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README updates, textbook polynomial cell, self-contained notebook

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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>
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Eric Furst 2026-05-04 10:18:10 -04:00
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04-semantic-search/README.md
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@ -83,19 +83,39 @@ We use `cross-encoder/ms-marco-MiniLM-L-12-v2` to re-rank the merged candidates
### Prerequisites
Everything from Part III, plus a few additional packages:
Everything from Part III, plus a few additional packages. Each section has its own `requirements.txt`.
**If you are using `uv` for the workshop** (recommended):
```bash
pip install llama-index-retrievers-bm25 nltk
cd /path/to/llm-workshop
uv add $(cat 04-semantic-search/requirements.txt)
```
A `requirements.txt` is provided with the full set of dependencies:
**With a plain venv:**
```bash
pip install -r requirements.txt
```
The cross-encoder model (`cross-encoder/ms-marco-MiniLM-L-12-v2`) will download automatically on first use via `sentence-transformers`. It is small (~130 MB).
The new packages over Part III are `llama-index-retrievers-bm25` (BM25 keyword retrieval) and `nltk` (used by `search_keywords.py` for part-of-speech tagging).
### Cache the models
**Required.** This section uses *two* models: the embedding model from Part III (cached if you ran `cache_model.py` already) and a cross-encoder for re-ranking. Both must be cached before `build_store.py` and `query_hybrid.py` will run, since the scripts run in offline mode.
If you have already cached the embedding model in Part III, point this section at it:
```bash
cd 04-semantic-search
ln -s ../03-rag/models models
```
Then pre-cache the cross-encoder (`cross-encoder/ms-marco-MiniLM-L-12-v2`, ~130 MB) by running a similar one-shot script (or temporarily set `HF_HUB_OFFLINE=0` in your shell for the first run of `build_store.py`).
If you have not cached the embedding model yet, run `python ../03-rag/cache_model.py` first.
### Pull the LLM
Make sure `ollama` is running and `command-r7b` is available: