fix(index): bound LLM concurrency at the leaf, not per gather call

The previous approach (bounded_gather building a fresh semaphore per call)
did NOT compose: the indexing call graph nests gathers
(tree_parser -> process_large_node_recursively -> recurse, plus each node's
check_title gather), and each level got its own independent cap. Peak
in-flight LLM calls grew ~N^depth, so a deep/wide document still exhausted
file descriptors (Errno 24) — the exact failure the cap was meant to
prevent — while the flat summary phase was over-serialized. Naively sharing
one semaphore across gather levels would instead deadlock (a parent holds a
slot while awaiting children that need slots).

Move the throttle to the single chokepoint every LLM call funnels through,
llm_acompletion: one shared semaphore per event loop, acquired only around
the litellm.acompletion network call. This gives a true global cap that
composes across any nesting and can't deadlock (a parent awaiting children
holds no slot). bounded_gather is gone; the call sites revert to plain
asyncio.gather.

Also:
- Reject bool in max_concurrency validation (bool is an int subclass, so
  set_max_concurrency(True) / IndexConfig(max_concurrency=True) previously
  became Semaphore(1) and silently serialized). Shared _validate_max_concurrency
  + a pydantic field_validator.
- Guard check_title_appearance_in_start_concurrent against an out-of-range
  or 0 physical_index (LLM can emit one): it was dereferenced during task
  construction, outside the gather's return_exceptions protection, aborting
  the whole build; 0 silently wrapped to the last page. Now marked 'no'.
- Propagate contextvars into agent.py's worker-thread run (mirrors
  pipeline._run_async) so ContextVar settings stay consistent.
- Tests rewritten to cover the nested case the old flat tests missed, the
  leaf-level throttle in llm_acompletion, bool rejection, and the
  out-of-range physical_index guard.

Claude-Session: https://claude.ai/code/session_01Kx5DgKbhK1N8autqXH8SmS

pageindex/agent.py

@@ -159,6 +159,10 @@ class AgentRunner:
             result = Runner.run_sync(agent, question)
         else:
             import concurrent.futures
+            import contextvars
+            # Copy the current context into the worker thread so ContextVar-based
+            # settings propagate (mirrors pipeline._run_async).
+            ctx = contextvars.copy_context()
             with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
-                result = pool.submit(asyncio.run, Runner.run(agent, question)).result()
+                result = pool.submit(ctx.run, asyncio.run, Runner.run(agent, question)).result()
         return result.final_output