Modern applications rely on PostgreSQL for its fully ACID‑compliant, expressive SQL, and rich ecosystem of extensions. The database handles relational workloads exceptionally well, but many projects also need to search for large text collections—prod...| VectorChord
In this post, I’ll share key insights and findings from building a practical text search application without using frameworks like LangChain or external APIs. I’ve also extended the app’s functionality to support Retrieval-Augmented Generation (RAG) capabilities using the Gemini Flash 1.5B model.| amritpandey.io
PS: thanks for all the interest, here you are some discussions about VectorVFS as well: Hacker News: discussion thread Reddit: discussion thread When I released The post VectorVFS: your filesystem as a vector database first appeared on Terra Incognita.| Terra Incognita
Emerging AI technologies like RAG and vector databases enhance business intelligence, driving efficiency through smarter, context-aware responses. The post RAG, vector databases, and LLM search: The future of AI-powered business intelligence first appeared on TechTalks.| TechTalks
We're excited to announce the release of VectorChord-BM25 version 0.2, our PostgreSQL extension designed to bring advanced BM25-based full-text search ranking capabilities directly into your database! VectorChord-BM25 allows you to leverage the power...| VectorChord blog
Hybrid search combining BM25 and pgvector compatible extension VectorChord, seamlessly integrated within PostgreSQL.| VectorChord blog
In this article, we will explain that RAG is really nothing more than saying: hey LLM, here is a bunch of data, can you tell me about it?| Luc van Donkersgoed's Notes
