Vector Utilities
Vector Utility API
The Vector Utility API provides a SQL function VECTOR_CHUNKS which processes text into pieces (chunks) in preparation for the generation of embeddings to be used with a vector index. The API is configurable in terms of size of chunks and rules for splitting chunks.
While it is possible for you to create your own chunking algorithms, utilizing this functionality could save you time and aid in faster development with a pre-packaged SQL function.
Chainable Utility Functions for Vectors
DBMS_VECTOR provides a set of utility functions for processing text for the creation of vector indexes. These functions may be chained together such that the output from one function is used as the input for the next.
This feature offers a straightforward yet very customizable method for you to turn textual content, like a PDF document or VARCHAR2 database field, into the embeddings necessary for a vector index. This capability enables you to seamlessly develop with vectors, facilitating the creation of the next generation of Artificial Intelligence applications with ease.
Vector Format Output for Feature Extraction Algorithm
Feature extraction algorithms produce a set of features that represent projections in a lower dimensional latent space. The output is typically numerical and dense. VECTOR type representation is the natural choice.
Feature extraction algorithms represent a principled approach to vectorizing relational data. The vectorized representation can be used for similarity search.
In-Database Algorithms Support for VECTOR Data Type Predictors
This feature enables users to include one or more columns of VECTOR data type as predictors along with structured enterprise data to in-database machine learning algorithms.
Vector representations of unstructured data can be a powerful input to traditional machine learning algorithms. They enable efficient data processing on text and image data, helping to speed data-driven decision making. Providing vectors as input to machine learning models enables handling a broader class of use cases.
JSON_TEXTCONTAINS and JSON_EXISTS Support in DBMS_HYBRID_VECTOR.SEARCH
The DBMS_HYBRID_VECTOR.SEARCH API now supports a way for users to specify optional JSON_EXISTS and JSON_TEXTCONTAINS clauses as part of their hybrid search.
Writing complex queries requires the ability to further constrain hybrid (semantic and textual) search by JSON_EXISTS and JSON_TEXTCONTAINS when working with complex JSON data. This feature lets you design sophisticated applications that better satisfy users' search criteria.