A New Way to Build Intelligent Data Pipelines with Vectorize and SingleStore
In the world of data, delay is a luxury few can afford. But, if you’re a developer building gen AI applications, lag isn’t just an inconvenience—it’s a liability. With AI applications, vast amounts of data need to be processed and retrieved in seconds. One misstep can throw off the entire system. Imagine the challenge of maintaining a pipeline that can’t just keep up but needs to adapt in real time as the data changes. To ensure accuracy and speed for your RAG applications, you need two things: real time RAG pipelines and a vector database that can keep up.
With Vectorize and our new integration to SingleStore, that’s exactly what you get.
Why SingleStore?
SingleStore combines high-performance vector search capabilities with real-time processing, making it particularly effective for RAG workloads. Its architecture handles both structured and unstructured data, while built-in vector functions support common similarity metrics like dot product and cosine similarity. This makes SingleStore a great fit for teams who need their RAG pipelines to scale without compromising on speed.
How Vectorize and SingleStore Work Together
Vectorize users can now use SingleStore to keep up with the demands of complex AI applications. This integration lets you store and retrieve vector embeddings directly in SingleStore, so your RAG pipelines process data and return results quickly. The combination of Vectorize’s flexible pipeline management and SingleStore’s optimized data handling means you get reliable, quick access to the insights you need. And with support for real-time updates, your vector search indexes stay fresh, so your models are always working with the latest data.
Streamline Data Extraction
Vectorize makes it easy to populate your SingleStore database from a growing number of data sources, including cloud storage, web crawlers, and specialized platforms like Discord and Intercom. Vectorize’s source connectors streamline data extraction and the creation of vector search indexes, ensuring your database stays current and your AI applications deliver accurate, relevant results.
Connecting a data source to your pipeline is quick and intuitive, and you can easily connect more than one source. For example, if you’re building an AI assistant that needs information from your documentation as well as files in Google Drive, you can configure your pipeline to include both sources with just a couple of clicks.
What This Means for Your RAG Pipelines
Vectorize automates everything from data extraction to ensuring that your vector search indexes remain optimized and accurate. You can focus on building your AI applications while Vectorize ensures your vector data in SingleStore is always up-to-date and ready to deliver the most accurate results.
Get Started with SingleStore and Vectorize
Ready to build smarter AI workflows? Start by creating a Vectorize account, add SingleStore to your setup, and see how Vectorize’s automated data vectorization and pipeline management make creating and deploying high-performance, accurate RAG applications in production easier.