feat: funciones Python datascience, finance, cybersecurity y pipelines

Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift,
diff_entities/relations, extract_entities/relations_llm, hotness_score, melt,
merge_graphs, pivot, build_entity/relation_schema_prompt.
Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order,
hawkes_intensity + módulo finance.py.
Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py.
Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

python/functions/pipelines/extraction_pipeline_test.py

@@ -0,0 +1,227 @@
+"""Tests para extraction_pipeline."""
+
+from __future__ import annotations
+
+import os
+import sys
+import tempfile
+
+_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+if _ROOT not in sys.path:
+    sys.path.insert(0, _ROOT)
+
+from python.functions.pipelines.extraction_pipeline import extraction_pipeline
+
+
+# ── LLM stubs ─────────────────────────────────────────────────────────────────
+
+def _llm_with_entities(messages: list[dict]) -> dict:
+    """LLM stub que retorna entidades fijas para el primer mensaje de extraccion."""
+    system_content = messages[0]["content"] if messages else ""
+    if "entity" in system_content.lower() or "entities" in system_content.lower():
+        return {
+            "entities": [
+                {
+                    "name": "John Smith",
+                    "type_ref": "osint_person_go_cybersecurity",
+                    "attributes": {"full_name": "John Smith", "nationality": "US"},
+                    "confidence": 0.95,
+                },
+                {
+                    "name": "evil-corp.com",
+                    "type_ref": "osint_domain_go_cybersecurity",
+                    "attributes": {"fqdn": "evil-corp.com"},
+                    "confidence": 0.88,
+                },
+            ]
+        }
+    # Llamada de relaciones
+    return {
+        "relations": [
+            {
+                "from_name": "John Smith",
+                "to_name": "evil-corp.com",
+                "relation_type": "operates",
+                "description": "John Smith operates evil-corp.com",
+                "confidence": 0.8,
+            }
+        ]
+    }
+
+
+def _llm_empty(messages: list[dict]) -> dict:
+    """LLM stub que retorna siempre resultado vacio."""
+    system_content = messages[0]["content"] if messages else ""
+    if "entit" in system_content.lower():
+        return {"entities": []}
+    return {"relations": []}
+
+
+ENTITY_PRESETS = [
+    {
+        "type_ref": "osint_person_go_cybersecurity",
+        "label": "Person",
+        "metadata_fields": ["full_name", "alias", "nationality"],
+    },
+    {
+        "type_ref": "osint_domain_go_cybersecurity",
+        "label": "Domain",
+        "metadata_fields": ["fqdn", "registrar"],
+    },
+]
+
+RELATION_TYPES = ["operates", "owns", "funds", "communicates_with", "related_to"]
+
+
+# ── Tests ──────────────────────────────────────────────────────────────────────
+
+def test_documento_con_entidades_y_relaciones():
+    """documento con entidades y relaciones retorna ExtractionResult completo"""
+    text = (
+        "John Smith, a US national, operates the domain evil-corp.com. "
+        "He was identified as the main administrator of the infrastructure."
+    )
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, encoding="utf-8") as f:
+        f.write(text)
+        tmp_path = f.name
+
+    try:
+        result = extraction_pipeline(
+            file_path=tmp_path,
+            entity_presets=ENTITY_PRESETS,
+            relation_types=RELATION_TYPES,
+            llm_chat_json=_llm_with_entities,
+            chunk_size=500,
+            chunk_overlap=50,
+            confidence_threshold=0.5,
+            dedup_threshold=0.85,
+        )
+        assert result is not None
+        assert len(result.entities) >= 1
+        assert result.stats.total_chunks >= 1
+        assert result.stats.total_chars > 0
+    finally:
+        os.unlink(tmp_path)
+
+
+def test_documento_vacio():
+    """documento vacio retorna ExtractionResult con listas vacias"""
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, encoding="utf-8") as f:
+        f.write("")
+        tmp_path = f.name
+
+    try:
+        result = extraction_pipeline(
+            file_path=tmp_path,
+            entity_presets=ENTITY_PRESETS,
+            relation_types=RELATION_TYPES,
+            llm_chat_json=_llm_empty,
+        )
+        assert result is not None
+        assert result.entities == []
+        assert result.relations == []
+        assert result.stats.total_chunks == 0
+    finally:
+        os.unlink(tmp_path)
+
+
+def test_documento_sin_entidades_detectables():
+    """documento sin entidades detectables retorna listas vacias"""
+    text = "The weather is nice today. The sun shines brightly over the mountains."
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, encoding="utf-8") as f:
+        f.write(text)
+        tmp_path = f.name
+
+    try:
+        result = extraction_pipeline(
+            file_path=tmp_path,
+            entity_presets=ENTITY_PRESETS,
+            relation_types=RELATION_TYPES,
+            llm_chat_json=_llm_empty,
+            confidence_threshold=0.5,
+        )
+        assert result is not None
+        assert result.entities == []
+        assert result.relations == []
+        assert result.stats.raw_entities_count == 0
+    finally:
+        os.unlink(tmp_path)
+
+
+def test_archivo_no_encontrado_lanza_filenotfounderror():
+    """archivo no encontrado lanza FileNotFoundError"""
+    import pytest
+    with pytest.raises(FileNotFoundError):
+        extraction_pipeline(
+            file_path="/tmp/no_existe_para_test_extraccion_pipeline.txt",
+            entity_presets=ENTITY_PRESETS,
+            relation_types=RELATION_TYPES,
+            llm_chat_json=_llm_empty,
+        )
+
+
+def test_entity_presets_vacio_lanza_valueerror():
+    """entity presets vacio lanza ValueError"""
+    import pytest
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, encoding="utf-8") as f:
+        f.write("some text")
+        tmp_path = f.name
+
+    try:
+        with pytest.raises(ValueError):
+            extraction_pipeline(
+                file_path=tmp_path,
+                entity_presets=[],
+                relation_types=RELATION_TYPES,
+                llm_chat_json=_llm_empty,
+            )
+    finally:
+        os.unlink(tmp_path)
+
+
+def test_progress_callback_se_invoca():
+    """progress callback se invoca durante la ejecucion"""
+    calls: list[tuple[str, float]] = []
+
+    def _on_progress(msg: str, pct: float) -> None:
+        calls.append((msg, pct))
+
+    text = "John Smith operates evil-corp.com."
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, encoding="utf-8") as f:
+        f.write(text)
+        tmp_path = f.name
+
+    try:
+        extraction_pipeline(
+            file_path=tmp_path,
+            entity_presets=ENTITY_PRESETS,
+            relation_types=RELATION_TYPES,
+            llm_chat_json=_llm_with_entities,
+            on_progress=_on_progress,
+        )
+        assert len(calls) > 0
+        messages = [c[0] for c in calls]
+        assert any("Extracting" in m or "Done" in m or "Dedup" in m for m in messages)
+    finally:
+        os.unlink(tmp_path)
+
+
+def test_stats_se_rellenan_correctamente():
+    """stats se rellenan correctamente con conteos y tiempo"""
+    text = "John Smith, a US national, operates the domain evil-corp.com."
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, encoding="utf-8") as f:
+        f.write(text)
+        tmp_path = f.name
+
+    try:
+        result = extraction_pipeline(
+            file_path=tmp_path,
+            entity_presets=ENTITY_PRESETS,
+            relation_types=RELATION_TYPES,
+            llm_chat_json=_llm_with_entities,
+        )
+        assert result.stats.total_chars > 0
+        assert result.stats.total_chunks >= 1
+        assert result.stats.processing_time_seconds >= 0.0
+    finally:
+        os.unlink(tmp_path)