Add LLM filtering pattern, .env.example, and workflows/lib

- Add .env.example with LLM_API_URL, LLM_MODEL, LLM_API_KEY
- Add .gitignore to exclude .env
- Add Pattern 5 (LLM filtering) to notebook-patterns.md
- Track workflows/lib with llm_call helper using mirascope
- Update README with LLM setup step and updated project structure

docs/notebook-patterns.md

@@ -425,6 +425,89 @@ def display_timeline(timeline_df):
     timeline_df
 ```
 
+## Pattern 5: LLM Filtering with `lib.llm`
+
+When you need to classify, score, or extract structured information from each entity (e.g. "is this meeting about project X?", "rate the relevance of this email"), use the `llm_call` helper from `workflows/lib`. It sends each item to an LLM and parses the response into a typed Pydantic model.
+
+**Prerequisites:** Copy `.env.example` to `.env` and fill in your `LLM_API_KEY`. Add `mirascope` and `pydantic` to the notebook's PEP 723 dependencies.
+
+```python
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "marimo",
+#     "httpx",
+#     "polars",
+#     "mirascope",
+#     "pydantic",
+# ]
+# ///
+```
+
+### Setup cell — import `llm_call`
+
+```python
+@app.cell
+def setup():
+    import httpx
+    import marimo as mo
+    import polars as pl
+    from lib.llm import llm_call
+    client = httpx.Client(timeout=30)
+    return (client, llm_call, mo, pl,)
+```
+
+### Define a response model
+
+Create a Pydantic model that describes the structured output you want from the LLM:
+
+```python
+@app.cell
+def models():
+    from pydantic import BaseModel
+
+    class RelevanceScore(BaseModel):
+        relevant: bool
+        reason: str
+        score: int  # 0-10
+
+    return (RelevanceScore,)
+```
+
+### Filter entities through the LLM
+
+Iterate over fetched entities and call `llm_call` for each one. Since `llm_call` is async, use `asyncio.gather` to process items concurrently:
+
+```python
+@app.cell
+async def llm_filter(meetings, llm_call, RelevanceScore, pl, mo):
+    import asyncio
+
+    _topic = "Greyhaven"
+
+    async def _score(meeting):
+        _text = meeting.get("summary") or meeting.get("title") or ""
+        _result = await llm_call(
+            prompt=f"Is this meeting about '{_topic}'?\n\nMeeting: {_text}",
+            response_model=RelevanceScore,
+            system_prompt="Score the relevance of this meeting to the given topic. Set relevant=true if score >= 5.",
+        )
+        return {**meeting, "llm_relevant": _result.relevant, "llm_reason": _result.reason, "llm_score": _result.score}
+
+    scored_meetings = await asyncio.gather(*[_score(_m) for _m in meetings])
+    relevant_meetings = [_m for _m in scored_meetings if _m["llm_relevant"]]
+
+    mo.md(f"**LLM filter:** {len(relevant_meetings)}/{len(meetings)} meetings relevant to '{_topic}'")
+    return (relevant_meetings,)
+```
+
+### Tips for LLM filtering
+
+- **Keep prompts short** — only include the fields the LLM needs (title, summary, snippet), not the entire raw entity.
+- **Use structured output** — always pass a `response_model` so you get typed fields back, not free-text.
+- **Batch wisely** — `asyncio.gather` sends all requests concurrently. For large datasets (100+ items), process in chunks to avoid rate limits.
+- **Cache results** — LLM calls are slow and cost money. If iterating on a notebook, consider storing scored results in a cell variable so you don't re-score on every edit.
+
 ## Do / Don't — Quick Reference for LLM Agents
 
 When generating marimo notebooks, follow these rules strictly. Violations cause `MultipleDefinitionError` at runtime.