Marketplace - OP Database - Partner connector Vector Operations
Demonstrates vector-based similarity search on database records using Manhattan, Euclidean, Cosine, and Dot distance calculations.
Solution Details
- Difficulty Level
- BEGINNER
- Solution Type
- Recipe
- Author
- OnePoint
- Published on
- May 16, 2025
- Last updated on
- Apr 22, 2026
Get Started
Get StartedKey Features
- Compute four vector distance types — Manhattan, Euclidean, Cosine, and Dot — in parallel branches.
- Support eight database platforms including PostgreSQL, MySQL, Oracle, SAP HANA, and IBM DB2.
- Generate AI embeddings via OP Intelligence and compare them against stored vectors in real time.
- Run all distance operations simultaneously using Boomi's parallel branching for faster results.
- Configure quickly with out-of-the-box vector operation support and JDBC driver compatibility.
How it works
AI-Powered Similarity Search
Enables developers to perform semantic similarity searches against database records using AI-generated embeddings, making it easy to find the most relevant matches from large datasets.
Multi-Metric Vector Distance Benchmarking
Runs all four major vector distance algorithms simultaneously on the same dataset, allowing teams to compare results and choose the most suitable metric for their use case.
Cross-Database Vector Integration Starter
Provides a ready-to-use foundation for building vector-enabled database workflows across eight supported platforms, reducing setup time for AI-powered search and recommendation features.
Applications Required
- OP Database
- OP Intelligence