Introduction to RAG ? What is Agentic RAG? Agentic RAG vs. Traditional RAG Agentic RAG Architecture and Components Understanding Adaptive RAG Types of Agentic RAG with Example Agentic RAG with LlamaIndex Agentic RAG with Cohere
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👉 In short: RAG makes LLMs smarter, more accurate, and context-aware by connecting them with real, external knowledge.
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Documents → Loads text data (your knowledge base).
Chunks → Splits into smaller parts for better search.
Embeddings + Vector DB → Encodes chunks and stores in FAISS.
Retriever + Prompt Template → Fetches relevant chunks when user queries.
LLM (GPT) → Generates final answer using both query + retrieved context.
Example,
from langchain_openai import OpenAIEmbeddings, ChatOpenAI from langchain_community.vectorstores import FAISS from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chains import RetrievalQA from langchain.docstore.document import Document
