td-vector-distance

You can calculate vector distance for similarity analysis in Teradata Vantage by using the TD_VectorDistance function, which computes various distance metrics between sets of vectors. It supports an automated workflow from data preprocessing to results analysis.

For semantic search in Teradata, the TD_VectorDistance function supports Euclidean, Manhattan, and Cosine distance metrics. These metrics enable you to measure document similarity and perform vector analysis on your data tables.

You compare document similarity using text analytics in Teradata by applying the TD_VectorDistance function to your document vectors. The skill provides automated feature engineering and SQL generation to streamline the comparison workflow.

Yes, the TD_VectorDistance function includes automated data preprocessing and feature engineering for vector analysis. It generates the necessary SQL to provide a complete analytical workflow from raw data to distance calculation results.

You can run vector distance calculations directly on existing database tables by referencing the table name in the skill. The automated workflow handles the SQL generation to compute similarity metrics without requiring external data extraction.

The best way to perform semantic search on text data in Teradata is using the TD_VectorDistance function to compute vector similarities. It handles the complete workflow including preprocessing, distance calculation, and result analysis for your text analytics.