In this tutorial, we walk through the implementation of an Agentic Retrieval-Augmented Generation (RAG) system. We design it so that the agent does more than just retrieve documents; it actively decides when retrieval is needed, selects the best retrieval strategy, and synthesizes responses with contextual awareness. By combining embeddings, FAISS indexing, and a mock LLM, we create a practical demonstration of how agentic decision-making can elevate the standard RAG pipeline into something more adaptive and intelligent. Check out the FULL CODES here. Copy CodeCopiedUse a different Browser import numpy as np import faiss from sentence_transformers import SentenceTransformer import json import re…