Pinecone Newsroom

How to use Pinecone Assistant to accelerate businesses that build RAG systems. Case study AI RAG

At Microsoft Ignite, Microsoft announced that Pinecone is one of their first GenAI Azure Native Integration partners. This follows their recent announcement at GitHub Universe of Pinecone as one of the providers in their new AI App Template Gallery. Why is this great news? In short, both of those updates mean it’s easier than ever for developers to build with Pinecone on Azure.

We've updated our Datadog integration and Prometheus endpoints for expanded performance and usage monitoring.

In September Pinecone serverless added an evaluation API to evaluate RAG and the ability to choose the LLM, among other updates.

Introducing the ability to bulk import from object storage for Pinecone’s serverless infrastructure. This new capability makes ingesting large amounts of data more efficient for developers building accurate, secure, and scalable AI applications.

Pinecone Assistant is now available in public preview, including new features like a built-in evaluation framework, expanded LLM support, and more.

In August Pinecone serverless became generally available to Google Cloud Platform (GCP) and Microsoft Azure and embedding and reranking were added to the Pinecone API.

Pinecone serverless is generally available (GA) on Microsoft Azure. Along with AWS and GCP, you can now build with serverless on the cloud and region that suits you best.

Pinecone serverless is generally available (GA) on Google Cloud and Google Cloud Marketplace. Along with AWS and Azure, you can now build with serverless on the cloud and region that suits you best.

Reranking capabilities are now available with Pinecone Inference, an API that provides instant access to fully managed models hosted on Pinecone’s infrastructure.