local RAG notebook with Ollama, ChromaDB, and optional agentic workflow

[Irfan-del-droid]

This PR adds a new notebook demonstrating a minimal, fully local Retrieval-Augmented Generation (RAG) pipeline.

Key features:

• Local LLM inference using Ollama
• Document embedding and retrieval with ChromaDB
• End-to-end RAG pipeline implementation
• Optional agentic workflow using LangGraph
• Optional OpenVINO integration for optimized inference

The notebook is designed to be:

• CPU-friendly (no GPU required)
• fully local (no external APIs)
• beginner-friendly and modular

Advanced components such as the agentic workflow and OpenVINO integration are clearly marked as optional to maintain accessibility.

This contribution aims to provide a practical introduction to local-first AI workflows aligned with OpenVINO notebook standards.

Happy to refine based on feedback.

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[Irfan-del-droid]

Hi! This notebook focuses on a minimal local RAG pipeline with optional extensions for agentic workflows and OpenVINO optimization. Please let me know if any adjustments are needed.