Pinecone and the Power of Vector Databases for AI
By Adam Pease
Pinecone and the Power of Vector Databases for AI
Last week, Pinecone, a provider of vector database software revealed that it had raised $100 million in a series B round led by Andreesen Horowitz. This blog discusses the investment news and reflects on the significance of vector databases for the AI market.
What Is Pinecone?
Pinecone is a New York-based startup that offers database solutions to support artificial intelligence. In particular, the provider is focused on helping organizations translate their proprietary data into vector embeddings that can be used to configure and customize AI to suit their use case.
The new funding round brings Pinecone to a $750 million valuation, suggesting that the market for vector database solutions is heating up. While many businesses have moved swiftly to adopt ChatGPT and other large language models into their processes, many have struggled with the limited memory of current state of the art chat models. Despite their levels of intelligence, today’s still struggle to retain conversational memory beyond a certain ‘token limit.’ Vector databases have emerged to address this problem and provide chatbots with longer-term, more continuous working memory.
What Are Vector Databases?
Vector databases work by reducing information to a compressed mathematical state known as a ‘vector embedding.’ When information is represented in this mathematical form it can be used by AI models to perform different operations, such as search, or data analysis. The key to understanding vector databases is that they are capable of transforming complex, heterogeneous information into a universally-legible format that can be digested by AI models.
For this reason, vector databases are already seeing significant adoption in the enterprise, especially when used in conjunction with emerging large language models. By enabling organizations to take complex and domain-specific data and translate it into a transparent and actionable format, organizations like Pinecone are helping to build the memory layer of large language models. Such applications will be vital in use cases such as the legal field, where large language models will need to be able to search, reference, and remember a great deal of specific information to perform effectively.
Bottom Line
Pinecone’s latest round of fundraising suggests that the database market is heating up for AI. Vector databases are a novel addition to the AI tech stack that are quickly becoming understood as critical infrastructure for tomorrow’s large language model applications.
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