In the previous blog, the upcoming SAP HANA Cloud Vector Engine was explored, discussing its potential use cases and its role in SAP's Gen AI strategy. In this blog, we will dive deeper into this new addition from a technical perspective. I am happy to share with you all an interview featuring Mathias Kemeter (follow him @mkemeter), the SAP HANA Cloud Multi-Model Engineering Lead, who leads the development team responsible for the Vector Engine. Join us in this exclusive interview where we delve into technical insights and real-world applications. Special thanks to our development colleagues for their dedication in implementing this innovative technology. Watch the video for a comprehensive exploration of technical aspects and practical uses of the Vector Engine.
Watch the detailed interview here:
Besides the questions tackled in the interview, here are a few more interesting areas to explore and few more inquisitive questions you might have on mind.
Decoding Data Chunking:
Q: Ever wondered how to handle those hefty documents or inputs? Well, when it comes to larger data, it's a good idea to break it into manageable chunks before diving into embedding models. So, what are the best practices for chunking your text documents?
A: Though there are no data chunking recommendations from SAP HANA Cloud at this time, in general when dealing with extensive documents or inputs, it's advised to break them into smaller chunks before feeding them into embedding models. Now, you might be wondering, how do you go about this chunking process? Here's the scoop: there are no one-size-fits-all rules. For text, it's typically a matter of considering the token limit of the language model and the embedding model. However, for images, a different approach is taken – reducing resolution. The key is to let the characteristics of your data guide your decision. No need to stress over strict recommendations, instead, consider factors like token limits and various chunking techniques, such as fixed-size or variable-sized methods, alongside performance metrics for your language and embedding models. It's all about finding that sweet spot for cost and performance.
Data Silo Dilemma:
Q: How does the Vector Engine in SAP HANA Cloud resolve the challenge of data silos?
A: With the Vector Engine smoothly integrated into SAP HANA Cloud, users can eliminate data silos and fragmented databases. Now, everything from business data to vector embeddings, alongside spatial, graph, and JSON data, can reside on the same platform i.e., SAP HANA Cloud. This integration not only eliminates the need for secondary databases dedicated solely to housing and analyzing vector data but also creates synergies and opportunities for advanced querying. By bringing together diverse data types, users can query based on geolocations, relationships in network data, and more, all within a unified environment. This holistic approach significantly reduces query processing delays and latency issues, enabling efficient data analysis and decision-making processes.
Langchain Link-Up:
Q: How compatible is the SAP HANA Cloud Vector Engine with Langchain, and what opportunities does it unlock for the development community?
A: Here is the good news!! The release of the SAP HANA Cloud Vector Engine is just around the corner, and it has already been smoothly integrated with Langchain. This compatibility opens up variety of possibilities for the development community with respect to building applications powered by Large Language Models. A big shout-out to our incredible development colleagues for making this a possibility very early on. Dive in and explore the synergies between the vector engine and Langchain for innovative and powerful applications. Here is the official link to start in this direction. Also, here is an in detail blog from our engineering colleague @MartinKolb, that explains how to use LangChain to create embeddings based on website content and feed them into SAP HANA Cloud (elaborating the RAG scenario).
Importing Vector Data:
Q: How does importing vector embeddings into HANA work?
A: Importing vector embeddings into HANA is as seamless as dealing with any other data type. However, the most common approach is though leveraging application interactions, particularly when it comes to vector storage and querying. Imagine an intelligent app that is built on top of SAP HANA Cloud, it's this app that takes the lead in reading and writing vector data, orchestrating a smooth flow in and out of SAP HANA Cloud. This dynamic interaction, where the app plays a pivotal role, is one of the streamlined ways of how vector embeddings can be housed in HANA.
In addition to the above technical deep dive QnA, here is an insightful demo video that provides a coding peek into the chapter of building intelligent data applications that leverages the new Vector Engine for Retrieval Augmented Generation (RAG) and Generative AI scenario.
I hope this journey through SAP HANA Cloud's Vector Engine has left you inspired and better informed about the latest advancements in data analytics technology. The unique technical insights have shed light on the inner workings of this feature, making it more accessible and actionable for developers and data enthusiasts alike. As we move forward into QRC1 2024 and beyond, the Vector Engine is sure to play a pivotal role in enhancing data processing and analytics.
Stay tuned for future developments and innovations in SAP HANA Cloud as we adapt to the constantly changing environment of data-driven solutions. As a next step, I highly recommend checking out our upcoming Early Adopter Care program, where you can take advantage of being an early adopter of the vector engine in SAP HANA Cloud. Register for the EAC program today!!
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