A Vector Database stores and searches embedding vectors for semantic retrieval, recommendations, and RAG. GROUNDING tracks vector indexes, filtering, hybrid retrieval, latency, and production reliability.
Topic: RAG Related: Embeddings RAG Hybrid Search
Recent Updates
- 2026-06-05: Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval (cs.CL updates on arXiv.org) · arxiv.org — arXiv Saisri Padmaja Jonnalagedda
- 2026-06-05: Analysis of the Odysseus Self-Hosted AI Workspace (Искусственный интеллект – AI, ANN и иные формы искусственного разума) · habr.com — Alibaba OpenAI Anthropic PewDiePie
- 2026-06-05: Why we chose recursive SQL over GraphQL for our knowledge graph (Искусственный интеллект – AI, ANN и иные формы искусственного разума) · habr.com — Google Gemini
- 2026-06-08: Your UnEmbedding Matrix is Secretly a Feature Lens for Text Embeddings (cs.CL updates on arXiv.org) · arxiv.org
- 2026-06-08: Building a Grounded, Citation-Based RAG System Locally (Искусственный интеллект – AI, ANN и иные формы искусственного разума) · habr.com — Ollama Russian Ministry of Sport FIBA CEV Gemma 4 BGE-M3 bge-reranker-v2-m3
FAQ
What is Vector Database?
A Vector Database stores and searches embedding vectors for semantic retrieval, recommendations, and RAG. GROUNDING tracks vector indexes, filtering, hybrid retrieval, latency, and production reliability.
Which topic does Vector Database belong to?
On the GROUNDING radar, Vector Database is grouped under the RAG topic.
Which concepts are related to Vector Database?
Related concepts tracked by the radar include Embeddings, RAG, Hybrid Search.