Working with Vectors and Text Embeddings in SAP HANA Cloud

Get Hands-On Experience with SAP HANA Cloud

In the world of machine learning and artificial intelligence, understanding language is a crucial challenge. Text embedding offers a powerful solution by converting words into numerical representations, enabling algorithms to process and interpret text with remarkable efficiency. This technique maps words into high-dimensional vector spaces, where those with similar meanings are positioned closer together—paving the way for smarter text analysis and retrieval.

Curious to see text embeddings in action? You can explore these powerful features firsthand by registering for the SAP HANA Cloud Basic Trial. The process is simple:

Sign up for a free Basic Trial and follow the easy registration steps.Access the guided workbook to walk through real-world use cases.Experiment with VECTOR_EMBEDDING, COSINE_SIMILARITY, and L2DISTANCE to perform text similarity searches and more.

Start your journey with SAP HANA Cloud today and discover how text embeddings can transform your data processing and analytics capabilities.

Introduction

Through the SAP HANA Cloud Basic Trial guided workbook, you can explore firsthand how SAP HANA Cloud’s VECTOR_EMBEDDING function converts text into vector representations. This transformation enhances text search and similarity queries, making it easier to analyze relationships between different text entries.

Additionally, key functions such as COSINE_SIMILARITY and L2DISTANCE, alongside the TO_REAL_VECTOR function from the built-in vector engine, enable precise measurement of relationships between text vectors. These tools refine search accuracy, improving the relevance of results.

Vector Engine

The SAP HANA Cloud vector engine supports the create, read, update, and delete (CRUD) operations involving vectors using SQL.

The given vector is compared to the vectors in the table and the similarity is calculated. As can be seen from the result, one vector is quite similar (0.99) while the other is not (0.59).

Text Embedding

Creating text embeddings in SAP HANA Cloud involves using the VECTOR_EMBEDDING function to convert text into vector representations. This functionality is useful for enabling advanced text search and similarity queries within the SAP HANA Cloud database.

Multi Model with Vectors and Text Embeddings

Combine multi-model engines of SAP HANA Cloud, including spatial, graph, relational, and the JSON document store, enabling a unified approach to data management. In a sales strategy scenario, relational engines store transactional data, while the graph engine maps relationships between sales entities.

The spatial engine enhances geographic analysis with interactive maps, and the JSON document store manages diverse product catalogs. The vector engine further refines inventory management by comparing product similarities.

Summary

The SAP HANA Cloud Basic Trial guided workbook provides hands-on experience with the VECTOR_EMBEDDING function, which converts text into vector representations to enhance text search and similarity queries. Functions like COSINE_SIMILARITY, L2DISTANCE, and TO_REAL_VECTOR enable precise measurement of relationships between text vectors, improving search accuracy and relevance.

The SAP HANA Cloud vector engine supports CRUD operations on vectors using SQL, allowing users to compare vectors and calculate similarity scores. This capability is essential for tasks like inventory management, where products can be analyzed and compared based on similarity.

SAP HANA Cloud also enables a multi-model approach by integrating spatial, graph, relational, and JSON document store engines. This allows businesses to combine various data types, such as transactional sales data, relationship hierarchies, geographic insights, and diverse product catalogs, providing a unified and comprehensive data management solution.

​ Get Hands-On Experience with SAP HANA CloudIn the world of machine learning and artificial intelligence, understanding language is a crucial challenge. Text embedding offers a powerful solution by converting words into numerical representations, enabling algorithms to process and interpret text with remarkable efficiency. This technique maps words into high-dimensional vector spaces, where those with similar meanings are positioned closer together—paving the way for smarter text analysis and retrieval.Curious to see text embeddings in action? You can explore these powerful features firsthand by registering for the SAP HANA Cloud Basic Trial. The process is simple:Sign up for a free Basic Trial and follow the easy registration steps.Access the guided workbook to walk through real-world use cases.Experiment with VECTOR_EMBEDDING, COSINE_SIMILARITY, and L2DISTANCE to perform text similarity searches and more.Start your journey with SAP HANA Cloud today and discover how text embeddings can transform your data processing and analytics capabilities. IntroductionThrough the SAP HANA Cloud Basic Trial guided workbook, you can explore firsthand how SAP HANA Cloud’s VECTOR_EMBEDDING function converts text into vector representations. This transformation enhances text search and similarity queries, making it easier to analyze relationships between different text entries.Additionally, key functions such as COSINE_SIMILARITY and L2DISTANCE, alongside the TO_REAL_VECTOR function from the built-in vector engine, enable precise measurement of relationships between text vectors. These tools refine search accuracy, improving the relevance of results.Vector EngineThe SAP HANA Cloud vector engine supports the create, read, update, and delete (CRUD) operations involving vectors using SQL.The given vector is compared to the vectors in the table and the similarity is calculated. As can be seen from the result, one vector is quite similar (0.99) while the other is not (0.59).Text EmbeddingCreating text embeddings in SAP HANA Cloud involves using the VECTOR_EMBEDDING function to convert text into vector representations. This functionality is useful for enabling advanced text search and similarity queries within the SAP HANA Cloud database.Multi Model with Vectors and Text EmbeddingsCombine multi-model engines of SAP HANA Cloud, including spatial, graph, relational, and the JSON document store, enabling a unified approach to data management. In a sales strategy scenario, relational engines store transactional data, while the graph engine maps relationships between sales entities.The spatial engine enhances geographic analysis with interactive maps, and the JSON document store manages diverse product catalogs. The vector engine further refines inventory management by comparing product similarities. SummaryThe SAP HANA Cloud Basic Trial guided workbook provides hands-on experience with the VECTOR_EMBEDDING function, which converts text into vector representations to enhance text search and similarity queries. Functions like COSINE_SIMILARITY, L2DISTANCE, and TO_REAL_VECTOR enable precise measurement of relationships between text vectors, improving search accuracy and relevance.The SAP HANA Cloud vector engine supports CRUD operations on vectors using SQL, allowing users to compare vectors and calculate similarity scores. This capability is essential for tasks like inventory management, where products can be analyzed and compared based on similarity.SAP HANA Cloud also enables a multi-model approach by integrating spatial, graph, relational, and JSON document store engines. This allows businesses to combine various data types, such as transactional sales data, relationship hierarchies, geographic insights, and diverse product catalogs, providing a unified and comprehensive data management solution.            Read More Technology Blogs by SAP articles 

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