On Vector Databases. Q&A with Neil Kanungo
Q1: What is a Vector Database?
A Vector Database is a specialized form of database that arranges data based on content similarity rather than just keywords. This is a game-changer for businesses that deal with unstructured data, which traditional databases struggle to handle. Imagine a library where books are not just arranged by genre but also by the author’s writing style, story plots, and more. That’s the level of granularity a vector database offers. It understands the context and meaning of the data, making it incredibly useful for applications like recommendation systems, chatbots, and more. From a customer problem-solving perspective, this means you no longer have to manually tag or label your data for specific lookups, saving time and reducing errors.
Q2: What is the difference between a traditional database and a vector database?
Traditional databases store data “as-is,” without any transformation. They are not designed to handle unstructured data, which makes up to 90% of all data today. In contrast, vector databases use “vector embeddings” to represent data, capturing thousands of characteristics. This allows them to process unstructured data with ease. For businesses, this means you can now efficiently handle various types of data, including text, images, and audio, without the limitations imposed by traditional databases. This opens up new avenues for customer engagement through more intelligent applications.
Q3: What is the role of vector database in generative AI?
Vector databases are pivotal in powering generative AI applications. They enable similarity search, which is fundamental to applications like chatbots, recommendation systems, and even anomaly detection. For instance, using vector databases, chatbot applications like ChatGPT can generate responses that are contextually relevant to the user’s query. This is a significant step up from the rule-based systems of the past. For customers, this means more personalized and accurate responses, enhancing user experience and solving problems more efficiently.
To get the full lowdown on vector databases, head over to https://kdb.ai/learning-hub/fundamentals/vector-database-101/.
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Neil Kanungo, VP of Product Led Growth, KX
Sponsored by KX