Retrieval

Introduction Retrieval‑Augmented Generation (RAG) has quickly become the de‑facto architecture for building knowledge‑aware applications powered by large language models (LLMs). By coupling a retrieval engine (often a vector store) with a generative model, RAG enables systems to answer questions, draft documents, or provide recommendations that are grounded in up‑to‑date, domain‑specific data. While prototypes can be assembled in a few hours using libraries like LangChain or LlamaIndex, moving a RAG pipeline to production introduces a whole new set of challenges: ...

Introduction Traditional RAG systems treat knowledge as a collection of text chunks—embedded, indexed, and retrieved based on semantic similarity. This works well for simple factual lookup, but fails when questions require understanding relationships, dependencies, or multi-hop reasoning. Graph RAG fundamentally reimagines how knowledge is represented: instead of flat documents, information is structured as a graph of entities and relationships. This enables LLMs to traverse connections, follow dependencies, and reason about how concepts relate to each other. ...

Introduction Retrieval-Augmented Generation (RAG) transformed how LLMs access external knowledge. But traditional RAG has a fundamental limitation: it’s passive. You retrieve once, hope it’s relevant, and generate an answer. If the retrieval fails, the entire system fails. Agentic RAG changes this paradigm. Instead of a single retrieve-then-generate pass, an AI agent actively plans retrieval strategies, evaluates results, reformulates queries, and iterates until it finds sufficient information—or determines that it cannot. ...