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Renn F
d7aee91b39
OptimalService.search / query (via _aggregate_citations) ran each index's plugin.search() sequentially, and every plugin.search re-ran HyDE + embed — so an N-index query made N LLM+embed round-trips in series (~28s across all indexes, even though the SQL is fast). Embed the query ONCE (BaseIndexPlugin.compute_query_embedding) and run every index's vector search concurrently against that single embedding (search_with_embedding + asyncio.gather). The search/query signatures and return contract are unchanged; behavior is identical, just ~Nx fewer embed calls and parallel fetch. Adds a regression test asserting one embed + per-index fan-out.
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
"""OptimalService.search embeds the query once and fans out across indexes.
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Guards the latency fix: previously each index re-ran HyDE + embed sequentially
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(N LLM calls, serial), so an all-index search took ~28s. Now the query is
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embedded once and every index's vector search runs concurrently with that single
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embedding.
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"""
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from __future__ import annotations
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from roboco.models.optimal import IndexType, SearchOutcome
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from roboco.services.optimal import OptimalService
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def _fake_plugin(index_type: IndexType) -> MagicMock:
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plugin = MagicMock()
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plugin.compute_query_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
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plugin.search_with_embedding = AsyncMock(
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return_value=SearchOutcome(results=[], success=True, index_type=index_type)
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)
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plugin.count = AsyncMock(return_value=1)
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return plugin
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@pytest.mark.asyncio
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async def test_search_embeds_once_and_fans_out(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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svc = OptimalService.__new__(OptimalService)
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p_docs = _fake_plugin(IndexType.DOCUMENTATION)
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p_journals = _fake_plugin(IndexType.JOURNALS)
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monkeypatch.setattr(svc, "_initialized", True, raising=False)
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monkeypatch.setattr(
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svc,
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"_plugins",
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{IndexType.DOCUMENTATION: p_docs, IndexType.JOURNALS: p_journals},
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raising=False,
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)
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await svc.search("anything")
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# Embedded exactly once total (on the first plugin), reused across indexes —
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# not once per index.
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embed_calls = (
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p_docs.compute_query_embedding.await_count
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+ p_journals.compute_query_embedding.await_count
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)
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assert embed_calls == 1
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# Every index ran a vector search with the pre-computed embedding.
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p_docs.search_with_embedding.assert_awaited_once()
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p_journals.search_with_embedding.assert_awaited_once()
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