Top Highlights

• 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.
• 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.
• "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. "So therefore it allows you to be more flexible with how you want to call LLMs."
• 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. Instead, a vector database can store and quickly find past responses to similar questions by understanding the meaning behind the statements with...

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