As AI advances, more tools are being introduced into the ecosystem, enabling engineers and developers to experiment and build custom AI apps. But it’s not that simple.
In fact, each advantage of AI is accompanied by a disadvantage. For example, with vector databases such as Chroma, the major challenge was efficient data processing. And many of the latest AI applications rely on vector embeddings as the core of LLMs.
Vector databases are designed to store and query unstructured data—inputs that lack a fixed schema, such as text,…
