907 lines
33 KiB
Python
907 lines
33 KiB
Python
import json
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from uuid import NAMESPACE_URL, uuid5
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import pytest
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import requests
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from tht.adapters.vector.qdrant import QdrantVectorStore, point_id
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from tht.ports.vector import VectorStoreError, VectorWriteRecord
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from tht.vectorstore.records import VectorRecord
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class FakeResponse:
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def __init__(self, status_code: int, payload=None, text: str | None = None):
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self.status_code = status_code
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self._payload = payload
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self.text = text if text is not None else (
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"" if payload is None else json.dumps(payload)
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)
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@property
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def ok(self) -> bool:
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return 200 <= self.status_code < 300
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def json(self):
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if isinstance(self._payload, Exception):
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raise self._payload
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return self._payload
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class FakeQdrantHttp:
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def __init__(self, *, dimension=1024, distance="Cosine"):
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self.dimension = dimension
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self.distance = distance
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self.collection = None
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self.sparse_vectors: dict[str, dict] | None = None
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self.payload_indexes: set[str] = set()
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self.points: dict[str, dict] = {}
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self.calls: list[tuple[str, str, dict | None]] = []
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self.fail_request: Exception | None = None
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self.malformed_query = False
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self.malformed_scroll = False
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self.scroll_pages: list[dict] | None = None
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self.drop_collection_on_points = False
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def request(self, method, url, *, json=None, timeout=None):
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self.calls.append((method, url, json))
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if self.fail_request is not None:
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raise self.fail_request
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path = url.split("://", 1)[-1].split("/", 1)[-1]
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path = "/" + path.split("?", 1)[0]
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if method == "GET" and path == "/collections/workspace-semantic":
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if self.collection is None:
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return FakeResponse(404, {"status": "error"})
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return FakeResponse(200, {
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"result": {
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"config": {
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"params": {
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"vectors": {"size": self.dimension, "distance": self.distance},
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**(
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{"sparse_vectors": self.sparse_vectors}
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if self.sparse_vectors is not None else {}
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),
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}
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},
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"payload_schema": {
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field: {"data_type": "keyword"} for field in sorted(self.payload_indexes)
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},
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}
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})
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if method == "PUT" and path == "/collections/workspace-semantic":
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self.collection = json
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self.dimension = json["vectors"]["size"]
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self.distance = json["vectors"]["distance"]
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self.sparse_vectors = json.get("sparse_vectors")
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return FakeResponse(200, {"status": "ok"})
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if method == "PUT" and path == "/collections/workspace-semantic/index":
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self.payload_indexes.add(json["field_name"])
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return FakeResponse(200, {"status": "ok"})
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if method == "PUT" and path == "/collections/workspace-semantic/points":
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if self.drop_collection_on_points:
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self.collection = None
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return FakeResponse(404, {"status": "error"})
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for point in json["points"]:
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self.points[point["id"]] = point
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return FakeResponse(200, {"result": {"status": "acknowledged"}})
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if method == "POST" and path == "/collections/workspace-semantic/points/query":
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if self.malformed_query:
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return FakeResponse(200, {"result": {"points": "nope"}})
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filter_value = json["filter"] if "filter" in json else json["prefetch"][0]["filter"]
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wanted = _match_points(self.points.values(), filter_value)
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scored = sorted(
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(
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{
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"id": point["id"],
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"score": point.get("score", 0.9),
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"payload": point["payload"],
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}
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for point in wanted
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),
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key=lambda point: (-point["score"], point["payload"]["record_key"]),
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)
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return FakeResponse(200, {"result": {"points": scored[: json["limit"]]}})
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if method == "POST" and path == "/collections/workspace-semantic/points/scroll":
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if self.malformed_scroll:
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return FakeResponse(200, {"result": {"points": "bad"}})
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if self.scroll_pages is not None:
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offset = json.get("offset")
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for page in self.scroll_pages:
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if page["offset"] == offset:
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filtered = _match_points(page["points"], json["filter"])
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return FakeResponse(200, {
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"result": {
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"points": filtered,
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"next_page_offset": page["next_page_offset"],
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}
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})
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raise AssertionError(("unexpected offset", offset, self.scroll_pages))
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wanted = sorted(
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_match_points(self.points.values(), json["filter"]),
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key=lambda point: point["payload"]["record_key"],
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)
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return FakeResponse(200, {"result": {"points": wanted, "next_page_offset": None}})
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if method == "POST" and path == "/collections/workspace-semantic/points/delete":
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doomed = [point["id"] for point in _match_points(self.points.values(), json["filter"])]
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for point_id_value in doomed:
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self.points.pop(point_id_value, None)
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return FakeResponse(200, {"result": {"status": "acknowledged"}})
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raise AssertionError((method, path, json))
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def _match_points(points, flt):
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matches = []
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must = flt["must"]
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for point in points:
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payload = point["payload"]
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if all(_match_clause(payload, clause) for clause in must):
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matches.append(point)
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return matches
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def _match_clause(payload, clause):
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key = clause["key"]
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match = clause["match"]
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if "value" in match:
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return payload.get(key) == match["value"]
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if "any" in match:
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return payload.get(key) in set(match["any"])
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raise AssertionError(clause)
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def _write_record(record_id: str, kind: str, *, metadata=None, sparse_text=None, sparse_language=None):
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return VectorWriteRecord(
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record=VectorRecord(
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id=record_id,
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kind=kind,
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ref=f"ref:{record_id}",
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title=f"title:{record_id}",
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content=f"content:{record_id}",
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metadata=metadata or {},
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),
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embedding=[0.1] * 1024,
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content_hash="sha256:" + "a" * 64,
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sparse_text=sparse_text,
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sparse_language=sparse_language,
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)
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def _store(
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fake: FakeQdrantHttp, *, collection_lifecycle: str = "self_heal"
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) -> QdrantVectorStore:
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return QdrantVectorStore(
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base_url="http://qdrant:6333",
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collection="workspace-semantic",
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workspace_id="demo",
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workspace_revision="a" * 40,
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expected_dimension=1024,
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collection_lifecycle=collection_lifecycle,
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request=fake.request,
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)
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def _ready_collection_with_bm25(fake: FakeQdrantHttp) -> None:
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fake.collection = {"vectors": {"size": 1024, "distance": "Cosine"}}
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fake.payload_indexes = {
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"content_hash", "document_id", "kind", "record_key", "record_kind",
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"vector_generation", "workspace_id", "workspace_revision",
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}
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fake.sparse_vectors = {"bm25": {"modifier": "idf"}}
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def test_point_id_is_deterministic_uuidv5():
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assert point_id("demo", "memory", "memory:1") == str(
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uuid5(NAMESPACE_URL, "thothii:demo:memory:memory:1")
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)
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def test_upsert_creates_collection_and_keyword_indexes_idempotently():
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fake = FakeQdrantHttp()
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store = _store(fake)
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assert store.upsert("memory", [_write_record("memory:1", "memory")]) == 1
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assert store.upsert("memory", [_write_record("memory:1", "memory")]) == 1
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creates = [call for call in fake.calls if call[0] == "PUT" and call[1].endswith("/collections/workspace-semantic")]
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assert len(creates) == 1
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assert creates[0][2] == {"vectors": {"size": 1024, "distance": "Cosine"}}
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assert fake.payload_indexes == {
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"content_hash",
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"document_id",
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"kind",
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"record_key",
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"record_kind",
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"vector_generation",
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"workspace_id",
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"workspace_revision",
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}
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def test_upsert_refuses_collection_dimension_or_distance_mismatch_without_recreating():
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fake = FakeQdrantHttp(dimension=384, distance="Dot")
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fake.collection = {"vectors": {"size": 384, "distance": "Dot"}}
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store = _store(fake)
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with pytest.raises(VectorStoreError, match="Qdrant collection configuration mismatch"):
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store.upsert("memory", [_write_record("memory:1", "memory")])
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creates = [call for call in fake.calls if call[0] == "PUT" and call[1].endswith("/collections/workspace-semantic")]
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assert creates == []
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def test_upsert_require_existing_refuses_missing_collection_without_creating():
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fake = FakeQdrantHttp()
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store = _store(fake, collection_lifecycle="require_existing")
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with pytest.raises(VectorStoreError, match="semantic_index_incompatible"):
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store.upsert("memory", [_write_record("memory:1", "memory")])
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assert fake.collection is None
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creates = [
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call
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for call in fake.calls
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if call[0] == "PUT" and call[1].endswith("/collections/workspace-semantic")
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]
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assert creates == []
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def test_upsert_require_existing_refuses_incompatible_collection_without_mutating():
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fake = FakeQdrantHttp(dimension=384, distance="Dot")
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fake.collection = {"vectors": {"size": 384, "distance": "Dot"}}
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store = _store(fake, collection_lifecycle="require_existing")
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with pytest.raises(VectorStoreError, match="semantic_index_incompatible"):
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store.upsert("memory", [_write_record("memory:1", "memory")])
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assert fake.payload_indexes == set()
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mutating = [call for call in fake.calls if call[0] == "PUT"]
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assert mutating == []
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def test_upsert_require_existing_writes_to_existing_compatible_collection():
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fake = FakeQdrantHttp()
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fake.collection = {"vectors": {"size": 1024, "distance": "Cosine"}}
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fake.payload_indexes = {
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"content_hash",
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"document_id",
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"kind",
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"record_key",
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"record_kind",
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"vector_generation",
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"workspace_id",
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"workspace_revision",
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}
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store = _store(fake, collection_lifecycle="require_existing")
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assert store.upsert("memory", [_write_record("memory:1", "memory")]) == 1
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create_or_index = [
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call
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for call in fake.calls
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if call[0] == "PUT" and not call[1].endswith("/points?wait=true")
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]
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assert create_or_index == []
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def test_upsert_require_existing_fails_if_collection_disappears_after_preflight():
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fake = FakeQdrantHttp()
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fake.collection = {"vectors": {"size": 1024, "distance": "Cosine"}}
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fake.payload_indexes = {
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"content_hash",
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"document_id",
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"kind",
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"record_key",
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"record_kind",
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"vector_generation",
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"workspace_id",
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"workspace_revision",
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}
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fake.drop_collection_on_points = True
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store = _store(fake, collection_lifecycle="require_existing")
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with pytest.raises(VectorStoreError, match="HTTP 404"):
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store.upsert("memory", [_write_record("memory:1", "memory")])
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creates = [
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call
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for call in fake.calls
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if call[0] == "PUT" and call[1].endswith("/collections/workspace-semantic")
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]
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assert creates == []
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def test_health_fails_when_the_bound_collection_is_missing():
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fake = FakeQdrantHttp()
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health = _store(fake).health()
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assert health.ok is False
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assert health.read_reachable is False
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assert health.write_reachable is False
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assert "missing" in (health.detail or "").lower()
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def test_health_fails_when_required_payload_indexes_are_missing_without_creating_them():
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fake = FakeQdrantHttp()
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fake.collection = {"vectors": {"size": 1024, "distance": "Cosine"}}
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health = _store(fake).health()
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assert health.ok is False
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assert fake.payload_indexes == set()
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def test_health_reports_when_evidence_bm25_is_unavailable_without_disabling_dense_callers():
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fake = FakeQdrantHttp()
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fake.collection = {"vectors": {"size": 1024, "distance": "Cosine"}}
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fake.payload_indexes = {
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"content_hash", "document_id", "kind", "record_key", "record_kind",
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"vector_generation", "workspace_id", "workspace_revision",
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}
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health = _store(fake).health()
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assert health.ok is True
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assert health.bm25_compatible is False
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@pytest.mark.parametrize(
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("record", "semantic_kind"),
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[
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(_write_record("schema_column:patients.id", "schema_column"), "schema"),
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(
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_write_record(
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"demo:gen:11111111111111111111111111111111:chunk:1",
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"evidence",
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metadata={
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"workspace_id": "demo",
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"vector_generation": "gen:11111111111111111111111111111111",
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"document_id": "doc:abc",
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},
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),
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"evidence",
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),
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(_write_record("memory:1", "memory"), "memory"),
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],
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)
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def test_upsert_serializes_qdrant_point_payloads(record, semantic_kind):
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fake = FakeQdrantHttp()
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store = _store(fake)
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store.upsert("memory" if semantic_kind == "memory" else "evidence" if semantic_kind == "evidence" else "schema_records", [record])
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point = next(iter(fake.points.values()))
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expected_revision = "a" * 40 if semantic_kind in ("schema_table", "schema_column", "evidence") else None
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assert point["id"] == point_id("demo", semantic_kind, record.record.id, expected_revision)
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assert point["vector"] == record.embedding
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assert point["payload"]["workspace_id"] == "demo"
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assert point["payload"]["workspace_revision"] == "a" * 40
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assert point["payload"]["kind"] == semantic_kind
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assert point["payload"]["record_kind"] == record.record.kind
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assert point["payload"]["record_key"] == record.record.id
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assert point["payload"]["content_hash"] == record.content_hash
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def test_evidence_upsert_sends_dense_and_server_side_italian_bm25():
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fake = FakeQdrantHttp()
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_ready_collection_with_bm25(fake)
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store = _store(fake)
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record = _write_record(
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"demo:gen:11111111111111111111111111111111:chunk:1",
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"evidence",
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metadata={
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"workspace_id": "demo",
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"vector_generation": "gen:11111111111111111111111111111111",
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"document_id": "doc:abc",
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},
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sparse_text="ricovero per cardiomiopatia dilatativa",
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sparse_language="italian",
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)
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store.upsert("evidence", [record])
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point = next(iter(fake.points.values()))
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assert point["vector"] == {
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"": record.embedding,
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"bm25": {
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"text": "ricovero per cardiomiopatia dilatativa",
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"model": "qdrant/bm25",
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"options": {"language": "italian"},
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},
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}
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def test_evidence_search_uses_filtered_dense_and_bm25_prefetches_with_default_rrf():
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fake = FakeQdrantHttp()
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_ready_collection_with_bm25(fake)
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store = _store(fake)
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generation = "gen:" + "1" * 32
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store.upsert("evidence", [
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_write_record(
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f"demo:{generation}:chunk:1",
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"evidence",
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metadata={"workspace_id": "demo", "vector_generation": generation, "document_id": "doc:abc"},
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sparse_text="ricovero per cardiomiopatia dilatativa",
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sparse_language="italian",
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)
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])
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store.search(
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["evidence"], [0.2] * 1024, limit=10, kinds=["evidence"],
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query_text="cardiomiopatia", query_language="italian",
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metadata_filter={"workspace_id": "demo", "vector_generation": generation, "document_ids": ["doc:abc"]},
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)
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query = next(call[2] for call in reversed(fake.calls) if call[1].endswith("/points/query"))
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assert query["query"] == {"rrf": {}}
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assert query["limit"] == 10
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assert query["prefetch"] == [
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{
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"query": [0.2] * 1024,
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"limit": 20,
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"filter": {"must": [
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{"key": "workspace_id", "match": {"value": "demo"}},
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{"key": "workspace_revision", "match": {"value": "a" * 40}},
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{"key": "kind", "match": {"any": ["evidence"]}},
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{"key": "record_kind", "match": {"any": ["evidence"]}},
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{"key": "vector_generation", "match": {"value": generation}},
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{"key": "document_id", "match": {"any": ["doc:abc"]}},
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]},
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},
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{
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"query": {
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"text": "cardiomiopatia", "model": "qdrant/bm25",
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"options": {"language": "italian"},
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},
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"using": "bm25",
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"limit": 20,
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"filter": {"must": [
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{"key": "workspace_id", "match": {"value": "demo"}},
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{"key": "workspace_revision", "match": {"value": "a" * 40}},
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{"key": "kind", "match": {"any": ["evidence"]}},
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{"key": "record_kind", "match": {"any": ["evidence"]}},
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{"key": "vector_generation", "match": {"value": generation}},
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{"key": "document_id", "match": {"any": ["doc:abc"]}},
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]},
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},
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]
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@pytest.mark.parametrize("retrieval_mode, expected_query", [
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("dense", None),
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("bm25", {"text": "cardiomiopatia", "model": "qdrant/bm25", "options": {"language": "italian"}}),
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])
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def test_evidence_diagnostic_branch_searches_use_the_runtime_filter(retrieval_mode, expected_query):
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fake = FakeQdrantHttp()
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_ready_collection_with_bm25(fake)
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store = _store(fake)
|
|
generation = "gen:" + "1" * 32
|
|
|
|
store.search(
|
|
["evidence"], [0.2] * 1024, limit=10, kinds=["evidence"],
|
|
query_text="cardiomiopatia", query_language="italian", retrieval_mode=retrieval_mode,
|
|
metadata_filter={"workspace_id": "demo", "vector_generation": generation, "document_ids": ["doc:abc"]},
|
|
)
|
|
|
|
query = next(call[2] for call in reversed(fake.calls) if call[1].endswith("/points/query"))
|
|
assert query["limit"] == 10
|
|
assert query["filter"]["must"][-2:] == [
|
|
{"key": "vector_generation", "match": {"value": generation}},
|
|
{"key": "document_id", "match": {"any": ["doc:abc"]}},
|
|
]
|
|
if retrieval_mode == "dense":
|
|
assert query["vector"] == [0.2] * 1024
|
|
else:
|
|
assert query["query"] == expected_query
|
|
assert query["using"] == "bm25"
|
|
|
|
|
|
def test_search_filters_by_workspace_and_allowed_record_kinds():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
store.upsert("memory", [_write_record("memory:1", "memory")])
|
|
other = next(iter(fake.points.values())).copy()
|
|
other["id"] = point_id("other", "memory", "memory:2")
|
|
other["payload"] = {**other["payload"], "workspace_id": "other", "record_key": "memory:2"}
|
|
fake.points[other["id"]] = other
|
|
solved = next(iter(fake.points.values())).copy()
|
|
solved["id"] = point_id("demo", "memory", "solved:1")
|
|
solved["payload"] = {**solved["payload"], "record_key": "solved:1", "record_kind": "solved_question"}
|
|
fake.points[solved["id"]] = solved
|
|
|
|
hits = store.search(["memory"], [0.2] * 1024, limit=5, kinds=["memory"])
|
|
|
|
assert [hit.id for hit in hits] == ["memory:1"]
|
|
query_call = next(call for call in fake.calls if call[0] == "POST" and call[1].endswith("/points/query?wait=true") is False and call[1].endswith("/points/query"))
|
|
assert query_call[2]["filter"] == {
|
|
"must": [
|
|
{"key": "workspace_id", "match": {"value": "demo"}},
|
|
{"key": "kind", "match": {"any": ["memory"]}},
|
|
{"key": "record_kind", "match": {"any": ["memory"]}},
|
|
]
|
|
}
|
|
|
|
|
|
def test_evidence_search_refuses_dense_only_fallback():
|
|
store = _store(FakeQdrantHttp())
|
|
|
|
with pytest.raises(VectorStoreError, match="hybrid query text"):
|
|
store.search(["evidence"], [0.2] * 1024, limit=5, kinds=["evidence"])
|
|
|
|
|
|
def test_hybrid_evidence_search_refuses_a_collection_without_bm25():
|
|
fake = FakeQdrantHttp()
|
|
_ready_collection_with_bm25(fake)
|
|
fake.sparse_vectors = None
|
|
store = _store(fake)
|
|
|
|
with pytest.raises(VectorStoreError, match="BM25 collection configuration"):
|
|
store.search(
|
|
["evidence"], [0.2] * 1024, limit=5, kinds=["evidence"],
|
|
query_text="cardiomiopatia", query_language="italian",
|
|
)
|
|
|
|
|
|
def test_search_excludes_inconsistent_semantic_kind_in_bound_workspace():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
store.upsert("memory", [_write_record("memory:1", "memory")])
|
|
contaminated = next(iter(fake.points.values())).copy()
|
|
contaminated["id"] = point_id("demo", "evidence", "memory:contaminated")
|
|
contaminated["payload"] = {
|
|
**contaminated["payload"],
|
|
"kind": "evidence",
|
|
"record_key": "memory:contaminated",
|
|
}
|
|
fake.points[contaminated["id"]] = contaminated
|
|
|
|
hits = store.search(["memory"], [0.2] * 1024, limit=5, kinds=["memory"])
|
|
|
|
assert [hit.id for hit in hits] == ["memory:1"]
|
|
|
|
|
|
def test_existing_hashes_health_and_exact_generation_inventory_and_delete():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
generation = "gen:" + "1" * 32
|
|
keep = "gen:" + "2" * 32
|
|
store.upsert("evidence", [
|
|
_write_record(
|
|
f"demo:{generation}:chunk:1",
|
|
"evidence",
|
|
metadata={"workspace_id": "demo", "vector_generation": generation, "document_id": "doc:1"},
|
|
),
|
|
_write_record(
|
|
f"demo:{keep}:chunk:2",
|
|
"evidence",
|
|
metadata={"workspace_id": "demo", "vector_generation": keep, "document_id": "doc:2"},
|
|
),
|
|
])
|
|
|
|
assert store.existing_hashes("evidence", ["evidence"]) == {
|
|
f"demo:{generation}:chunk:1": "sha256:" + "a" * 64,
|
|
f"demo:{keep}:chunk:2": "sha256:" + "a" * 64,
|
|
}
|
|
assert store.list_evidence_generations("evidence", "demo") == [generation, keep]
|
|
assert store.delete_generation("evidence", generation, "demo") == 1
|
|
assert store.list_evidence_generations("evidence", "demo") == [keep]
|
|
|
|
health = store.health()
|
|
assert health.ok is True
|
|
assert health.read_reachable is True
|
|
assert health.write_reachable is True
|
|
assert health.observed_dimensions == (1024,)
|
|
assert health.dimension_compatible is True
|
|
|
|
|
|
def test_evidence_inventory_and_delete_ignore_inconsistent_semantic_kind():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
generation = "gen:" + "1" * 32
|
|
contaminated_generation = "gen:" + "2" * 32
|
|
store.upsert("evidence", [
|
|
_write_record(
|
|
f"demo:{generation}:chunk:1",
|
|
"evidence",
|
|
metadata={
|
|
"workspace_id": "demo",
|
|
"vector_generation": generation,
|
|
"document_id": "doc:1",
|
|
},
|
|
),
|
|
])
|
|
contaminated_delete = next(iter(fake.points.values())).copy()
|
|
contaminated_delete["id"] = point_id("demo", "memory", "evidence:contaminated-delete")
|
|
contaminated_delete["payload"] = {
|
|
**contaminated_delete["payload"],
|
|
"kind": "memory",
|
|
"record_key": "evidence:contaminated-delete",
|
|
}
|
|
fake.points[contaminated_delete["id"]] = contaminated_delete
|
|
contaminated_list = next(iter(fake.points.values())).copy()
|
|
contaminated_list["id"] = point_id("demo", "memory", "evidence:contaminated-list")
|
|
contaminated_list["payload"] = {
|
|
**contaminated_list["payload"],
|
|
"kind": "memory",
|
|
"record_key": "evidence:contaminated-list",
|
|
"vector_generation": contaminated_generation,
|
|
}
|
|
fake.points[contaminated_list["id"]] = contaminated_list
|
|
|
|
assert store.list_evidence_generations("evidence", "demo") == [generation]
|
|
assert store.delete_generation("evidence", generation, "demo") == 1
|
|
assert contaminated_delete["id"] in fake.points
|
|
assert contaminated_list["id"] in fake.points
|
|
|
|
|
|
def test_metadata_search_rejects_a_workspace_id_different_from_the_bound_adapter():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
generation = "gen:" + "1" * 32
|
|
store.upsert("evidence", [
|
|
_write_record(
|
|
f"demo:{generation}:chunk:1",
|
|
"evidence",
|
|
metadata={
|
|
"workspace_id": "demo", "vector_generation": generation,
|
|
"document_id": "doc:shared",
|
|
},
|
|
),
|
|
])
|
|
foreign = next(iter(fake.points.values())).copy()
|
|
foreign["id"] = point_id("other", "evidence", f"other:{generation}:chunk:1")
|
|
foreign["payload"] = {
|
|
**foreign["payload"],
|
|
"workspace_id": "other",
|
|
"record_key": f"other:{generation}:chunk:1",
|
|
"ref": "ref:foreign",
|
|
"title": "foreign",
|
|
"content": "foreign",
|
|
}
|
|
fake.points[foreign["id"]] = foreign
|
|
|
|
with pytest.raises(VectorStoreError, match="workspace namespace does not match"):
|
|
store.search(
|
|
["evidence"], [0.2] * 1024, limit=5, kinds=["evidence"],
|
|
metadata_filter={
|
|
"workspace_id": "other",
|
|
"vector_generation": generation,
|
|
"document_ids": ["doc:shared"],
|
|
},
|
|
)
|
|
|
|
|
|
def test_generation_inventory_rejects_a_workspace_id_different_from_the_bound_adapter():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
|
|
with pytest.raises(VectorStoreError, match="workspace namespace does not match"):
|
|
store.list_evidence_generations("evidence", "other")
|
|
assert not any(call[1].endswith("/points/scroll") for call in fake.calls)
|
|
|
|
|
|
def test_generation_delete_cannot_mutate_foreign_workspace_or_non_evidence_points():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
generation = "gen:" + "1" * 32
|
|
store.upsert("evidence", [
|
|
_write_record(
|
|
f"demo:{generation}:chunk:1",
|
|
"evidence",
|
|
metadata={
|
|
"workspace_id": "demo", "vector_generation": generation,
|
|
"document_id": "doc:demo",
|
|
},
|
|
),
|
|
])
|
|
demo = next(iter(fake.points.values()))
|
|
foreign = demo.copy()
|
|
foreign["id"] = point_id("other", "evidence", f"other:{generation}:chunk:1")
|
|
foreign["payload"] = {
|
|
**demo["payload"], "workspace_id": "other",
|
|
"record_key": f"other:{generation}:chunk:1",
|
|
}
|
|
fake.points[foreign["id"]] = foreign
|
|
memory = demo.copy()
|
|
memory["id"] = point_id("other", "memory", "memory:foreign")
|
|
memory["payload"] = {
|
|
**demo["payload"], "workspace_id": "other", "kind": "memory",
|
|
"record_kind": "memory", "record_key": "memory:foreign",
|
|
}
|
|
fake.points[memory["id"]] = memory
|
|
before = set(fake.points)
|
|
|
|
with pytest.raises(VectorStoreError, match="workspace namespace does not match"):
|
|
store.delete_generation("evidence", generation, "other")
|
|
|
|
assert set(fake.points) == before
|
|
assert not any(call[1].endswith("/points/delete?wait=true") for call in fake.calls)
|
|
|
|
|
|
def test_delete_kinds_is_workspace_scoped_and_preserves_other_semantic_kinds():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
|
|
store.upsert("memory", [_write_record("memory:1", "memory")])
|
|
store.upsert("memory", [_write_record("solved:1", "solved_question")])
|
|
store.upsert("schema_records", [_write_record("schema_table:patients", "schema_table")])
|
|
|
|
other_workspace_memory = next(
|
|
point for point in fake.points.values() if point["payload"]["record_key"] == "memory:1"
|
|
).copy()
|
|
other_workspace_memory["id"] = point_id("other", "memory", "memory:other")
|
|
other_workspace_memory["payload"] = {
|
|
**other_workspace_memory["payload"],
|
|
"workspace_id": "other",
|
|
"record_key": "memory:other",
|
|
"title": "title:memory:other",
|
|
"content": "content:memory:other",
|
|
"ref": "ref:memory:other",
|
|
}
|
|
fake.points[other_workspace_memory["id"]] = other_workspace_memory
|
|
|
|
assert store.delete_kinds("memory", ["memory"]) == 1
|
|
|
|
delete_call = next(
|
|
call
|
|
for call in fake.calls
|
|
if call[0] == "POST" and call[1].endswith("/points/delete?wait=true")
|
|
)
|
|
assert delete_call[2]["filter"] == {
|
|
"must": [
|
|
{"key": "workspace_id", "match": {"value": "demo"}},
|
|
{"key": "kind", "match": {"any": ["memory"]}},
|
|
{"key": "record_kind", "match": {"any": ["memory"]}},
|
|
]
|
|
}
|
|
assert {
|
|
point["payload"]["record_key"]: point["payload"]["record_kind"]
|
|
for point in fake.points.values()
|
|
} == {
|
|
"solved:1": "solved_question",
|
|
"schema_table:patients": "schema_table",
|
|
"memory:other": "memory",
|
|
}
|
|
|
|
|
|
def test_existing_hashes_and_delete_kinds_ignore_inconsistent_semantic_kind():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
store.upsert("memory", [_write_record("memory:1", "memory")])
|
|
contaminated = next(iter(fake.points.values())).copy()
|
|
contaminated["id"] = point_id("demo", "evidence", "memory:contaminated")
|
|
contaminated["payload"] = {
|
|
**contaminated["payload"],
|
|
"kind": "evidence",
|
|
"record_key": "memory:contaminated",
|
|
}
|
|
fake.points[contaminated["id"]] = contaminated
|
|
|
|
assert store.existing_hashes("memory", ["memory"]) == {
|
|
"memory:1": "sha256:" + "a" * 64,
|
|
}
|
|
assert store.delete_kinds("memory", ["memory"]) == 1
|
|
assert contaminated["id"] in fake.points
|
|
|
|
|
|
def test_sanitizes_timeout_and_malformed_responses():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
fake.fail_request = requests.Timeout("dial tcp 10.0.0.9:6333: i/o timeout")
|
|
|
|
with pytest.raises(VectorStoreError, match="Qdrant request failed") as timeout:
|
|
store.search(["memory"], [0.2] * 1024, limit=1)
|
|
assert "10.0.0.9" not in str(timeout.value)
|
|
|
|
fake.fail_request = None
|
|
store.upsert("memory", [_write_record("memory:1", "memory")])
|
|
fake.malformed_query = True
|
|
with pytest.raises(VectorStoreError, match="Qdrant returned malformed query response"):
|
|
store.search(["memory"], [0.2] * 1024, limit=1)
|
|
|
|
fake.malformed_query = False
|
|
fake.malformed_scroll = True
|
|
with pytest.raises(VectorStoreError, match="Qdrant returned malformed scroll response"):
|
|
store.existing_hashes("memory", ["memory"])
|
|
|
|
|
|
def test_upsert_payload_keeps_canonical_identity_when_metadata_collides():
|
|
fake = FakeQdrantHttp()
|
|
store = _store(fake)
|
|
record = _write_record(
|
|
"memory:1",
|
|
"memory",
|
|
metadata={
|
|
"workspace_id": "evil",
|
|
"kind": "evil",
|
|
"record_kind": "evil",
|
|
"record_key": "evil",
|
|
"content_hash": "evil",
|
|
},
|
|
)
|
|
|
|
store.upsert("memory", [record])
|
|
|
|
payload = next(iter(fake.points.values()))["payload"]
|
|
assert payload["workspace_id"] == "demo"
|
|
assert payload["kind"] == "memory"
|
|
assert payload["record_kind"] == "memory"
|
|
assert payload["record_key"] == "memory:1"
|
|
assert payload["content_hash"] == "sha256:" + "a" * 64
|
|
|
|
|
|
def test_scroll_based_operations_paginate_until_next_page_offset_is_absent():
|
|
fake = FakeQdrantHttp()
|
|
generation_a = "gen:" + "1" * 32
|
|
generation_b = "gen:" + "2" * 32
|
|
fake.scroll_pages = [
|
|
{
|
|
"offset": None,
|
|
"points": [
|
|
{
|
|
"id": "p1",
|
|
"payload": {
|
|
"workspace_id": "demo",
|
|
"kind": "evidence",
|
|
"record_kind": "evidence",
|
|
"record_key": f"demo:{generation_a}:chunk:1",
|
|
"content_hash": "sha256:" + "a" * 64,
|
|
"vector_generation": generation_a,
|
|
},
|
|
}
|
|
],
|
|
"next_page_offset": "page-2",
|
|
},
|
|
{
|
|
"offset": "page-2",
|
|
"points": [
|
|
{
|
|
"id": "p2",
|
|
"payload": {
|
|
"workspace_id": "demo",
|
|
"kind": "evidence",
|
|
"record_kind": "evidence",
|
|
"record_key": f"demo:{generation_a}:chunk:2",
|
|
"content_hash": "sha256:" + "b" * 64,
|
|
"vector_generation": generation_a,
|
|
},
|
|
},
|
|
{
|
|
"id": "p3",
|
|
"payload": {
|
|
"workspace_id": "demo",
|
|
"kind": "evidence",
|
|
"record_kind": "evidence",
|
|
"record_key": f"demo:{generation_b}:chunk:3",
|
|
"content_hash": "sha256:" + "c" * 64,
|
|
"vector_generation": generation_b,
|
|
},
|
|
},
|
|
],
|
|
"next_page_offset": None,
|
|
},
|
|
]
|
|
store = _store(fake)
|
|
|
|
assert store.existing_hashes("evidence", ["evidence"]) == {
|
|
f"demo:{generation_a}:chunk:1": "sha256:" + "a" * 64,
|
|
f"demo:{generation_a}:chunk:2": "sha256:" + "b" * 64,
|
|
f"demo:{generation_b}:chunk:3": "sha256:" + "c" * 64,
|
|
}
|
|
assert store.list_evidence_generations("evidence", "demo") == [generation_a, generation_b]
|
|
assert store.delete_generation("evidence", generation_a, "demo") == 2
|
|
offsets = [
|
|
call[2].get("offset")
|
|
for call in fake.calls
|
|
if call[0] == "POST" and call[1].endswith("/points/scroll")
|
|
]
|
|
assert offsets[:2] == [None, "page-2"]
|