fix: extract years of experience and accept French notes

src/job_research/profile/cv_extractor.py

@@ -145,6 +145,11 @@ EDUCATION_ENTRY_SEPARATORS = (" at ", ", ", " - ", " – ", " — ")
 
 LEADING_BULLET_MARKERS = {"-", "*", "+", "•", "‣", "∙", "●", "◦", "–", "—"}
 
+YEARS_OF_EXPERIENCE_PATTERNS = (
+    re.compile(r"^years of experience\s*:\s*(\d+)\s*$", re.IGNORECASE),
+    re.compile(r"^ann[ée]es d['’]exp[ée]rience\s*:\s*(\d+)\s*$", re.IGNORECASE),
+)
+
 
 def extract_pdf_text(path: Path) -> str:
     reader = PdfReader(str(path))
@@ -166,12 +171,18 @@ def extract_cv_signals(text: str) -> dict[str, object]:
     skills: list[str] = []
     experience_entries: list[dict[str, str]] = []
     education_entries: list[dict[str, str]] = []
+    years_of_experience: int | None = None
     in_education_section = False
     pending_education_credential: str | None = None
 
     for line in non_empty_lines[1:]:
         lowered = line.lower()
 
+        years_of_experience_line = _parse_years_of_experience(line)
+        if years_of_experience_line is not None:
+            years_of_experience = years_of_experience_line
+            continue
+
         if lowered.startswith(("education:", "formation:")):
             in_education_section = True
             pending_education_credential = None
@@ -225,7 +236,7 @@ def extract_cv_signals(text: str) -> dict[str, object]:
         if experience_entry:
             experience_entries.append(experience_entry)
 
-    return {
+    payload = {
         "name": name,
         "location": location,
         "languages": languages,
@@ -234,6 +245,11 @@ def extract_cv_signals(text: str) -> dict[str, object]:
         "education_entries": education_entries,
     }
 
+    if years_of_experience is not None:
+        payload["years_of_experience"] = years_of_experience
+
+    return payload
+
 
 def _parse_csv_field(line: str) -> list[str]:
     _, value = line.split(":", 1)
@@ -251,6 +267,17 @@ def _normalize_line(line: str) -> str:
     return stripped
 
 
+def _parse_years_of_experience(line: str) -> int | None:
+    normalized = line.replace("’", "'")
+
+    for pattern in YEARS_OF_EXPERIENCE_PATTERNS:
+        match = pattern.match(normalized)
+        if match:
+            return int(match.group(1))
+
+    return None
+
+
 def _parse_education_entry(line: str) -> dict[str, str] | None:
     for separator in EDUCATION_ENTRY_SEPARATORS:
         if separator not in line:

src/job_research/profile/merge.py

@@ -9,14 +9,20 @@ from job_research.models import (
 from job_research.profile.profile_parser import AuthoredProfile
 
 
+EXPERIENCE_NOTE_MARKERS = (
+    "experience",
+    "expérience",
+    "années d'expérience",
+    "annees d'experience",
+)
+
+
 def build_candidate_profile_output(
     cv_signals: dict[str, object], authored: AuthoredProfile
 ) -> CandidateProfileOutput:
     warnings: list[WarningItem] = []
 
-    if "years_of_experience" in cv_signals and any(
-        "experience" in note.lower() for note in authored.notes
-    ):
+    if "years_of_experience" in cv_signals and _notes_mention_experience(authored.notes):
         warnings.append(
             WarningItem(
                 field="years_of_experience",
@@ -54,3 +60,12 @@ def build_candidate_profile_output(
         education_entries=[EducationEntry.model_validate(item) for item in education_entries],
         warnings=warnings,
     )
+
+
+def _notes_mention_experience(notes: list[str]) -> bool:
+    for note in notes:
+        normalized = note.casefold().replace("’", "'")
+        if any(marker in normalized for marker in EXPERIENCE_NOTE_MARKERS):
+            return True
+
+    return False

tests/profile/test_cv_extractor.py

@@ -1,6 +1,8 @@
 from textwrap import dedent
 from pathlib import Path
 
+import pytest
+
 from job_research.profile.cv_extractor import extract_cv_signals
 from job_research.profile.cv_extractor import extract_pdf_text
 
@@ -27,6 +29,29 @@ def test_extract_cv_signals_reads_basic_fields_from_text() -> None:
     assert len(extracted["experience_entries"]) == 2
 
 
+@pytest.mark.parametrize(
+    ("line", "expected"),
+    [
+        ("Years of experience: 2", 2),
+        ("Années d'expérience : 3", 3),
+    ],
+)
+def test_extract_cv_signals_extracts_years_of_experience_from_explicit_line(
+    line: str, expected: int
+) -> None:
+    text = dedent(
+        f"""
+        Tonio
+        Location: France
+        {line}
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["years_of_experience"] == expected
+
+
 def test_extract_cv_signals_allows_single_word_titles() -> None:
     text = dedent(
         """

tests/profile/test_merge.py

@@ -26,3 +26,27 @@ def test_build_candidate_profile_output_writes_warning_when_facts_conflict() ->
     assert output.summary == "Junior data engineer focused on GCP."
     assert output.constraints == ["CDI only"]
     assert output.warnings[0].field == "years_of_experience"
+
+
+def test_build_candidate_profile_output_writes_warning_for_french_experience_note() -> None:
+    cv_signals = {
+        "name": "Tonio",
+        "location": "France",
+        "languages": ["French", "English"],
+        "skills": ["Python", "SQL"],
+        "experience_entries": [{"title": "Data Engineer", "company": "A"}],
+        "education_entries": [],
+        "years_of_experience": 2,
+    }
+    authored = AuthoredProfile(
+        summary="Junior data engineer focused on GCP.",
+        target_roles=["Data Engineer"],
+        strengths=["Python"],
+        skills_to_emphasize=["BigQuery", "GCP"],
+        constraints=["CDI only"],
+        notes=["Années d'expérience semble plus proche de 3."],
+    )
+
+    output = build_candidate_profile_output(cv_signals, authored)
+
+    assert output.warnings[0].field == "years_of_experience"