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Loading...Introduction to Vector Databases
Last quarter, our team discovered the importance of vector databases in building efficient AI-driven search and recommendation systems. After scaling our application to handle over 10 million requests per day, we realized that traditional relational databases were no longer sufficient. This led us to explore vector databases, specifically Weaviate 1.16, Pinecone 1.4, and Qdrant 0.12.
The Problem with Traditional Databases
Traditional databases are designed to handle structured data and are not optimized for the complex, high-dimensional data used in AI models. As a result, querying and retrieving data from these databases becomes inefficient, leading to increased latency and decreased performance. Vector databases, on the other hand, are designed to handle high-dimensional data and provide efficient querying and retrieval mechanisms.
Weaviate 1.
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