chatbot_v3/tests/fakes.py

"""Hand-written fakes for narrow application-owned ports (ADR-0016)."""

import asyncio
import uuid
from collections.abc import Sequence
from dataclasses import dataclass, field
from datetime import datetime

from src.application.ingestion.models import SparseVector
from src.application.points.models import ChunkPoint


@dataclass
class FakeObjectStorage:
    """In-memory `ObjectStorage`. `fail_next` simulates one upload failure."""

    objects: dict[str, bytes] = field(default_factory=dict)
    fail_next: bool = False

    async def put_object(self, *, key: str, data: bytes, content_type: str) -> None:
        if self.fail_next:
            self.fail_next = False
            raise OSError("simulated object storage failure")
        self.objects[key] = data


@dataclass
class FakeDenseEmbedder:
    """A scripted `DenseEmbedder`. Returns a fixed-dimension zero vector per
    text by default; `fail_next` simulates one batch failure.
    """

    name: str
    dimensions: int = 4
    value: float = 0.0
    """Component value of every returned vector. Non-zero where a real Qdrant
    has to score the result, since a zero vector has no direction to compare."""
    model_version: str = "fake-dense-v1"
    calls: list[list[str]] = field(default_factory=list)
    fail_next: bool = False
    delay_seconds: float = 0.0
    """Simulates a slow provider call, e.g. to exercise timeout handling."""

    async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
        self.calls.append(list(texts))
        if self.delay_seconds:
            await asyncio.sleep(self.delay_seconds)
        if self.fail_next:
            self.fail_next = False
            raise RuntimeError("simulated embedder failure")
        return [[self.value] * self.dimensions for _ in texts]


@dataclass
class FakeSparseEmbedder:
    """A scripted `SparseEmbedder`. Returns an empty sparse vector per text."""

    name: str = "sparse"
    model_version: str = "fake-sparse-v1"
    calls: list[list[str]] = field(default_factory=list)
    fail_next: bool = False

    def embed_batch(self, texts: Sequence[str], *, query: bool = False) -> list[SparseVector]:
        self.calls.append(list(texts))
        if self.fail_next:
            self.fail_next = False
            raise RuntimeError("simulated embedder failure")
        return [SparseVector(indices=[], values=[]) for _ in texts]


@dataclass
class FakePointStorage:
    """An in-memory `PointStorage`.

    `points` is keyed by point id, so a re-upsert of the same deterministic id
    overwrites rather than accumulating — the property a test asserting "a
    retry produces no duplicate points" needs the fake to actually model.

    `fail_on_batch` fails the Nth (0-based) upsert batch, which is how a test
    checks that the soft-delete sweep never runs after a partial failure.
    """

    points: dict[str, ChunkPoint] = field(default_factory=dict)
    upsert_batches: list[int] = field(default_factory=list)
    deactivate_calls: list[dict[str, object]] = field(default_factory=list)
    fail_on_batch: int | None = None
    fail_deactivate: bool = False
    max_in_flight: int = 0
    _in_flight: int = 0

    async def upsert_points(self, points: Sequence[ChunkPoint]) -> None:
        self._in_flight += 1
        self.max_in_flight = max(self.max_in_flight, self._in_flight)
        try:
            # Yield so concurrent batches actually overlap; without this the
            # in-flight ceiling is trivially 1 and the bound goes untested.
            await asyncio.sleep(0)
            index = len(self.upsert_batches)
            self.upsert_batches.append(len(points))
            if self.fail_on_batch is not None and index == self.fail_on_batch:
                raise RuntimeError("simulated point storage failure")
            for point in points:
                self.points[str(point.point_id)] = point
        finally:
            self._in_flight -= 1

    async def deactivate_points_from_index(
        self,
        *,
        tenant_id: uuid.UUID,
        file_id: uuid.UUID,
        from_chunk_index: int,
        deleted_at: datetime,
        updated_by: str,
    ) -> int:
        self.deactivate_calls.append(
            {
                "tenant_id": tenant_id,
                "file_id": file_id,
                "from_chunk_index": from_chunk_index,
                "updated_by": updated_by,
            }
        )
        if self.fail_deactivate:
            raise RuntimeError("simulated deactivate failure")

        def is_stale(point: ChunkPoint) -> bool:
            chunk_index = point.payload.get("chunk_index")
            return (
                point.payload.get("file_id") == str(file_id)
                and point.payload.get("tenant_id") == str(tenant_id)
                and point.payload.get("is_active") is True
                and isinstance(chunk_index, int)
                and chunk_index >= from_chunk_index
            )

        stale = [point for point in self.points.values() if is_stale(point)]
        for point in stale:
            point.payload["is_active"] = False
            point.payload["deleted_at"] = deleted_at.isoformat()
        return len(stale)