chore(examples): scrub, standardize, reframe 04

Examples scrub-and-reframe phase, first wave.

- Delete examples/01-linear-pipeline/ (covered by 00-hello-world).
- Renumber 02-05 down by one to fill the gap.
- Standardize 01-03 (LLM-using) on openarmature.llm.OpenAIProvider +
  SystemMessage + UserMessage, lifting hardcoded vLLM endpoints to
  LLM_BASE_URL / LLM_MODEL / LLM_API_KEY env vars (same shape as
  00-hello-world).
- Reframe 03-observer-hooks to register OTelObserver alongside the
  plain console_tracer, showing observability backends as pluggable
  behind one uniform hook.
- Reframe 04-nested-subgraphs around a real use case (question
  answering against a tiny baked-in document corpus: outer
  coordinator, doc-QA subgraph, section-extract subgraph). LLM-using
  throughout; the depth-aware observer is a side observation rather
  than the headline.
- Strip spec/proposal references from comments and docstrings across
  examples/ and examples/README.md. Internal src/ scope is unchanged.
- Update tests/test_examples_smoke.py parametrize list to the five
  surviving demos.

Author: chris-colinsky

examples/01-linear-pipeline/main.py

@@ -10,8 +10,7 @@
 custom `ProjectionStrategy` for the parent ↔ subgraph boundary, and the
 `merge` reducer for dict accumulation.
 
-This is the second demo in the series. It exercises three graph features
-that `01-linear-pipeline/` didn't:
+Three graph features that `00-hello-world` only touched lightly:
 
   1. **Conditional edges.** The entry node classifies the question and the
      graph routes to one of two branches based on that classification.
@@ -22,27 +21,33 @@
      from its own schema's defaults. To pass the user's question in (and
      shape what comes back out), we write a `ProjectionStrategy` by hand.
 
-And for good measure it also demonstrates the **merge reducer** for dict
-accumulation, alongside the **append reducer** already seen in 01-linear-pipeline.
+LLM calls go through ``openarmature.llm.OpenAIProvider`` (same pattern as
+``00-hello-world``) so the example reads as the recommended path rather
+than as "openai with some openarmature on top."
+
+**Configuration** (env vars; OpenAI defaults shown):
+
+- ``LLM_BASE_URL`` defaults to ``https://api.openai.com``. **Host root
+  only** — the provider adds the path itself.
+- ``LLM_MODEL`` defaults to ``gpt-4o-mini``.
+- ``LLM_API_KEY`` required (empty for local servers that don't authenticate).
 
 Run with:
-    uv run python main.py "what year did the moon landing happen"             # → quick branch
-    uv run python main.py "is espresso actually more caffeinated than drip?"  # → research branch
+
+    uv sync --group examples
+    cd examples/01-routing-and-subgraphs
+    LLM_API_KEY=sk-... uv run python main.py "what year did the moon landing happen"
+    LLM_API_KEY=sk-... uv run python main.py "is espresso actually more caffeinated than drip?"
 """
 
 from __future__ import annotations
 
 import asyncio
+import os
 import sys
 from collections.abc import Mapping
 from typing import Annotated, Any
 
-from openai import AsyncOpenAI
-from openai.types.chat import (
-    ChatCompletionMessageParam,
-    ChatCompletionSystemMessageParam,
-    ChatCompletionUserMessageParam,
-)
 from pydantic import Field
 
 from openarmature.graph import (
@@ -54,11 +59,22 @@
     append,
     merge,
 )
+from openarmature.llm import OpenAIProvider, SystemMessage, UserMessage
+
+# Lazy-initialized so importing this module (test harnesses, doc builders,
+# IDE inspection) doesn't open an httpx.AsyncClient connection pool.
+_provider_instance: OpenAIProvider | None = None
 
-VLLM_BASE_URL = "http://localhost:8000/v1"
-MODEL = "dark-side-of-the-code/Mistral-Small-24B-Instruct-2501-AWQ"
 
-client = AsyncOpenAI(base_url=VLLM_BASE_URL, api_key="not-needed")
+def _get_provider() -> OpenAIProvider:
+    global _provider_instance
+    if _provider_instance is None:
+        _provider_instance = OpenAIProvider(
+            base_url=os.environ.get("LLM_BASE_URL", "https://api.openai.com"),
+            model=os.environ.get("LLM_MODEL", "gpt-4o-mini"),
+            api_key=os.environ.get("LLM_API_KEY") or None,
+        )
+    return _provider_instance
 
 
 # ----------------------------------------------------------------------------
@@ -77,9 +93,9 @@
 #   - Boundaries are auditable. To find "what does the subgraph see?" you read
 #     one projection class, not a scattered naming convention.
 #
-# Both schemas below use the same reducer patterns we introduced in
-# 01-linear-pipeline: `append` on a `trace` list, `merge` on a dict. Fields without
-# an `Annotated[..., reducer]` get `last_write_wins` by default.
+# Both schemas below use the standard reducer set: `append` on the
+# `trace` list, `merge` on a dict. Fields without an
+# `Annotated[..., reducer]` get `last_write_wins` by default.
 
 
 class AssistantState(State):
@@ -103,30 +119,27 @@ class ResearchState(State):
 
 
 # ----------------------------------------------------------------------------
-# LLM helper (not openarmature — plumbing)
+# LLM helper
 # ----------------------------------------------------------------------------
+# Thin wrapper over Provider.complete that takes a system + user pair and
+# returns the assistant's reply as a string. Keeps the node bodies focused
+# on graph logic (state in → state update out) rather than provider
+# plumbing. Production code would typically inline the call.
 
 
 async def _chat(system: str, user: str) -> str:
-    messages: list[ChatCompletionMessageParam] = [
-        ChatCompletionSystemMessageParam(role="system", content=system),
-        ChatCompletionUserMessageParam(role="user", content=user),
-    ]
-    resp = await client.chat.completions.create(
-        model=MODEL,
-        messages=messages,
-        temperature=0.3,
-        stream=False,
+    response = await _get_provider().complete(
+        [SystemMessage(content=system), UserMessage(content=user)],
     )
-    return (resp.choices[0].message.content or "").strip()
+    return (response.message.content or "").strip()
 
 
 # ----------------------------------------------------------------------------
 # Outer-graph nodes
 # ----------------------------------------------------------------------------
-# Every node is the same shape as in 01-linear-pipeline: `async def(state) -> dict`,
-# returning ONLY the fields it wants to change. The engine applies per-field
-# reducers and re-validates.
+# Standard node shape: `async def(state) -> dict`, returning ONLY the
+# fields it wants to change. The engine applies per-field reducers and
+# re-validates.
 #
 # Three things worth noticing as you read these:
 #
@@ -334,9 +347,8 @@ def build_research_subgraph() -> CompiledGraph[ResearchState]:
 #
 #   - `project_in`: DELIBERATELY LIMITED. It builds a fresh subgraph state
 #     from its schema's defaults — `subgraph_state_cls()`. The parent's
-#     state is ignored. This is the spec's default and it's an explicit
-#     design choice (v0.1.1 §2 Subgraph): subgraphs don't see the outer
-#     world unless the author opts in.
+#     state is ignored. Subgraphs don't see the outer world unless the
+#     author opts in — encapsulation is the point.
 #
 # For this demo we absolutely need the question in the subgraph. So we write
 # a projection class that implements the `ProjectionStrategy` Protocol (see
@@ -444,15 +456,19 @@ def build_graph() -> CompiledGraph[AssistantState]:
 async def main() -> None:
     question = " ".join(sys.argv[1:]) or "is espresso actually more caffeinated than drip coffee?"
     graph = build_graph()
-    final = await graph.invoke(AssistantState(question=question))
-
-    print(f"question: {final.question}")
-    print(f"route:    {final.route}")
-    print()
-    print(f"answer:\n{final.answer}")
-    print()
-    print(f"trace:   {final.trace}")
-    print(f"tallies: {final.tallies}")
+    try:
+        final = await graph.invoke(AssistantState(question=question))
+        print(f"question: {final.question}")
+        print(f"route:    {final.route}")
+        print()
+        print(f"answer:\n{final.answer}")
+        print()
+        print(f"trace:   {final.trace}")
+        print(f"tallies: {final.tallies}")
+    finally:
+        await graph.drain()
+        if _provider_instance is not None:
+            await _provider_instance.aclose()
 
 
 if __name__ == "__main__":