Merge branch 'feature/candidate-profile-ingestion'

docs/profile-template.md

@@ -0,0 +1,22 @@
+# Candidate Profile
+
+## Summary
+One short paragraph describing your target profile.
+
+## Target Roles
+- Data Engineer
+
+## Strengths
+- Python
+- SQL
+
+## Skills To Emphasize
+- GCP
+- BigQuery
+
+## Constraints
+- CDI only
+- France only
+
+## Notes
+- Anything the CV parser might miss but future ranking should understand.

pyproject.toml

@@ -1,7 +1,28 @@
+[build-system]
+requires = ["setuptools>=69"]
+build-backend = "setuptools.build_meta"
+
 [project]
 name = "job-research"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
-dependencies = []
+dependencies = [
+    "pydantic>=2.7,<3",
+    "pypdf>=5.0,<6",
+    "pyyaml>=6.0,<7",
+    "typer>=0.12,<1",
+]
+
+[dependency-groups]
+dev = ["pytest>=8.2,<9"]
+
+[project.scripts]
+job-research = "job_research.cli:main"
+
+[tool.setuptools]
+package-dir = {"" = "src"}
+
+[tool.setuptools.packages.find]
+where = ["src"]

src/job_research/__init__.py

@@ -0,0 +1,3 @@
+__all__ = ["__version__"]
+
+__version__ = "0.1.0"

src/job_research/cli.py

@@ -0,0 +1,67 @@
+from pathlib import Path
+
+import typer
+
+from job_research.profile.cv_extractor import extract_cv_signals, extract_pdf_text
+from job_research.profile.merge import build_candidate_profile_output
+from job_research.profile.profile_parser import parse_profile_markdown
+from job_research.storage import save_candidate_profile_yaml
+
+app = typer.Typer(help="Build one canonical candidate profile YAML")
+
+
+@app.callback()
+def main_command() -> None:
+    pass
+
+@app.command("build-profile")
+def build_profile(
+    cv: Path = typer.Option(
+        ...,
+        "--cv",
+        exists=True,
+        dir_okay=False,
+        readable=True,
+        help="Path to the CV PDF or UTF-8 text file.",
+    ),
+    profile: Path = typer.Option(
+        ...,
+        "--profile",
+        exists=True,
+        dir_okay=False,
+        readable=True,
+        help="Path to the light-template markdown profile.",
+    ),
+    out: Path = typer.Option(
+        Path("data/candidate-profile.yaml"),
+        "--out",
+        dir_okay=False,
+        help="Path to write the canonical YAML profile.",
+    ),
+) -> None:
+    """Build candidate-profile.yaml from CV and markdown profile."""
+
+    cv_text = extract_pdf_text(cv) if cv.suffix.lower() == ".pdf" else cv.read_text(encoding="utf-8")
+    if not cv_text.strip():
+        raise ValueError("No readable text found in CV input")
+
+    authored_profile = parse_profile_markdown(profile.read_text(encoding="utf-8"))
+    cv_signals = extract_cv_signals(cv_text)
+    candidate_profile = build_candidate_profile_output(cv_signals, authored_profile)
+
+    save_candidate_profile_yaml(out, candidate_profile)
+
+    typer.echo(f"candidate profile written to {out}")
+    warning_count = len(candidate_profile.warnings)
+    if warning_count:
+        typer.echo(f"Warnings included: {warning_count}")
+    else:
+        typer.echo("No warnings included.")
+
+
+def main() -> None:
+    app()
+
+
+if __name__ == "__main__":
+    main()

src/job_research/models.py

@@ -0,0 +1,37 @@
+from pydantic import BaseModel, Field
+
+
+class ExperienceEntry(BaseModel):
+    company: str
+    title: str
+    start: str | None = None
+    end: str | None = None
+    highlights: list[str] = Field(default_factory=list)
+
+
+class EducationEntry(BaseModel):
+    institution: str
+    credential: str
+    start: str | None = None
+    end: str | None = None
+
+
+class WarningItem(BaseModel):
+    field: str
+    message: str
+
+
+class CandidateProfileOutput(BaseModel):
+    name: str | None = None
+    summary: str | None = None
+    target_roles: list[str] = Field(default_factory=list)
+    strengths: list[str] = Field(default_factory=list)
+    skills_to_emphasize: list[str] = Field(default_factory=list)
+    constraints: list[str] = Field(default_factory=list)
+    notes: list[str] = Field(default_factory=list)
+    location: str | None = None
+    languages: list[str] = Field(default_factory=list)
+    skills: list[str] = Field(default_factory=list)
+    experience_entries: list[ExperienceEntry] = Field(default_factory=list)
+    education_entries: list[EducationEntry] = Field(default_factory=list)
+    warnings: list[WarningItem] = Field(default_factory=list)

src/job_research/profile/__init__.py

@@ -0,0 +1 @@
+__all__ = ["profile_parser"]

src/job_research/profile/cv_extractor.py

@@ -0,0 +1,371 @@
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+from pypdf import PdfReader
+
+
+EXPERIENCE_LINE_CONNECTORS = (" at ", " chez ", " au ", " à ")
+
+EXPERIENCE_TITLE_STOPWORDS = {
+    "a",
+    "an",
+    "and",
+    "as",
+    "at",
+    "after",
+    "before",
+    "by",
+    "for",
+    "from",
+    "in",
+    "into",
+    "of",
+    "on",
+    "or",
+    "the",
+    "to",
+    "with",
+    "within",
+    "without",
+    "while",
+    "during",
+    "since",
+    "because",
+}
+
+EXPERIENCE_TITLE_ACTION_STARTS = {
+    "built",
+    "created",
+    "delivered",
+    "designed",
+    "developed",
+    "deployed",
+    "implemented",
+    "improved",
+    "managed",
+    "migrated",
+    "maintained",
+    "worked",
+}
+
+EXPERIENCE_TITLE_LABELS = {
+    "education",
+    "experience",
+    "formation",
+    "languages",
+    "langues",
+    "location",
+    "profile",
+    "skills",
+    "compétences",
+    "competences",
+    "summary",
+}
+
+EXPERIENCE_PROSE_MARKERS = {
+    "after",
+    "before",
+    "because",
+    "during",
+    "from",
+    "joined",
+    "joining",
+    "left",
+    "leaving",
+    "in",
+    "moved",
+    "moving",
+    "relocated",
+    "relocating",
+    "since",
+    "studying",
+    "then",
+    "to",
+    "toward",
+    "towards",
+    "transferring",
+    "transitioning",
+    "until",
+    "while",
+    "working",
+    "work",
+    "worked",
+}
+
+EXPERIENCE_PROSE_COMPANY_PATTERNS = (
+    re.compile(r"\bau\s+sein\s+de\b", re.IGNORECASE),
+    re.compile(r"\b(before|after|during|while|since|because)\b", re.IGNORECASE),
+    re.compile(r"\b(joined|joining|moved|moving|relocated|relocating|worked|working)\b", re.IGNORECASE),
+    re.compile(
+        r"\bin\s+(?:paris|london|lyon|france|berlin|amsterdam|madrid|rome|marseille|bordeaux|toulouse|nantes|lille|grenoble|strasbourg|nice|rennes|montpellier|remote)\b",
+        re.IGNORECASE,
+    ),
+)
+
+EDUCATION_TITLE_KEYWORDS = {
+    "bachelor",
+    "degree",
+    "diploma",
+    "diplome d'ingenieur",
+    "diplome d'ingénieur",
+    "diplôme d'ingénieur",
+    "diplôme d'ingenieur",
+    "engineering school",
+    "bac+5",
+    "licence",
+    "bsc",
+    "master",
+    "msc",
+}
+
+EDUCATION_INSTITUTION_MARKERS = {
+    "academy",
+    "college",
+    "centrale",
+    "ecole",
+    "école",
+    "ens",
+    "epita",
+    "epitech",
+    "hec",
+    "imt",
+    "institute",
+    "institut",
+    "insa",
+    "polytech",
+    "school",
+    "university",
+    "universite",
+    "université",
+}
+
+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))
+    page_texts = [
+        text.strip()
+        for text in (page.extract_text() or "" for page in reader.pages)
+        if text.strip()
+    ]
+
+    if not page_texts:
+        raise ValueError("No extractable text found in PDF CV")
+
+    return "\n".join(page_texts)
+
+
+def extract_cv_signals(text: str) -> dict[str, object]:
+    lines = [_normalize_line(line) for line in text.splitlines()]
+    non_empty_lines = [line for line in lines if line]
+
+    name = non_empty_lines[0] if non_empty_lines else None
+    location = None
+    languages: list[str] = []
+    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
+            remainder = line.split(":", 1)[1].strip()
+            if remainder:
+                education_entry = _parse_education_entry(remainder)
+                if education_entry:
+                    education_entries.append(education_entry)
+                elif _looks_like_education_credential(remainder):
+                    pending_education_credential = remainder
+            continue
+
+        if in_education_section:
+            if pending_education_credential and _looks_like_institution(line):
+                education_entries.append(
+                    {
+                        "credential": pending_education_credential,
+                        "institution": line,
+                    }
+                )
+                pending_education_credential = None
+                continue
+
+            education_entry = _parse_education_entry(line)
+            if education_entry:
+                education_entries.append(education_entry)
+                pending_education_credential = None
+                continue
+
+            if _looks_like_education_credential(line):
+                pending_education_credential = line
+                continue
+
+            pending_education_credential = None
+            in_education_section = False
+
+        if lowered.startswith("location:"):
+            location = line.split(":", 1)[1].strip() or None
+            continue
+        if lowered.startswith(("languages:", "langues:")):
+            languages = _parse_csv_field(line)
+            continue
+        if lowered.startswith(("skills:", "compétences:")):
+            skills = _parse_csv_field(line)
+            continue
+        education_entry = _parse_education_entry(line)
+        if education_entry:
+            education_entries.append(education_entry)
+            continue
+        experience_entry = _parse_experience_entry(line)
+        if experience_entry:
+            experience_entries.append(experience_entry)
+
+    payload = {
+        "name": name,
+        "location": location,
+        "languages": languages,
+        "skills": skills,
+        "experience_entries": experience_entries,
+        "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)
+    return [item.strip() for item in value.split(",") if item.strip()]
+
+
+def _normalize_line(line: str) -> str:
+    stripped = line.strip()
+    if not stripped:
+        return ""
+
+    if stripped[0] in LEADING_BULLET_MARKERS:
+        return stripped[1:].lstrip()
+
+    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:
+            continue
+
+        left, right = (part.strip() for part in line.split(separator, 1))
+        if not left or not right:
+            continue
+
+        if _looks_like_education_credential(left) and _looks_like_institution(right):
+            return {"credential": left, "institution": right}
+
+        if _looks_like_education_credential(right) and _looks_like_institution(left):
+            return {"credential": right, "institution": left}
+
+    return None
+
+
+def _looks_like_education_credential(text: str) -> bool:
+    lowered = text.lower().replace(".", "")
+    return any(
+        re.search(rf"\b{re.escape(keyword)}\b", lowered)
+        for keyword in EDUCATION_TITLE_KEYWORDS
+    )
+
+
+def _looks_like_institution(text: str) -> bool:
+    lowered = text.lower()
+    return any(marker in lowered for marker in EDUCATION_INSTITUTION_MARKERS)
+
+
+def _parse_experience_entry(line: str) -> dict[str, str] | None:
+    if " au sein " in line.lower():
+        return None
+
+    for connector in EXPERIENCE_LINE_CONNECTORS:
+        if line.count(connector) != 1:
+            continue
+
+        title, company = (part.strip() for part in line.split(connector, 1))
+        if not title or not company:
+            continue
+
+        if not _looks_like_experience_title(title):
+            continue
+
+        if _looks_like_prose_company(company):
+            continue
+
+        return {
+            "title": title,
+            "company": company,
+        }
+
+    return None
+
+
+def _looks_like_experience_line(line: str) -> bool:
+    return _parse_experience_entry(line) is not None
+
+
+def _looks_like_experience_title(title: str) -> bool:
+    title_words = [word.strip(".,;:!?()[]{}") for word in title.split()]
+    if not title_words:
+        return False
+
+    first_word = title_words[0]
+    normalized_title = " ".join(title_words).casefold()
+    if normalized_title in EXPERIENCE_TITLE_LABELS:
+        return False
+
+    if first_word.casefold() in EXPERIENCE_TITLE_STOPWORDS:
+        return False
+
+    if first_word.casefold() in EXPERIENCE_TITLE_ACTION_STARTS:
+        return False
+
+    if len(title_words) == 1 and len(first_word) < 2:
+        return False
+
+    return not any(
+        word.casefold() in EXPERIENCE_PROSE_MARKERS for word in title_words if word
+    )
+
+
+def _looks_like_prose_company(company: str) -> bool:
+    return any(pattern.search(company) for pattern in EXPERIENCE_PROSE_COMPANY_PATTERNS)

src/job_research/profile/merge.py

@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+import re
+
+from job_research.models import (
+    CandidateProfileOutput,
+    EducationEntry,
+    ExperienceEntry,
+    WarningItem,
+)
+from job_research.profile.profile_parser import AuthoredProfile
+
+
+EXPERIENCE_NOTE_MARKERS = (
+    "experience",
+    "expérience",
+    "années d'expérience",
+    "annees d'experience",
+)
+
+
+MISSING_CV_FACT_WARNINGS = (
+    ("name", "No candidate name was extracted from the CV."),
+    ("experience_entries", "No experience entries were extracted from the CV."),
+    ("skills", "No skills were extracted from the CV."),
+    ("education_entries", "No education entries were extracted from the CV."),
+)
+
+
+def build_candidate_profile_output(
+    cv_signals: dict[str, object], authored: AuthoredProfile
+) -> CandidateProfileOutput:
+    warnings: list[WarningItem] = []
+
+    _append_years_of_experience_warning(cv_signals, authored.notes, warnings)
+    _append_missing_cv_fact_warnings(cv_signals, warnings)
+
+    merged_skills: list[str] = []
+    for skill in [
+        *(cv_signals.get("skills") or []),
+        *authored.strengths,
+        *authored.skills_to_emphasize,
+    ]:
+        if skill not in merged_skills:
+            merged_skills.append(skill)
+
+    experience_entries = cv_signals.get("experience_entries") or []
+    education_entries = cv_signals.get("education_entries") or []
+
+    return CandidateProfileOutput(
+        name=cv_signals.get("name"),
+        summary=authored.summary,
+        target_roles=authored.target_roles,
+        strengths=authored.strengths,
+        skills_to_emphasize=authored.skills_to_emphasize,
+        constraints=authored.constraints,
+        notes=authored.notes,
+        location=cv_signals.get("location"),
+        languages=cv_signals.get("languages") or [],
+        skills=merged_skills,
+        experience_entries=[ExperienceEntry.model_validate(item) for item in experience_entries],
+        education_entries=[EducationEntry.model_validate(item) for item in education_entries],
+        warnings=warnings,
+    )
+
+
+def _append_years_of_experience_warning(
+    cv_signals: dict[str, object], notes: list[str], warnings: list[WarningItem]
+) -> None:
+    years_of_experience = cv_signals.get("years_of_experience")
+    if years_of_experience is None:
+        return
+
+    for note in notes:
+        note_years_of_experience = _note_years_of_experience(note)
+        if note_years_of_experience is None or note_years_of_experience == years_of_experience:
+            continue
+
+        warnings.append(
+            WarningItem(
+                field="years_of_experience",
+                message=(
+                    "CV-derived years of experience does not match a markdown note. "
+                    "Review manually."
+                ),
+            )
+        )
+
+        return
+
+
+def _append_missing_cv_fact_warnings(
+    cv_signals: dict[str, object], warnings: list[WarningItem]
+) -> None:
+    for field, message in MISSING_CV_FACT_WARNINGS:
+        if cv_signals.get(field):
+            continue
+
+        warnings.append(WarningItem(field=field, message=message))
+
+
+def _note_years_of_experience(note: str) -> int | None:
+    normalized = note.casefold().replace("’", "'")
+    if not any(marker in normalized for marker in EXPERIENCE_NOTE_MARKERS):
+        return None
+
+    matches = re.findall(r"\b(\d{1,2})\b", normalized)
+    if len(matches) != 1:
+        return None
+
+    return int(matches[0])

src/job_research/profile/profile_parser.py

@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from dataclasses import dataclass, field
+
+
+LIST_MARKERS = ("- ", "* ", "+ ")
+
+
+@dataclass
+class AuthoredProfile:
+    summary: str | None = None
+    target_roles: list[str] = field(default_factory=list)
+    strengths: list[str] = field(default_factory=list)
+    skills_to_emphasize: list[str] = field(default_factory=list)
+    constraints: list[str] = field(default_factory=list)
+    notes: list[str] = field(default_factory=list)
+
+
+REQUIRED_SECTIONS = {
+    "summary",
+    "target roles",
+    "strengths",
+    "skills to emphasize",
+    "constraints",
+    "notes",
+}
+
+
+def parse_profile_markdown(markdown: str) -> AuthoredProfile:
+    sections: dict[str, list[str]] = defaultdict(list)
+    current_section: str | None = None
+
+    for raw_line in markdown.splitlines():
+        line = raw_line.strip()
+        if line.startswith("## "):
+            current_section = line[3:].strip().lower()
+            continue
+        if not line or current_section is None:
+            continue
+        sections[current_section].append(line)
+
+    missing = REQUIRED_SECTIONS - set(sections)
+    if missing:
+        missing_text = ", ".join(sorted(missing))
+        raise ValueError(f"Missing required markdown sections: {missing_text}")
+
+    return AuthoredProfile(
+        summary=" ".join(sections["summary"]),
+        target_roles=_parse_list_section("target roles", sections["target roles"]),
+        strengths=_parse_list_section("strengths", sections["strengths"]),
+        skills_to_emphasize=_parse_list_section(
+            "skills to emphasize", sections["skills to emphasize"]
+        ),
+        constraints=_parse_list_section("constraints", sections["constraints"]),
+        notes=_parse_notes_section(sections["notes"]),
+    )
+
+
+def _parse_list_section(section_name: str, lines: list[str]) -> list[str]:
+    items: list[str] = []
+
+    for line in lines:
+        item = _strip_list_marker(line)
+        if item is None:
+            raise ValueError(f"Unsupported content in section '{section_name}': {line}")
+        items.append(item)
+
+    return items
+
+
+def _parse_notes_section(lines: list[str]) -> list[str]:
+    notes: list[str] = []
+
+    for line in lines:
+        item = _strip_list_marker(line)
+        notes.append(item if item is not None else line)
+
+    return notes
+
+
+def _strip_list_marker(line: str) -> str | None:
+    for marker in LIST_MARKERS:
+        if line.startswith(marker):
+            return line[len(marker):].strip()
+
+    return None

src/job_research/storage.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from job_research.models import CandidateProfileOutput
+
+
+def save_candidate_profile_yaml(path: Path, profile: CandidateProfileOutput) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    payload = profile.model_dump(mode="json")
+    path.write_text(yaml.safe_dump(payload, sort_keys=False, allow_unicode=True), encoding="utf-8")
+
+
+def load_yaml(path: Path) -> dict[str, Any]:
+    payload = yaml.safe_load(path.read_text(encoding="utf-8"))
+    if not isinstance(payload, Mapping):
+        raise ValueError("candidate-profile YAML root must be a mapping")
+
+    return dict(payload)

tests/profile/test_cv_extractor.py

@@ -0,0 +1,414 @@
+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
+
+
+def test_extract_cv_signals_reads_basic_fields_from_text() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Languages: French, English
+        Skills: Python, SQL, Terraform, GCP, BigQuery
+        Data Engineer at Company A
+        Analytics Engineer at Company B
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["name"] == "Tonio"
+    assert extracted["location"] == "France"
+    assert extracted["languages"] == ["French", "English"]
+    assert extracted["skills"] == ["Python", "SQL", "Terraform", "GCP", "BigQuery"]
+    assert extracted["experience_entries"][0]["title"] == "Data Engineer"
+    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(
+        """
+        Tonio
+        Location: France
+        Consultant at Accenture
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == [
+        {"title": "Consultant", "company": "Accenture"}
+    ]
+
+
+def test_extract_cv_signals_allows_lowercase_company_names() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Data Engineer at leboncoin
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == [
+        {"title": "Data Engineer", "company": "leboncoin"}
+    ]
+
+
+def test_extract_cv_signals_ignores_prose_after_company_name() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Senior engineer at Microsoft before moving to Paris.
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == []
+
+
+def test_extract_cv_signals_normalizes_bullet_prefixed_fields_and_experience() -> None:
+    text = dedent(
+        """
+        Tonio
+        - Location: France
+        - Data Engineer at Company A
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["location"] == "France"
+    assert extracted["experience_entries"] == [
+        {"title": "Data Engineer", "company": "Company A"}
+    ]
+
+
+def test_extract_cv_signals_normalizes_en_dash_bullet_prefixed_fields_and_experience() -> None:
+    text = dedent(
+        """
+        Tonio
+        – Location: France
+        — Data Engineer at Company A
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["location"] == "France"
+    assert extracted["experience_entries"] == [
+        {"title": "Data Engineer", "company": "Company A"}
+    ]
+
+
+def test_extract_cv_signals_recognizes_french_field_labels() -> None:
+    text = dedent(
+        """
+        Tonio
+        Formation:
+        M.Sc. in Data Engineering at EPITA
+        Langues: French, English
+        Compétences: Python, SQL
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["languages"] == ["French", "English"]
+    assert extracted["skills"] == ["Python", "SQL"]
+    assert extracted["education_entries"] == [
+        {"credential": "M.Sc. in Data Engineering", "institution": "EPITA"}
+    ]
+
+
+def test_extract_cv_signals_parses_french_experience_connectors() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Ingénieur chez Dassault Systèmes
+        Développeur au CNRS
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == [
+        {"title": "Ingénieur", "company": "Dassault Systèmes"},
+        {"title": "Développeur", "company": "CNRS"},
+    ]
+
+
+def test_extract_cv_signals_parses_clear_titles_with_french_and_english_connectors() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Ingénieur à Thales
+        Ingénieur Data chez BNP Paribas
+        Consultant BI chez Accenture
+        Head of Data at Qonto
+        Product Owner at Qonto
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == [
+        {"title": "Ingénieur", "company": "Thales"},
+        {"title": "Ingénieur Data", "company": "BNP Paribas"},
+        {"title": "Consultant BI", "company": "Accenture"},
+        {"title": "Head of Data", "company": "Qonto"},
+        {"title": "Product Owner", "company": "Qonto"},
+    ]
+
+
+def test_extract_cv_signals_accepts_lowercase_short_and_real_company_titles() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        data engineer at Company A
+        iOS Engineer at Company A
+        3D Artist at Studio
+        R Developer at Company A
+        Data Engineer at Made in Design
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == [
+        {"title": "data engineer", "company": "Company A"},
+        {"title": "iOS Engineer", "company": "Company A"},
+        {"title": "3D Artist", "company": "Studio"},
+        {"title": "R Developer", "company": "Company A"},
+        {"title": "Data Engineer", "company": "Made in Design"},
+    ]
+
+
+def test_extract_cv_signals_ignores_french_prose_continuations() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Ingénieur au sein de BNP Paribas
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == []
+
+
+def test_extract_cv_signals_rejects_label_like_lines_without_colons() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location at Paris
+        Summary at a glance
+        Profile at LinkedIn
+        Education at EPITA
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == []
+
+
+def test_extract_cv_signals_rejects_narrative_bullet_experience_lines() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Implemented data pipelines at Airbnb
+        Designed dashboards at Company A
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == []
+
+
+def test_extract_cv_signals_ignores_in_paris_prose_tail() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Data Engineer at Microsoft in Paris
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == []
+
+
+def test_extract_cv_signals_extracts_education_entries_after_heading() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Education:
+        - Master of Science in Data Science at University of Paris
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["education_entries"] == [
+        {
+            "credential": "Master of Science in Data Science",
+            "institution": "University of Paris",
+        }
+    ]
+
+
+def test_extract_cv_signals_extracts_common_french_education_entries() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Education:
+        Diplôme d'ingénieur
+        CentraleSupélec
+        MSc
+        University of Paris
+        BSc
+        University of Oxford
+        Bac+5
+        École Polytechnique
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["education_entries"] == [
+        {
+            "credential": "Diplôme d'ingénieur",
+            "institution": "CentraleSupélec",
+        },
+        {"credential": "MSc", "institution": "University of Paris"},
+        {"credential": "BSc", "institution": "University of Oxford"},
+        {"credential": "Bac+5", "institution": "École Polytechnique"},
+    ]
+
+
+def test_extract_cv_signals_extracts_dotted_degree_variants() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Education:
+        M.Sc. in Data Engineering at EPITA
+        B.Sc. in Computer Science at University X
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["education_entries"] == [
+        {"credential": "M.Sc. in Data Engineering", "institution": "EPITA"},
+        {
+            "credential": "B.Sc. in Computer Science",
+            "institution": "University X",
+        },
+    ]
+
+
+def test_extract_cv_signals_ignores_before_moving_prose_tail() -> None:
+    text = dedent(
+        """
+        Tonio
+        Location: France
+        Senior Engineer at Microsoft Before moving to Paris.
+        """
+    ).strip()
+
+    extracted = extract_cv_signals(text)
+
+    assert extracted["experience_entries"] == []
+
+
+def test_extract_pdf_text_skips_blank_pages(monkeypatch) -> None:
+    class FakePage:
+        def __init__(self, text: str | None) -> None:
+            self._text = text
+
+        def extract_text(self) -> str | None:
+            return self._text
+
+    class FakePdfReader:
+        def __init__(self, path: str) -> None:
+            self.path = path
+            self.pages = [
+                FakePage(None),
+                FakePage("Tonio"),
+                FakePage(""),
+                FakePage("Data Engineer"),
+            ]
+
+    seen_paths: list[str] = []
+
+    def fake_pdf_reader(path: str) -> FakePdfReader:
+        seen_paths.append(path)
+        return FakePdfReader(path)
+
+    monkeypatch.setattr("job_research.profile.cv_extractor.PdfReader", fake_pdf_reader)
+
+    extracted = extract_pdf_text(Path("cv.pdf"))
+
+    assert extracted == "Tonio\nData Engineer"
+    assert seen_paths == ["cv.pdf"]
+
+
+def test_extract_pdf_text_rejects_textless_pdfs(monkeypatch) -> None:
+    class FakePage:
+        def extract_text(self) -> str | None:
+            return "   "
+
+    class FakePdfReader:
+        def __init__(self, path: str) -> None:
+            self.path = path
+            self.pages = [FakePage(), FakePage()]
+
+    def fake_pdf_reader(path: str) -> FakePdfReader:
+        return FakePdfReader(path)
+
+    monkeypatch.setattr("job_research.profile.cv_extractor.PdfReader", fake_pdf_reader)
+
+    with pytest.raises(ValueError, match="No extractable text"):
+        extract_pdf_text(Path("cv.pdf"))

tests/profile/test_merge.py

@@ -0,0 +1,74 @@
+from job_research.profile.merge import build_candidate_profile_output
+from job_research.profile.profile_parser import AuthoredProfile
+
+
+def test_build_candidate_profile_output_writes_warning_when_facts_conflict() -> 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=["Years of experience feels closer to 3."],
+    )
+
+    output = build_candidate_profile_output(cv_signals, authored)
+
+    assert output.summary == "Junior data engineer focused on GCP."
+    assert output.constraints == ["CDI only"]
+    assert any(item.field == "years_of_experience" for item in output.warnings)
+
+
+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 any(item.field == "years_of_experience" for item in output.warnings)
+
+
+def test_build_candidate_profile_output_warns_on_missing_core_cv_facts() -> None:
+    cv_signals = {
+        "location": "France",
+        "languages": ["French", "English"],
+        "skills": [],
+        "experience_entries": [],
+        "education_entries": [],
+    }
+    authored = AuthoredProfile(
+        summary="Junior data engineer focused on GCP."
+    )
+
+    output = build_candidate_profile_output(cv_signals, authored)
+
+    assert [item.field for item in output.warnings] == [
+        "name",
+        "experience_entries",
+        "skills",
+        "education_entries",
+    ]

tests/profile/test_profile_parser.py

@@ -0,0 +1,74 @@
+import pytest
+
+from textwrap import dedent
+
+from job_research.profile.profile_parser import parse_profile_markdown
+
+
+def test_parse_profile_markdown_reads_light_required_template() -> None:
+    markdown = dedent(
+        """
+        # Candidate Profile
+
+        ## Summary
+        Junior data engineer focused on Python and GCP.
+
+        ## Target Roles
+        * Data Engineer
+        + Analytics Engineer
+
+        ## Strengths
+        - Python
+        - SQL
+
+        ## Skills To Emphasize
+        - BigQuery
+        - Terraform
+
+        ## Constraints
+        - CDI only
+        - France only
+
+        ## Notes
+        Slight preference for French listings.
+        """
+    ).strip()
+
+    profile = parse_profile_markdown(markdown)
+
+    assert profile.summary == "Junior data engineer focused on Python and GCP."
+    assert profile.target_roles == ["Data Engineer", "Analytics Engineer"]
+    assert profile.strengths == ["Python", "SQL"]
+    assert profile.skills_to_emphasize == ["BigQuery", "Terraform"]
+    assert profile.constraints == ["CDI only", "France only"]
+    assert profile.notes == ["Slight preference for French listings."]
+
+
+def test_parse_profile_markdown_rejects_unsupported_list_content() -> None:
+    markdown = dedent(
+        """
+        # Candidate Profile
+
+        ## Summary
+        Junior data engineer focused on Python and GCP.
+
+        ## Target Roles
+        - Data Engineer
+        Analytics Engineer
+
+        ## Strengths
+        - Python
+
+        ## Skills To Emphasize
+        - BigQuery
+
+        ## Constraints
+        - CDI only
+
+        ## Notes
+        - Slight preference for French listings.
+        """
+    ).strip()
+
+    with pytest.raises(ValueError, match="Unsupported content in section 'target roles'"):
+        parse_profile_markdown(markdown)

tests/test_cli.py

@@ -0,0 +1,191 @@
+from subprocess import run
+from textwrap import dedent
+
+from job_research.storage import load_yaml
+
+
+def test_installed_cli_help_exposes_build_profile_subcommand() -> None:
+    result = run(["uv", "run", "job-research", "--help"], capture_output=True, text=True, check=False)
+
+    assert result.returncode == 0
+    assert "build-profile" in result.stdout
+    assert "Build candidate-profile.yaml from CV and markdown profile." in result.stdout
+
+
+def test_installed_cli_subcommand_help_works() -> None:
+    result = run(["uv", "run", "job-research", "build-profile", "--help"], capture_output=True, text=True, check=False)
+
+    assert result.returncode == 0
+    assert "Usage: job-research build-profile" in result.stdout
+
+
+def test_build_profile_writes_yaml_from_cv_and_profile(tmp_path) -> None:
+    cv = tmp_path / "cv.txt"
+    cv.write_text(
+        dedent(
+            """
+            Tonio Example
+            Location: France
+            Languages: French, English
+            Skills: Python, SQL, Terraform
+            Years of experience: 3
+            Data Engineer at Acme
+            Education: Master of Science at Example University
+            """
+        ).strip(),
+        encoding="utf-8",
+    )
+    profile = tmp_path / "profile.md"
+    profile.write_text(
+        dedent(
+            """
+            # Candidate Profile
+
+            ## Summary
+            Junior data engineer focused on Python and GCP.
+
+            ## Target Roles
+            - Data Engineer
+
+            ## Strengths
+            - Python
+            - SQL
+
+            ## Skills To Emphasize
+            - GCP
+            - BigQuery
+
+            ## Constraints
+            - CDI only
+            - France only
+
+            ## Notes
+            - Slight preference for French listings.
+            - Years of experience feels closer to 4.
+            """
+        ).strip(),
+        encoding="utf-8",
+    )
+    out = tmp_path / "candidate-profile.yaml"
+
+    result = run(
+        ["uv", "run", "job-research", "build-profile", "--cv", str(cv), "--profile", str(profile), "--out", str(out)],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+
+    assert result.returncode == 0
+    assert f"candidate profile written to {out}" in result.stdout
+    assert "Warnings included: 1" in result.stdout
+
+    payload = load_yaml(out)
+
+    assert payload["name"] == "Tonio Example"
+    assert payload["summary"] == "Junior data engineer focused on Python and GCP."
+    assert payload["target_roles"] == ["Data Engineer"]
+    assert payload["skills"] == ["Python", "SQL", "Terraform", "GCP", "BigQuery"]
+    assert any(item["field"] == "years_of_experience" for item in payload["warnings"])
+
+
+def test_build_profile_reports_when_no_warnings_are_included(tmp_path) -> None:
+    cv = tmp_path / "cv.txt"
+    cv.write_text(
+        dedent(
+            """
+            Tonio Example
+            Location: France
+            Languages: French, English
+            Skills: Python, SQL
+            Data Engineer at Acme
+            Education: Master of Science at Example University
+            """
+        ).strip(),
+        encoding="utf-8",
+    )
+    profile = tmp_path / "profile.md"
+    profile.write_text(
+        dedent(
+            """
+            # Candidate Profile
+
+            ## Summary
+            Junior data engineer focused on Python and GCP.
+
+            ## Target Roles
+            - Data Engineer
+
+            ## Strengths
+            - Python
+            - SQL
+
+            ## Skills To Emphasize
+            - GCP
+            - BigQuery
+
+            ## Constraints
+            - CDI only
+            - France only
+
+            ## Notes
+            - Slight preference for French listings.
+            """
+        ).strip(),
+        encoding="utf-8",
+    )
+    out = tmp_path / "candidate-profile.yaml"
+
+    result = run(
+        ["uv", "run", "job-research", "build-profile", "--cv", str(cv), "--profile", str(profile), "--out", str(out)],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+
+    assert result.returncode == 0
+    assert f"candidate profile written to {out}" in result.stdout
+    assert "No warnings included." in result.stdout
+
+
+def test_build_profile_rejects_empty_cv_text_before_writing(tmp_path) -> None:
+    cv = tmp_path / "cv.txt"
+    cv.write_text("   \n", encoding="utf-8")
+    profile = tmp_path / "profile.md"
+    profile.write_text(
+        dedent(
+            """
+            # Candidate Profile
+
+            ## Summary
+            Junior data engineer.
+
+            ## Target Roles
+            - Data Engineer
+
+            ## Strengths
+            - Python
+
+            ## Skills To Emphasize
+            - BigQuery
+
+            ## Constraints
+            - CDI only
+
+            ## Notes
+            - Slight preference for French listings.
+            """
+        ).strip(),
+        encoding="utf-8",
+    )
+    out = tmp_path / "candidate-profile.yaml"
+
+    result = run(
+        ["uv", "run", "job-research", "build-profile", "--cv", str(cv), "--profile", str(profile), "--out", str(out)],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+
+    assert result.returncode != 0
+    assert not out.exists()
+    assert "No readable text found in CV input" in result.stderr

tests/test_storage.py

@@ -0,0 +1,39 @@
+import pytest
+
+from job_research.models import CandidateProfileOutput, ExperienceEntry, WarningItem
+from job_research.storage import save_candidate_profile_yaml, load_yaml
+
+
+def test_save_candidate_profile_yaml_round_trips_readable_output(tmp_path) -> None:
+    profile = CandidateProfileOutput(
+        name="Tonio",
+        summary="Junior data engineer focused on Python and GCP.",
+        target_roles=["Data Engineer"],
+        strengths=["Python", "SQL"],
+        skills_to_emphasize=["GCP", "BigQuery"],
+        constraints=["CDI only", "France only"],
+        notes=["Slight preference for French listings."],
+        location="France",
+        languages=["French", "English"],
+        skills=["Python", "SQL", "Terraform", "GCP", "BigQuery"],
+        experience_entries=[ExperienceEntry(company="A", title="Data Engineer")],
+        education_entries=[],
+        warnings=[WarningItem(field="years_of_experience", message="CV and profile disagree.")],
+    )
+
+    out = tmp_path / "candidate-profile.yaml"
+    save_candidate_profile_yaml(out, profile)
+
+    payload = load_yaml(out)
+
+    assert payload["name"] == "Tonio"
+    assert payload["constraints"] == ["CDI only", "France only"]
+    assert payload["warnings"][0]["field"] == "years_of_experience"
+
+
+def test_load_yaml_rejects_non_mapping_root(tmp_path) -> None:
+    path = tmp_path / "candidate-profile.yaml"
+    path.write_text("[]", encoding="utf-8")
+
+    with pytest.raises(ValueError, match="mapping"):
+        load_yaml(path)

uv.lock

@@ -2,7 +2,303 @@ version = 1
 revision = 3
 requires-python = ">=3.13"
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
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