"""M7: the knowledge read paths must not grow a query per source or per passage.

The same discipline `test_context_performance.py` holds for M6, applied to the
four paths M7 adds. Each of them lists or joins over rows that a real library
has many of, and each could plausibly have been written one query at a time:

    source list           a chunk count and an embedded count per row
    source detail         the source, and its passages
    retrieval             lexical candidates, semantic candidates, their rows
    context build         all of the above, inside a prompt assembly

The assertions are on **growth**, not on an exact count: a fixed number breaks
on any unrelated query and teaches the next person to raise it. What matters is
that four times the library does not cost four times the queries.

Also asserted here: candidates are bounded *in the database* before the Python
reranking runs. "Do not load every chunk in the campaign merely to find the top
few" is a statement about the SQL, so it is tested against the SQL.

    python -m pytest tests/test_knowledge_performance.py -v
"""

import asyncio

import pytest
from fastapi import Depends
from fastapi.testclient import TestClient
from sqlalchemy import event, select

from app import auth, limits, memorybank, models
from app.database import Base, SessionLocal, engine, get_db
from app.knowledge import embeddings, retrieval
from app.main import app
from app.routers import adventures

from fakes import ScriptedProvider, state_block


class StubEmbedder:
    async def embed(self, texts):
        return [[1.0, float(len(t) % 5), 0.5] for t in texts]


@pytest.fixture()
def sql_log():
    statements: list[str] = []

    def record(conn, cursor, statement, parameters, context, executemany):
        statements.append(statement)

    event.listen(engine, "before_cursor_execute", record)
    try:
        yield statements
    finally:
        event.remove(engine, "before_cursor_execute", record)


@pytest.fixture()
def client(monkeypatch):
    Base.metadata.create_all(bind=engine)
    memorybank._vector_cache.clear()
    embeddings._cache.clear()
    setup = SessionLocal()
    user = models.User(is_guest=False, email="m7perf@example.com")
    setup.add(user)
    setup.flush()
    setup.add(models.Settings(
        user_id=user.id, model="test-model", embedding_model="nomic-embed-text",
        context_token_budget=8000, max_output_tokens=400,
    ))
    adventure = models.Adventure(user_id=user.id, title="Performance")
    setup.add(adventure)
    setup.flush()
    setup.add(models.Action(adventure_id=adventure.id, type="start",
                            text="Aldric stands in the crypt beneath the Old Abbey."))
    setup.commit()
    adv_id, user_id = adventure.id, user.id
    setup.close()

    monkeypatch.setattr(limits, "check_row_cap", lambda *a, **k: None)
    monkeypatch.setattr(adventures.turns, "OpenAICompatibleProvider", ScriptedProvider)
    monkeypatch.setattr(memorybank, "embedding_provider", lambda s: StubEmbedder())
    monkeypatch.setattr(memorybank, "summary_provider", lambda s: StubEmbedder())
    app.dependency_overrides[auth.get_current_user] = (
        lambda db=Depends(get_db): db.get(models.User, user_id)
    )
    test_client = TestClient(app)
    test_client.adv_id = adv_id
    test_client.user_id = user_id
    try:
        yield test_client
    finally:
        app.dependency_overrides.clear()
        memorybank._vector_cache.clear()
        embeddings._cache.clear()
        Base.metadata.drop_all(bind=engine)


def add_sources(client, count, paragraphs=6, prefix="lore"):
    """Imports `count` sources, each with several passages of crypt-ish prose."""
    for n in range(count):
        body = "\n\n".join(
            f"## {prefix} {n} section {p}\n\n"
            + ("The crypt beneath the Old Abbey at Westhaven is vaulted in "
               "stone, and the stair descends past niches cut for the dead. ") * 8
            for p in range(paragraphs)
        )
        response = client.post(
            f"/api/adventures/{client.adv_id}/knowledge",
            files={"file": (f"{prefix}-{n}.md", body.encode(), "text/markdown")},
            data={"classification": ["canon", "reference", "inspiration"][n % 3],
                  "allow_duplicate": "true"},
        )
        assert response.status_code == 201, response.text[:200]


def counts(client):
    with SessionLocal() as db:
        sources = len(db.execute(select(models.KnowledgeSource)).scalars().all())
        chunks = len(db.execute(select(models.KnowledgeChunk)).scalars().all())
    return sources, chunks


def measure(sql_log, call):
    sql_log.clear()
    result = call()
    return len(sql_log), result


def retrieve(client):
    with SessionLocal() as db:
        adventure = db.get(models.Adventure, client.adv_id)
        settings = db.execute(select(models.Settings).where(
            models.Settings.user_id == client.user_id)).scalars().first()
        return asyncio.run(retrieval.retrieve(adventure, settings))


# --------------------------------------------------------------------- tests

def test_the_source_list_does_not_cost_a_query_per_source(client, sql_log):
    add_sources(client, 4)
    small, _ = measure(sql_log, lambda: client.get(
        f"/api/adventures/{client.adv_id}/knowledge").json())

    add_sources(client, 12, prefix="more")
    large, rows = measure(sql_log, lambda: client.get(
        f"/api/adventures/{client.adv_id}/knowledge").json())

    assert len(rows) == 16
    assert large == small, f"{small} queries for 4 sources, {large} for 16"
    # ...and the counts it shows are real, so the fixed query count is not
    # because the counts were dropped.
    assert all(row["chunk_count"] > 0 for row in rows)


def test_source_detail_does_not_cost_a_query_per_passage(client, sql_log):
    add_sources(client, 1, paragraphs=3)
    small_id = client.get(f"/api/adventures/{client.adv_id}/knowledge").json()[0]["id"]
    small, _ = measure(sql_log, lambda: client.get(
        f"/api/adventures/{client.adv_id}/knowledge/{small_id}/chunks").json())

    add_sources(client, 1, paragraphs=24, prefix="big")
    big_id = client.get(f"/api/adventures/{client.adv_id}/knowledge").json()[-1]["id"]
    large, chunks = measure(sql_log, lambda: client.get(
        f"/api/adventures/{client.adv_id}/knowledge/{big_id}/chunks").json())

    assert len(chunks) > 3
    assert large == small, f"{small} queries for a small source, {large} for a big one"


def test_retrieval_does_not_grow_with_the_library(client, sql_log):
    add_sources(client, 4)
    embed_pending(client)
    small, small_result = measure(sql_log, lambda: retrieve(client))

    add_sources(client, 16, prefix="more")
    embed_pending(client)
    embeddings.forget_cached(client.adv_id)
    large, large_result = measure(sql_log, lambda: retrieve(client))

    sources, chunks = counts(client)
    assert sources == 20 and chunks > 40
    assert small_result.candidates and large_result.candidates
    assert large <= small + 1, f"{small} queries at 4 sources, {large} at 20"


def test_the_context_build_does_not_grow_with_the_library(client, sql_log):
    add_sources(client, 4)
    embed_pending(client)
    ScriptedProvider.replies = [f"The crypt is cold.\n{state_block([])}"]
    client.post(f"/api/adventures/{client.adv_id}/actions",
                json={"type": "do", "text": "Aldric descends into the crypt."})
    small, _ = measure(sql_log, lambda: client.get(
        f"/api/adventures/{client.adv_id}/context").json())

    add_sources(client, 16, prefix="more")
    embed_pending(client)
    embeddings.forget_cached(client.adv_id)
    large, report = measure(sql_log, lambda: client.get(
        f"/api/adventures/{client.adv_id}/context").json())

    assert report["knowledge"]["used"]
    assert large <= small + 1, f"{small} queries at 4 sources, {large} at 20"


def test_candidates_are_bounded_in_sql_before_the_python_ranking(client, sql_log):
    """"Do not load every chunk merely to find the top few", asserted on the SQL."""
    add_sources(client, 20, paragraphs=8)
    embed_pending(client)
    embeddings.forget_cached(client.adv_id)
    _sources, chunks = counts(client)
    assert chunks > retrieval.LEXICAL_CANDIDATES * 2, chunks

    sql_log.clear()
    result = retrieve(client)

    # The lexical query names a LIMIT, and the merged candidate set is bounded
    # by the two per-path caps rather than by the size of the library.
    lexical = [s for s in sql_log if "knowledge_fts" in s and "MATCH" in s]
    assert lexical, sql_log
    assert all("LIMIT" in s for s in lexical)
    assert result.considered <= (
        retrieval.LEXICAL_CANDIDATES + retrieval.SEMANTIC_CANDIDATES
    )
    assert result.considered < chunks, (result.considered, chunks)

    # The row fetch for those candidates is one query, not one per candidate.
    loads = [s for s in sql_log
             if "knowledge_chunks" in s and "knowledge_sources" in s
             and " IN " in s.upper()]
    assert len(loads) <= 2, loads


def test_the_semantic_scan_reads_only_narrow_columns(client, sql_log):
    """A vector is 6 kB; the catalogue read must not fetch passage text."""
    add_sources(client, 6)
    embed_pending(client)
    embeddings.forget_cached(client.adv_id)

    sql_log.clear()
    retrieve(client)
    catalogue = [s for s in sql_log
                 if "knowledge_embeddings.chunk_id" in s
                 and "knowledge_embeddings.vector" not in s]
    assert catalogue, "the semantic catalogue read was not found"
    assert all("knowledge_chunks.text" not in s for s in catalogue)


def embed_pending(client):
    with SessionLocal() as db:
        adventure = db.get(models.Adventure, client.adv_id)
        settings = db.execute(select(models.Settings).where(
            models.Settings.user_id == client.user_id)).scalars().first()
        asyncio.run(embeddings.embed_pending(db, adventure, settings))
        db.commit()