"""POST /api/chat — a RAG chat turn streamed over SSE (PLAN §3/§4).

Flow (LOCKED A7/A15): embed the question → hybrid retrieval (cosine
top-N ∪ Postgres FTS top-N, RRF-fused) → the **honesty gate** → locked
persona prompt (PLAN §6) → ``turbo`` streamed as ``delta`` events → final
``done`` event (``deflected``, ``sources``, ``suggestions``) +
``query_log`` row + the per-turn log line (PLAN §9).
Mid-stream failures become a structured ``error`` event; a pre-stream DB
outage is a plain 503 JSON.

Thinking (phase 17, PLAN §4 extension, owner permission 2026-08-23): the
model's reasoning arrives ahead of the answer and is streamed as
``thinking`` events before the ``delta`` events of the same turn. Each
turn's thinking is counted in the per-turn log line
(``thinking_chars=N``); ``BOR_STREAM_THINKING=0`` suppresses the
``thinking`` frames (the pieces are still counted).

Honesty gate (A8, revised 2026-08-21): LOW — deflection — only when the
best cosine is strictly below ``BOR_RELEVANCE_THRESHOLD`` **and** no
candidate chunk FTS-matches the question (``fts_hits == 0``). A
name-your-tool question with weak vector overlap but a lexical hit still
gets a grounded answer. Deflection mode carries weak-hit *titles only*
(never document content) plus deterministic "Maybe try" chips, and the
``done`` event / ``query_log`` row record ``deflected=true``, the weak
score and the ``fts_hits`` count.

Steering (phase 15): the owner's stored tuning notes are loaded per turn
(oldest first) and injected into the system prompt as a ``<tuning>``
section — both the HIGH and the LOW prompt carry it. The per-turn log
line records ``tuning=N`` (the number of injected notes).

Summaries (phase 30): a lite-model summary chunk's parent *is* the
source document, so a summary hit resolves to the full source document
through the unchanged chunk→document mapping (A7 revised) — context
assembly is untouched. ``TurnPlan.summary_hits`` counts the hit chunks
with ``is_summary`` whose parent document landed in the selected
top-N context, and the per-turn log line records ``summary_hits=N``
after ``fts_hits`` (PLAN §9 line extension).

KB overview (phase 31): the lite-generated outline of the knowledge
base (single ``kb_overview`` row) is read per turn (one indexed PK
lookup — no LLM call) and injected into **both** prompts as the
``<knowledge_base>`` section, ordered ``<relevance>`` →
``<knowledge_base>`` → ``<tuning>`` → mode body. With an empty row the
prompts stay byte-identical to the pre-phase text (phase 15
convention); ``TurnPlan.kb_chars`` records the length of the stored
outline (0 when absent) and the per-turn log line records
``kb_chars=N`` after ``tuning=N`` (PLAN §9 line extension).

Agent document tools (phase 37, PLAN §4 extension, owner permission
2026-08-26; phase 45 removed the per-tool budgets — owner permission
2026-08-27; phase 70 aligned the surface to the harness-trained
``ls`` / ``read`` / ``grep`` — owner permission 2026-09-03): a
**grounded** turn (``not plan.deflected``) no longer streams a bare
``chat_stream`` — it runs the agent loop (``app.rag.agent.run_agent``),
which offers the model the three server-side tools
``ls`` / ``read`` / ``grep`` for the whole turn (as many calls as the
model wants, re-lists and re-greps included) until it answers or the
round cap (``BOR_AGENT_MAX_ROUNDS``, default 10) forces one final
no-tools answer. Each model-requested call streams as an SSE ``tool``
event — ``{"type": "tool", "name": …, "argument": … | null}`` — ahead
of the answer's ``delta`` frames: ``argument`` is the single string the
model passed — ``read``'s ``path`` (the combined ``source/path``),
``grep``'s ``pattern``, ``ls``'s ``path`` — or null (a non-string
value — a model error the backend refuses — and an omitted argument
both yield null).

Tool-result frames (phase 95, ``TODO.md`` L5 — A15 extension, owner
permission 2026-09-10; the event-type list grows from six to SEVEN,
``tool_result`` among them; PLAN.md is being redone by the owner): a
``read`` whose document is longer than ``BOR_READ_MAX_CHARS`` streams,
AFTER the matching ``tool`` frame (the line is already on screen — the
marker lands a beat later, the phase-37/48 tool-line timing is
untouched) and BEFORE the next model round, exactly one optional
``tool_result`` event — ``{"type": "tool_result", "name": "read",
"argument": …, "truncated": true, "chars_shown": N, "chars_total": M}``
— the additive truncation notice the UI turns into the
"(truncated — showing N of M chars)" marker on the Reading line. A
non-truncated read streams NO such frame (one frame = one noteworthy
event), deflected turns never stream one (the agent never runs, A8),
and every pre-existing frame is byte-identical — clients that do not
know the type ignore it. ``done.sources``, ``query_log.sources`` and the
per-turn log line all report the same combined source list (retrieval
docs + the agent's read docs, deduped by ``(source, path)``, order
preserved — a grep adds no source; it is a locator, locked A5), and the
log line records ``tool_calls=N`` after ``thinking_chars=N`` (PLAN §9
line extension — ``N`` counts executed tool calls; rejected calls do not
count). **Deflected turns keep the direct ``chat_stream`` —
byte-identical to the pre-phase path (A8):**
the LOW prompt never carries tools, and with ``agent_max_rounds`` at
**0** ``run_agent`` makes exactly one ``tools=None`` request,
reproducing the pre-phase behavior (the kill switch).

LLM retries (phase 67, ``BOR_LLM_RETRIES`` / ``BOR_LLM_RETRY_DELAY``,
owner-locked 2026-09-01): when the aipi endpoint dies before a request
has streamed its first output frame (locked A2), the request is
restarted — up to ``llm_retries`` times (default 3), a flat
``llm_retry_delay`` (default 5 s) between attempts. Every restart is
announced with an SSE ``retry`` frame (``{"type": "retry",
"attempt": n, "max_attempts": N}`` — the attempt about to be tried,
1-based, ahead of the pre-retry wait) so the UI can show the transient
"Communication interrupted — retrying (n of N)…" status (locked A4);
exhaustion and any failure after the first frame keep the existing
terminal ``error`` frames. The pre-stream question embedding retries on
the same budget, and the deflected answer stream goes through
``chat_stream_retried`` (the agent loop retries per round — task 03).
The per-turn log line records ``retries=N`` after ``total_ms=N`` (0
when nothing was retried — the field is uniform across all turn
shapes).

Tool-scaffolding guardrail (phase 71, deterministic only — owner
permission 2026-09-03: "deterministic guardrails only right now, forget
using a model for that"): the raw chat-template tokens
``<|tool_call_start|>…<|tool_call_end|>`` the ``lite`` model sometimes
emits as plain answer text can never reach the user. The DEFLECTED
path's request runs ``delta.content`` through a caller-owned
``ScaffoldingFilter`` (content only — thinking stays raw); when the
filter wipes the whole reply (visible content 0, ``stripped_chars > 0``)
the turn gets exactly ONE bounded recovery: the same messages with
``agent.CORRECTION_INSTRUCTION`` folded into the single system prompt,
``tools=None``, a fresh filter, the same phase-67 retry budget, streamed
through the same piece loop (extracted as the inner ``_pump`` helper).
A recovery that also comes back empty — and any grounded round where the
recovery policy in ``run_agent`` fails — settles with the dedicated
structured ``error`` frame (``MalformedReplyError`` caught before the
generic ``LLMError`` handler): no ``done``, no ``query_log`` row,
byte-for-byte the existing terminal-error shape. The per-turn log line
records ``scaffold_stripped=N`` after ``retries=N`` — the sum across the
turn's requests (grounded: the agent's rounds + forced final + any
recovery, via the holder; deflected: this turn's filters), 0 on clean
turns (the field is uniform, the phase-67 ``retries=N`` pattern); the
recovery does not bump ``retries=N`` (it is not a phase-67
endpoint-retry).

Chat history (phase 74, TODO L4, owner-locked A2/A3/A4 2026-09-08):
``POST /api/chat`` accepts an optional ``history`` — the client's prior
turns, oldest first (the ``bor.chat.v1`` record minus the current
question; the endpoint stays stateless per A10 — nothing is stored). It
is mapped ONCE per turn by :func:`app.rag.prompts.history_to_messages`
— trimmed newest-first against the settings budgets
(``history_max_turns`` / ``history_max_chars``; a capped-out turn is
dropped whole, never truncated) — and fed to the model on BOTH turn
branches: the deflected path splices it between the system prompt and
the current user message (the phase-71 recovery still rebuilds from
``messages[1:]`` — unchanged), and the grounded agent receives it as
``run_agent(..., history=hist)``. Prior brain turns' thinking travels
as ``reasoning_content`` on the assistant message (the preserve-
thinking wire convention, A4). The per-turn log line records
``history_msgs=N`` after ``kb_chars=N`` (0 when the request carries no
history — the two-message request stays byte-identical).
"""
from __future__ import annotations

import asyncio
import json
import logging
import time
from collections.abc import AsyncIterator, Sequence
from dataclasses import dataclass
from typing import Any

from fastapi import APIRouter, Depends
from fastapi.responses import JSONResponse, StreamingResponse
from sqlalchemy.orm import Session

from app.api.steering import load_steering_notes
from app.config import Settings, get_settings
from app.core.auth import require_user
from app.db import db_available, get_db
from app.models import Document, QueryLog
from app.rag.agent import (
    CORRECTION_INSTRUCTION,  # phase 71: the harness-owned recovery line
    AgentHolder,
    MalformedReplyError,  # phase 71: the recovery policy's terminal signal
    run_agent,
)
from app.rag.llm import (
    EmbeddingError,
    LLMClient,
    LLMError,
    RetryPiece,  # phase 67: one LLM request restart (an SSE retry frame)
    StreamPiece,  # type of the answer pieces streamed by the agent loop
    ToolCallPiece,  # phase 37: one model-requested tool call
    ToolResultPiece,  # phase 95: one truncated tool result (SSE frame = task 02)
    chat_stream_retried,  # phase 67: the retry-before-first-piece primitive
)
from app.rag.overview import load_kb_overview
from app.rag.prompts import build_deflect_prompt, build_high_prompt, history_to_messages
from app.rag.retriever import RetrievedChunk, retrieve, select_documents, weak_hit_titles
from app.rag.scaffolding import ScaffoldingFilter  # phase 71: the streaming filter
from app.rag.suggestions import derive_suggestions
from app.schemas import (
    ChatDoneEvent,
    ChatErrorEvent,
    ChatRequest,
    ChatRetryEvent,
    ChatThinkingEvent,
    ChatToolEvent,
    ChatToolResultEvent,
    SourceRef,
)

logger = logging.getLogger("app.chat")
router = APIRouter(tags=["chat"])

#: Streaming hints: no proxy buffering, no client caching (PLAN A15).
SSE_HEADERS = {"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}

_llm: LLMClient | None = None


def get_llm() -> LLMClient:
    """Shared LLM client (FastAPI dependency so tests can override it)."""
    global _llm
    if _llm is None:
        _llm = LLMClient(get_settings())
    return _llm


def sse_event(payload: dict[str, Any]) -> str:
    """Serialize one SSE frame: ``data: <json>\\n\\n`` (PLAN §4)."""
    return f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"


@dataclass
class TurnPlan:
    """What one chat turn sends to the LLM and reports on ``done``."""

    top_score: float  # best cosine across candidates (query_log.top_score)
    fts_hits: int  # lexical (OR-tsquery) candidates matched
    deflected: bool
    system_prompt: str
    docs: list[Document]  # cited sources (weak hits when deflected)
    suggestions: list[str]  # "Maybe try" chips (deflected turns only)
    tuning_count: int = 0  # steering notes injected into the system prompt
    #: Hit chunks with ``is_summary`` whose parent document made it into
    #: *docs* (phase 30; per-turn log line ``summary_hits=N``).
    summary_hits: int = 0
    #: Length of the stored KB overview injected as the
    #: ``<knowledge_base>`` section (phase 31; per-turn log line
    #: ``kb_chars=N``). 0 when no non-empty row exists.
    kb_chars: int = 0


def plan_turn(
    chunks: Sequence[RetrievedChunk],
    settings: Settings,
    notes: Sequence[str] | None = None,
    kb_overview: str | None = None,
) -> TurnPlan:
    """Apply the honesty gate (A8, revised) and assemble prompt + context.

    * **HIGH (grounded)** when ``best_cosine >= threshold`` **or**
      ``fts_hits > 0``: HIGH prompt with the full top-N documents, no
      suggestions. A cosine exactly at the threshold is an answer — the
      gate is strict (``< threshold``).
    * **LOW (deflected)** only when ``best_cosine < threshold`` **and**
      ``fts_hits == 0`` (or no hits at all): LOW prompt (``DEFLECT_MODE``)
      with weak-hit titles only — never document content — plus
      deterministic alternative-question chips derived from those titles.

    ``top_score`` (stored in ``query_log``) is the best cosine, so the
    gate input is always a pure vector-similarity number; the lexical
    signal is recorded separately as ``fts_hits``.

    *notes* are the owner's steering notes (phase 15, oldest first):
    when non-empty, both the HIGH and the LOW prompt carry the
    ``<tuning>`` section; with no notes the prompts are unchanged.

    *kb_overview* is the stored KB outline (phase 31, one PK lookup per
    turn): when non-empty, both prompts carry the ``<knowledge_base>``
    section (between ``<relevance>`` and ``<tuning>``) and
    ``kb_chars`` records the outline's length; with no outline the
    prompts are byte-identical to the pre-phase text and ``kb_chars``
    is 0.

    ``summary_hits`` (phase 30) counts the hit chunks with
    ``is_summary`` whose parent document is among the selected
    top-N documents — both the HIGH and the LOW branch record it.
    """
    steering = list(notes or [])
    kb_text = (kb_overview or "").strip()
    kb_chars = len(kb_text)
    best_cosine = max((c.cosine for c in chunks), default=0.0)
    fts_hits = sum(1 for c in chunks if c.fts_hit)
    docs = select_documents(chunks, n=settings.top_n_docs)
    selected_ids = {d.id for d in docs}
    summary_hits = sum(1 for c in chunks if c.is_summary and c.document.id in selected_ids)
    if best_cosine >= settings.relevance_threshold or fts_hits > 0:
        return TurnPlan(
            best_cosine,
            fts_hits,
            False,
            build_high_prompt(docs, notes=steering, kb_overview=kb_text),
            docs,
            [],
            len(steering),
            summary_hits,
            kb_chars,
        )
    titles = weak_hit_titles(chunks)
    return TurnPlan(
        best_cosine,
        fts_hits,
        True,
        build_deflect_prompt(titles, notes=steering, kb_overview=kb_text),
        docs,
        derive_suggestions(titles, settings.suggestions),
        len(steering),
        summary_hits,
        kb_chars,
    )


@router.post("/chat")
async def chat(
    request: ChatRequest,
    _user: None = Depends(require_user),  # noqa: B008  # phase 79: admin or live token
    db: Session = Depends(get_db),  # noqa: B008
    llm: LLMClient = Depends(get_llm),  # noqa: B008
):
    """One chat turn: SSE stream of ``delta`` events + a final ``done``.

    User-gated (phase 79): anonymous callers get 401 ``authentication
    required`` before any streaming — the ONLY anonymous content is the
    shared chats.
    """
    if not db_available():
        return JSONResponse(
            status_code=503,
            content={
                "detail": (
                    "The knowledge base is offline — start Postgres with "
                    "`podman compose up -d db`, then ask again."
                )
            },
        )

    started = time.monotonic()

    async def stream() -> AsyncIterator[str]:
        # Phase 48: one terminal flag — ``True`` at every terminal exit
        # (the ``done`` yield; every ``error``-then-``return``). The
        # ``finally`` below logs the cancelled-turn line only when the
        # consumer went away before any terminal frame; it must not
        # yield (GeneratorExit handling).
        settled = False
        retries_used = 0  # phase 67: LLM requests restarted this turn (log line)
        try:
            settings = get_settings()
            # Phase 74 (TODO L4): the client's prior turns, mapped ONCE
            # per turn — trimmed newest-first against the settings
            # budgets, assistant turns carrying their prior thinking as
            # ``reasoning_content`` (A4). BOTH branches below (deflected
            # + grounded agent) reuse the same block; an absent/empty
            # history yields ``[]`` (the byte-identical two-message
            # request, A2).
            hist = history_to_messages(request.history, settings)

            # 1. Embed the question.
            #    Phase 67: a dead embeddings endpoint is retried before any
            #    frame has left the server — up to ``llm_retries`` restarts,
            #    a flat ``llm_retry_delay`` between attempts, one SSE
            #    ``retry`` frame per restart (the UI shows the transient
            #    "retrying" status, not an error — locked A4). The final
            #    failure keeps the EXISTING terminal ``error`` frame (the
            #    copy reads correctly after N tries); ``llm_retries=0`` is
            #    byte-identical to the pre-phase-67 single attempt.
            t0 = time.monotonic()
            max_attempts = settings.llm_retries + 1
            attempt = 1
            while True:
                try:
                    question_vec = await llm.embed_one(request.message)
                    break
                except EmbeddingError as e:
                    embed_ms = int((time.monotonic() - t0) * 1000)
                    if attempt >= max_attempts:
                        total_ms = int((time.monotonic() - started) * 1000)
                        logger.error(
                            "chat: question=%r embed_ms=%d total_ms=%d — embedding failed: %s",
                            request.message,
                            embed_ms,
                            total_ms,
                            e,
                        )
                        settled = True  # terminal: the error frame settles the turn
                        yield sse_event(
                            ChatErrorEvent(
                                detail="I couldn't reach the embedding model — please try again."
                            ).model_dump()
                        )
                        return
                    logger.warning(
                        "chat: question=%r embedding failed (attempt %d/%d) — "
                        "retrying in %.1fs: %s",
                        request.message,
                        attempt,
                        max_attempts,
                        settings.llm_retry_delay,
                        e,
                    )
                    retries_used += 1
                    yield sse_event(
                        ChatRetryEvent(
                            attempt=attempt + 1, max_attempts=max_attempts
                        ).model_dump()
                    )
                    await asyncio.sleep(settings.llm_retry_delay)
                    attempt += 1
            embed_ms = int((time.monotonic() - t0) * 1000)

            # 2. Retrieve top-K chunks, load the owner's steering notes
            #    (phase 15), then the honesty gate (A8) picks the HIGH
            #    (grounded) or LOW (deflected) prompt + context.
            try:
                steering_notes = load_steering_notes(db)
                # KB overview (phase 31): one indexed PK lookup per turn —
                # the outline is generated at import time, never per chat
                # turn.
                kb_overview = load_kb_overview(db)
                chunks = retrieve(db, request.message, question_vec)
                plan = plan_turn(chunks, settings, notes=steering_notes, kb_overview=kb_overview)
            except Exception:  # noqa: BLE001 — DB failure mid-turn
                logger.exception(
                    "chat: retrieval failed question=%r total_ms=%d",
                    request.message,
                    int((time.monotonic() - started) * 1000),
                )
                settled = True  # terminal: the error frame settles the turn
                yield sse_event(
                    ChatErrorEvent(
                        detail="The knowledge base went offline mid-question — is Postgres up?"
                    ).model_dump()
                )
                return
            messages: list[dict[str, Any]] = [
                {"role": "system", "content": plan.system_prompt},
                *hist,  # phase 74: the trimmed prior turns (empty by default)
                {"role": "user", "content": request.message},
            ]

            # 3. Stream the answer (grounded, or an honest deflection).
            #    Phase 17: thinking pieces stream as ``thinking`` events
            #    ahead of the ``delta`` events (PLAN §4 extension); the
            #    kill-switch (``BOR_STREAM_THINKING=0``) suppresses the
            #    frames, not the counting.
            #    Phase 37: a grounded turn runs the agent loop instead of
            #    a bare ``chat_stream`` — its ``ToolCallPiece``s stream
            #    as ``tool`` events ahead of the answer. A deflected turn
            #    keeps the direct ``chat_stream`` (byte-identical, A8):
            #    the LOW prompt never carries tools, and with
            #    ``agent_max_rounds=0`` ``run_agent`` is a single
            #    ``tools=None`` request anyway (the kill switch).
            holder = AgentHolder()
            answer_stream: AsyncIterator[
                StreamPiece | ToolCallPiece | RetryPiece | ToolResultPiece
            ]
            deflected_filter: ScaffoldingFilter | None = None
            if plan.deflected:
                # Phase 67: the deflected stream goes through the retry
                # primitive — a dead endpoint is restarted (SSE ``retry``
                # frames) only before its first piece (locked A2); the
                # grounded path stays a plain ``run_agent`` call (task 03
                # makes IT retry internally) — its ``RetryPiece``s flow
                # through the shared piece loop below. Phase 71: the
                # request's content also runs through a caller-owned
                # filter (one per request) — a scaffolding-only reply
                # streams zero ``delta`` frames instead of raw tokens, and
                # the filter's ``stripped_chars`` drives the recovery
                # decision after the piece loop.
                deflected_filter = ScaffoldingFilter()
                answer_stream = chat_stream_retried(
                    llm,
                    messages,
                    tools=None,
                    retries=settings.llm_retries,
                    delay=settings.llm_retry_delay,
                    scaffolding=deflected_filter,
                )
            else:
                answer_stream = run_agent(
                    llm,
                    db,
                    system_prompt=plan.system_prompt,
                    user_message=request.message,
                    seed_docs=plan.docs,
                    settings=settings,
                    holder=holder,
                    history=hist,  # phase 74: the same trimmed prior turns
                )
            thinking_chars = 0
            content_chars = 0  # phase 71: the turn's visible (clean) content
            scaffold_stripped = 0  # phase 71: sum across the turn's requests

            async def _pump(
                pieces: AsyncIterator[
                    StreamPiece | ToolCallPiece | RetryPiece | ToolResultPiece
                ],
            ) -> AsyncIterator[str]:
                """One request's piece loop (phase 71 extraction): the
                thinking/tool/retry/tool_result/delta handling shared by
                the turn's first pass and — deflected path only — the one
                bounded recovery. Behavior-preserving for the first pass
                (pinned by the existing integration suite). Phase 95:
                the ``ToolResultPiece`` branch emits the additive
                ``tool_result`` SSE frame (the seventh, optional event
                type — the A15 extension)."""
                nonlocal thinking_chars, content_chars, retries_used
                async for piece in pieces:  # StreamPiece | ToolCallPiece | RetryPiece
                    if isinstance(piece, ToolCallPiece):
                        # Phase 37 (PLAN §4 extension; phase 70): one SSE
                        # ``tool`` frame per model-requested call.
                        # ``argument`` is the single string the model
                        # passed — ``read``'s ``path`` (the combined
                        # ``source/path``), ``grep``'s ``pattern``,
                        # ``ls``'s ``path`` — or null (a non-string value
                        # is a model error the backend refuses, as is an
                        # omitted argument).
                        argument = piece.arguments.get(
                            "pattern" if piece.name == "grep" else "path"
                        )
                        argument = argument if isinstance(argument, str) else None
                        yield sse_event(
                            ChatToolEvent(name=piece.name, argument=argument).model_dump()
                        )
                        continue
                    if isinstance(piece, RetryPiece):
                        # Phase 67: the answer stream was restarted before
                        # its first piece (locked A2) — a transient status
                        # frame, never an error. No other state changes:
                        # the thinking/clock/timeout handling is the
                        # client's job.
                        retries_used += 1
                        yield sse_event(
                            ChatRetryEvent(
                                attempt=piece.attempt, max_attempts=piece.max_attempts
                            ).model_dump()
                        )
                        continue
                    if isinstance(piece, ToolResultPiece):
                        # Phase 95 (A15 extension, task 02): one optional
                        # ``tool_result`` frame per truncated ``read`` —
                        # emitted HERE, right where the agent loop yielded
                        # the piece: AFTER the matching ``tool`` frame and
                        # BEFORE the next model round. Additive: a
                        # non-truncated read yields no piece at all (no
                        # frame), and the other six event types are
                        # byte-identical.
                        yield sse_event(
                            ChatToolResultEvent(
                                name=piece.name,
                                argument=piece.argument,
                                truncated=piece.truncated,
                                chars_shown=piece.chars_shown,
                                chars_total=piece.chars_total,
                            ).model_dump()
                        )
                        continue
                    if piece.kind == "thinking":
                        thinking_chars += len(piece.text)
                        if settings.stream_thinking:
                            yield sse_event(ChatThinkingEvent(text=piece.text).model_dump())
                    else:
                        content_chars += len(piece.text)
                        yield sse_event({"type": "delta", "text": piece.text})

            try:
                async for frame in _pump(answer_stream):
                    yield frame
                if plan.deflected and deflected_filter is not None:
                    scaffold_stripped = deflected_filter.stripped_chars
                    # Phase 71: the deflected reply's visible content was
                    # wiped by the filter (the scaffolding was the whole
                    # "answer") — the ONE bounded recovery: the same
                    # messages with the correction folded into the single
                    # system prompt, ``tools=None``, a FRESH filter, the
                    # same phase-67 retry budget, streamed through the
                    # same piece loop. A round with real visible content
                    # needs no recovery (the clean content stands).
                    if content_chars == 0 and deflected_filter.stripped_chars > 0:
                        logger.warning(
                            "chat: deflected reply was pure tool-scaffolding "
                            "(%d chars stripped) — running the one bounded "
                            "recovery",
                            deflected_filter.stripped_chars,
                        )
                        recovery_filter = ScaffoldingFilter()
                        recovery_stream = chat_stream_retried(
                            llm,
                            [
                                {
                                    "role": "system",
                                    "content": (
                                        plan.system_prompt + "\n"
                                        + CORRECTION_INSTRUCTION
                                    ),
                                },
                                *messages[1:],
                            ],
                            tools=None,
                            retries=settings.llm_retries,
                            delay=settings.llm_retry_delay,
                            scaffolding=recovery_filter,
                        )
                        async for frame in _pump(recovery_stream):
                            yield frame
                        scaffold_stripped += recovery_filter.stripped_chars
                        if content_chars == 0:
                            # The second empty reply is terminal (at most
                            # one recovery per turn) — the dedicated error
                            # frame below (no done, no query_log row).
                            logger.warning(
                                "chat: the recovery reply was still empty "
                                "(scaffold_stripped=%d) — settling with a "
                                "malformed-reply error",
                                scaffold_stripped,
                            )
                            raise MalformedReplyError(
                                "the deflected model answered in raw "
                                "tool-scaffolding twice in a row — no "
                                "clean answer to stream"
                            )
                else:
                    # Grounded turns: the agent's rounds + forced final +
                    # any recovery already accumulated the turn total on
                    # the holder (the deflected fallback is 0 — the agent
                    # never runs, so this branch is grounded-only).
                    scaffold_stripped = holder.scaffold_stripped
            except MalformedReplyError as e:
                # Phase 71: the recovery policy's terminal signal —
                # caught BEFORE the generic LLMError handler (it
                # subclasses it), so the dedicated copy reaches the UI;
                # the generic "dropped the connection" copy stays for
                # transport failures.
                logger.error(
                    "chat: malformed reply after the one bounded recovery "
                    "question=%r total_ms=%d — %s",
                    request.message,
                    int((time.monotonic() - started) * 1000),
                    e,
                )
                settled = True  # terminal: the error frame settles the turn
                yield sse_event(
                    ChatErrorEvent(
                        detail="The model returned a malformed reply — please try again."
                    ).model_dump()
                )
                return
            except LLMError as e:
                logger.error(
                    "chat: LLM stream failed question=%r total_ms=%d — %s",
                    request.message,
                    int((time.monotonic() - started) * 1000),
                    e,
                )
                settled = True  # terminal: the error frame settles the turn
                yield sse_event(
                    ChatErrorEvent(
                        detail="The chat model dropped the connection — try again?"
                    ).model_dump()
                )
                return
            except Exception:  # noqa: BLE001 — a tool call hit the DB mid-stream
                # Phase 37: tool execution (the drill-down ``ls`` /
                # ``read`` / ``grep`` lookups) runs inside the stream
                # now; a mid-turn DB failure gets the same structured
                # ``error`` event as the pre-stream retrieval path.
                logger.exception(
                    "chat: tool execution failed question=%r total_ms=%d",
                    request.message,
                    int((time.monotonic() - started) * 1000),
                )
                settled = True  # terminal: the error frame settles the turn
                yield sse_event(
                    ChatErrorEvent(
                        detail="The knowledge base went offline mid-question — is Postgres up?"
                    ).model_dump()
                )
                return

            # 4. Durable record + required per-turn log line (PLAN §9).
            #    Phase 37: the agent's read documents join the
            #    retrieval's — deduped by (source, path), order preserved
            #    — and the same combined list feeds done.sources,
            #    query_log.sources and the log line (empty on deflected
            #    turns: the agent never runs). A cancelled turn (the
            #    generator closed by the consumer) never reaches this
            #    step — no query_log row.
            cited_docs: list[Document] = []
            seen: set[tuple[str, str]] = set()
            for doc in [*plan.docs, *holder.read_docs]:
                key = (doc.source, doc.path)
                if key not in seen:
                    seen.add(key)
                    cited_docs.append(doc)
            source_paths = [f"{d.source}/{d.path}" for d in cited_docs]
            total_ms = int((time.monotonic() - started) * 1000)
            try:
                db.add(
                    QueryLog(
                        question=request.message,
                        top_score=plan.top_score,
                        fts_hits=plan.fts_hits,
                        chunk_hits=len(chunks),
                        deflected=plan.deflected,
                        sources=", ".join(source_paths),
                        latency_ms=total_ms,
                    )
                )
                db.commit()
            except Exception:  # noqa: BLE001 — the answer already went out
                logger.exception("chat: failed to write query_log question=%r", request.message)

            logger.info(
                "question=%r embed_ms=%d top_score=%.3f fts_hits=%d summary_hits=%d tuning=%d "
                "kb_chars=%d history_msgs=%d threshold=%.2f deflected=%s sources=%r "
                "thinking_chars=%d tool_calls=%d total_ms=%d retries=%d scaffold_stripped=%d",
                request.message,
                embed_ms,
                plan.top_score,
                plan.fts_hits,
                plan.summary_hits,
                plan.tuning_count,
                plan.kb_chars,
                len(hist),
                settings.relevance_threshold,
                plan.deflected,
                source_paths,
                thinking_chars,
                holder.tool_calls,
                total_ms,
                retries_used,
                scaffold_stripped,
            )
            settled = True  # terminal: the done frame settles the turn
            yield sse_event(
                ChatDoneEvent(
                    deflected=plan.deflected,
                    sources=[
                        SourceRef(source=d.source, path=d.path, title=d.title) for d in cited_docs
                    ],
                    suggestions=plan.suggestions,
                ).model_dump()
            )
        finally:
            # Phase 48 (owner-locked): a cancelled turn — the SSE
            # consumer went away before any terminal frame — settles
            # with one warning line and skips query_log entirely (the
            # write above is simply never reached when the generator is
            # closed). The finally must not yield (GeneratorExit
            # handling).
            if not settled:
                logger.warning(
                    "chat: turn cancelled question=%r total_ms=%d",
                    request.message,
                    int((time.monotonic() - started) * 1000),
                )

    return StreamingResponse(stream(), media_type="text/event-stream", headers=SSE_HEADERS)