Advanced RAG
What happens when your RAG system retrieves the wrong documents?. Or when the retrieved context is not enough to answer the question? A traditional RAG pipeline usually doesn't think twice and its path is so its a single attempt generated answer. "Retrieve → Generate → Answer" But real world AI applications aren't always that simple. Sometimes the system needs to search again in a different way,…
Traditional Retrieval Augmented Generation (RAG) systems execute a straightforward sequence of steps: Retrieve → Generate → Answer. However, real-world AI applications don't always adhere to such simplicity. In these cases, systems may require a different retrieval approach, evaluation of the found context, alteration of their strategy, confirmation of the answer, or even human assistance before proceeding. This is where Agentic RAG comes into play.
Agentic RAG introduces the ability for a system to reason about the retrieval process, enabling it to make informed decisions about the next course of action. This becomes increasingly important as RAG applications tackle more complex documents. Simply retrieving the top-k documents may not yield accurate results. Agentic RAG aims to build systems that are more intelligent, robust, and reliable by allowing them to adapt their approach rather than following a rigid pipeline.
The ultimate objective of Agentic RAG isn't about increasing complexity; it's about creating systems that can handle uncertainty and ambiguity in a more effective manner. It's about empowering RAG systems to reason, verify, and even escalate queries to human experts when necessary. To illustrate this concept, the author has created a series of self-contained notebooks utilizing LangGraph.
Each notebook demonstrates a practical Agentic RAG pattern that readers can comprehend, experiment with, and adapt for their own AI projects. The free version can be found at https://github.com/ChandulaSenevirathna/Agentic_RAG, while the advanced version is available at https://chandula7.gumroad.com/l/Advanced_RAG_LangGraph_Patterns.
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