Integrating LLMs into Business Applications

Integrating Large Language Models (LLMs) into business applications using SAP’s methodical approach involves several steps. Some of these steps can be:

• Needs Assessment:

  Identify the use cases where an LLM application or usage can add value, such as natural language understanding, sentiment analysis, or chatbots, using ideation to validation phases.

• Use data frameworks in SAP Business Technology Platform (SAP BTP):

  SAP BTP supports leading data frameworks, such as LangChain and LlamaIndex, for LLM development. Just like LLMs, they are used in a SaaS setup, where they are trained and hosted in the cloud and can be integrated directly into applications, products, or services.

  • LangChain is a framework designed for developing data-aware applications powered by LLMs. It excels in integrating multiple tools and building intelligent agents capable of performing various tasks. Developers can use Python and JavaScript with LangChain.

  • On the other hand, LlamaIndex, formerly known as GPT Index, focuses on data ingestion, structuring, and access. It shines in smart search, retrieval, and efficient data structuring, offering various engines for natural language access to data.

• Select the Right LLM Framework:

  You can choose, for example, between LangChain and LlamaIndex. While LangChain provides a broad framework for LLM applications, LlamaIndex specializes in data indexing and retrieval.

  • Choose LangChain for its versatility in agent-based applications.

  • Opt for LlamaIndex when efficient data handling is crucial.

  You can even consider integrating LlamaIndex into LangChain to optimize retrieval capabilities within SAP BTP.

It is important to note that this integration is not just a technical feat; it’s a strategic orchestration of data, intelligence, and user experience.