RAG, LangGraph & AI Agents Course in India

What is RAG?

Retrieval-Augmented Generation grounds an LLM in your own data. Instead of guessing, the model retrieves relevant chunks from a vector store and answers with citations. RAG is what turns a generic chatbot into a trustworthy assistant over private documents, support tickets, codebases or product catalogues.

Why LangGraph matters

Linear LangChain chains break down once your AI needs branching logic, retries, or human-in-the-loop. LangGraph models AI workflows as state machines — explicit nodes, edges and conditional transitions — which is how production GenAI systems are actually built.

RAG + LangGraph + Agents architecture

• Ingestion: parsing PDFs, HTML, transcripts and DB rows into clean chunks.
• Embedding strategy: model choice, chunk size, overlap, hybrid keyword + vector.
• Vector store: FAISS for local, Chroma for prototyping, Pinecone for scale.
• Retrieval: dense, sparse, hybrid and reranking with cross-encoders.
• Orchestration: LangGraph nodes for retrieve → reason → tool-call → respond.
• Agentic upgrade: turning a RAG pipeline into a tool an agent can invoke.
• Evaluation: faithfulness, answer relevance, retrieval hit-rate.

Architecture example

A typical project graph: Ingestion job → Vector store → LangGraph workflow (Router node → Retriever node → Claude reasoning node → Tool node → Reflection node) → FastAPI endpoint → Streamlit UI. You will build, deploy and debug this end-to-end stack, not just the LLM call.