feat(rag): pass chat history with prior thinking to the LLM

Phase 74 (TODO.md L4): a follow-up question now reaches the model WITH
the conversation so far — every prior user/brain turn and the prior
thinking blocks on brain turns (preserve-thinking) — while
POST /api/chat stays stateless (A10): the client provides the history
in the request body and the server stores nothing new.

Server (task 01):
- ChatRequest.history: optional list[HistoryTurn] (who: user|brain,
  text, optional thinking) — absent/empty keeps the request
  byte-identical to pre-phase-74 (the two-message [system, user]
  request; the kill-switch semantics are pinned in the integration
  suite).
- app.rag.prompts.history_to_messages: pure mapper — walks the turns
  newest-first against the settings budgets (history_max_turns=40 /
  history_max_chars=24000, BOR_HISTORY_MAX_TURNS /
  BOR_HISTORY_MAX_CHARS); a capped turn is dropped WHOLE (never cut
  mid-answer); the kept window is returned oldest-first; brain turns
  carry their thinking as reasoning_content (A4) only when
  non-empty.
- Both branches feed it: the deflected path splices it between the
  system prompt and the current user message (the phase-71 recovery
  still rebuilds from messages[1:]), the grounded agent receives
  run_agent(..., history=hist); llm.py's message params widen to
  list[dict[str, Any]] (string-only messages stay byte-identical on
  the wire — the SDK passes message dicts through verbatim).
- The per-turn log line (PLAN §9) gains history_msgs=N after
  kb_chars=N.
- Pins: tests/unit/test_history.py (mapper: mapping, reasoning
  gating, both budgets, drop-whole, ordering, empty default),
  tests/unit/test_config.py (the two settings + env overrides),
  tests/unit/test_agent.py (the history splice + the default),
  tests/integration/test_chat_api.py (deflected AND grounded forward
  the history incl. reasoning_content, no-history byte-identity, 422
  pins, the log field).

Client (task 02):
- runTurn — the single funnel for fresh send / phase-49 retry /
  phase-53 stale-regen — sends history = the conversation record
  minus the current question, with thinking only on brain records
  that streamed one (undefined drops the key from the JSON, the
  record's convention); the question is never duplicated into the
  history.

Wire proof (task 03):
- The mock's echo my history marker (HISTORY_TRIGGER) answers with
  the deterministic history echo — history: N prior messages; last
  answer tail: <last 24 chars>; thinking: yes|no — checked BEFORE
  the DEFLECT_MODE branch (like TABLE_TRIGGER), so it fires on both
  turn branches whatever the gate says; the module docstring records
  the user/assistant-only history invariant that keeps every
  existing (tool-result-classified) marker flow unaffected.
- tests/e2e/test_llm_history.py (isolated): a grounded follow-up and
  a deflected follow-up both receive history: 2 prior messages +
  thinking: yes + the byte-exact tail of turn 1's answer (derived
  from the persisted bor.chat.v1 record — the same array the client
  maps into the body); a cold start receives history: 0 prior
  messages / last answer tail: none / thinking: no.
- Regressions green in isolation: chat_rag, chat_history (phase 50),
  agent_document_tools, harness_aligned_tools, stop_generation,
  retry_answer, response_to_docs.

app/rag/llm.py

@@ -298,7 +298,7 @@ class LLMClient:
         return vec
 
     async def chat(
-        self, messages: list[dict[str, str]], model: str | None = None
+        self, messages: list[dict[str, Any]], model: str | None = None
     ) -> str:
         """One-shot (non-streaming) completion (A5 extended, phase 30).
 
@@ -341,12 +341,20 @@ class LLMClient:
 
     async def chat_stream(
         self,
-        messages: list[dict[str, str]],
+        messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None = None,
         scaffolding: ScaffoldingFilter | None = None,
     ) -> AsyncGenerator[StreamPiece | ToolCallPiece, None]:
         """Stream assistant pieces from the chat model (PLAN A5/A15, phase 17).
 
+        Messages are passed to the request body VERBATIM: string-only
+        ``{role, content}`` dicts are byte-identical on the wire to the
+        pre-phase-74 requests, and an assistant message may additionally
+        carry ``reasoning_content`` (the client's prior thinking, phase
+        74 — the same wire field the model uses for its OWN reasoning on
+        the response side; the ``openai`` SDK passes message dicts
+        through untouched, so no transport change).
+
         ``stream=True`` against the OpenAI-compatible endpoint, yielding
         typed :class:`StreamPiece` values. Wire convention (verified live
         against aipi's ``turbo`` on 2026-08-23): the model's reasoning
@@ -488,7 +496,7 @@ class LLMClient:
 
 async def chat_stream_retried(
     llm: LLMClient,
-    messages: list[dict[str, str]],
+    messages: list[dict[str, Any]],
     *,
     tools: list[dict[str, Any]] | None = None,
     retries: int = 0,