Pinecone Blog

Pinecone Assistant now has support for new OpenAI (gpt-4.1, o4-mini) and Anthropic (claude-3-7-sonnet) models, as well as Gemini (gemini-2.5-pro). In addition to the new selection of models, Pinecone Assistant now gives developers more control by exposing the temperature parameter for the chosen LLM.

Pinecone sponsored and attended events across Tech Week by A16Z

This blog post explores how multi-vector retrieval improves search accuracy by capturing rich query-document interactions, while addressing its scalability challenges. It introduces a practical, staged retrieval pipeline that balances speed and effectiveness, starting with fast retrieval, refining with multi-vector embeddings, and finishing with cross-encoder reranking. The post highlights ConstBERT, a constant-space multi-vector model co-developed by Pinecone and academic collaborators, and shows how to integrate it into Pinecone to build efficient, scalable, and accurate search systems. ConstBERT is now available in open source.

We recently partnered with the School of Code during their AI Week. The students used Pinecone to build working AI applications in just a few days. A short video captures their projects and impressions. While the apps are indeed beginner-level, they show that even newcomers can build practical AI experiences with modern tools. The signal is clear: Pinecone is not just for enterprises; - it’s for anyone who wants to build knowledgeable AI -- today.

As AI systems become agentic and more autonomous, enterprises need infrastructure that delivers secure data access, real-time retrieval, and production-grade performance. Rubrik Annapurna, powered by Pinecone’s vector database, provides the infrastructure to deploy generative AI at scale, with RAG and agentic workloads in mind.