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from __future__ import annotations
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import hashlib
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import sqlite3
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from pathlib import Path
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import pytest
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import yaml
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from gewu.adapters.git_repository import GitCanonicalRepository
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from gewu.adapters.sqlite_search import SearchIndexError, SQLiteSearchIndex
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from gewu.adapters.sqlite_vectors import SQLiteVectorIndex
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from gewu.application.identity import Principal
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from gewu.application.search import CanonicalSearchService, HybridSearchIndex, SearchError
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from gewu.domain.contracts import (
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Confidence,
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EdgeEndpoint,
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NodeType,
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Scope,
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SearchRequest,
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TypedEdge,
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)
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from tests.domain.proposal_fixtures import NODE
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class FixtureEmbeddings:
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dimensions = 2
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model_id = "fixture-e5@v1"
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artifact_sha256 = "b" * 64
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def embed_queries(self, texts: tuple[str, ...]) -> tuple[tuple[float, ...], ...]:
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return tuple((1.0, 0.0) for _ in texts)
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def embed_passages(self, texts: tuple[str, ...]) -> tuple[tuple[float, ...], ...]:
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return tuple(
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(1.0, 0.0) if text.startswith("Semantic match") else (0.0, 1.0) for text in texts
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)
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def _record_path(node_id: str, project_id: str) -> str:
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project_key = hashlib.sha256(project_id.encode()).hexdigest()
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node_key = hashlib.sha256(node_id.encode()).hexdigest()
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return f"projects/{project_key}/nodes/{node_key}.yaml"
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def _repository(tmp_path: Path) -> GitCanonicalRepository:
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repository = GitCanonicalRepository(tmp_path / "knowledge.git")
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nodes = [
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NODE.model_copy(
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update={
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"node_id": f"dome:alpha-{number:02}",
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"title": f"Alpha candidate {number:02}",
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"summary": f"Alpha lexical candidate {number:02}.",
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"node_type": NodeType.FINDING,
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"confidence": Confidence.MEDIUM,
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}
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)
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for number in range(12)
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]
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nodes.extend(
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(
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NODE.model_copy(
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update={
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"node_id": "dome:semantic-match",
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"title": "Semantic match for the intent",
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"summary": "A close meaning with no shared query word.",
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"node_type": NodeType.TECHNIQUE,
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"confidence": Confidence.HIGH,
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}
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),
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NODE.model_copy(
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update={
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"node_id": "dome:graph-related",
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"title": "Connected nonlexical technique",
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"summary": "A related result found through one typed edge.",
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"node_type": NodeType.TECHNIQUE,
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"confidence": Confidence.MEDIUM,
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}
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),
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NODE.model_copy(
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update={
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"node_id": "video:semantic-match",
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"project_id": "interactive-video",
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"title": "Semantic match in another project",
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"summary": "Must remain outside the requested project scope.",
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"node_type": NodeType.TECHNIQUE,
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"confidence": Confidence.HIGH,
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}
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),
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)
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)
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edges = (
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TypedEdge(
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edge_id="dome:search-expansion-edge",
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edge_type="compared_with",
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source=EdgeEndpoint(kind="node", id="dome:alpha-00"),
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target=EdgeEndpoint(kind="node", id="dome:graph-related"),
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interpretation_rationale="Both notes inform the same audience-attendance question.",
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),
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TypedEdge(
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edge_id="dome:search-cross-project-edge",
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edge_type="compared_with",
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source=EdgeEndpoint(kind="node", id="dome:alpha-00"),
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target=EdgeEndpoint(kind="node", id="video:semantic-match"),
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interpretation_rationale="Cross-project edge must not widen search scope.",
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),
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)
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files = {
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_record_path(node.node_id, node.project_id): yaml.safe_dump(
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node.model_dump(mode="json")
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).encode()
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for node in nodes
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}
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files.update(
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{
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f"edges/{hashlib.sha256(edge.edge_id.encode()).hexdigest()}.yaml": yaml.safe_dump(
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edge.model_dump(mode="json")
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).encode()
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for edge in edges
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}
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)
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repository.publish_files(
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expected_head=repository.head(),
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files=files,
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message="Add hybrid search fixtures",
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index_directory=tmp_path / "publishing",
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)
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return repository
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def test_hybrid_search_fuses_ranks_and_preserves_scope_and_filters(
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tmp_path: Path, monkeypatch
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) -> None:
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from gewu.application import search as search_module
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monkeypatch.setattr(search_module, "_VECTOR_CANDIDATE_LIMIT", 1)
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repository = _repository(tmp_path)
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search_path = tmp_path / "search.sqlite3"
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lexical = SQLiteSearchIndex(search_path, repository)
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vectors = SQLiteVectorIndex(search_path)
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hybrid = HybridSearchIndex(
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lexical=lexical,
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vectors=vectors,
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embeddings=FixtureEmbeddings(),
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)
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lexical.configure_vector_generation(
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extension_loader=vectors.load_extension,
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builder=hybrid.build_vector_generation,
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is_current=hybrid.vector_generation_is_current,
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)
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service = CanonicalSearchService(
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index=hybrid,
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project_ids_provider=lambda: ("dome-performance", "interactive-video"),
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canonical_head=repository.head,
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)
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principal = Principal(
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"reader", "reader-key", frozenset({"viewer"}), frozenset({"dome-performance"})
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)
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request = SearchRequest(
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query="alpha",
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scope=Scope(projects=("dome-performance",), include_shared=False),
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limit=20,
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budget_tokens=50_000,
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)
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response = service.search(request, principal=principal)
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first_generation = vectors.active_generation()
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assert first_generation is not None
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assert lexical.path == vectors.path
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assert first_generation.revision == response.index_revision
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assert first_generation.node_count == 15
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result_ids = [item["node"]["node_id"] for item in response.data]
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assert response.retrieval_mode == "hybrid"
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assert result_ids.index("dome:semantic-match") < result_ids.index("dome:alpha-11")
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graph_hit = next(
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item for item in response.data if item["node"]["node_id"] == "dome:graph-related"
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)
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assert graph_hit["retrieval_path"] == "graph_expansion"
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assert graph_hit["expanded_from"] == "dome:alpha-00"
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assert graph_hit["expansion_edge"]["edge_type"] == "compared_with"
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assert not any(node_id.startswith("video:") for node_id in result_ids)
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assert all("retrieval_score" in item for item in response.data)
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first_page = service.search(request.model_copy(update={"limit": 5}), principal=principal)
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assert first_page.next_cursor is not None
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second_page = service.search(
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request.model_copy(update={"limit": 5, "cursor": first_page.next_cursor}),
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principal=principal,
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)
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first_ids = {item["node"]["node_id"] for item in first_page.data}
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second_ids = {item["node"]["node_id"] for item in second_page.data}
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assert len(first_ids) == len(second_ids) == 5
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assert first_ids.isdisjoint(second_ids)
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class ChangedModelEmbeddings(FixtureEmbeddings):
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model_id = "fixture-e5@v2"
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changed_hybrid = HybridSearchIndex(
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lexical=lexical,
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vectors=vectors,
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embeddings=ChangedModelEmbeddings(),
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)
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changed_profile_service = CanonicalSearchService(
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index=changed_hybrid,
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project_ids_provider=lambda: ("dome-performance", "interactive-video"),
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canonical_head=repository.head,
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)
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with pytest.raises(SearchError, match="cursor is invalid"):
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changed_profile_service.search(
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request.model_copy(update={"limit": 5, "cursor": first_page.next_cursor}),
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principal=principal,
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)
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filtered = service.search(
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request.model_copy(
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update={"node_types": (NodeType.TECHNIQUE,), "confidences": (Confidence.HIGH,)}
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),
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principal=principal,
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)
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assert [item["node"]["node_id"] for item in filtered.data] == ["dome:semantic-match"]
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assert filtered.retrieval_mode == "hybrid"
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lexical.configure_vector_generation(
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extension_loader=vectors.load_extension,
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builder=changed_hybrid.build_vector_generation,
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is_current=changed_hybrid.vector_generation_is_current,
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)
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changed_response = changed_profile_service.search(
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request.model_copy(update={"limit": 5}), principal=principal
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)
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changed_generation = vectors.active_generation()
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assert changed_response.retrieval_mode == "hybrid"
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assert changed_generation is not None and changed_generation.model_id == "fixture-e5@v2"
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assert vectors.generation(first_generation.generation_id) is not None
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class FailingEmbeddings(FixtureEmbeddings):
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model_id = "fixture-e5@failed-build"
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def embed_passages(self, texts: tuple[str, ...]) -> tuple[tuple[float, ...], ...]:
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raise RuntimeError("fixture model failure")
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failing_hybrid = HybridSearchIndex(
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lexical=lexical,
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vectors=vectors,
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embeddings=FailingEmbeddings(),
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)
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lexical.configure_vector_generation(
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extension_loader=vectors.load_extension,
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builder=failing_hybrid.build_vector_generation,
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is_current=failing_hybrid.vector_generation_is_current,
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)
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degraded = CanonicalSearchService(
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index=failing_hybrid,
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project_ids_provider=lambda: ("dome-performance", "interactive-video"),
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canonical_head=repository.head,
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).search(request.model_copy(update={"limit": 5}), principal=principal)
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assert degraded.retrieval_mode == "keyword"
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assert any(item.code == "HYBRID_RETRIEVAL_DEGRADED" for item in degraded.warnings)
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assert degraded.next_cursor is None
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def test_fts_and_vector_publication_roll_back_as_one_transaction(tmp_path: Path) -> None:
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repository = _repository(tmp_path)
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search_path = tmp_path / "search.sqlite3"
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lexical = SQLiteSearchIndex(search_path, repository)
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vectors = SQLiteVectorIndex(search_path)
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hybrid = HybridSearchIndex(
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lexical=lexical,
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vectors=vectors,
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embeddings=FixtureEmbeddings(),
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)
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def fail_after_vector_publish(connection, revision, nodes) -> None:
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hybrid.build_vector_generation(connection, revision, nodes)
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raise sqlite3.OperationalError("simulated termination before index commit")
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lexical.configure_vector_generation(
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extension_loader=vectors.load_extension,
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builder=fail_after_vector_publish,
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is_current=hybrid.vector_generation_is_current,
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)
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with pytest.raises(SearchIndexError):
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lexical.search(
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query="alpha",
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project_ids=("dome-performance",),
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include_shared=False,
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limit=5,
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)
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assert lexical.diagnostic_status()[0] is None
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assert vectors.active_generation() is None
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lexical.configure_vector_generation(
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extension_loader=vectors.load_extension,
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builder=hybrid.build_vector_generation,
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is_current=hybrid.vector_generation_is_current,
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)
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recovered = hybrid.search(
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query="alpha",
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project_ids=("dome-performance",),
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include_shared=False,
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limit=5,
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)
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active = vectors.active_generation()
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assert recovered.retrieval_mode == "hybrid"
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assert active is not None and active.revision == recovered.index_revision
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