Oracle AI Vector Search: Part 1

In this episode, Senior Principal APEX and Apps Dev Instructor Brent Dayley joins hosts Lois Houston and Nikita Abraham to discuss Oracle AI Vector Search. Brent provides an in-depth overview, shedding light on the brand-new vector data type, vector embeddings, and the vector workflow.   Oracle Database 23ai: Oracle AI Vector Search Fundamentals: https://mylearn.oracle.com/ou/course/oracle-database-23ai-oracle-ai-vector-search-fundamentals/140188/   Oracle Database 23ai: SQL Workshop: https://mylearn.oracle.com/ou/course/oracle-database-23ai-sql-workshop/137830/   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 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs here at Oracle University. Joining me as always is our Team Lead of our Editorial Services, Nikita Abraham. Nikita: Hi everyone! Thanks for tuning in over the last few months as we’ve been discussing all the Oracle Database 23ai new features. We’re coming to the end of the season, and to close things off, in this episode and the next one, we’re going to be talking about the fundamentals of Oracle AI Vector Search. In today’s episode, we’ll try to get an overview of what vector search is, why Oracle Vector Search stands out, and dive into the new vector data type. We’ll also get insights into vector embedding models and the vector workflow. 01:11 Lois: To take us through all of this, we’re joined by Brent Dayley, who is a Senior Principal APEX and Apps Development Instructor with Oracle University. Hi Brent! Thanks for joining us today. Can you tell us about the new vector data type? Brent: So this data type was introduced in Oracle Database 23ai. And it allows you to store vector embeddings alongside other business data. Now, the vector data type allows a foundation to store vector embeddings. 01:42 Lois: And what are vector embeddings, Brent? Brent: Vector embeddings are mathematical representations of data points. They assign mathematical representations based on meaning and context of your unstructured data. You have to generate vector embeddings from your unstructured data either outside or within the Oracle Database. In order to get vector embeddings, you can either use ONNX embedding machine learning models or access third-party REST APIs. Embeddings can be used to represent almost any type of data, including text, audio, or visual, such as pictures. And they are used in proximity searches. 02:28 Nikita: Hmmm, proximity search. And similarity search, right? Can you break down what similarity search is and how it functions? Brent: So vector data tends to be unevenly distributed and clustered into groups that are semantically related. Doing a similarity search based on a given query vector is equivalent to retrieving the k nearest vectors to your query vector in your vector space. What this means is that basically you need to find an ordered list of vectors by ranking them, where the first row is the closest or most similar vector to the query vector. The second row in the list would be the second closest vector to the query vector, and so on, depending on your data set. What we need to do is to find the relative order of distances. And that's really what matters rather than the actual distance. Now, similarity searches tend to get data from one or more clusters, depending on the value of the query vector and the fetch size. Approximate searches using vector indexes can limit the searches to specific clusters. Exact searches visit vectors across all clusters. 03:44 Lois: Ok. I want to move on to vector embedding models. What are they and why are they valuable? Brent: Vector embedding models allow you to assign meaning to what a word, or a sentence, or the pixels in an image, or perhaps audio. It allows you to quantify features or dimensions. Most modern vector embeddings use a transformer model. Bear in mind that convolutional neural networks can also be used. Depending on the type of your data, you can use different pretrained open source models to create vector embeddings. As an example, for textual data, sentence transformers can transform words, sentences, or paragraphs into vector embeddings. 04:33 Nikita: And what about visual data? Brent: For visual data, you can use residual network also known as ResNet to generate vector embeddings. You can also use visual spectrogram representation for audio data. And that allows us to use the audio data to fall back into the visual data case. Now, these can also be based on your own data set. Each model also determines the number of dimensions for your vectors. 05:02 Lois: Can you give us some examples of this, Brent? Brent: Cohere's embedding model, embed English version 3.0, has 1,024 dimensions. Open AI's embedding model, text-embedding-3-large, has 3,072 dimensions. 05:24 Want to get the inside scoop on Oracle University? Head over to the Oracle University Learning Community. Attend exclusive events. Read up on the latest news. Get first-hand access to new products. Read the OU Learning Blog. Participate in Challenges. And stay up-to-date with upcoming certification opportunities. Visit mylearn.oracle.com to get started.  05:50 Nikita: Welcome back! Let’s now get into the practical side of things. Brent, how do you import embedding models? Brent: Although you can generate vector embeddings outside the Oracle Database using pre-trained open source embeddings or your own embedding models, you also have the option of doing those within the Oracle Database. In order to use those within the Oracle Database, you need to use models that are compatible with the Open Neural Network Exchange Standard, or ONNX, also known as Onyx. Oracle Database implements an Onyx runtime directly within the database, and this is going to allow you to generate vector embeddings directly inside the Oracle Database using SQL. 06:35 Lois: Brent, why should people choose to use Oracle AI Vector Search? Brent: Now one of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data, all in one single system. This is very powerful, and also a lot more effective because you don't need to add a specialized vector database. And this eliminates the pain of data fragmentation between multiple systems. It also supports Retrieval Augmented Generation, also known as RAG. Now this is a breakthrough generative AI technique that combines large language models and private business data. And this allows you to deliver responses to natural language questions. RAG provides higher accuracy and avoids having to expose private data by including it in the large language model training data. 07:43 Nikita: In the last part of our conversation today, I want to ask you about the Oracle AI Vector Search workflow, starting with generating vector embeddings. Brent: Generate vector embeddings from your data, either outside the database or within the database. Now, embeddings are a mathematical representation of what your data meaning is. So what does this long sentence mean, for instance? What are the main keywords out of it? You can also generate embeddings not only on your typical string type of data, but you can also generate embeddings on other types of data, such as pictures or perhaps maybe audio wavelengths. 08:28 Lois: Could you give us some examples? Brent: Maybe we want to convert text strings to embeddings or convert files into text. And then from text, maybe we can chunk that up into smaller chunks and then generate embeddings on those chunks. Maybe we want to convert files to embeddings, or maybe we want to use embeddings for end-to-end search. Now you have to generate vector embeddings from your unstructured data, either outside or within the Oracle Database. You can either use the ONNX embedding machine learning models or you can access third-party REST APIs. You can import pre-trained models in ONNX format for vector generation within the database. You can download pre-trained embedding machine learning models, convert them into the ONNX format if they are not already in that format. Then you can import those models into the Oracle Database and generate vector embeddings from your data within the database. Oracle also allows you to convert pre-trained models to the ONNX format using Oracle machine learning for Python. This enables the use of text transformers from different companies. 09:51 Nikita: Ok, so that was about generating vector embeddings. What about the next step in the workflow—storing vector embeddings? Brent: So you can create one or more columns of the vector data type in your standard relational data tables. You can also store those in secondary tables that are related to the primary tables using primary key foreign key relationships. You can store vector embeddings on structured data and relational business data in the Oracle Database. You do store the resulting vector embeddings and associated unstructured data with your relational business data inside the Oracle Database. 10:30 Nikita: And the third step is creating vector indexes? Brent: Now you may want to create vector indexes in the event that you have huge vector spaces. This is an optional step, but this is beneficial for running similarity searches over those huge vector spaces. So once you have generated the vector embeddings and stored those vector embeddings and possibly created the vector indexes, you can then query your data with similarity searches. This allows for Native SQL operations and allows you to combine similarity searches with relational searches in order to retrieve relevant data. 11:15 Lois: Ok. I think I’ve got it. So, Step 1, generate the vector embeddings from your unstructured data. Step 2, store the vector embeddings. Step 3, create vector indices. And Step 4, combine similarity and keyword search. Brent: Now there is another optional step. You could generate a prompt and send it to a large language model for a full RAG inference. You can use the similarity search results to generate a prompt and send it to your generative large language model in order to complete your RAG pipeline. 11:59 Lois: Thank you for sharing such valuable insights about Oracle AI Vector Search, Brent. We can’t wait to have you back next week to talk about vector indices and memory. Nikita: And if you want to know more about Oracle AI Vector Search, visit mylearn.oracle.com and check out the Oracle Database 23ai: Oracle AI Vector Search Fundamentals course. Lois: Yes, and if you're serious about advancing in your development journey, we recommend taking the Oracle Database 23ai SQL workshop. It’s designed for those who might be familiar with SQL from other database platforms or even those completely new to SQL. Nikita: Yeah, we’ll add the link to the workshop in the show notes so you can find it easily. Until next week, this is Nikita Abraham… Lois: And Lois Houston signing off! 12:45 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.