This demo will introduce developers to Pattern Similarity for temporal data search in KDB.AI.
In the demo, temporal and unstructured document data are embedded into the vector database. A combined search approach allows end users to find when documents have references against market price trends. At the end, similar trends can easily be queried using pattern similarity scores.
This demo showcases KDB.AI's ability to work with structured and unstructured data, to find similar data across time and meaning, and to extend the knowledge of Large Language Models.