feat(nlp-service): full extract() pipeline — assembles all steps

Also adds regex year-fallback in extract_dates() for de/es spaCy small
models that don't tag bare 4-digit years as DATE entities, and widens
the direction-token window to 2 tokens back to handle Spanish "antes de".

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nlp-service/extractor.py

@@ -132,9 +132,28 @@ def _year_end(d: date) -> date:
     return d
 
 
+def _find_year_spans(doc) -> list:
+    """Fallback: find tokens that look like 4-digit years (1000–2999) when NER
+    produces no DATE entities.  Returns a list of single-token pseudo-spans
+    (spaCy Span objects) labelled 'DATE'."""
+    spans = []
+    for token in doc:
+        if _YEAR_RE.match(token.text):
+            year = int(token.text)
+            if 1000 < year < 3000:
+                span = doc[token.i : token.i + 1]
+                spans.append(span)
+    return spans
+
+
 def extract_dates(doc, lang: str) -> tuple[str | None, str | None]:
     """Return (date_from, date_to) as ISO strings or None."""
     date_spans = [ent for ent in doc.ents if ent.label_ == "DATE"]
+
+    # Fallback: some spaCy small models (de, es) don't tag bare years as DATE
+    if not date_spans:
+        date_spans = _find_year_spans(doc)
+
     if not date_spans:
         return None, None
 
@@ -160,11 +179,16 @@ def extract_dates(doc, lang: str) -> tuple[str | None, str | None]:
     if not d:
         return None, None
 
-    prev_lower = doc[span.start - 1].lower_ if span.start > 0 else ""
+    # Check up to 2 tokens before the date span to handle multi-word prepositions
+    # like Spanish "antes de 1920" where the keyword is 2 tokens back.
+    prev_tokens = [
+        doc[span.start - i].lower_
+        for i in range(1, min(3, span.start + 1))
+    ]
 
-    if prev_lower in before_tokens:
+    if any(t in before_tokens for t in prev_tokens):
         return None, _year_end(d).isoformat()
-    if prev_lower in after_tokens:
+    if any(t in after_tokens for t in prev_tokens):
         return d.isoformat(), None
     # Bare year/date — closed year-range
     return d.isoformat(), _year_end(d).isoformat()
@@ -195,3 +219,27 @@ def extract_keywords(doc, excluded_spans: list) -> list[str]:
             keywords.append(lemma)
 
     return keywords
+
+
+# ── Step 5: Assembly ─────────────────────────────────────────────────────────
+
+def extract(query: str, lang: str) -> ParseResponse:
+    """Run the full NLP pipeline and return a ParseResponse."""
+    nlp = get_nlp(lang)
+    doc = nlp(query)
+
+    per_spans = [ent for ent in doc.ents if ent.label_ == "PER"]
+
+    person_names = extract_person_names(doc)
+    person_role = detect_person_role(doc, per_spans, lang)
+    date_from, date_to = extract_dates(doc, lang)
+    keywords = extract_keywords(doc, list(doc.ents))
+
+    return ParseResponse(
+        personNames=person_names,
+        personRole=person_role,
+        dateFrom=date_from,
+        dateTo=date_to,
+        keywords=keywords,
+        rawQuery=query,
+    )