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Paul_P
Product and Topic Expert
Product and Topic Expert

AI Foundation is SAP’s all-in-one AI toolkit, offering developers AI that’s ready-to-use, customizable, grounded in business data, and supported by leading generative AI foundation models. It is also the basis for AI capabilities that SAP embeds across its portfolio.

If you are not already familiar with the different capabilities of the AI Foundation stack represented below, read this introductory blog post.

AI-Foundation-SAP-BTP.png
Let's delve into the Q1 2024 release highlights of the AI Foundation, including innovations for the Document Information Extraction service, SAP Translation Hub, SAP AI Core and SAP HANA Cloud. 

AI Services 

Document Processing - Document Information Extraction

New: Combine Different Setup Types When Adding Data Fields to Schemas

Users can now combine header fields with different setup types in the same schema.
They can add header fields with the following setup types to a schema created for a standard document type (e.g. invoice) or custom document type:  

  • auto (with and without a default extractor) 
  • manual 

This extends the scope of the existing standard schemas from SAP, such as for the invoice document and reduce time to value for new business fields (key-value pairs) that need to be extracted from documents. 

New: Conversion of Country Specific Unit of Measure Values to ISO Format

The conversion of country specific unit of measure values into ISO format for invoice documents has been improved. For instance, the processing of certain locale-specific unit of measures (e.g. German 'Stk.' for 'Stück' / 'piece'). Users can expect improved quality of extraction and faster business process execution. 

New: Purchase order number extraction from line-item level 

Users can extract purchase order numbers that are available on-line item field level from invoice documents. It allows a faster execution of accounts payable processes when there are multiple PO numbers listed in the tables of supplier invoices.

New: New Invoice Supported Language – Japanese

The Document Information Extraction service supports now the Japanese language for invoice documents, improving global coverage.

New: Better Models for the Extraction of Standard Document Types

The machine learning models for the extraction of invoice, paymentAdvice, and purchaseOrder documents have been improved. Users can expect improvements in particular when extracting dates, amounts, tax ID, bank accounts. 

  • Invoices: higher extraction accuracy can be expected for Japan, Hungary, Türkiye, and Romania.  
  • Purchase orders: improved extraction accuracy can be expected for Spanish purchase orders.
  • Payment advice: more consistent column extraction and improved extraction of amounts in line-items.

Get started with Document Information Extraction.

Machine Translation - SAP Translation Hub

New: User settings for the document translation UI 

Users can define the preferred UI language and theme in the application, which will be carried over across software and document translation UIs, thus improving the overall user experience. They can also define the preferred source and target languages to translate content using the application, which will apply only while using the document translation UI. It automates the translation process by not having to define the source and target language every time the application opens. 

New: Updated tile in SAP BTP

As part of the efforts of migrating the application for software translation to the multi-cloud environment, the tile available on SAP Business Technology Platform has been renamed, from Document Translation to SAP Translation Hub. That’s one of the last steps towards completing the migration planned in Q2 2024.

Get started with SAP Translation Hub.

Generative AI Management & AI Workload Management

SAP AI Core

New: Availability of additional large language models in the generative AI hub

Integration of additional large language models (LLM):

  • Google PaLM 2 for text (text-bison), PaLM 2 for chat (chat-bison), and embeddings for text (textembedding-gecko)
  • Google Gemini Pro
  • Updates to Microsoft Azure OpenAI model versions

You can now leverage a greater selection of "best-to-fit" LLMs for your use case, ease exploration over market-leading generative AI models, without the need to go through lengthy contractual, legal discussions and finally harmonize lifecycle management across models.

Find more information here.

New: SDK for support of large language models

We introduced a series of features in SAP AI Core: 

  • Technical libraries that simplify inference on large language models (LLMs) by automatically injecting the correct headers and paths into each request. It improves the extensibility, allowing users to add additional adaptations as needed. 
  • Tooling for the effective integration and use of LLMs with LangChain in the context of the generative AI hub. It simplifies the developer experience with ready-to-use libraries for access to LLMs deployed using the generative AI hub. 
  • A new library, the ai-core-llm-sdk, in addition to enhancements to the existing ai-core-sdk, to accommodate the required changes to support LLM access. It boosts efficiency when working with various LLM models by streamlining the deployment of LLM models and the querying of available models.

Get started with SAP AI Core.

Business Data & Context 

SAP HANA Cloud

New: Support for storage and retrieval of vector embeddings in SAP HANA Cloud, called SAP HANA Cloud vector engine  

A vector datastore manages unstructured data - such as text, images, or audio - in high-dimensional vector space as vector embeddings, to provide long-term memory and better context to AI models. This makes it easy to find and retrieve similar objects quickly, for example, by asking a question using natural language. This both simplifies interactions with large language models (LLMs) and empowers developers to securely implement generative AI in applications. 

SAP HANA Cloud vector engine can now natively store and search vector embeddings, which are numerical representations of objects, along with business data as part of its industry-leading multi-model processing capabilities to power intelligent data applications.

With these vector capabilities, SAP HANA Cloud will enable Retrieval Augmented Generation (RAG), facilitating the combination of LLMs with private business data. These applications learn and adapt to new information, enabling automated decision-making.

Key benefits of the SAP HANA Cloud vector engine include:

  • Multi-model: Users can unify all types of data into a single database to build innovative applications using an efficient data architecture and in-memory performance. By adding vector storage and processing to the same database already storing relational, graph, spatial, and even JSON data, application developers can create next-generation solutions that interact more naturally with the user.
  • Enhanced search and analysis: Businesses can now apply semantic and similarity search to business processes using documents like contracts, design specifications, and even service call notes.
  • Personalized recommendations: Users can benefit from an improved overall experience with more accurate and personalized suggestions.
  • Optimized large language models: The output of LLMs is augmented with more effective and contextual data.

To deep dive into the SAP HANA Cloud Vector Engine, read these blog posts by our experts:

Build business-ready AI applications with SAP and stay updated! 

  • Leverage the AI Foundation capabilities by visiting the SAP Discovery Center. Compare and select the service that fits most to your business needs. 
  • Explore the AI Foundation roadmap to discover past and upcoming innovations. 
  • Engage with our community of SAP experts through Q&A and blog posts.

See you next quarter for exciting innovations!