README updates, textbook polynomial cell, self-contained notebook

Same set of changes as che-computing-dev/LLMs:
- 03/04/05 READMEs: uv add workflow, required model caching
- 05-tool-use: add Setup section, requirements.txt
- 06-neural-networks: textbook cubic polynomial comparison cell
- 06-neural-networks: add nn_workshop_colab.ipynb (self-contained, inline data)
- vocab.md: catch up with terms from 02-05

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

05-tool-use/README.md

@@ -18,6 +18,42 @@ The LLM tools you use every day are not bare language models. They are agentic s
 
 ---
 
+## Setup
+
+This section uses the [Ollama Python library](https://github.com/ollama/ollama-python) to call local models programmatically.
+
+### Install the Python library
+
+A `requirements.txt` is provided. **If you are using `uv` for the workshop** (recommended):
+
+```bash
+cd /path/to/llm-workshop
+uv add $(cat 05-tool-use/requirements.txt)
+```
+
+**With a plain venv:**
+
+```bash
+pip install -r requirements.txt
+```
+
+The only direct dependency is `ollama`. If you completed sections 03 or 04, this is likely already installed as a transitive dependency of `llama-index-llms-ollama`.
+
+### Pull the model
+
+Both scripts use `llama3.1:8b`:
+
+```bash
+ollama pull llama3.1:8b
+```
+
+You can substitute another tool-calling model by editing the scripts. See https://ollama.com/search?c=tool for the current list of models that support function calling. Smaller options like `llama3.2:3b` work for the simple examples; larger models tend to handle multi-step tool calls more reliably.
+
+### A note on Exercise 8
+
+The optional advanced exercise wires the RAG pipeline from sections 03-04 into a LlamaIndex `ReActAgent`. If you plan to attempt it, you'll need the LlamaIndex packages from those sections already installed and a working RAG store from `03-rag/`.
+
+
 ## 1. From LLM to agent: what changed?
 
 ### The early days