a6491df5a6
* refactor(memory): lazy-init embedder env reads * feat(memory): reindex log to .rag/reindex.log * refactor(memory): search handles None embedder * test(memory): cover None embedder in search and index * docs(handoff): add handoff and ADR for lazy-init embedder * docs(handoff): set PR number * docs(handoff): rename to pr-100 prefix for pipeline detection * refactor(memory): split run_search to satisfy xenon rank A * fix(memory): cast search result fields to satisfy mypy * style(memory): ruff format search.py --------- Co-authored-by: opencode-agent <agent@opencode.local>
128 lines
4 KiB
Python
128 lines
4 KiB
Python
import json
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import os
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from argparse import Namespace
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from pathlib import Path
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from unittest.mock import patch
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import numpy as np
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import pytest
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os.environ.setdefault("OPENAI_BASE_URL", "http://test/v1")
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import src.memory.search as search_mod
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from src.memory.search import _cosine_sim
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class TestCosineSim:
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def test_identical(self) -> None:
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v = np.array([1.0, 2.0, 3.0])
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assert _cosine_sim(v, v) == pytest.approx(1.0)
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def test_orthogonal(self) -> None:
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a = np.array([1.0, 0.0])
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b = np.array([0.0, 1.0])
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assert _cosine_sim(a, b) == pytest.approx(0.0)
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def test_zero_vector(self) -> None:
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a = np.array([0.0, 0.0])
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b = np.array([1.0, 1.0])
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assert _cosine_sim(a, b) == pytest.approx(0.0)
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def test_parallel(self) -> None:
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a = np.array([1.0, 2.0])
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b = np.array([2.0, 4.0])
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assert _cosine_sim(a, b) == pytest.approx(1.0)
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def test_opposite(self) -> None:
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a = np.array([1.0, 1.0])
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b = np.array([-1.0, -1.0])
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assert _cosine_sim(a, b) == pytest.approx(-1.0)
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class TestSearchOutput:
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def test_search_json_format(self, tmp_path: Path) -> None:
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index_dir = tmp_path / ".rag"
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index_dir.mkdir()
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index = {
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"files": [
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{
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"source": "test.md",
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"text": "hello world",
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"embedding": [1.0, 0.0, 0.0],
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},
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],
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}
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(index_dir / "index.json").write_text(json.dumps(index))
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def fake_embed_texts(texts):
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return [[1.0, 0.0, 0.0]]
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with patch.object(search_mod, "embed_texts", fake_embed_texts):
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args = Namespace(index_dir=str(index_dir), query="hello", k=5, json=True)
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search_mod.run_search(args)
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def test_dedup_by_source(self, tmp_path: Path, capsys) -> None:
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index_dir = tmp_path / ".rag"
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index_dir.mkdir()
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index = {
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"files": [
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{
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"source": "doc.md",
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"text": "chunk 0",
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"embedding": [1.0, 0.0, 0.0],
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},
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{
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"source": "doc.md",
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"text": "chunk 1",
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"embedding": [0.9, 0.1, 0.0],
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},
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{
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"source": "other.md",
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"text": "chunk 0",
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"embedding": [0.5, 0.5, 0.0],
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},
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],
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}
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(index_dir / "index.json").write_text(json.dumps(index))
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def fake_embed_texts(texts):
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return [[1.0, 0.0, 0.0]]
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with patch.object(search_mod, "embed_texts", fake_embed_texts):
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args = Namespace(index_dir=str(index_dir), query="q", k=5, json=True)
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search_mod.run_search(args)
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captured = capsys.readouterr()
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result = json.loads(captured.out)
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assert len(result) == 2
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sources = [r["source"] for r in result]
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assert "doc.md" in sources
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assert "other.md" in sources
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assert sources.count("doc.md") == 1
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doc_entry = next(r for r in result if r["source"] == "doc.md")
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assert doc_entry["text"] == "chunk 0"
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def test_search_returns_empty_when_embedder_none(self, tmp_path: Path, capsys) -> None:
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index_dir = tmp_path / ".rag"
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index_dir.mkdir()
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index = {
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"files": [
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{
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"source": "test.md",
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"text": "hello world",
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"embedding": [1.0, 0.0, 0.0],
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},
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],
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}
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(index_dir / "index.json").write_text(json.dumps(index))
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def fake_embed_texts(texts):
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return None
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with patch.object(search_mod, "embed_texts", fake_embed_texts):
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args = Namespace(index_dir=str(index_dir), query="hello", k=5, json=True)
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search_mod.run_search(args)
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captured = capsys.readouterr()
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result = json.loads(captured.out)
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assert result == []
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