mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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520f2d26f2
Unified Reranker Architecture: - Add BaseReranker ABC with factory pattern - Implement 4 backends: ONNX (default), API, LiteLLM, Legacy - Add .env configuration parsing for API credentials - Migrate from sentence-transformers to optimum+onnxruntime File Watcher Module: - Add real-time file system monitoring with watchdog - Implement IncrementalIndexer for single-file updates - Add WatcherManager with signal handling and graceful shutdown - Add 'codexlens watch' CLI command - Event filtering, debouncing, and deduplication - Thread-safe design with proper resource cleanup Tests: 16 watcher tests + 5 reranker test files 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
"""Tests for LiteLLMReranker (LLM-based reranking)."""
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from __future__ import annotations
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import sys
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import types
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from dataclasses import dataclass
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import pytest
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from codexlens.semantic.reranker.litellm_reranker import LiteLLMReranker
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def _install_dummy_ccw_litellm(
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monkeypatch: pytest.MonkeyPatch, *, responses: list[str]
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) -> None:
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@dataclass(frozen=True, slots=True)
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class ChatMessage:
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role: str
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content: str
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class LiteLLMClient:
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def __init__(self, model: str = "default", **kwargs) -> None:
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self.model = model
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self.kwargs = kwargs
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self._responses = list(responses)
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self.calls: list[list[ChatMessage]] = []
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def chat(self, messages, **kwargs):
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self.calls.append(list(messages))
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content = self._responses.pop(0) if self._responses else ""
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return types.SimpleNamespace(content=content)
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dummy = types.ModuleType("ccw_litellm")
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dummy.ChatMessage = ChatMessage
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dummy.LiteLLMClient = LiteLLMClient
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monkeypatch.setitem(sys.modules, "ccw_litellm", dummy)
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def test_score_pairs_parses_numbers_and_normalizes_scales(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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_install_dummy_ccw_litellm(monkeypatch, responses=["0.73", "7", "80"])
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reranker = LiteLLMReranker(model="dummy")
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scores = reranker.score_pairs([("q", "d1"), ("q", "d2"), ("q", "d3")])
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assert scores == pytest.approx([0.73, 0.7, 0.8])
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def test_score_pairs_parses_json_score_field(monkeypatch: pytest.MonkeyPatch) -> None:
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_install_dummy_ccw_litellm(monkeypatch, responses=['{"score": 0.42}'])
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reranker = LiteLLMReranker(model="dummy")
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scores = reranker.score_pairs([("q", "d")])
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assert scores == pytest.approx([0.42])
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def test_score_pairs_uses_default_score_on_parse_failure(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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_install_dummy_ccw_litellm(monkeypatch, responses=["N/A"])
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reranker = LiteLLMReranker(model="dummy", default_score=0.123)
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scores = reranker.score_pairs([("q", "d")])
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assert scores == pytest.approx([0.123])
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def test_rate_limiting_sleeps_between_requests(monkeypatch: pytest.MonkeyPatch) -> None:
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_install_dummy_ccw_litellm(monkeypatch, responses=["0.1", "0.2"])
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reranker = LiteLLMReranker(model="dummy", min_interval_seconds=1.0)
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import codexlens.semantic.reranker.litellm_reranker as litellm_reranker_module
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sleeps: list[float] = []
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times = iter([100.0, 100.0, 100.1, 100.1])
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monkeypatch.setattr(litellm_reranker_module.time, "monotonic", lambda: next(times))
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monkeypatch.setattr(
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litellm_reranker_module.time, "sleep", lambda seconds: sleeps.append(seconds)
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)
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_ = reranker.score_pairs([("q", "d1"), ("q", "d2")])
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assert sleeps == pytest.approx([0.9])
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