queries, vector search and retrieval‑augmented generation, letting AI agents store context and act without juggling separate stores. Dive into the details and see how this open‑source DB could reshape your stack. #SurrealDB #RetrievalAugmentedGeneration #VectorSearch #MultiModalData

🔗 https://aidailypost.com/news/surrealdb-30-stores-agent-memory-business-logic-multimodal-data-one-db

The first beta of #EclipseStore v4 is now online — featuring the new Vector Index for GigaMap. HNSW-based similarity search with persistent storage, on-disk scalability, PQ compression & more.

Docs: https://docs.eclipsestore.io/manual/gigamap/indexing/jvector/index.html
Code: https://github.com/eclipse-store/store/tree/main/gigamap/jvector

#Java #VectorSearch #AI

The first beta of #EclipseStore v4 is now online — featuring the new Vector Index for GigaMap. HNSW-based similarity search with persistent storage, on-disk scalability, PQ compression & more.

Docs: https://docs.eclipsestore.io/manual/gigamap/indexing/jvector/index.html
Code: https://github.com/eclipse-store/store/tree/main/gigamap/jvector

#Java #VectorSearch #AI