Semantic Search - A Summary

If you've registered on ourai.applicloud.com and tried semantic search at SemanticDemo, you may have noticed that its usage is quite different from traditional full-text search. Understanding how to use it effectively requires a bit of learning.

In many cases, the text or document segment found by semantic search may match the intended meaning of your query, but it's not always the exact information you were looking for. This is because semantic search relies on vector similarity, which identifies related content based on meaning rather than exact keyword matches.

Combining Semantic Search with Traditional Techniques

In real-world applications, purely vector-based semantic search is often combined with:

• Full-text search for precise keyword matching.
• Pre-processing to refine the search input.
• Post-processing to rank and filter results more effectively.

These enhancements are typically handled by additional AI system layers to improve accuracy and relevance.

Where Semantic Search Shines

Despite its differences from traditional search, semantic search has valuable applications. It's particularly useful in:

• Chatbots, where natural language queries require flexible matching.
• Automated text processing, such as summarization or document classification.

Exploring Vector Storage in ChatGPT

Another exciting method — specific to ChatGPT — is the use of an integrated vector store. This powerful feature enables efficient semantic search directly within the ChatGPT environment.

Tomorrow, we'll explore this feature in detail and compare the results it delivers when searching the same dataset using different methods. Stay tuned!