How Vector Databases Work with RAG Vector databases store data using vector embeddings, allowing efficient management of high-dimensional vectors.

Bring AI to your database! Learn how to build smarter apps with vector search in SQL Server & Azure Cosmos DB -- no extra AI stack required.

RAG with Postgres in two parts In this exploration we will do our coding in two parts. First, we will ingest the text of multiple Wikipedia entries into a single vector database.

Going beyond RAG, Docugami uses vector databases to support its agentic systems by building knowledge graphs, where the AI system can track semantic elements and relationships across hundreds of ...

Recently, a new sentiment has emerged in AI security circles: "RAG is dead." I've observed firsthand how organizations are increasingly abandoning Retrieval-Augmented Generation (RAG) architectures in ...

Azure SQL Database now supports native vector storage and processing, streamlining AI development by integrating vector search with SQL queries. This update simplifies database management ...

Since 2023, so many database systems have announced vector search as a core feature that it hardly distinguishes them from the competition. For example, MongoDB, Cassandra, PostgreSQL, Snowflake ...

Vector databases aren’t just for engineers — they’re a strategic differentiator for modern marketing leaders.

Timescale debuts open source pgai Vectorizer for AI embeddings as it looks to push vector database beyond RAG.

Running a vector database in the cloud will be easier now that Pinecone is offering its vector database as a serverless offerings in Google Cloud and Microsoft Azure, to go along with an existing ...