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VincentLambert
941edd1269
Fix reranker using tokenized text (10–94x lower scores) instead of natural chunk text.
132 lines
4.7 KiB
Python
132 lines
4.7 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""Unit tests for the reranker input built by Dealer.rerank_by_model.
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External rerankers must be scored on the natural chunk text
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(``content_with_weight``, markup preserved), not the tokenized
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``content_ltks``: neural rerankers score stemmed / accent-split tokens far
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lower, which collapses relevance scores. The tokenized ``ins_tw`` used for the
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keyword-similarity part of the blend must stay unchanged.
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"""
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import sys
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import types
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import numpy as np
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import pytest
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# Stub the heavy / circular-importing dependencies before importing search,
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# mirroring test_search_pagination.py so the module imports in isolation.
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_fake_query = types.ModuleType("rag.nlp.query")
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class _DummyFulltextQueryer:
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pass
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_fake_query.FulltextQueryer = _DummyFulltextQueryer
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sys.modules.setdefault("rag.nlp.query", _fake_query)
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sys.modules.setdefault("common.settings", types.ModuleType("common.settings"))
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from rag.nlp.search import Dealer # noqa: E402
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pytestmark = pytest.mark.p1
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class _CapturingRerank:
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"""Reranker double that records the documents it is asked to score."""
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def __init__(self):
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self.docs = None
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def similarity(self, query, docs):
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self.docs = list(docs)
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return np.zeros(len(docs)), 0
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def _dealer(token_similarity_spy=None):
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dealer = Dealer.__new__(Dealer)
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class _Queryer:
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def question(self, query):
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return None, ["kw"]
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def token_similarity(self, keywords, ins_tw):
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if token_similarity_spy is not None:
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token_similarity_spy["ins_tw"] = ins_tw
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return [0.0] * len(ins_tw)
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dealer.qryr = _Queryer()
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# Tag/pagerank feature scoring is out of scope here.
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dealer._rank_feature_scores = lambda rank_feature, sres: np.zeros(len(sres.ids))
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return dealer
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def _sres(field_by_id):
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return Dealer.SearchResult(total=len(field_by_id), ids=list(field_by_id), field=field_by_id)
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def test_reranker_receives_natural_text():
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rr = _CapturingRerank()
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sres = _sres({"c1": {"content_with_weight": "Paris is the capital of France.", "content_ltks": "pari capit franc"}})
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_dealer().rerank_by_model(rr, sres, "q")
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assert rr.docs == ["Paris is the capital of France."]
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def test_natural_text_is_passed_as_is_without_space_cleanup():
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# remove_redundant_spaces() is ASCII-oriented: it treats accented letters as
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# punctuation and collapses "sécurité des" into "sécuritédes". Natural text
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# must reach the reranker untouched.
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rr = _CapturingRerank()
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text = "La sécurité des données : voir l'annexe A."
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sres = _sres({"c1": {"content_with_weight": text, "content_ltks": "secur donne annex"}})
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_dealer().rerank_by_model(rr, sres, "q")
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assert rr.docs == [text]
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@pytest.mark.parametrize("field", [{}, {"content_with_weight": ""}, {"content_with_weight": None}])
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def test_falls_back_to_tokenized_when_natural_absent(field):
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rr = _CapturingRerank()
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chunk = {"content_ltks": "pari capit franc", "title_tks": "titre", "important_kwd": ["kw1"], "question_tks": "quest"}
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chunk.update(field)
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sres = _sres({"c1": chunk})
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_dealer().rerank_by_model(rr, sres, "q")
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assert rr.docs == ["pari capit franc titre kw1 quest"]
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def test_fallback_still_cleans_redundant_spaces():
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rr = _CapturingRerank()
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sres = _sres({"c1": {"content_ltks": "( pari ) capit !"}})
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_dealer().rerank_by_model(rr, sres, "q")
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assert rr.docs == ["(pari) capit!"]
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def test_markup_is_preserved_not_stripped():
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rr = _CapturingRerank()
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html = "<table> <thead> <tr> <th>Contact</th> </tr> </thead> </table>"
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sres = _sres({"c1": {"content_with_weight": html, "content_ltks": "contact"}})
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_dealer().rerank_by_model(rr, sres, "q")
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assert "<table>" in rr.docs[0] and "<th>Contact</th>" in rr.docs[0]
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def test_keyword_similarity_still_uses_tokenized_tokens():
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spy = {}
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rr = _CapturingRerank()
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sres = _sres({"c1": {"content_with_weight": "Natural prose.", "content_ltks": "natur pros", "important_kwd": ["kw1"]}})
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_dealer(token_similarity_spy=spy).rerank_by_model(rr, sres, "q")
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# The keyword blend input stays the tokenized tokens, not the natural text.
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assert spy["ins_tw"] == [["natur", "pros", "kw1"]]
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