Supplied Vector Utility PL/SQL Packages

Use either a lightweight DBMS_VECTOR package or a more advanced DBMS_VECTOR_CHAIN package with full capabilities.

• DBMS_VECTOR:

  This package simplifies common operations with Oracle AI Vector Search, such as chunking text into smaller segments, extracting vector embeddings from user data, or generating text for a given prompt.

  Subprogram	Operation	Provider	Implementation
  Chainable Utility Functions	UTL_TO_CHUNKS to perform chunking	Oracle AI Database	Calls the VECTOR_CHUNKS SQL function under the hood
  	UTL_TO_EMBEDDING and UTL_TO_EMBEDDINGS to generate one or more embeddings	Oracle AI Database	Calls the embedding model in ONNX format stored in the database
  		Third-party REST providers	Calls the specified third-party embedding model
  	UTL_TO_GENERATE_TEXT to generate text for prompts and images	Third-party REST providers	Calls the specified third-party text generation model
  	UTL_TO_RERANK to retrieve more relevant search output	Third-party REST providers	Calls the specified third-party embedding model
  Credential Helper Procedures	CREATE_CREDENTIAL and DROP_CREDENTIAL to manage credentials for third-party service providers	Oracle AI Database	Stores credentials securely for use in Chainable Utility Functions

  For detailed information on this package, see DBMS_VECTOR.

• DBMS_VECTOR_CHAIN:

  This package provides chunking and embedding functions along with some text generation and summarization capabilities. It is more suitable for text processing with similarity search and hybrid search, using functionality that can be pipelined together for an end-to-end search.

  Subprogram	Operation	Provider	Implementation
  Chainable Utility Functions	UTL_TO_TEXT to extract plain text data from documents	Oracle AI Database	Uses the Oracle Text component (CONTEXT) of Oracle AI Database
  	UTL_TO_CHUNKS to perform chunking	Oracle AI Database	Calls the VECTOR_CHUNKS SQL function under the hood
  	UTL_TO_EMBEDDING and UTL_TO_EMBEDDINGS to generate one or more embeddings	Oracle AI Database	Calls the embedding model in ONNX format stored in the database
  		Third-party REST providers	Calls the specified third-party embedding model
  	UTL_TO_SUMMARY to generate summaries	Oracle AI Database	Uses Oracle Text
  		Third-party REST providers	Calls the specified third-party text summarization model
  	UTL_TO_GENERATE_TEXT to generate text for prompts and images	Third-party REST providers	Calls the specified third-party text generation model
  	UTL_TO_RERANK to retrieve more relevant search output	Third-party REST providers	Calls the specified third-party embedding model
  Credential Helper Procedures	CREATE_CREDENTIAL and DROP_CREDENTIAL to manage credentials for third-party service providers	Oracle AI Database	Stores credentials securely for use in Chainable Utility Functions
  Preference Helper Procedures	CREATE_PREFERENCE and DROP_PREFERENCE to manage preferences for hybrid vector indexes	Oracle AI Database	Creates vectorizer preferences for use in hybrid vector indexing pipelines
  Chunker Helper Procedures	CREATE_VOCABULARY and DROP_VOCABULARY to manage custom token vocabularies	Oracle AI Database	Uses Oracle Text
  	CREATE_LANG_DATA and DROP_LANG_DATA to manage language-specific data (abbreviation tokens)	Oracle AI Database	Uses Oracle Text

  Note:

  The DBMS_VECTOR_CHAIN package requires you to install the CONTEXT component of Oracle Text, an Oracle AI Database technology that provides indexing, term extraction, text analysis, text summarization, word and theme searching, and other utilities.

  Due to underlying dependance on the text processing capabilities of Oracle Text, note that both the UTL_TO_TEXT and UTL_TO_SUMMARY chainable utility functions and all the chunker helper procedures are available only in this package through Oracle Text.

  For detailed information on this package, see DBMS_VECTOR_CHAIN.