Identifying RAG Use Cases

You are aware of the challenges that Large Language Models (LLMs) can face. You have also learned that successful optimization involves consistent evaluation of outputs and iteration between different techniques, such as Retrieval Augmented Generation (RAG) and fine-tuning.

In this unit, you will identify a process and a typical architecture for RAG. It also includes an introduction to SAP HANA Cloud vector engine, which enables RAG applications. You will also learn about fine-tuning, agents, functions, and tools to optimize the performance of LLMs for your use case.

As we saw, LLMs output can be factually incorrect or irrelevant for your use case. RAG or Retrieval Augmented Generation can help in improving these responses, making them more precise and reliable for your use case.

See the video to identify the RAG architecture.

To handle complex and unstructured vector data efficiently in enterprise environments, you can use SAP HANA vector engine.

An SAP HANA vector engine is a component of the SAP HANA database designed to handle complex and unstructured vector data, such as embeddings used in machine learning and AI applications. It allows for the storage, analysis, and processing of vector data, enabling the development of intelligent data applications and adding more context in scenarios involving generative AI.

The HANA vector engine is optimized for executing vectorized operations on large datasets, allowing for efficient parallel processing.

Techniques, such as RAG, extends the capabilities of the vector engine by incorporating retrieval operations, such as filtering and sorting, into the generation process itself. This means that the data is retrieved and processed in a single operation, eliminating the need for extra round-trips between the CPU and storage.

Here are some key benefits of using an SAP HANA vector engine.

• Performance: It provides a high-performance vector store that can handle large volumes of data, which is crucial for AI applications.

• AI Integration: The Vector Engine facilitates the integration of AI models, like those used in RAG, with enterprise-grade databases for enhanced query responses.

• Scalability: It supports the development of scalable AI applications that can grow with the needs of the business.

• Data Analysis: The engine allows for the seamless processing, comparison, and utilization of vector data, which is vital for building intelligent data applications.

• Contextual Understanding: By storing and analyzing vector embeddings, the Vector Engine adds more context to generative AI scenarios, improving the relevance and accuracy of the AI’s output.

• Frameworks Integration: With the integration of frameworks like LangChain, it becomes easier to build chat-based applications that can answer technical questions or provide information spread across many pages of a website.

In summary, an SAP HANA vector engine is designed to meet the demands of modern data-driven enterprises, enabling them to apply the power of AI for data analysis and decision-making processes. For example, it facilitates the use of RAG by providing a high-performance vector store that can build scalable and efficient AI applications grounded in factual data.