CREATE_VOCABULARY

Use the DBMS_VECTOR_CHAIN.CREATE_VOCABULARY chunker helper procedure to load your own token vocabulary file into the database.

Purpose

To create custom token vocabulary that is recognized by the tokenizer used by your vector embedding model.

A vocabulary contains a set of tokens (words and word pieces) that are collected during a model’s statistical training process. You can supply this data to the chunker to help in accurately selecting the text size that approximates the maximum input limit imposed by the tokenizer of your embedding model.

Usage Notes

Usually, the supported vocabulary files (containing recognized tokens) are included as part of a model’s distribution. Oracle recommends to use the vocabulary files associated with your model.

If a vocabulary file is not available, then you may download one of the following files depending on the tokenizer type:
- WordPiece:
  
  Vocabulary file (vocab.txt) for the “bert-base-uncased” (English) or “bert-base-multilingual-cased” model
- Byte-Pair Encoding (BPE):
  
  Vocabulary file (vocab.json) for the “GPT2” model
  
  Use the following python script to extract the file:
```
import json
import sys

with open(sys.argv[1], encoding="utf-8") as f:
  d = json.load(f)
  for term in d:
    print(term)
```
- SentencePiece:
  
  Vocabulary file (tokenizer.json) for the “xlm-roberta-base” model
  
  Use the following python script to extract the file:
```
import json
import sys

with open(sys.argv[1], encoding="utf-8") as f:
  d = json.load(f)
  for entry in d["model"]["vocab"]:
    print(entry[0])
```
Ensure to save your vocabulary files in UTF-8 encoding.
You can create a vocabulary based on the tokens loaded in the schema.table.column, using a user-specified vocabulary name (vocabulary_name).

After loading your vocabulary data, you can use the by vocabulary chunking mode (with VECTOR_CHUNKS or UTL_TO_CHUNKS) to split input data by counting the number of tokens.
You can query these data dictionary views to access existing vocabulary data:
- ALL_VECTOR_VOCAB displays all available vocabularies.
- USER_VECTOR_VOCAB displays vocabularies from the schema of the current user.
- ALL_VECTOR_VOCAB_TOKENS displays a list of tokens from all available vocabularies.
- USER_VECTOR_VOCAB_TOKENS displays a list of tokens from the vocabularies owned by the current user.

Syntax

DBMS_VECTOR_CHAIN.CREATE_VOCABULARY(
    PARAMS      IN JSON default NULL
);

PARAMS

Specify the input parameters in JSON format:

{
    table_name,
    column_name,
    vocabulary_name,
    format,
    cased
}

Table 22 Parameter Details

Parameter	Description	Required	Default Value
`table_name`	Name of the table (along with the optional table owner) in which you want to load the vocabulary file	Yes	No value
`column_name`	Column name in the vocabulary table in which you want to load the vocabulary file	Yes	No value
`vocabulary_name`	User-specified name of the vocabulary, along with the optional owner name (if other than the current owner)	Yes	No value
`format`	`xlm` for SentencePiece tokenization `bert` for WordPiece tokenization `gpt2` for BPE tokenization	Yes	No value
`cased`	Character-casing of the vocabulary, that is, vocabulary to be treated as cased or uncased	No	`false`

Example

DECLARE
  params clob := '{"table_name"       : "doc_vocabtab",
                   "column_name"      : "token",
                   "vocabulary_name"  : "doc_vocab",
                   "format"           : "bert",
                   "cased"            : false}';

BEGIN
  dbms_vector_chain.create_vocabulary(json(params));
END;
/

End-to-end example:

To run an end-to-end example scenario using this procedure, see Create and Use Custom Vocabulary.

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