feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

Extrae al registry funciones del proyecto interno footprint_aurgi:
- core (6): slugify_ascii, normalize_for_join, cp_provincia_es, infer_provincia_from_cp, safe_read_csv_fallback, csv_to_parquet_duckdb
- geo puras (7): haversine_km, point_in_ring, point_in_polygon, point_in_polygons_bbox, polygon_bbox, extent_with_padding, distance_bucket
- geo I/O (4): load_geojson_polygons, load_boundary_gdf, add_basemap_osm, add_basemap_with_timeout
- valhalla client (4): valhalla_route, valhalla_isochrone, valhalla_isochrones_async, valhalla_matrix_1_to_n
- datascience stats (7): trimmed_mean, geometric_mean, detect_distribution_type, best_central_tendency, summary_stats, kde_density_levels, alpha_shape_concave_hull
- datascience fuzzy (3): fuzzy_merge_adaptive (rapidfuzz), words_to_dataset, remove_words_from_column
- datascience viz (2): plot_kde_2d, plot_heatmap_log
- infra (4): compress_pdf_ghostscript, render_table_page_pdfpages, add_header_logo, osm2pgsql_ingest
- pipelines (4): setup_geo_stack_docker, compute_centers_reachability, generate_isochrones_by_zone, count_points_per_zone
- types geo (4): LonLat, BBox, IsochroneRequest, Centro

Incluye:
- apps/footprint_geo_stack/ (PostGIS + Martin + Valhalla via docker-compose)
- 131/132 tests pasan (1 skip esperado: osm2pgsql en PATH)
- Issue tracker dev/issues/0052-footprint-aurgi-extraction.md
- Atribucion uniforme: source_repo internal:footprint_aurgi, source_license internal-aurgi
- Build con 9 agentes en paralelo (8 wave 1 + 1 wave 2 pipelines)

Tambien commitea trabajo previo no commiteado: aggregate_extraction_results, chunk_with_overlap, clean_pdf_text, merge_entity_aliases, extract_graph_gliner2, extract_relations_mrebel, extract_triples_spacy_es, gliner2/mrebel/marianmt/rebel/spacy_es load_model, parse_rebel_output, translate_es_to_en, issue 0050/0051.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

python/functions/pipelines/extract_graph_from_text.md

@@ -0,0 +1,89 @@
+---
+name: extract_graph_from_text
+kind: pipeline
+lang: py
+domain: pipelines
+version: "1.0.0"
+purity: impure
+signature: "def extract_graph_from_text(text: str, entity_labels: list[str], relation_labels: list | dict, allowed: dict, model: Any, threshold: float = 0.3, max_chars_per_chunk: int = 1500, overlap_sentences: int = 2) -> dict"
+description: "Pipeline E2E: texto -> grafo de entidades y relaciones. Orquesta chunking, extraccion con GLiNER2 por chunk, agregacion, filtrado tipado y resolucion de alias. Refactorizacion del playground del analisis gliner_glirel_tuning."
+tags: [pipeline, graph, ner, relation-extraction, gliner2, nlp, e2e, knowledge-graph, datascience, python]
+uses_functions:
+  - chunk_with_overlap_py_core
+  - extract_graph_gliner2_py_datascience
+  - aggregate_extraction_results_py_core
+  - filter_relations_by_entity_types_py_core
+  - merge_entity_aliases_py_core
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [time, typing.Any]
+params:
+  - name: text
+    desc: "Texto de entrada de cualquier longitud. Se auto-chunkea si supera max_chars_per_chunk. Recomendado: pre-limpiar con clean_pdf_text si viene de un PDF."
+  - name: entity_labels
+    desc: "Tipos de entidad para GLiNER2. E.g. ['person', 'organization', 'location']. Usar snake_case (mejor recall segun notebook 08)."
+  - name: relation_labels
+    desc: "Tipos de relacion. Lista de strings o dict {label: description}. E.g. ['works_at', 'ceo_of'] o {'ceo_of': 'person is CEO of organization'}."
+  - name: allowed
+    desc: "Reglas de filtrado tipado {rel_type: (head_types, tail_types)}. Pasar {} para desactivar el filtrado. E.g. {'ceo_of': (['person'], ['organization'])}."
+  - name: model
+    desc: "Instancia GLiNER2 cargada con gliner2_load_model. Inyectada por el caller para permitir cache entre llamadas."
+  - name: threshold
+    desc: "Umbral de confianza GLiNER2 (0-1). 0.3 validado empiricamente. Menor = mas recall, mas ruido."
+  - name: max_chars_per_chunk
+    desc: "Maximo de caracteres por chunk antes de dividir. 1500 es el valor optimo para GLiNER2-large."
+  - name: overlap_sentences
+    desc: "Frases de overlap entre chunks consecutivos. 2 evita perder entidades en los bordes de chunk."
+output: "Dict con 'nodes' (lista de {id, type, count}), 'edges' (lista de {from, to, kind}) y 'stats' ({n_chunks, n_nodes, n_edges, n_dropped_typed, elapsed_s}). Listo para serializar a JSON y visualizar con Sigma/D3."
+tested: true
+tests:
+  - "texto corto produce nodos y aristas esperados con stub model"
+  - "stats tiene todos los campos requeridos"
+test_file_path: "python/functions/pipelines/tests/test_extract_graph_from_text.py"
+file_path: "python/functions/pipelines/extract_graph_from_text.py"
+notes: |
+  Refactorizacion directa del playground/server.py del analisis
+  projects/osint_graph/analysis/gliner_glirel_tuning.
+  Todas las recetas validadas empiricamente en los notebooks 04, 06 y 08:
+  - threshold=0.3 (notebook 04)
+  - overlap_sentences=2 (notebook 06)
+  - filtrado tipado (notebook 08)
+  - coreference normalize+substring (playground/server.py)
+
+  Para PDFs: usar extract_pdf_text + clean_pdf_text antes de llamar a este pipeline.
+  Para OpenIE sin vocabulario fijo: usar extract_triples_spacy_es como alternativa.
+---
+
+## Ejemplo
+
+```python
+from datascience.gliner2_load_model import gliner2_load_model
+from pipelines.extract_graph_from_text import extract_graph_from_text
+
+model = gliner2_load_model(device="auto")
+
+ENTITY_LABELS = ["person", "organization", "location"]
+RELATION_LABELS = ["works_at", "ceo_of", "headquartered_in", "president_of"]
+ALLOWED = {
+    "ceo_of":       (["person"], ["organization"]),
+    "president_of": (["person"], ["organization"]),
+    "works_at":     (["person"], ["organization"]),
+    "headquartered_in": (["organization"], ["location"]),
+}
+
+text = """Carlos Torres Blanco es el presidente de BBVA.
+BBVA tiene su sede corporativa en Bilbao, aunque opera en mas de 30 paises."""
+
+graph = extract_graph_from_text(
+    text=text,
+    entity_labels=ENTITY_LABELS,
+    relation_labels=RELATION_LABELS,
+    allowed=ALLOWED,
+    model=model,
+)
+# graph["nodes"] -> [{"id": "Carlos Torres Blanco", "type": "person", "count": 1}, ...]
+# graph["edges"] -> [{"from": "Carlos Torres Blanco", "to": "BBVA", "kind": "president_of"}]
+# graph["stats"] -> {"n_chunks": 1, "n_nodes": 3, "n_edges": 2, ...}
+```