brain-of-reese/app/api/chat.py

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

Flow (LOCKED A7/A15): embed the question → pgvector cosine top-K chunks →
distinct parent documents (full text, capped) → 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.

The honesty gate (LOW relevance → deflection) lands in phase 04; every
turn in this phase is grounded (``deflected=false``).
"""
from __future__ import annotations

import json
import logging
import time
from collections.abc import AsyncIterator
from typing import Any

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

from app.config import get_settings
from app.db import db_available, get_db
from app.models import QueryLog
from app.rag.llm import EmbeddingError, LLMClient, LLMError
from app.rag.prompts import build_high_prompt
from app.rag.retriever import retrieve, select_documents
from app.schemas import ChatDoneEvent, 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"


@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:
            logger.error("chat: embedding failed question=%r — %s", request.message, e)
            yield sse_event(
                {
                    "type": "error",
                    "detail": "I couldn't reach the embedding model — please try again.",
                }
            )
            return
        embed_ms = int((time.monotonic() - t0) * 1000)

        # 2. Retrieve top-K chunks → top-N full parent documents.
        try:
            chunks = retrieve(db, question_vec)
            docs = select_documents(chunks)
        except Exception:  # noqa: BLE001 — DB failure mid-turn
            logger.exception("chat: retrieval failed question=%r", request.message)
            yield sse_event(
                {
                    "type": "error",
                    "detail": "The knowledge base went offline mid-question — is Postgres up?",
                }
            )
            return

        top_score = chunks[0].score if chunks else 0.0
        source_paths = [f"{d.source}/{d.path}" for d in docs]
        messages = [
            {"role": "system", "content": build_high_prompt(docs)},
            {"role": "user", "content": request.message},
        ]

        # 3. Stream the grounded answer.
        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 — %s", request.message, e)
            yield sse_event(
                {"type": "error", "detail": "The chat model dropped the connection — try again?"}
            )
            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=top_score,
                    chunk_hits=len(chunks),
                    deflected=False,
                    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 threshold=%.2f deflected=%s "
            "sources=%r total_ms=%d",
            request.message,
            embed_ms,
            top_score,
            get_settings().relevance_threshold,
            False,
            source_paths,
            total_ms,
        )
        yield sse_event(
            ChatDoneEvent(
                deflected=False,
                sources=[
                    SourceRef(source=d.source, path=d.path, title=d.title) for d in docs
                ],
                suggestions=[],
            ).model_dump()
        )

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