Add self-correcting RAG agent notebook with LangGraph

notebooks/llm-agent-langgraph/README.md

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+# Self-Correcting RAG Agent with LangGraph and OpenVINO
+
+Standard Retrieval-Augmented Generation (RAG) pipelines follow a linear path: retrieve documents, then generate an answer. If retrieval returns irrelevant documents, the model often hallucinates an answer instead of acknowledging uncertainty.
+
+This notebook demonstrates a **self-correcting RAG agent** powered by [LangGraph](https://langchain-ai.github.io/langgraph/) and [OpenVINO](https://docs.openvino.ai/). The agent implements a stateful workflow with conditional branching:
+
+1. **Retrieve** documents from a ChromaDB vector store
+2. **Grade** each document for relevance using an OpenVINO-optimized LLM
+3. **Generate** an answer if relevant documents are found
+4. **Rewrite** the query and re-retrieve if documents are irrelevant
+
+All LLM inference is accelerated with OpenVINO on Intel CPUs, GPUs, and NPUs.
+
+## Notebook Contents
+
+The tutorial consists of the following steps:
+
+- Install prerequisites
+- Select and convert a model (Phi-3-mini, Llama-3.2-1B, or Qwen2.5-1.5B) to OpenVINO IR with INT4 compression
+- Build a knowledge base with document chunking and ChromaDB
+- Define the LangGraph agent with four nodes: retrieve, grade, generate, rewrite
+- Run the self-correcting agent on sample queries
+- Interactive demo with Gradio UI showing agent reasoning
+
+## Installation Instructions
+
+This is a self-contained example that relies solely on its own code.</br>
+We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
+For details, please refer to [Installation Guide](../../README.md).
+
+<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/llm-agent-langgraph/README.md" />