"""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.

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.
"""
from __future__ import annotations

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.config import Settings, get_settings
from app.db import db_available, get_db
from app.models import Document, QueryLog
from app.rag.llm import EmbeddingError, LLMClient, LLMError
from app.rag.prompts import build_deflect_prompt, build_high_prompt
from app.rag.retriever import RetrievedChunk, retrieve, select_documents, weak_hit_titles
from app.rag.suggestions import derive_suggestions
from app.schemas import ChatDoneEvent, ChatErrorEvent, ChatRequest, 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)


def plan_turn(chunks: Sequence[RetrievedChunk], settings: Settings) -> 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``.
    """
    best_cosine = max((c.cosine for c in chunks), default=0.0)
    fts_hits = sum(1 for c in chunks if c.fts_hit)
    if best_cosine >= settings.relevance_threshold or fts_hits > 0:
        docs = select_documents(
            chunks, n=settings.top_n_docs, max_chars=settings.max_context_chars
        )
        return TurnPlan(best_cosine, fts_hits, False, build_high_prompt(docs), docs, [])
    titles = weak_hit_titles(chunks)
    return TurnPlan(
        best_cosine,
        fts_hits,
        True,
        build_deflect_prompt(titles),
        select_documents(chunks, n=settings.top_n_docs, max_chars=settings.max_context_chars),
        derive_suggestions(titles, settings.suggestions),
    )


@router.post("/chat")
async def chat(
    request: ChatRequest,
    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``."""
    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]:
        # 1. Embed the question.
        t0 = time.monotonic()
        try:
            question_vec = await llm.embed_one(request.message)
        except EmbeddingError as e:
            embed_ms = int((time.monotonic() - t0) * 1000)
            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,
            )
            yield sse_event(
                ChatErrorEvent(
                    detail="I couldn't reach the embedding model — please try again."
                ).model_dump()
            )
            return
        embed_ms = int((time.monotonic() - t0) * 1000)

        # 2. Retrieve top-K chunks, then the honesty gate (A8) picks the
        #    HIGH (grounded) or LOW (deflected) prompt + context.
        settings = get_settings()
        try:
            chunks = retrieve(db, request.message, question_vec)
            plan = plan_turn(chunks, settings)
        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),
            )
            yield sse_event(
                ChatErrorEvent(
                    detail="The knowledge base went offline mid-question — is Postgres up?"
                ).model_dump()
            )
            return
        source_paths = [f"{d.source}/{d.path}" for d in plan.docs]
        messages = [
            {"role": "system", "content": plan.system_prompt},
            {"role": "user", "content": request.message},
        ]

        # 3. Stream the answer (grounded, or an honest deflection).
        try:
            async for piece in llm.chat_stream(messages):
                yield sse_event({"type": "delta", "text": piece})
        except LLMError as e:
            logger.error(
                "chat: LLM stream failed question=%r total_ms=%d — %s",
                request.message,
                int((time.monotonic() - started) * 1000),
                e,
            )
            yield sse_event(
                ChatErrorEvent(
                    detail="The chat model dropped the connection — try again?"
                ).model_dump()
            )
            return

        # 4. Durable record + required per-turn log line (PLAN §9).
        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 threshold=%.2f "
            "deflected=%s sources=%r total_ms=%d",
            request.message,
            embed_ms,
            plan.top_score,
            plan.fts_hits,
            settings.relevance_threshold,
            plan.deflected,
            source_paths,
            total_ms,
        )
        yield sse_event(
            ChatDoneEvent(
                deflected=plan.deflected,
                sources=[
                    SourceRef(source=d.source, path=d.path, title=d.title) for d in plan.docs
                ],
                suggestions=plan.suggestions,
            ).model_dump()
        )

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