Zilliz - Provides a managed vector database service.
Cossmology Profile: https://dub.sh/HxFCcoG
Key People: Charles Xie, James Luan
Zilliz - Provides a managed vector database service.
Cossmology Profile: https://dub.sh/HxFCcoG
Key People: Charles Xie, James Luan

Learn what text embeddings are, how they power RAG and semantic search, and how to call embedding APIs from Python using Ollama or an OpenAI-compatible server (for example llama.cpp). Includes persistence, retrieval, and links to chunking, vector stores, and reranking on this site.
Databases for #AI: Should you use a vector #database? ๐ค
This article compares #opensource projects competing to handle modern #AI workloads, including #machinelearning and #LLMs. Discover which databases best meet todayโs AI challenges: https://lpi.org/636x
(Disclaimer: This post contains an AI-generated image.)
#AndyOram #AI #vectordatabase #machinelearning #LLMs #SQL #opensource #hybridsearch #generativeAI #MariaDB #MongoDB #Milvus #Qdrant #Weaviate #Vespa #ChromaDB #LanceDB

Your RAGโs Secret Backdoor: Leaking Data Through Vector Databases
This article exposes a vulnerability in Retrieval-Augmented Generation (RAG) systems, where misconfigured vector databases can lead to sensitive data leakage. By improperly securing these databases, attackers can gain access to internal documents such as HR policies and top-secret product roadmaps. The RAG system works by storing document chunks as embeddings in a special-purpose vector database and querying it to provide context for the LLM. The focus on securing the LLM while neglecting the vector database leaves it vulnerable to data exfiltration. The attacker can exploit weak access controls and clever retrieval attacks to gain access to sensitive data. Key lesson: Secure vector databases to prevent data breaches caused by RAG system vulnerabilities. #BugBounty #ArtificialIntelligence #DataLeak #Infosec #VectorDatabase
[zvec - ์ด๊ฒฝ๋ยท์ด๊ณ ์ ์ธํ๋ก์ธ์ค ๋ฒกํฐ DB
Zvec๋ ์ด๊ฒฝ๋ยท์ด๊ณ ์ ์ธํ๋ก์ธ์ค ๋ฒกํฐ DB๋ก, Alibaba์ Proxima ์์ง ๊ธฐ๋ฐ์ผ๋ก ๊ตฌ์ถ๋์ด ๋๊ท๋ชจ ์ ์ฌ๋ ๊ฒ์์ ์ต์ ์ค์ ์ผ๋ก ์ํํ๋๋ก ์ค๊ณ๋์์ต๋๋ค. ๋ฐ์ง ๋ฐ ํฌ์ ๋ฒกํฐ๋ฅผ ๋ชจ๋ ์ง์ํ๋ฉฐ, ํ์ด๋ธ๋ฆฌ๋ ๊ฒ์ ๊ธฐ๋ฅ์ ํตํด ์๋ฏธ์ ์ ์ฌ๋์ ๊ตฌ์กฐ์ ํํฐ๋ง์ ๊ฒฐํฉํ ์ ๋ฐ ๊ฒ์์ ์ ๊ณตํฉ๋๋ค. C++ ๊ธฐ๋ฐ ํต์ฌ ์์ง๊ณผ SWIGยทPython ๋ฐ์ธ๋ฉ ๊ตฌ์กฐ๋ก ๊ตฌ์ฑ๋์ด ๊ณ ์ฑ๋ฅ ์ฐ์ฐ๊ณผ ๋ค์ํ ์ธ์ด ํตํฉ์ ์ง์ํ๋ฉฐ, Apache-2.0 ๋ผ์ด์ ์ค๋ก ์ ๊ณต๋ฉ๋๋ค.
https://news.hada.io/topic?id=27147
#vectordatabase #algorithms #machinelearning #opensource #search
via @dotnet : Vector Data in .NET โ Building Blocks for AI Part 2
https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorStoโฆ
Did you know? Our pgedge-vectorizer tool (on GitHub: https://github.com/pgEdge/pgedge-vectorizer) automatically chunks text content and generates vector embeddings with the help of background workers.
OpenAI, Voyage AI, and Ollama are supported as embedding providers, and a simple SQL interface allows you to enable vectorization on any table. (Thereโs even built-in views and functions for monitoring queue status.)
#github #opensource #semanticsearch #vector #vectordatabase #openai #ollama #voyageai

A PostgreSQL extension to create chunk tables for existing text data, and populate them with embeddings using your favourite LLM. - pgEdge/pgedge-vectorizer

Step-by-step RAG tutorial: build retrieval-augmented generation systems with vector databases, hybrid search, reranking, and web search. Architecture, implementation, and production best practices.