However, the author warns against blindly following trends and adopting technologies without understanding their relevance and usefulness. The article points out the limitations of vector DBs, stating that they cannot fully replace traditional databases and still lack comprehensive search functionality. The author emphasizes the importance of understanding use cases and testing scenarios before implementing new technologies, and advises against being swayed by hype or popularity.
Key takeaways:
- Every organization needs an AI roadmap to stay competitive, and this can be achieved by assembling a team, getting API keys from OpenAI, and using vector databases and embeddings.
- Vector-based representations are crucial in modern natural language processing and have deep roots, with the concept of distributional semantics being expanded upon in George Miller's 1951 book "Language and Communication".
- Vector databases are becoming crowded, and while they offer semantic retrieval, they can't fully replace traditional databases and are still catching up in terms of supporting the text processing features needed for comprehensive search functionality.
- Enterprise search is complex and requires meticulous planning and execution. It's important to understand your use case and validate your test scenarios, rather than being lured by popular "shiny objects".