RheoData_23ai_Vector-Datatype-Webinar-2024.pptx

Copyright, 2024 RheoData and affiliates
bobby.curtis@rheodata.com | www.rheodata.com | @rheodatallc
Oracle 23ai:
AI Vector Search:
The What, The How, & The
Possibilities
Will start about 3 minutes
after the hour.

Webinar
Keeping
Use Q&A dialog to ask
questions. Will answer as
pauses occur or at end of
webinar.

Speaker
Bobby Curtis, MBA
Atlanta, GA
• Author
• Speaker
@dbasolved | @rheodatallc
https://dbasolved.com | https://www.rheodata.com/blog
bobby.curtis@rheodata.com
https://www.slideshare.net/BobbyCurtisMBA

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Prerequisites
• Oracle Database 23c 23ai
• oracle-database-free-23c-1.0-1.el8.x86_64
(23.4 ((LA1))
• oracle-database-ee-23c-1.0-1.el8.x86_64
(23.4(LA2))
• OCI Oracle Database Service
• On-premises (here) - Free
• Python 3.11/3.12
• python-oracledb
• oracledb-2.2.0
• Langchain
• https://python.langchain.com/docs/get_
started/introduction
• LLM API Key
• Cohere, ChatGPT, etc.

The What?

Vector Database are a new
classification of specialized databases
designed for AI workloads that allows
you to query based on semantics,
rather than keywords.

Vector Search enhances information
retrieval by mapping queries to relevant
data in your database based on
semantics, instead of precise matches,
using vectors to measure similarity.

45
68
16
50
42
Vector
Vectors are a sequence of numbers,
called dimensions, used to capture the
important “features” of the data.
Vectors in AI represent semantics of
unstructured data such as images,
documents, videos, etc.

45
68
16
50
42
Vector
The mathematical distance between two vectors
indicate how similar they are
Vectors represent the semantic content
of data, not the underlying words or
pixels

Online Embedding Models (Cohere)
English Multi-Lingual
embed-english-v3.0 (1024) embed-multilingual-v3.0 (1024)
embed-english-light-v3.0 (384) embed-multilingual-light-v3.0 (384)
embed-english-light-v2.0 (1024)

Online Embedding Models (Cohere)
English Multi-Lingual
embed-english-light-v3.0 (384) embed-multilingual-light-v3.0 (384)
embed-english-light-v2.0 (1024)
Database Embedding Models (Hugging Face/OMLUtils)

Input
Data
Object
Embedding
Model 4 8 42 72
Vector Search
Relevant Content
Data Object
Retrieval
Vector ID
Matches
Vector Embedding

The How? -> The Math

The Math?
Euclidean/Squared
Euclidean Distance
Cosine Similarity
Reflects the distance
between each of the
vector coordinates being
compared -> straight line
distance between vectors
Measures the cosine of
the angle between two
vectors.

The Math?
DOT Product Similarity Manhattan Distance
Similarity of two vectors
are viewed by multiplying
the size of each vector by
the cosine of their angle.
Calculated by summing
the distance between
the coordinates of two
vectors.

The Math?
Hamming Distance
The distance between two vectors
represent the number of coordinates where
they differ.

The How? -> Oracle

Vector data types can be declared in the following forms:
Vector Datatype
Vector Arbitrary number of dimensions and
format
Vector (*,*) Vector = Vector(*,*)
Vector (number_of_dimensions, *) Vectors must all have the specified
number of dimensions, or an error is
thrown. No format modification.
Vector(*, dimension_element_format) Arbitrary number of dimensions, but
format will be up-converted or down-
converted to the specific dimension
(INT8, FLOAT32, FLOAT64).
Vector(number_of_dimensions,
dimension_element_format)
Must have all the specified number of
dimensions, or error thrown. Each will be
up-converted or down-converted to the
specified format (INT8, FLOAT32,
FLOAT64).
Vectors can be NULL but dimensions cannot:
[1.1, NULL, 2.2]
Same Thing

Different Types of Vectors Supported!
drop table vector.rd_vector;
CREATE TABLE vector.rd_vector (
v1 VECTOR,
v2 VECTOR(3, FLOAT32),
v3 VECTOR(2, FLOAT64),
v4 VECTOR(1, INT8),
v5 VECTOR(1, *),
v6 VECTOR(*, FLOAT32),
v7 VECTOR(*, *)
);

Vector Base Operations
TO_VECTOR || VECTOR Constructors for vectors
FROM_VECTOR || VECTOR_SERIALIZE Functions take a vector and return as
string in VARCHAR2 or CLOB as output
VECTOR_NORM Function returns the Euclidean norm of
the vector in BINARY_DOUBLE
(SQRT(SUM((xi-yi)2))
VECTOR_DIMENSION_COUNT Function returns the number of
dimensions of a vector as an Oracle
number
VECTOR_DIMENSION_FORMAT Returns the storage format of the vector
in VARCHAR2 format (INT8, FLOAT32,
FLOAT64)
VECTOR_DISTANCE* Function returns Squared Euclidean
distance. Default for Oracle AI Vector
Search
There are a few base functions that are used with Vectors.

VECTOR_DISTANCE
vector_distance (expr1, expr2, [,
metric])
L1_distance (expr1,
expr2)
L2_distance (expr1,
expr2)
cosine_distance (expr1,
expr2)
inner_distance (expr1,
expr2)
Manhattan/Taxi Cab
Euclidean_Squared
DOT
Cosine

How do I create a “vector”?

Declare vector datatypes during table creation.
Example 1:
CREATE TABLE text_vec
(
id NUMBER,
embeddings VECTOR
);
Example 2:
(
id NUMBER,
embeddings VECTOR(384, INT8)
);

Vector datatypes can be added to tables later as well.
Example 1:
(
id NUMBER,
col1 varchar2(50),
);
ALTER TABLE text_vec add
(
vec vector
);

sql> create table
vec_tab
(
id number,
vec vector(2)
);
sql> insert into vec_tab
values (1, ’[1,2]’);
sql> insert into vec_tab
values (2, ’[2,3]’);
import oracledb
connection =
oracledb.connection()
cursor = connection.cursor()
cursor.execute()
cursor.setinputsizes(None,
oracledb.DB_TYPE_VECTOR)
cursor.execute()
connection.commit()
Available
Create Create & Populate

Let’s go with Python!

Python Approach – 1 of 4
import oracledb
import cohere
import os
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter

with open("./Scripts/oracle/vector/cities.txt") as f:
cities = f.read()
#print(cities)
text_splitter = CharacterTextSplitter(separator="n",
chunk_size=35, chunk_overlap=5, length_function=len,
is_separator_regex=False)
texts = text_splitter.split_text(cities)

api_key = ”API Key"
os.environ["COHERE_API_KEY"] = api_key
print(os.environ["COHERE_API_KEY"])
co = cohere.Client(os.getenv("COHERE_API_KEY"))
Cohere offers two types of API Keys:
• Development (Limit 10 calls per minute)
• Production (unlimited calls per minute)

Python Approach – 3.5 of 4
def database_connection():
try:
connection = oracledb.connect(
user="vector",
password="vector",
dsn="100.130.230.100:1521/freepdb1"
)
print('connected’)
return connection
except:
print('Could not make a connection')

connection = database_connection()
id_val = 1
for i in texts:
response = co.embed(
texts=[i],
model='embed-english-light-v3.0',
input_type='classification'
)
vtext = i
vec = response.embeddings[0]
#print(vtext)
#print(len(vec))
#print(vec)
cursor = connection.cursor()
cursor.setinputsizes(None, oracledb.DB_TYPE_VECTOR)
cursor.execute("insert into vector.text_vec (id, txt_vec, text)
values (:1, :2, :3)",[id_val, vec, vtext])
connection.commit()
#print("recorded inserted")
id_val = id_val + 1
print(str(id_val-1) + " inserted")
cursor.execute("select * from vector.text_vec")
for row in cursor:
print(row)

Create Vector Demo (Video)

Create Vectors
select text
from vector.text_vec
order by vector_distance(txt_vec,(
select txt_vec
from vector.text_vec_lookup
where id = :1), DOT)
fetch first :2 rows only;

Similarly Searches (Demo)

Similarly Searches (Demo)
Explain Plan
• No indexes
• Selected 43 rows in milliseconds – all text
New webinar: Vector Indexes, coming soon.

How do I embed a LLM within the database?

ONNX Files
ONNX = Open Neural Network Exchange
An open-source format designed for machine-
learning models. Ensuring cross-platform
compatibility and supports major languages
and frameworks, facilitating easy and efficient
model exchanges.

$ export
ORACLE_HOME_23ai=/opt/oracle/product/23ai/dbhome_1
$ cd $ORACLE_HOME_23ai/python/bin
$ python -V
$ export PATH=$ORACLE_HOME_23ai/python/bin:$PATH
$ python -v
• Verify that the correct Python is installed.
• Python 3.12 (min)

$ cd ~
$ mkdir onnx
• Make directory for all ONNX files

$ cd ~/onnx
$ unzip ./omlutils.zip -d
• Change directory to ONNX directory
• Unzip OMLUTILS.zip file

$ cd ~/onnx
$ python -m pip install -r requirements.txt
$ python -m pip install omlutils-0.13.0-cp312-cp312-
linux_x86_64.whl
• Install all the necessary requirments
• Install OMLUTILS wheel file

$ cd ~/onnx
$ python
>>> from omlutils import EmbeddingModel,
EmbeddingModelConfig
>>> em = EmbeddingModel(model_name="sentence-
transformers/all-MiniLM-L6-v2”)
>>> em.export2file("all-MiniLM-L6-v2",output_dir=".”)
>>> exit()
$
• Install requested LLM into database using Python
Interactive Prompt
• View model in Oracle Data Dictonary

New Database Views
*_DATA_MODELS
SELECT * FROM *_DATA_MODELS;

Using the Embedded Model
(TO_VECTOR &
VECTOR_EMBEDDING)
SELECT
to_vector(vector_embedding(VECTOR.DOC_MODE
L using url as data)) as embedding
from ggai.base;

Updating existing table with
Vectors
update ggai.base_url
set urlvec = (SELECT
to_vector(vector_embedding(VECTOR.DOC_MODEL using
url as data)) from ggai.base_url where id = 1)
where client = upper('test');

The Possibilities?

The possibilities of Oracle Vector Search are going
to drive growth in everything!

Retrieval Augmented Generation (RAG)
A technique for enhancing the accuracy
and reliability of generative AI models with facts
from external sources

Use Cases:
• Search (known use case)
• Natural-Language Processing/Search (semantic search)
• Recommendation Systems (retail, customer service)
• Biometric and Anomaly Detection (healthcare)
• Drug discovery and genomics (healthcare)
• Patient similarity Analysis (healthcare)
• Personalized Digital Assistants

• Private data shared via Vector databases/data type need to be
secure.
• Organizations should evaluate what data is vectorized (shared)
before using with public LLMs.
• Consider building private LLMs to keep data secure.

Questions

Next webinars!
 June 13, 2024 @ 1 pm EST:
Oracle GoldenGate 23ai: What is new and how to upgrade
 June 27, 2024 @ 1 pm EST (tentative):
Oracle Database 23ai: Vector Indexes – What and how to use

solutions@rheodata.com
@rheodatallc
http://rheodata.com

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