📊 Semantic Vector Database Search
🔎 Query Results
Most semantically similar results retrieved via cosine similarity:
No results yet. Enter a query and click “Search”.
How It Works ⚙️
This tool demonstrates semantic vector search — the foundation of AI knowledge retrieval and RAG (Retrieval-Augmented Generation).
Each document or record in your system is encoded as a
high-dimensional embedding vector by an AI model such as
text-embedding-3-large. These vectors capture meaning, not just keywords.
When you type a query, it is also embedded and compared against all stored vectors using cosine similarity or dot product distance.
The results shown on the left are the items closest to your query in vector space — representing the most conceptually relevant information in your database.
In short: this page lets your AI think in meaning, not in words.