Oracle AI Vector Search: Part 2

Oracle University Podcast

This week, Lois Houston and Nikita Abraham continue their exploration of Oracle AI Vector Search with a deep dive into vector indexes and memory considerations. Senior Principal APEX and Apps Dev Instructor Brent Dayley breaks down what vector indexes are, how they enhance the efficiency of search queries, and the different types supported by Oracle AI Vector Search. Oracle Database 23ai: Oracle AI Vector Search Fundamentals: https://mylearn.oracle.com/ou/course/oracle-database-23ai-oracle-ai-vector-search-fundamentals/140188/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ Twitter: https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:26 Nikita: Welcome back to the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services at Oracle University, and with me is Lois Houston, Director of Innovation Programs.

Lois: Hi everyone! Last week was Part 1 of our discussion on Oracle AI Vector Search. We talked about what it is, its benefits, the new vector data type, vector embedding models, and the overall workflow. In Part 2, we’re going to focus on vector indices and memory.

00:56

Nikita: And to help us break it all down, we’ve got Brent Dayley back with us. Brent is a Senior Principal APEX and Apps Dev Instructor with Oracle University. Hi Brent! Thanks for being with us today. So, let’s jump right in! What are vector indexes and how are they useful?

Brent: Now, vector indexes are specialized indexing data structures that can make your queries more efficient against your vectors. They use techniques such as clustering, and partitioning, and neighbor graphs. Now, they greatly reduce the search space, which means that your queries happen quicker. They're also extremely efficient. They do require that you enable the vector pool in the SGA.

01:42

Lois: Brent, walk us through the different types of vector indices that are supported by Oracle AI Vector Search. How do they integrate into the overall process?

Brent: So Oracle AI Vector Search supports two types of indexes, in-memory neighbor graph vector index. HNSW is the only type of in-memory neighbor graph vector index that is supported. These are very efficient indexes for vector approximate similarity search. HNSW graphs are structured using principles from small world networks along with layered hierarchical organization.

And neighbor partition vector index, inverted file flat index, is the only type of neighbor partition index supported. It is a partition-based index which balances high search quality with reasonable speed.

02:35

Nikita: Brent, you mentioned that enabling the vector pool in the SGA is a requirement when working with vector indexes. Can you explain that process for us?

Brent: In order for you to be able to use vector indexes, you do need to enable the vector pool area. And in order to do that, what you need to do is set the vector memory size parameter.

You can set it at the container database level. And the PDB inherits it from the CDB. Now bear in mind that the database does have to be balanced when you set the vector pool.

03:12

Lois: Ok. Are there any other considerations to keep in mind when using vector indices?

Brent: Vector indexes are stored in this pool, and vector metadata is also stored here. And you do need to restart the database. So large vector indexes do need lots of RAM, and RAM constrains the vector index size. You should use IVF indexes when there is not enough RAM. IVF indexes use both the buffer cache as well as disk.

03:42

Nikita: And what about memory considerations?

Brent: So to remind you, a vector is a numerical representation of text, images, audio, or video that encodes the features or semantic meaning of the data, instead of the actual contents, such as the words or pixels of an image. So the vector is a list of numerical values known as dimensions with a specified format.

Now, Oracle does support the int8 format, the float32 format, and the float64 format. Depending on the format depends on the number of bytes. For instance, int8 is one byte, float32 is four bytes. Now, Oracle AI Vector Search supports vectors with up to 65,535 dimensions.

04:34

Lois: What should we know about creating a table with a vector column?

Brent: Now, Oracle Database 23ai does have a new vector data type. The new data type was created in order to support vector search.

The definition can include the number of dimensions and can include the format. Bear in mind that either one of those are optional when you define your column. The possible dimension formats are int, float 32, and float 64. Float 32 and float 64 are IEEE standards, and Oracle Database will automatically cast the value if needed.

05:18

Nikita: Can you give us a few declaration examples?

Brent: Now, if we just do a vector type, then the vectors can have any arbitrary number of dimensions and formats. If we describe the vector type as vector * , *, then that means that vectors can have an arbitrary number of dimensions and formats. Vector and vector * , * are equivalent. Vector with the number of dimensions specified, followed by a comma, and then an asterisk, is equivalent to vector number of dimensions.

Vectors must all have the specified number of dimensions, or an error will be thrown. Every vector will have its dimension stored without format modification. And if we do vector asterisk common dimension element format, what that means is that vectors can have an arbitrary number of dimensions, but their format will be up-converted or down-converted to the specified dimension element format, either INT8, float 32, or float 64.

06:25

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06:52

Nikita: Welcome back! Brent, what is the vector constructor and why is it useful?

Brent: Now, the vector constructor is a function that allows us to create vectors without having to store those in a column in a table. These are useful for learning purposes. You use these usually with a smaller number of dimensions. Bear in mind that most embedding models can contain thousands of different dimensions. You get to specify the vector values, and they usually represent two-dimensional like xy coordinates. The dimensions are optional, and the format is optional as well.

07:29

Lois: Right. Before we wrap up, can you tell us how to calculate vector distances?

Brent: Now, vector distance uses the function VECTOR_DISTANCE as the main function. This allows you to calculate distances between two vectors and, therefore, takes two vectors as parameters. Optionally, you can specify a metric. If you do not specify a metric, then the default metric, COSINE, would be used. You can optionally use other shorthand functions, too. These include L1 distance, L2 distance, cosine distance, and inner product. All of these functions also take two vectors as input and return the distance between them. Now the VECTOR_DISTANCE function can be used to perform a similarity search. If a similarity search query does not specify a distance metric, then the default cosine metric will be used for both exact and approximate searches.

If a similarity search does specify a distance metric in the VECTOR_DISTANCE function, then an exact search with that distance metric is used if it conflicts with the distance metric specified in a vector index. If the two distance metrics are the same, then this will be used for both exact as well as approximate searches.

08:58

Nikita: I was wondering Brent, what vector distance metrics do we have access to?

Brent: We have Euclidean and Euclidean squared distances. We have cosine similarity, dot product similarity, Manhattan distance, and Hamming similarity. Let's take a closer look at the first of these metrics, Euclidean and Euclidean squared distances. This gives us the straight-line distance between two vectors. It does use the Pythagorean theorem. It is sensitive to both the vector size as well as the direction.

With Euclidean distances, comparing squared distances is equivalent to comparing distances. So when ordering is more important than the distance values themselves, the squared Euclidean distance is very useful as it is faster to calculate than the Euclidean distance, which avoids the square root calculation.

09:58

Lois: And the cosine similarity metrics?

Brent: It is one of the most widely used similarity metrics, especially in natural language processing. The smaller the angle means they are more similar. While cosine distance measures how different two vectors are, cosine similarity measures how similar two vectors are.

Dot product similarity allows us to multiply the size of each vector by the cosine of their angle. The corresponding geometrical interpretation of this definition is equivalent to multiplying the size of one of the vectors by the size of the projection of the second vector onto the first one or vice versa. Larger means that they are more similar. Smaller means that they are less similar.

Manhattan distance is useful for describing uniform grids. You can imagine yourself walking from point A to point B in a city such as Manhattan. Now, since there are buildings in the way, maybe we need to walk down one street and then turn and walk down the next street in order to get to our result. As you can imagine, this metric is most useful for vectors describing objects on a uniform grid such as city blocks, power grids, or perhaps a chessboard.

11:27

Nikita: And finally, we have Hamming similarity, right?

Brent: This describes where vector dimensions differ. They are binary vectors, and it tells us the number of bits that require change to match. It compares the position of each bit in the sequence. Now, these are usually used in order to detect network errors.

11:53 Nikita: Brent, thanks for joining us these last two weeks and explaining what Oracle AI Vector Search is. If you want to learn more about what we discussed today, visit mylearn.oracle.com and search for the Oracle Database 23ai: Oracle AI Vector Search Fundamentals course.

Lois: This concludes our season on Oracle Database 23ai New Features for administrators. In our next episode, we’re going to talk about database backup and recovery, but more on that later! Until then, this is Lois Houston…

Nikita: And Nikita Abraham signing off!

12:29

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