![]() If a developer is building a chatbot application on top of OpenAI's GPT-4, they may not want to call the AI model every time a question is asked, if similar questions have been asked by users in the past. Vector databases have grown in popularity as developers look for more efficient and accurate ways to build applications on top of LLMs.įor instance, one common use case for this technology is searching through data for similar items, or similarity search. "So therefore it allows you to be more flexible with how you want to call LLMs." LLMs refers to large language models like OpenAI's GPT-4. "It basically provides a database or long-term memory around all the calls and responses of unstructured data," Essence VC managing partner Tim Chen said. They accomplish this through vector embeddings, or representations of unstructured data that describe them as numeric values in hundreds or thousands of different dimensions, allowing them to be more easily grouped together based on similarities. Vector databases help users search across complex, unstructured data like text, images, and videos using their actual content instead of human-generated tags or labels. Similar to the investor excitement around generative AI, VCs are now flocking to another area within the buzzy AI ecosystem. Within the span of just a few days in the past month, three startups - Chroma, Weaviate, and Pinecone - received term sheets and valuations ranging as high as $700 million, highlighting new investor interest in an AI category titled vector databases, Insider has learned. ![]() These large rounds continue a trend of hype around startups in the buzzy generative AI ecosystem.VCs like Andreessen Horowitz and Index Ventures are offering valuations ranging up to $700 million.Vector database startups like Pinecone have received term sheets from top-tier VCs, Insider has learned.Account icon An icon in the shape of a person's head and shoulders.
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