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

README.md

@@ -228,6 +228,26 @@ Made with [`contrib.rocks`](https://contrib.rocks).
 * [What is the first CPU generation you support with OpenVINO?](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
 * [Are there any success stories about deploying real-world solutions with OpenVINO?](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html)
 
+## 🔍 Training Data Transparency
+
+This repository focuses on demonstrating **inference and model optimization workflows** using the OpenVINO™ Toolkit with pre-trained models.
+
+- The notebooks do **not perform model training, re-training, or fine-tuning**.
+- Instead, they leverage pre-trained models and showcase **efficient inference and deployment techniques** across different hardware backends.
+- In addition to inference, the notebooks demonstrate **model optimization workflows**, including:
+  - model conversion (e.g., FP32 → FP16 / INT8)
+  - quantization and compression techniques
+  - performance tuning for CPU, GPU, and other supported devices
+
+Training data details, dataset composition, and associated biases are defined by the **original model providers** and are not modified within this repository.
+
+Users are encouraged to:
+- Review the original model documentation for dataset sources and training details
+- Understand potential biases and limitations of pre-trained models
+- Evaluate models appropriately for their specific use cases and deployment environments
+- Follow responsible AI and data governance practices when integrating these models into applications
+
+This section aims to clarify the scope of the repository and improve transparency regarding how models are used within OpenVINO notebooks.
 ---
 
 \* Other names and brands may be claimed as the property of others.

notebooks/local-agentic-rag/README.md

@@ -0,0 +1,110 @@
+# 🤖 Local RAG Pipeline with Ollama and Optional Agentic Workflow
+
+This notebook demonstrates a **minimal, fully local Retrieval-Augmented Generation (RAG) pipeline** using Ollama, ChromaDB, and an optional agentic workflow with LangGraph.
+
+The implementation is designed to be **educational, modular, and CPU-friendly**, requiring no cloud APIs after initial setup.
+
+---
+
+## 📚 Overview
+
+This notebook walks through building a complete local AI pipeline:
+
+- Local LLM inference using Ollama
+- Document embedding and storage with ChromaDB
+- Retrieval-Augmented Generation (RAG)
+- Optional agentic workflow using LangGraph
+- Optional OpenVINO™ integration for optimized inference
+
+The goal is to provide a **clear and reproducible introduction** to local-first AI systems.
+
+---
+
+## 🔍 What is RAG?
+
+**Retrieval-Augmented Generation (RAG)** enhances LLM responses by retrieving relevant context from a knowledge base before generating an answer.
+
+This helps:
+- Reduce hallucinations
+- Incorporate domain-specific knowledge
+- Improve factual accuracy
+
+---
+
+## 🤖 Optional Agentic Workflow
+
+This notebook includes an **optional agentic extension** using LangGraph.
+
+In this setup, the system can:
+- Decide whether retrieval is needed
+- Route queries dynamically
+- Use simple tools such as a calculator
+
+> ⚠️ This section is optional and intended for learning purposes.  
+> The core RAG pipeline works independently without the agentic extension.
+
+---
+---
+
+## 🔧 Recent Changes & Fixes
+
+### Added: Dependency Check (Before Agent Section)
+
+**Issue:** Running the LangGraph agent section (Section 7) would fail with `NameError: name 'ask_llm' is not defined` if prerequisite cells were not executed first.
+
+**Solution:** Added an automatic dependency check that:
+- ✅ Verifies all required functions are available before building the agent
+- ✅ Provides clear error messages if functions are missing
+- ✅ Shows exactly which sections to run and in what order
+
+**Impact:** Users can now run cells in any order—the dependency check catches missing prerequisites with helpful instructions.
+
+### Execution Order Requirements
+
+To run the full pipeline successfully, execute sections in this order:
+
+1. **Section 1:** Environment Setup (install packages)
+2. **Section 1** (Ollama): Configuration & model verification
+3. **Section 2:** Basic LLM Inference (`ask_llm` function)
+4. **Section 3:** Document Preparation (creates chunks)
+5. **Section 4:** ChromaDB Setup (vector store)
+6. **Section 5:** Retrieval (`retrieve_documents` function)
+7. **Section 6:** RAG Pipeline (`build_rag_prompt` function)
+8. **Section 7+:** Agentic workflow (now safe to run)
+
+> 💡 The dependency check will remind you if you skip steps!
+
+---
+## ⚡ OpenVINO™ Integration
+
+OpenVINO™ is Intel’s toolkit for optimizing and deploying deep learning models.
+
+This notebook is designed to be **compatible with OpenVINO optimization workflows**, including:
+
+- Model conversion (FP32 → FP16 / INT8)
+- Quantization and compression
+- CPU, GPU, and NPU performance optimization
+
+> 💡 OpenVINO integration is optional. The notebook can run without it.
+
+---
+
+## 💻 Requirements
+
+| Component | Requirement |
+|----------|-------------|
+| Python | 3.9+ |
+| RAM | 8 GB minimum, 16 GB recommended |
+| Storage | ~5 GB free |
+| OS | Windows, Linux, or macOS |
+
+> ✅ No GPU is required.
+
+---
+
+## 🛠️ Setup Instructions
+
+### 1. Install Python dependencies
+
+```bash
+pip install ollama chromadb langgraph langchain sentence-transformers jupyter