"""Embedding worker (T97, Phase 4).

The worker drains a queue of EmbeddingJobs and emits ``embedding_indexed``
events. Mirrors test_significance.py's BackgroundWorker tests in shape:
seed a memory, enqueue jobs, call ``stop()`` to drain via sentinel, then
assert on the projected ``embeddings`` table and the underlying event_log.
"""

from __future__ import annotations

from pathlib import Path

from chat.db.connection import open_db
from chat.db.migrate import apply_migrations
from chat.eventlog.log import append_event
from chat.eventlog.projector import project
from chat.services.embedding_worker import EmbeddingJob, EmbeddingWorker
from chat.services.embeddings import (
    DEFAULT_EMBEDDING_MODEL,
    EmbeddingResult,
    FALLBACK_EMBEDDING_MODEL,
)

# Trigger handler registration for projection.
import chat.state.embeddings  # noqa: F401
import chat.state.entities  # noqa: F401
import chat.state.memory  # noqa: F401
import chat.state.world  # noqa: F401


def _seed_memories(db_path: Path, count: int) -> list[int]:
    """Seed ``count`` memory rows for ``bot_a`` and return their ids."""
    with open_db(db_path) as conn:
        append_event(
            conn,
            kind="bot_authored",
            payload={
                "id": "bot_a",
                "name": "BotA",
                "persona": "...",
                "voice_samples": [],
                "traits": [],
                "backstory": "",
                "initial_relationship_to_you": "",
                "kickoff_prose": "",
            },
        )
        append_event(
            conn,
            kind="chat_created",
            payload={
                "id": "chat_bot_a",
                "host_bot_id": "bot_a",
                "initial_time": "2026-04-26T20:00:00+00:00",
                "narrative_anchor": "Day 1",
                "weather": "",
            },
        )
        for i in range(count):
            append_event(
                conn,
                kind="memory_written",
                payload={
                    "owner_id": "bot_a",
                    "chat_id": "chat_bot_a",
                    "pov_summary": f"memory text {i}",
                    "witness_you": 1,
                    "witness_host": 1,
                    "witness_guest": 0,
                    "source": "direct",
                    "reliability": 1.0,
                    "significance": 1,
                    "pinned": 0,
                    "auto_pinned": 0,
                },
            )
        project(conn)
        return [
            r[0]
            for r in conn.execute(
                "SELECT id FROM memories WHERE owner_id = 'bot_a' ORDER BY id"
            ).fetchall()
        ]


async def test_worker_drains_jobs_and_emits_indexed_events(tmp_path):
    """Three jobs in -> three ``embedding_indexed`` events out, all
    projected into the ``embeddings`` table."""
    db = tmp_path / "t.db"
    apply_migrations(db)
    memory_ids = _seed_memories(db, count=3)

    worker = EmbeddingWorker(
        conn_factory=lambda: open_db(db),
        client=None,  # pseudo path — no client needed
    )
    await worker.start()
    for mid in memory_ids:
        worker.enqueue(EmbeddingJob(memory_id=mid, text=f"text-{mid}"))
    await worker.stop()

    with open_db(db) as conn:
        # Three embedding_indexed events landed.
        cur = conn.execute(
            "SELECT COUNT(*) FROM event_log WHERE kind = 'embedding_indexed'"
        )
        assert cur.fetchone()[0] == 3
        # Three rows in the embeddings table — one per memory.
        cur = conn.execute(
            "SELECT memory_id, model, dim FROM embeddings ORDER BY memory_id"
        )
        rows = cur.fetchall()
        assert len(rows) == 3
        for (mid, model, dim), expected_mid in zip(rows, memory_ids):
            assert mid == expected_mid
            assert model == DEFAULT_EMBEDDING_MODEL
            assert dim > 0


async def test_worker_skips_fallback_results(tmp_path, monkeypatch):
    """A fallback EmbeddingResult must NOT produce an embedding_indexed
    event — backfill can retry later when a real embedding is available."""
    db = tmp_path / "t.db"
    apply_migrations(db)
    memory_ids = _seed_memories(db, count=1)

    async def _fake_generate(client, *, text, model, dim, timeout_s=30.0):
        return EmbeddingResult(
            vector=[0.0] * dim, model=FALLBACK_EMBEDDING_MODEL, dim=dim
        )

    # Patch the symbol the worker resolved at import time.
    import chat.services.embedding_worker as worker_mod

    monkeypatch.setattr(worker_mod, "generate_embedding", _fake_generate)

    worker = EmbeddingWorker(
        conn_factory=lambda: open_db(db),
        client=None,
    )
    await worker.start()
    worker.enqueue(EmbeddingJob(memory_id=memory_ids[0], text="anything"))
    await worker.stop()

    with open_db(db) as conn:
        cur = conn.execute(
            "SELECT COUNT(*) FROM event_log WHERE kind = 'embedding_indexed'"
        )
        assert cur.fetchone()[0] == 0
        cur = conn.execute("SELECT COUNT(*) FROM embeddings")
        assert cur.fetchone()[0] == 0


async def test_worker_handles_concurrent_jobs_serially(tmp_path):
    """Five jobs queued back-to-back must process in FIFO order — the
    single-task design respects the Featherless 2-conn cap (and keeps
    event_log ordering deterministic)."""
    db = tmp_path / "t.db"
    apply_migrations(db)
    memory_ids = _seed_memories(db, count=5)

    worker = EmbeddingWorker(
        conn_factory=lambda: open_db(db),
        client=None,
    )
    await worker.start()
    # Enqueue all five before yielding to the loop — exercises the queue
    # rather than a one-at-a-time drain.
    for mid in memory_ids:
        worker.enqueue(EmbeddingJob(memory_id=mid, text=f"text-{mid}"))
    await worker.stop()

    with open_db(db) as conn:
        # Events landed in enqueue order (FIFO).
        cur = conn.execute(
            "SELECT json_extract(payload_json, '$.memory_id') "
            "FROM event_log WHERE kind = 'embedding_indexed' "
            "ORDER BY id"
        )
        seen = [r[0] for r in cur.fetchall()]
        assert seen == memory_ids

        # All five embeddings projected.
        cur = conn.execute("SELECT COUNT(*) FROM embeddings")
        assert cur.fetchone()[0] == 5