d06277408b
Disjoint corpora give every database's rank-r candidate the same RRF score, and the stable sort resolved those ties to lancedb.databases declaration order, discarding the retrieval scores entirely. Ties now break on the raw retrieval score, which is uncalibrated across indexes but only ever orders candidates within one rank tier: the databases in a fusion share an embedder and ran the same search type, and it can never lift a candidate above another rank. Hybrid per-database scores are themselves rank-derived, so exact agreement still ties and keeps configured order, deterministically. The n > limit depth quota is unchanged, pending the retrieval eval.
691 lines
28 KiB
Python
691 lines
28 KiB
Python
"""Searching several databases and fusing what they return."""
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import pytest
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from docling_core.types.doc.document import DoclingDocument
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from docling_core.types.doc.labels import DocItemLabel
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from haiku.rag.client import HaikuRAG
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from haiku.rag.client.session import FederatedSession
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from haiku.rag.config import get_config
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from haiku.rag.embeddings import EmbedderWrapper
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from haiku.rag.store.exceptions import (
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ConfigMismatchError,
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SourceUnavailableError,
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UnknownDatabaseError,
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)
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from haiku.rag.store.models import Chunk, DocumentItem
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from tests.multi_db.helpers import (
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StubReranker,
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_config,
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_restore_embedder,
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_seed,
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)
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class TestFederatedSearch:
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@pytest.mark.asyncio
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async def test_results_carry_their_source(self, tmp_path):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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async with HaikuRAG(config=config) as rag:
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results = await rag.search("cats", limit=10, search_type="fts")
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assert {r.source for r in results} == {"alpha", "beta"}
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for r in results:
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assert r.source is not None
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assert r.source in r.content
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@pytest.mark.asyncio
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async def test_sources_selects_a_subset(self, tmp_path):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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async with HaikuRAG(config=config) as rag:
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results = await rag.search(
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"cats", limit=10, search_type="fts", sources=["alpha"]
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)
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assert {r.source for r in results} == {"alpha"}
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@pytest.mark.asyncio
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async def test_unknown_source_is_rejected(self, tmp_path):
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config = _config(tmp_path, ["alpha"])
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await _seed(config, "alpha", ["alpha document about cats"])
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async with HaikuRAG(config=config) as rag:
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with pytest.raises(UnknownDatabaseError, match="nope"):
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await rag.search("cats", search_type="fts", sources=["nope"])
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@pytest.mark.asyncio
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async def test_an_unopenable_database_fails_the_query(self, tmp_path):
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config = _config(tmp_path, ["alpha", "missing"])
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await _seed(config, "alpha", ["alpha document about cats"])
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with pytest.raises(SourceUnavailableError, match="missing"):
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async with HaikuRAG(config=config) as rag:
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await rag.search("cats", search_type="fts")
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class TestSingleDatabaseUnchanged:
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@pytest.mark.asyncio
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async def test_source_is_unset_without_configured_databases(self, temp_db_path):
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async with HaikuRAG(temp_db_path, create=True) as rag:
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doc = DoclingDocument(name="one")
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doc.add_text(label=DocItemLabel.TEXT, text="a document about cats")
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await rag.import_document(
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doc,
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[
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Chunk(
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content="a document about cats",
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embedding=[0.1] * get_config().embeddings.model.vector_dim,
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order=0,
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)
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],
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uri="test://one",
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)
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results = await rag.search("cats", search_type="fts")
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assert results
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assert all(r.source is None for r in results)
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class TestOneQueryVector:
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@pytest.mark.asyncio
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async def test_a_search_embeds_the_query_once_for_the_whole_set(
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self, tmp_path, query_embedding
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):
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"""Each database owns an embedder, so embedding per database costs a
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round trip each on a remote endpoint."""
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config = _config(tmp_path, ["alpha", "beta", "gamma"])
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for name in ("alpha", "beta", "gamma"):
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await _seed(config, name, [f"{name} one"])
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async with HaikuRAG(config=config, read_only=True) as rag:
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await rag.search("one")
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assert query_embedding == ["one"]
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class TestOneEmbedderAcrossTheSet:
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"""A set is searched with one query vector, so the databases in a
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selection must share an embedder."""
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@pytest.mark.asyncio
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async def test_disagreeing_databases_cannot_be_searched_together(self, tmp_path):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha one"])
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await _seed(config, "beta", ["beta one"])
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await _restore_embedder(config, "beta", model_name="some-other-model")
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async with HaikuRAG(config=config, read_only=True) as rag:
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with pytest.raises(ConfigMismatchError, match="different embedders"):
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await rag.search("one")
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@pytest.mark.asyncio
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async def test_a_database_asked_for_alone_is_never_compared(
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self, tmp_path, query_embedding
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):
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"""Only databases searched together have to agree."""
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha one"])
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await _seed(config, "beta", ["beta one"])
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await _restore_embedder(config, "beta", model_name="some-other-model")
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async with HaikuRAG(config=config, read_only=True) as rag:
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assert await rag.search("one", sources=["alpha"]) is not None
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assert await rag.count_documents(filter=None) is not None
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@pytest.mark.asyncio
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async def test_full_text_search_needs_no_agreement(self, tmp_path):
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"""Full-text search embeds nothing, so which model wrote each database
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does not come into it."""
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha one"])
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await _seed(config, "beta", ["beta one"])
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await _restore_embedder(config, "beta", model_name="some-other-model")
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async with HaikuRAG(config=config, read_only=True) as rag:
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results = await rag.search("one", search_type="fts")
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assert {r.source for r in results} == {"alpha", "beta"}
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@pytest.mark.asyncio
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async def test_agreeing_databases_search_together(self, tmp_path, query_embedding):
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"""The databases agree with each other; that they were written by a
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differently-spelled provider than the config is the soft case."""
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha one"])
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await _seed(config, "beta", ["beta one"])
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await _restore_embedder(config, "alpha", provider="openai")
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await _restore_embedder(config, "beta", provider="openai")
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async with HaikuRAG(config=config, read_only=True) as rag:
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assert len(await rag.search("one")) > 0
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class TestRerankerFusion:
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@pytest.mark.asyncio
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async def test_the_reranker_scores_the_union_and_owners_survive(
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self, tmp_path, monkeypatch
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):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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stub = StubReranker()
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monkeypatch.setattr(HaikuRAG, "reranker", property(lambda self: stub))
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async with HaikuRAG(config=config) as rag:
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results = await rag.search("cats", limit=2, search_type="fts")
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# It saw both databases' candidates, not one database at a time.
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assert len(stub.seen) == 2
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assert {c.split()[0] for c in stub.seen} == {"alpha", "beta"}
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# Each result still knows which database it came from.
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for r in results:
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assert r.source is not None
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assert r.content.startswith(r.source)
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@pytest.mark.asyncio
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async def test_a_closing_failure_does_not_mask_the_exit(self, tmp_path):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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rag = HaikuRAG(config=config)
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await rag.__aenter__()
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await rag.clients_for(["alpha", "beta"])
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assert isinstance(rag._session, FederatedSession)
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sessions = rag._session._sessions
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async def boom():
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raise RuntimeError("close failed")
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sessions["alpha"].aclose = boom # ty: ignore[invalid-assignment]
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beta = sessions["beta"].store
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await rag.__aexit__(None, None, None)
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# The failure is swallowed, and the sibling is still closed after it.
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assert rag._clients == {}
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assert rag._session._sessions == {}
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assert not beta.db.is_open()
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@pytest.mark.asyncio
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async def test_multimodal_reranking_attaches_each_database_own_pictures(
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self, tmp_path, monkeypatch
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):
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"""Picture self_refs repeat across databases exactly as they do across
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documents, so the pre-rerank attach must stay per database."""
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config = _config(tmp_path, ["alpha", "beta"])
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config.reranking.multimodal = True
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dim = get_config().embeddings.model.vector_dim
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for name in ("alpha", "beta"):
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async with HaikuRAG(config=config, create=True, sources=[name]) as rag:
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doc = DoclingDocument(name=name)
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doc.add_text(label=DocItemLabel.TEXT, text=f"{name} figure of cats")
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await rag.import_document(
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doc,
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[
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Chunk(
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content=f"{name} figure of cats",
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embedding=[0.1] * dim,
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order=0,
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metadata={
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"doc_item_refs": ["#/pictures/0"],
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"labels": ["picture"],
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},
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)
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],
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uri=f"test://{name}/figure",
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)
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[document] = await rag.list_documents()
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assert document.id is not None
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await rag.document_item_repository.create_items(
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document.id,
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[
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DocumentItem(
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document_id=document.id,
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position=0,
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self_ref="#/pictures/0",
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label="picture",
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text=f"caption {name}",
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picture_data=f"bytes-{name}".encode(),
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)
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],
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)
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stub = StubReranker()
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monkeypatch.setattr(HaikuRAG, "reranker", property(lambda self: stub))
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async with HaikuRAG(config=config) as rag:
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await rag.search("cats", limit=2, search_type="fts")
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assert stub.attached == {"alpha": b"bytes-alpha", "beta": b"bytes-beta"}
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class TestOverFetchingForAReranker:
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"""A reranker needs more candidates than it returns. Ranking without one does
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not, and an image query keeps its vector ranking either way."""
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@staticmethod
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def _limits_asked(monkeypatch) -> list[int]:
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from haiku.rag.store.repositories.chunk import ChunkRepository
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asked: list[int] = []
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search = ChunkRepository.search
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async def spy(self, *args, **kwargs):
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asked.append(kwargs["limit"])
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return await search(self, *args, **kwargs)
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monkeypatch.setattr(ChunkRepository, "search", spy)
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return asked
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@pytest.mark.asyncio
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async def test_a_text_query_over_fetches_for_a_reranker(
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self, tmp_path, monkeypatch
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):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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monkeypatch.setattr(HaikuRAG, "reranker", property(lambda self: StubReranker()))
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asked = self._limits_asked(monkeypatch)
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async with HaikuRAG(config=config) as rag:
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await rag.search("cats", limit=3, search_type="fts")
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per_database = list(asked)
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asked.clear()
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await rag.search("cats", limit=3, search_type="fts", sources=["alpha"])
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assert per_database == [30, 30]
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assert asked == [30]
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@pytest.mark.asyncio
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async def test_a_text_query_without_a_reranker_fetches_what_it_returns(
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self, tmp_path, monkeypatch
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):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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monkeypatch.setattr(HaikuRAG, "reranker", property(lambda self: None))
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asked = self._limits_asked(monkeypatch)
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async with HaikuRAG(config=config) as rag:
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await rag.search("cats", limit=3, search_type="fts")
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per_database = list(asked)
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asked.clear()
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await rag.search("cats", limit=3, search_type="fts", sources=["alpha"])
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assert per_database == [3, 3]
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assert asked == [3]
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@pytest.mark.asyncio
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async def test_an_image_query_fetches_what_it_returns(self, tmp_path, monkeypatch):
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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monkeypatch.setattr(HaikuRAG, "reranker", property(lambda self: StubReranker()))
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dim = get_config().embeddings.model.vector_dim
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async def embed_image(self, image): # noqa: ARG001
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return [0.1] * dim
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monkeypatch.setattr(EmbedderWrapper, "supports_images", True)
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monkeypatch.setattr(EmbedderWrapper, "embed_image", embed_image)
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asked = self._limits_asked(monkeypatch)
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async with HaikuRAG(config=config) as rag:
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await rag.search(b"\x89PNG\r\n\x1a\n", limit=3)
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assert asked == [3, 3]
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class TestOneReranker:
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@pytest.mark.asyncio
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async def test_the_set_builds_one_reranker_for_a_text_query(
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self, tmp_path, monkeypatch
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):
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"""Local rerankers load model weights per instance, so a set of
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databases must build one, not one each."""
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built = []
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monkeypatch.setattr(
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"haiku.rag.client.get_reranker",
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lambda config: built.append(config) or StubReranker(),
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)
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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built.clear()
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async with HaikuRAG(config=config) as rag:
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await rag.search("cats", limit=2, search_type="fts")
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assert len(built) == 1, f"built {len(built)} rerankers"
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@pytest.mark.asyncio
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async def test_an_image_query_builds_no_reranker(self, tmp_path, monkeypatch):
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"""An image query has no text to score against, so it keeps its vector
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ranking. The query type is checked before the reranker, which loads
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model weights for a local one on first access."""
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built = []
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monkeypatch.setattr(
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"haiku.rag.client.get_reranker",
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lambda config: built.append(config) or StubReranker(),
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)
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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built.clear()
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dim = get_config().embeddings.model.vector_dim
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async def embed_image(self, image): # noqa: ARG001
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return [0.1] * dim
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monkeypatch.setattr(EmbedderWrapper, "supports_images", True)
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monkeypatch.setattr(EmbedderWrapper, "embed_image", embed_image)
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async with HaikuRAG(config=config) as rag:
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results = await rag.search(b"\x89PNG\r\n\x1a\n")
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# The whole path ran: over-fetching, embedding and fusing all saw an
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# image query, and none of them reached for a reranker.
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assert {r.source for r in results} == {"alpha", "beta"}
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assert built == []
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@pytest.mark.asyncio
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async def test_the_reranker_is_closed_once(self, tmp_path, monkeypatch):
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"""The federator owns the reranker: it hands the same object to every
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database and closes it once."""
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closes = []
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class CountingReranker(StubReranker):
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async def aclose(self):
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closes.append(1)
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monkeypatch.setattr(
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"haiku.rag.client.get_reranker", lambda config: CountingReranker()
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)
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats"])
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await _seed(config, "beta", ["beta document about cats"])
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async with HaikuRAG(config=config) as rag:
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await rag.search("cats", limit=2, search_type="fts")
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assert closes == [1], f"closed {len(closes)} times"
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class TestNarrowingToOneDatabase:
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"""A selection of one is an ordinary search. Fusion exists to reconcile
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rankings from separate indexes, and there is nothing to reconcile."""
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@pytest.mark.asyncio
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async def test_narrowing_keeps_the_database_s_own_scores(self, tmp_path):
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"""RRF scores position; a selection of one keeps the database's own
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hybrid scores."""
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config = _config(tmp_path, ["alpha", "beta"])
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await _seed(config, "alpha", ["alpha document about cats", "alpha on dogs"])
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await _seed(config, "beta", ["beta document about cats"])
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async with HaikuRAG(config=config) as covering:
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narrowed = await covering.search(
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"cats", search_type="fts", sources=["alpha"]
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)
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async with HaikuRAG(config=config, sources=["alpha"]) as one:
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native = await one.search("cats", search_type="fts")
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assert [r.chunk_id for r in narrowed] == [r.chunk_id for r in native]
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assert [r.score for r in narrowed] == [r.score for r in native]
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assert all(r.source == "alpha" for r in narrowed)
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@pytest.mark.asyncio
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async def test_narrowing_does_not_embed_for_a_filter_matching_nothing(
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self, tmp_path, monkeypatch
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):
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"""One database embeds inside the repository, which returns early when
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the filter matches no document."""
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|
from haiku.rag.embeddings import EmbedderWrapper
|
|
|
|
config = _config(tmp_path, ["alpha", "beta"])
|
|
await _seed(config, "alpha", ["alpha document about cats"])
|
|
await _seed(config, "beta", ["beta document about cats"])
|
|
|
|
def explode(self, query):
|
|
raise AssertionError("embedded a query no document could match")
|
|
|
|
monkeypatch.setattr(EmbedderWrapper, "embed_query", explode)
|
|
|
|
async with HaikuRAG(config=config) as covering:
|
|
results = await covering.search(
|
|
"cats", filter="uri = 'test://nothing'", sources=["alpha"]
|
|
)
|
|
|
|
assert results == []
|
|
|
|
|
|
class TestReciprocalRankFusion:
|
|
"""Without a reranker, scores from separate indexes are not comparable, so
|
|
fusion ranks by position. These pin what that produces."""
|
|
|
|
@staticmethod
|
|
def _ranked(source: str, count: int, top: float) -> list[tuple[Chunk, float]]:
|
|
return [
|
|
(Chunk(id=f"{source}{i}", content=f"{source} {i}"), top - i / 100)
|
|
for i in range(count)
|
|
]
|
|
|
|
def _lopsided(self, count: int) -> list[list[tuple[Chunk, float]]]:
|
|
"""Every native score in the first database beats every one in the
|
|
second, so score order and position order disagree."""
|
|
return [self._ranked("a", count, 0.9), self._ranked("b", count, 0.2)]
|
|
|
|
async def _fuse_over(self, tmp_path, per_source, limit):
|
|
from haiku.rag.client.search import _fuse
|
|
|
|
config = _config(tmp_path, ["alpha", "beta"])
|
|
await _seed(config, "alpha", ["alpha one"])
|
|
await _seed(config, "beta", ["beta one"])
|
|
async with HaikuRAG(config=config) as rag:
|
|
assert rag.reranker is None
|
|
clients = await rag.clients_for(["alpha", "beta"])
|
|
fused = await _fuse(rag, clients, "cats", per_source, limit)
|
|
return [(owner.source, chunk.id, score) for owner, chunk, score in fused]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_databases_interleave_by_rank(self, tmp_path):
|
|
"""Each contributes its rank-1 before either contributes its rank-2."""
|
|
fused = await self._fuse_over(tmp_path, self._lopsided(3), 10)
|
|
|
|
assert [(source, cid) for source, cid, _ in fused] == [
|
|
("alpha", "a0"),
|
|
("beta", "b0"),
|
|
("alpha", "a1"),
|
|
("beta", "b1"),
|
|
("alpha", "a2"),
|
|
("beta", "b2"),
|
|
]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_the_score_is_the_reciprocal_of_the_rank(self, tmp_path):
|
|
fused = await self._fuse_over(tmp_path, self._lopsided(2), 10)
|
|
|
|
assert [score for _, _, score in fused] == [
|
|
1 / 61,
|
|
1 / 61,
|
|
1 / 62,
|
|
1 / 62,
|
|
]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_ranks_tie_and_the_retrieval_score_breaks_them(self, tmp_path):
|
|
"""Every rank ties across databases, so the tiebreak decides all of it,
|
|
and it must be relevance, not the order databases were configured in."""
|
|
per_source = [self._ranked("a", 2, 0.2), self._ranked("b", 2, 0.9)]
|
|
fused = await self._fuse_over(tmp_path, per_source, 10)
|
|
|
|
assert [source for source, _, _ in fused] == ["beta", "alpha", "beta", "alpha"]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_the_configured_order_does_not_matter(self, tmp_path):
|
|
"""The same candidates fuse to the same list whichever database is
|
|
declared first."""
|
|
forward = await self._fuse_over(tmp_path, self._lopsided(3), 10)
|
|
(tmp_path / "swapped").mkdir()
|
|
backward = await self._fuse_over(
|
|
tmp_path / "swapped",
|
|
[self._ranked("b", 3, 0.2), self._ranked("a", 3, 0.9)],
|
|
10,
|
|
)
|
|
|
|
assert [(cid, score) for _, cid, score in forward] == [
|
|
(cid, score) for _, cid, score in backward
|
|
]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_exact_ties_keep_the_configured_order(self, tmp_path):
|
|
"""Hybrid scores are rank-derived and tie exactly when databases agree,
|
|
so a genuine tie must still resolve deterministically."""
|
|
per_source = [self._ranked("a", 2, 0.9), self._ranked("b", 2, 0.9)]
|
|
fused = await self._fuse_over(tmp_path, per_source, 10)
|
|
|
|
assert [source for source, _, _ in fused] == ["alpha", "beta", "alpha", "beta"]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_the_retrieval_score_never_overrides_the_rank(self, tmp_path):
|
|
"""Raw scores are not calibrated across indexes, so a database with
|
|
inflated scores still contributes one candidate per rank."""
|
|
fused = await self._fuse_over(tmp_path, self._lopsided(2), 10)
|
|
|
|
assert [cid for _, cid, _ in fused] == ["a0", "b0", "a1", "b1"]
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_the_limit_cuts_the_fused_list(self, tmp_path):
|
|
"""Each database was asked for enough to fill the window on its own."""
|
|
fused = await self._fuse_over(tmp_path, self._lopsided(5), 3)
|
|
|
|
assert [(source, cid) for source, cid, _ in fused] == [
|
|
("alpha", "a0"),
|
|
("beta", "b0"),
|
|
("alpha", "a1"),
|
|
]
|
|
|
|
|
|
class TestFusingWhatARerankerReturns:
|
|
@pytest.mark.asyncio
|
|
async def test_a_reranker_returning_copies_is_named(self, tmp_path):
|
|
"""Candidates are mapped back to their database by identity, because
|
|
chunk ids repeat between copies of one. A reranker that rebuilds its
|
|
chunks loses that, and saying so beats a KeyError."""
|
|
from haiku.rag.client.search import _fuse
|
|
|
|
config = _config(tmp_path, ["alpha", "beta"])
|
|
await _seed(config, "alpha", ["alpha document about cats"])
|
|
await _seed(config, "beta", ["beta document about cats"])
|
|
|
|
class Rebuilds:
|
|
async def rerank(self, query, chunks, top_n=10):
|
|
return [(chunk.model_copy(), 1.0) for chunk in chunks[:top_n]]
|
|
|
|
async with HaikuRAG(config=config) as rag:
|
|
clients = await rag.clients_for(["alpha", "beta"])
|
|
rag.__dict__["_own_reranker"] = Rebuilds()
|
|
per_source = [
|
|
await c.chunk_repository.search("cats", 5, "fts") for c in clients
|
|
]
|
|
|
|
with pytest.raises(ValueError, match="objects from the list"):
|
|
await _fuse(rag, clients, "cats", per_source, 5)
|
|
|
|
|
|
class TestRememberingTheStoredEmbedder:
|
|
@pytest.mark.asyncio
|
|
async def test_creating_a_database_records_the_embedder_it_wrote(self, tmp_path):
|
|
"""Creating writes the settings this database will be read with, so a
|
|
client that created one can be compared against a client that opened
|
|
one."""
|
|
config = _config(tmp_path, ["alpha", "beta"])
|
|
|
|
async with HaikuRAG(config=config, create=True, sources=["alpha"]) as created:
|
|
assert created.store.stored_embedding is not None
|
|
written = created.store.stored_embedding
|
|
|
|
async with HaikuRAG(config=config, sources=["alpha"]) as reopened:
|
|
assert reopened.store.stored_embedding == written
|
|
|
|
|
|
class TestComparingEmbedders:
|
|
@pytest.mark.asyncio
|
|
async def test_a_database_recording_no_embedder_is_not_compared(self, tmp_path):
|
|
"""A database whose settings never recorded one cannot disagree with a
|
|
database that did, so there is nothing to reject."""
|
|
config = _config(tmp_path, ["alpha", "beta"])
|
|
await _seed(config, "alpha", ["alpha document about cats"])
|
|
await _seed(config, "beta", ["beta document about cats"])
|
|
|
|
async with HaikuRAG(config=config) as rag:
|
|
alpha, beta = await rag.clients_for(["alpha", "beta"])
|
|
recorded = beta.store.stored_embedding
|
|
assert recorded is not None and recorded != ("other", "model", 7)
|
|
|
|
# Disagreeing on the record is what is rejected...
|
|
beta.store.stored_embedding = ("other", "model", 7)
|
|
with pytest.raises(ConfigMismatchError, match="different embedders"):
|
|
rag._require_one_embedder([alpha, beta])
|
|
|
|
# ...and having no record is not a disagreement.
|
|
beta.store.stored_embedding = None
|
|
rag._require_one_embedder([alpha, beta])
|
|
results = await rag.search("cats", search_type="fts")
|
|
|
|
assert {r.source for r in results} == {"alpha", "beta"}
|
|
|
|
|
|
class TestOneNamedDatabase:
|
|
@pytest.mark.asyncio
|
|
async def test_a_single_named_database_keeps_its_name(self, tmp_path):
|
|
"""Named in config is named in results, even as the only entry."""
|
|
config = _config(tmp_path, ["alpha"])
|
|
await _seed(config, "alpha", ["alpha document about cats"])
|
|
|
|
async with HaikuRAG(config=config) as rag:
|
|
results = await rag.search("cats", search_type="fts")
|
|
|
|
assert results
|
|
assert all(r.source == "alpha" for r in results)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_selecting_nothing_at_construction_is_rejected(self, tmp_path):
|
|
config = _config(tmp_path, ["alpha", "beta"])
|
|
|
|
with pytest.raises(ValueError, match="selects no database"):
|
|
async with HaikuRAG(config=config, sources=[]):
|
|
pass
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_selecting_nothing_means_the_same_with_one_database(self, tmp_path):
|
|
"""`sources=[]` selects nothing whether one database is configured or
|
|
several."""
|
|
config = _config(tmp_path, ["alpha"])
|
|
await _seed(config, "alpha", ["alpha document about cats"])
|
|
|
|
async with HaikuRAG(config=config) as rag:
|
|
assert await rag.search("cats", search_type="fts", sources=[]) == []
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_selecting_nothing_per_query_returns_nothing(self, tmp_path):
|
|
config = _config(tmp_path, ["alpha", "beta"])
|
|
await _seed(config, "alpha", ["alpha document about cats"])
|
|
await _seed(config, "beta", ["beta document about cats"])
|
|
|
|
async with HaikuRAG(config=config) as rag:
|
|
assert await rag.search("cats", search_type="fts", sources=[]) == []
|