Retrieval-Augmented Generation in Production with Haystack

Chapter 6. Advanced RAG and Keeping Pace with AI Developments

Artificial intelligence is evolving at an unprecedented pace, with new breakthroughs and technologies emerging almost daily. As these advancements unfold, they significantly change how we approach and implement RAG systems. In this chapter, we look at cutting-edge developments that are reshaping the landscape of RAG and explore how to stay current with these rapid changes.

We will examine four key areas where recent AI innovations are having a profound impact on RAG:

AI agents

Intelligent agents can enhance responses by invoking tools to solve complex queries.

Multimodal RAG

As AI becomes adept at processing various types of data, incorporating multiple modalities (text, images, audio, etc.) can create more comprehensive and versatile RAG systems.

Knowledge graphs for RAG

Integrating knowledge graphs adds relational information to RAG.

SQL RAG

The intersection of RAG with SQL opens up new possibilities for interacting with databases and generating precise, data-driven responses.

AI Agents

So far, we have discussed how RAG harnesses the power of LLMs on industry data use cases. However, a RAG approach for individual queries is still deterministic and limited to solving conceptually simple tasks. On the other hand, an agentic approach to AI unlocks the power of GenAI on entire workflows. An AI agent is an autonomous system that leverages an LLM as its core decision-making and orchestration component. The agent ...