Automatic Metadata Extraction is now available in Vectorize! This powerful addition transforms how you work with documents in your RAG pipelines. This feature allows you to extract structured information from your documents automatically, enhancing your retrieval capabilities and providing more context for your language models. The Challenge of Unstructured Data Organizations deal with vast amounts […]
We’re excited to announce our new integration with Supabase Vector, bringing Vectorize’s retrieval-augmented generation (RAG) pipelines to one of the fastest-growing open-source developer platforms. If you’re already using Supabase and want to power smarter search, structured extraction, or multimodal AI experiences — this integration is for you. Why Supabase? Supabase has earned its spot as […]
With the release of the latest JFK assassination files this week, we saw an opportunity to showcase Vectorize’s capabilities while providing a valuable service to the public. Today, I’m excited to share JFK Files Explorer, a free tool that allows anyone to have conversations with these historically significant documents. The Challenge: Scale and Complexity The […]
If you’re implementing retrieval augmented generation (RAG), there’s one crucial component you might be missing: a reranking model. While vector similarity search has become the go-to method for retrieving relevant context, relying solely on similarity scores can lead to suboptimal results. Let me show you why reranking is not just an optional enhancement, but a […]
The article looks at the benefits of modularity in agentic systems, enhancing clarity, maintainability, and reducing complexity.
This guide outlines how to create efficient agentic systems by focusing on three layers: tools, reasoning, and action. Each layer presents unique challenges that can impact overall system performance.
At Vectorize, we want to make it fast and easy for our users to create retrieval-augmented generation (RAG) pipelines to power their AI applications. We’ve tried to make it as intuitive as possible and continue to iterate on our user interface towards this goal. However, when you support dozens of integrations with third-party vector databases, […]
“This thing is a tangled mess.” I was relieved to hear the presenter say the words I was thinking. He had just finished walking me through a new AI agent, which I’m going to call Sherpa throughout this article. Sherpa was a proof of concept for a new AI agent their team had been working […]
Building multimodal RAG applications can be tricky. These design patterns will help you provide users with richer, more detailed insights
Anyone who’s built an AI application using retrieval-augmented generation (RAG) knows how frustrating it can be. Most of your data is in places it’s not easy to access: buried deep in file systems, trapped inside SaaS platforms, or tucked away in knowledge bases. Once you’ve figured out a way to extract your data, you still […]
