From 40bea8160304432360481040ad5086413375f831 Mon Sep 17 00:00:00 2001 From: fn-registry agent Date: Tue, 28 Apr 2026 22:13:08 +0200 Subject: [PATCH] chore: initial sync --- .claude/CLAUDE.md | 40 + .gitignore | 12 + .python-version | 1 + README.md | 0 extract.py | 540 ++++++ lib/__init__.py | 0 lib/build_entity_schema_prompt.py | 43 + lib/build_relation_schema_prompt.py | 22 + lib/core_functions.py | 814 +++++++++ lib/deduplicate_entities.py | 283 +++ lib/deduplicate_relations.py | 189 ++ lib/deduplication_result.py | 22 + lib/entity_candidate.py | 34 + lib/extract_entities_llm.py | 145 ++ lib/extract_relations_llm.py | 141 ++ lib/extract_text_from_file.py | 92 + lib/extraction_pipeline.py | 208 +++ lib/extraction_result.py | 20 + lib/extraction_stats.py | 25 + lib/merge_entity_attributes.py | 78 + lib/normalize_entity_name.py | 81 + lib/relation_candidate.py | 35 + lib/render_sigma_html.py | 234 +++ lib/split_text_into_chunks.py | 66 + main.py | 6 + notebooks/01_extraction.ipynb | 935 ++++++++++ pyproject.toml | 15 + registry.db | 0 run-jupyter-lab.sh | 45 + uv.lock | 2549 +++++++++++++++++++++++++++ 30 files changed, 6675 insertions(+) create mode 100644 .claude/CLAUDE.md create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 README.md create mode 100644 extract.py create mode 100644 lib/__init__.py create mode 100644 lib/build_entity_schema_prompt.py create mode 100644 lib/build_relation_schema_prompt.py create mode 100644 lib/core_functions.py create mode 100644 lib/deduplicate_entities.py create mode 100644 lib/deduplicate_relations.py create mode 100644 lib/deduplication_result.py create mode 100644 lib/entity_candidate.py create mode 100644 lib/extract_entities_llm.py create mode 100644 lib/extract_relations_llm.py create mode 100644 lib/extract_text_from_file.py create mode 100644 lib/extraction_pipeline.py create mode 100644 lib/extraction_result.py create mode 100644 lib/extraction_stats.py create mode 100644 lib/merge_entity_attributes.py create mode 100644 lib/normalize_entity_name.py create mode 100644 lib/relation_candidate.py create mode 100644 lib/render_sigma_html.py create mode 100644 lib/split_text_into_chunks.py create mode 100644 main.py create mode 100644 notebooks/01_extraction.ipynb create mode 100644 pyproject.toml create mode 100644 registry.db create mode 100755 run-jupyter-lab.sh create mode 100644 uv.lock diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md new file mode 100644 index 0000000..36a65c7 --- /dev/null +++ b/.claude/CLAUDE.md @@ -0,0 +1,40 @@ +# JUPYTER HABILITADO EN ESTE ANALISIS + +## Reglas OBLIGATORIAS para Claude + +### 1. CODIGO INMUTABLE — NUNCA MODIFICAR CELDAS EXISTENTES +- **PROHIBIDO** usar NotebookEdit para reemplazar celdas existentes +- **SIEMPRE** anadir celdas NUEVAS al final del notebook +- Si hay un error en una celda, crear celda nueva con la correccion +- El historial de trabajo debe quedar intacto para trazabilidad + +### 2. PROGRAMACION FUNCIONAL OBLIGATORIA +- **Funciones puras**: sin efectos secundarios, mismo input -> mismo output +- **Inmutabilidad**: nunca mutar datos, crear copias transformadas +- **Composicion**: funciones pequenas que se combinan +- Preferir: `map`, `filter`, `reduce`, list comprehensions +- Evitar: loops con mutacion, `global`, modificar argumentos in-place + +### 3. SIEMPRE usar MCP jupyter para ejecutar codigo Python +- Las ejecuciones se ven en tiempo real en Jupyter Lab del usuario +- Compartimos variables y estado del kernel +- **NUNCA usar bash para ejecutar Python en este analisis** + +### 4. Verificar Jupyter activo ANTES de ejecutar +- Si no esta activo: pedir al usuario que ejecute `./run-jupyter-lab.sh` + +### 5. Gestion de notebooks +- Notebooks en la carpeta `notebooks/` o subcarpetas +- Si un notebook tiene >50 celdas, crear uno nuevo +- Nombrar descriptivamente: `01_exploracion.ipynb`, `02_limpieza.ipynb` + +### 6. Gestion de Python +- **SIEMPRE usar `uv`** para gestionar dependencias +- Anadir paquetes con `uv add nombre_paquete` + +### 7. Acceso al fn_registry +- `FN_REGISTRY_ROOT` apunta a la raiz del registry +- Para importar funciones Python: `sys.path.insert(0, os.path.join(os.environ["FN_REGISTRY_ROOT"], "python", "functions"))` +- Para consultar registry.db: `sqlite3` o `import sqlite3` con la ruta `$FN_REGISTRY_ROOT/registry.db` + + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6c9a63d --- /dev/null +++ b/.gitignore @@ -0,0 +1,12 @@ +.venv/ +.mcp.json +.jupyter-port +.jupyter/ +.jupyter_ystore.db +.ipython/ +__pycache__/ +*.pyc +.ipynb_checkpoints/ +bin/ +data/ +.DS_Store diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/extract.py b/extract.py new file mode 100644 index 0000000..9704a02 --- /dev/null +++ b/extract.py @@ -0,0 +1,540 @@ +"""Extracción de grafo ontológico desde un documento. + +Uso: python extract.py + python extract.py data/condiciones-generales-bizum.pdf + +Optimizaciones vs extraction_pipeline: +- 1 sola llamada LLM por chunk (entities + relations + tipos sugeridos) +- Chunks de 2000 chars +- Paralelizado con ThreadPoolExecutor +""" + +import sys +import os +import json +import subprocess +import time +from concurrent.futures import ThreadPoolExecutor, as_completed + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "lib")) + +from extract_text_from_file import extract_text_from_file +from core_functions import preprocess_text, extract_json_from_llm +from split_text_into_chunks import split_text_into_chunks +from deduplicate_entities import deduplicate_entities +from deduplicate_relations import deduplicate_relations +from entity_candidate import EntityCandidate +from relation_candidate import RelationCandidate +from render_sigma_html import render_sigma_html + +# ── Presets ──────────────────────────────────────────────────────────────────── + +OSINT_PRESETS = [ + {"type_ref": "person", "label": "Person", + "metadata_fields": ["full_name", "alias", "nationality", "dob", "gender", "risk_score"]}, + {"type_ref": "organization", "label": "Organization", + "metadata_fields": ["legal_name", "country", "sector", "founded", "risk_score"]}, + {"type_ref": "location", "label": "Location", + "metadata_fields": ["lat", "lon", "address", "country", "city"]}, + {"type_ref": "event", "label": "Event", + "metadata_fields": ["event_type", "date", "location", "description", "severity"]}, + {"type_ref": "email", "label": "Email", + "metadata_fields": ["address", "provider", "verified", "breached"]}, + {"type_ref": "domain", "label": "Domain", + "metadata_fields": ["fqdn", "registrar", "created_date", "expires_date"]}, + {"type_ref": "ip_address", "label": "IP Address", + "metadata_fields": ["ip", "asn", "country", "isp", "geolocation"]}, + {"type_ref": "phone", "label": "Phone", + "metadata_fields": ["number", "country_code", "carrier", "phone_type"]}, + {"type_ref": "social_media", "label": "Social Media Account", + "metadata_fields": ["platform", "username", "url", "followers", "verified"]}, + {"type_ref": "document", "label": "Document", + "metadata_fields": ["title", "format", "classification", "source"]}, + {"type_ref": "crypto_wallet", "label": "Crypto Wallet", + "metadata_fields": ["address", "blockchain", "balance"]}, + {"type_ref": "malware", "label": "Malware", + "metadata_fields": ["family", "hash_sha256", "threat_level"]}, + {"type_ref": "vulnerability", "label": "Vulnerability", + "metadata_fields": ["cve_id", "cvss", "affected_product", "exploited"]}, +] + +GENERIC_PRESETS = [ + {"type_ref": "concept", "label": "Concept", + "metadata_fields": ["name", "category", "definition"]}, + {"type_ref": "url", "label": "URL/Link", + "metadata_fields": ["url", "domain", "context"]}, + {"type_ref": "date_reference", "label": "Date/Time", + "metadata_fields": ["date", "precision", "context"]}, + {"type_ref": "quantity", "label": "Quantity/Amount", + "metadata_fields": ["value", "unit", "context"]}, + {"type_ref": "coordinates", "label": "Coordinates", + "metadata_fields": ["lat", "lon", "label"]}, + {"type_ref": "text_fragment", "label": "Key Text Fragment", + "metadata_fields": ["text", "category", "relevance"]}, +] + +# ── Custom presets (acumulativo, pensado para promoción al registry) ─────────── + +CUSTOM_PRESETS_PATH = os.path.join(os.path.dirname(__file__), "data", "custom_presets.json") + + +def load_custom_presets() -> list[dict]: + """Carga presets custom desde data/custom_presets.json si existe.""" + if not os.path.exists(CUSTOM_PRESETS_PATH): + return [] + with open(CUSTOM_PRESETS_PATH) as f: + data = json.load(f) + return data.get("presets", []) + + +def save_custom_presets(presets: list[dict]) -> None: + """Guarda presets custom en data/custom_presets.json. + + Formato pensado para promoción al registry: + { + "presets": [ + { + "type_ref": "snake_case_id", + "label": "Human Label", + "metadata_fields": ["field1", "field2"], + "reason": "why this type exists", + "source_doc": "document where it was first discovered", + "promoted": false // true cuando se registre en el registry + } + ] + } + """ + os.makedirs(os.path.dirname(CUSTOM_PRESETS_PATH), exist_ok=True) + with open(CUSTOM_PRESETS_PATH, "w") as f: + json.dump({"presets": presets}, f, ensure_ascii=False, indent=2) + + +def merge_suggested_into_custom(suggested: list[dict], source_doc: str) -> list[dict]: + """Mergea tipos sugeridos con custom existentes. Dedup por type_ref.""" + existing = load_custom_presets() + existing_refs = {p["type_ref"] for p in existing} + + added = [] + for s in suggested: + ref = s.get("type_ref", "") + if not ref or ref in existing_refs: + continue + existing_refs.add(ref) + preset = { + "type_ref": ref, + "label": s.get("label", ref), + "metadata_fields": s.get("metadata_fields", []), + "reason": s.get("reason", ""), + "source_doc": source_doc, + "promoted": False, + } + existing.append(preset) + added.append(preset) + + if added: + save_custom_presets(existing) + + return added + + +RELATION_TYPES = [ + "employs", "works_for", "founded", "owns", "controls", + "member_of", "affiliated_with", "collaborates_with", + "communicates_with", "sent_to", "received_from", + "located_in", "headquartered_in", "traveled_to", "operates_in", + "participated_in", "caused", "occurred_at", "occurred_on", + "mentions", "references", "describes", "authored", "published", + "funds", "transacted_with", "invested_in", + "hosts", "resolves_to", "exploits", "targets", + "related_to", "part_of", "instance_of", "has_attribute", +] + +# ── LLM wrapper ─────────────────────────────────────────────────────────────── + +def claude_haiku_json(messages: list[dict]) -> dict: + parts = [] + for msg in messages: + if msg["role"] == "system": + parts.append(f"[SYSTEM]\n{msg['content']}") + elif msg["role"] == "user": + parts.append(f"[USER]\n{msg['content']}") + prompt = "\n\n".join(parts) + + result = subprocess.run( + ["claude", "-p", "--model", "haiku", "--output-format", "json", prompt], + capture_output=True, text=True, timeout=120, + ) + if result.returncode != 0: + raise RuntimeError(f"claude -p failed: {result.stderr[:200]}") + + envelope = json.loads(result.stdout) + return extract_json_from_llm(envelope.get("result", "")) + +# ── Unified prompt ───────────────────────────────────────────────────────────── + +def build_unified_prompt(presets, rel_types): + type_lines = [] + for p in presets: + fields = ", ".join(p.get("metadata_fields", [])) + type_lines.append(f"- {p['label']} (type_ref: {p['type_ref']}): [{fields}]") + + return ( + "You are an entity and relation extraction expert. " + "Given text, extract ALL entities and relations in a single pass.\n\n" + "ENTITY TYPES:\n" + "\n".join(type_lines) + "\n\n" + "RELATION TYPES: " + ", ".join(rel_types) + "\n\n" + 'OUTPUT FORMAT (strict JSON):\n' + '{\n' + ' "entities": [{"name": "...", "type_ref": "...", "attributes": {...}, "confidence": 0.9}],\n' + ' "relations": [{"from_name": "...", "to_name": "...", "relation_type": "...", "confidence": 0.8, "description": "..."}],\n' + ' "suggested_types": [{"type_ref": "snake_case_id", "label": "Human Label", "metadata_fields": ["f1","f2"], "reason": "..."}]\n' + '}\n\n' + "RULES:\n" + "- Extract ALL entities explicitly mentioned\n" + "- Use exact type_ref from schema. Unknown attributes = null\n" + "- Confidence: 1.0=explicit, 0.7=strongly implied, 0.5=weakly implied\n" + "- Relations: from_name/to_name MUST match entity names exactly\n" + "- suggested_types: for important entities that do NOT fit any type, suggest a new type. " + "Use those suggested type_refs for those entities in the entities array.\n" + '- If no new types needed: "suggested_types": []\n' + "- Respond in the same language as the text for descriptions" + ) + +# ── Process one chunk ────────────────────────────────────────────────────────── + +def process_chunk(chunk_idx: int, chunk_text: str, system_prompt: str): + """Procesa un chunk: extrae entities + relations + suggested_types.""" + try: + resp = claude_haiku_json([ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": chunk_text}, + ]) + except Exception as e: + print(f" [WARN] chunk {chunk_idx}: {e}") + return [], [], [] + + raw_entities = resp.get("entities", []) + raw_relations = resp.get("relations", []) + suggested = resp.get("suggested_types", []) + + entities = [] + for ent in raw_entities: + name = ent.get("name", "").strip() + if not name: + continue + entities.append(EntityCandidate( + name=name, + type_ref=ent.get("type_ref", "concept"), + attributes=ent.get("attributes", {}), + confidence=float(ent.get("confidence", 0.5)), + source_chunk_indices=[chunk_idx], + )) + + relations = [] + for rel in raw_relations: + fn = rel.get("from_name", "").strip() + tn = rel.get("to_name", "").strip() + if not fn or not tn: + continue + relations.append(RelationCandidate( + from_name=fn, + to_name=tn, + relation_type=rel.get("relation_type", "related_to"), + confidence=float(rel.get("confidence", 0.5)), + description=rel.get("description", ""), + source_chunk_index=chunk_idx, + )) + + return entities, relations, suggested + +# ── Sigma conversion ─────────────────────────────────────────────────────────── + +TYPE_COLORS = { + "person": "#e74c3c", + "organization": "#3498db", + "location": "#2ecc71", + "event": "#f39c12", + "email": "#9b59b6", + "domain": "#1abc9c", + "ip_address": "#e67e22", + "phone": "#95a5a6", + "social_media": "#e91e63", + "document": "#607d8b", + "crypto_wallet": "#ff9800", + "malware": "#f44336", + "vulnerability": "#ff5722", + "concept": "#00bcd4", + "url": "#8bc34a", + "date_reference": "#cddc39", + "quantity": "#ffc107", + "coordinates": "#4caf50", + "text_fragment": "#78909c", +} + +def to_sigma(entities, relations, entity_id_map): + # Build name→UUID lookup from dedup map + # entity_id_map: {name_variant -> uuid, ...} + # Invert to uuid→canonical_name using entities list + uuid_to_name = {} + name_to_uuid = {} + for e in entities: + # Find this entity's UUID in the map + uuid = entity_id_map.get(e.name, entity_id_map.get(e.name.lower().strip(), e.name)) + uuid_to_name[uuid] = e.name + name_to_uuid[e.name] = uuid + + degree = {} + for r in relations: + fid = r.from_id or r.from_name + tid = r.to_id or r.to_name + degree[fid] = degree.get(fid, 0) + 1 + degree[tid] = degree.get(tid, 0) + 1 + + nodes = [] + seen_uuids = set() + for e in entities: + uuid = name_to_uuid.get(e.name, e.name) + if uuid in seen_uuids: + continue + seen_uuids.add(uuid) + # Filter out 'type' — sigma.js reserves it for node render program + reserved = {"type", "hidden", "x", "y"} + attrs = {k: str(v) for k, v in (e.attributes or {}).items() if v is not None and k not in reserved} + nodes.append({ + "key": uuid, + "attributes": { + "label": e.name, + "color": TYPE_COLORS.get(e.type_ref, "#aaaaaa"), + "size": 4 + min(degree.get(uuid, 0) * 2, 20), + "entity_type": e.type_ref, + **attrs, + }, + }) + + node_keys = {n["key"] for n in nodes} + edges = [] + seen_edges = set() + for i, r in enumerate(relations): + fid = r.from_id or r.from_name + tid = r.to_id or r.to_name + if fid in node_keys and tid in node_keys and fid != tid: + edge_key = (fid, tid, r.relation_type) + if edge_key in seen_edges: + continue + seen_edges.add(edge_key) + edges.append({ + "key": f"e{i}", + "source": fid, + "target": tid, + "attributes": {"label": r.relation_type}, + }) + + return {"nodes": nodes, "edges": edges} + +# ── Reclasificación de entidades genéricas ───────────────────────────────────── + +GENERIC_TYPE_REFS = {"concept", "text_fragment", "url", "date_reference", "quantity", "coordinates"} + + +def reclassify_generic_entities(entities, new_presets, workers=4): + """Reclasifica entidades genéricas usando los tipos recién descubiertos. + + En vez de re-procesar chunks, hace 1 llamada batch a haiku con las entidades + genéricas y los nuevos presets para reclasificarlas in-place. + """ + generic = [(i, e) for i, e in enumerate(entities) if e.type_ref in GENERIC_TYPE_REFS] + if not generic or not new_presets: + return 0 + + # Construir prompt de reclasificación + type_lines = [] + for p in new_presets: + fields = ", ".join(p.get("metadata_fields", [])) + type_lines.append(f"- {p['label']} (type_ref: {p['type_ref']}): [{fields}]") + + system = ( + "You reclassify entities into more specific types. " + "For each entity, decide if it fits one of the NEW types below better than its current generic type. " + "If it fits, return the new type_ref and updated attributes. If not, return null.\n\n" + "NEW TYPES:\n" + "\n".join(type_lines) + "\n\n" + 'OUTPUT: {"reclassified": [{"index": 0, "type_ref": "new_type", "attributes": {...}}, ...]}\n' + "Only include entities that should change. Omit those that should stay as-is." + ) + + # Procesar en batches de 30 entidades para no exceder contexto + batch_size = 30 + total_changed = 0 + + def _reclassify_batch(batch): + items = [{"index": idx, "name": e.name, "current_type": e.type_ref, + "attributes": e.attributes} for idx, e in batch] + try: + resp = claude_haiku_json([ + {"role": "system", "content": system}, + {"role": "user", "content": json.dumps(items, ensure_ascii=False)}, + ]) + return resp.get("reclassified", []) + except Exception: + return [] + + batches = [generic[i:i+batch_size] for i in range(0, len(generic), batch_size)] + + with ThreadPoolExecutor(max_workers=workers) as pool: + futures = {pool.submit(_reclassify_batch, b): b for b in batches} + for future in as_completed(futures): + for item in future.result(): + idx = item.get("index") + new_ref = item.get("type_ref", "") + if idx is not None and new_ref and 0 <= idx < len(entities): + entities[idx].type_ref = new_ref + if item.get("attributes"): + entities[idx].attributes.update(item["attributes"]) + total_changed += 1 + + return total_changed + + +# ── Main ─────────────────────────────────────────────────────────────────────── + +def main(): + if len(sys.argv) < 2: + print("Uso: python extract.py ") + sys.exit(1) + + file_path = sys.argv[1] + if not os.path.isabs(file_path): + file_path = os.path.join(os.path.dirname(__file__), file_path) + + workers = int(sys.argv[2]) if len(sys.argv) > 2 else 4 + + print(f"=== Ontology Graph Extraction ===") + print(f"File: {file_path}") + print(f"Workers: {workers}") + start = time.monotonic() + + # 1. Extraer y preprocesar texto + print("\n[1/5] Extracting text...") + raw = extract_text_from_file(file_path) + text = preprocess_text(raw) + print(f" {len(text)} chars") + + # 2. Chunking + print("[2/5] Chunking...") + chunks = split_text_into_chunks(text, chunk_size=2000, overlap=200) + print(f" {len(chunks)} chunks") + + # 3. Extracción paralela + custom = load_custom_presets() + # Solo usar custom no promovidos (los promovidos ya estarán en el registry) + active_custom = [p for p in custom if not p.get("promoted", False)] + all_presets = OSINT_PRESETS + GENERIC_PRESETS + active_custom + print(f" Presets: {len(OSINT_PRESETS)} OSINT + {len(GENERIC_PRESETS)} generic + {len(active_custom)} custom") + system_prompt = build_unified_prompt(all_presets, RELATION_TYPES) + + print(f"[3/5] Extracting entities + relations ({workers} workers)...") + all_entities = [] + all_relations = [] + all_suggested = [] + + with ThreadPoolExecutor(max_workers=workers) as pool: + futures = { + pool.submit(process_chunk, i, chunk, system_prompt): i + for i, chunk in enumerate(chunks) + } + for future in as_completed(futures): + idx = futures[future] + ents, rels, sugg = future.result() + all_entities.extend(ents) + all_relations.extend(rels) + all_suggested.extend(sugg) + print(f" chunk {idx+1}/{len(chunks)}: {len(ents)} entities, {len(rels)} relations" + + (f", {len(sugg)} new types" if sugg else "")) + + # 4. Deduplicación + print(f"\n[4/5] Deduplicating...") + print(f" Raw: {len(all_entities)} entities, {len(all_relations)} relations") + + dedup = deduplicate_entities(all_entities, name_threshold=0.85) + final_entities = dedup.entities + entity_id_map = dedup.name_to_id + + final_relations = deduplicate_relations(all_relations, entity_id_map) + + print(f" Final: {len(final_entities)} entities, {len(final_relations)} relations") + print(f" Merged: {dedup.total_before - dedup.total_after} entities, " + f"{len(all_relations) - len(final_relations)} relations") + + # Registrar tipos sugeridos en custom_presets.json + unique_suggested = [] + if all_suggested: + seen = set() + for s in all_suggested: + key = s.get("type_ref", "") + if key and key not in seen: + seen.add(key) + unique_suggested.append(s) + + source_doc = os.path.basename(file_path) + added = merge_suggested_into_custom(unique_suggested, source_doc) + total_custom = len(load_custom_presets()) + + if added: + print(f"\n New types registered ({len(added)}):") + for p in added: + print(f" + {p['label']} ({p['type_ref']}): {p['metadata_fields']}") + print(f" Reason: {p['reason']}") + print(f" Total custom presets: {total_custom} (in {CUSTOM_PRESETS_PATH})") + + # Reclasificar entidades genéricas con los tipos recién descubiertos + n_generic = sum(1 for e in final_entities if e.type_ref in GENERIC_TYPE_REFS) + if n_generic > 0: + print(f"\n Reclassifying {n_generic} generic entities with new types...") + changed = reclassify_generic_entities(final_entities, added, workers=workers) + print(f" Reclassified: {changed}/{n_generic}") + else: + print(f"\n {len(unique_suggested)} suggested types already registered ({total_custom} total custom)") + + # Stats por tipo + type_counts = {} + for e in final_entities: + type_counts[e.type_ref] = type_counts.get(e.type_ref, 0) + 1 + print(f"\n Entity types:") + for t, c in sorted(type_counts.items(), key=lambda x: -x[1]): + print(f" {t}: {c}") + + rel_counts = {} + for r in final_relations: + rel_counts[r.relation_type] = rel_counts.get(r.relation_type, 0) + 1 + print(f" Relation types:") + for t, c in sorted(rel_counts.items(), key=lambda x: -x[1]): + print(f" {t}: {c}") + + # 5. Visualización + print(f"\n[5/5] Generating graph...") + graph = to_sigma(final_entities, final_relations, entity_id_map) + out_dir = os.path.join(os.path.dirname(__file__), "data") + html_path = render_sigma_html(graph, os.path.join(out_dir, "ontology_graph.html"), "Ontology Graph") + print(f" {len(graph['nodes'])} nodes, {len(graph['edges'])} edges") + print(f" HTML: file://{html_path}") + + # Guardar JSON intermedio + json_path = os.path.join(out_dir, "extraction_result.json") + with open(json_path, "w") as f: + json.dump({ + "entities": [{"name": e.name, "type_ref": e.type_ref, + "confidence": e.confidence, "attributes": e.attributes} + for e in final_entities], + "relations": [{"from": r.from_name, "to": r.to_name, + "type": r.relation_type, "confidence": r.confidence, + "description": r.description} + for r in final_relations], + "suggested_types": [dict(s) for s in (unique_suggested if all_suggested else [])], + }, f, ensure_ascii=False, indent=2) + print(f" JSON: {json_path}") + + elapsed = time.monotonic() - start + print(f"\nDone in {elapsed:.1f}s") + + +if __name__ == "__main__": + main() diff --git a/lib/__init__.py b/lib/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/lib/build_entity_schema_prompt.py b/lib/build_entity_schema_prompt.py new file mode 100644 index 0000000..23cc063 --- /dev/null +++ b/lib/build_entity_schema_prompt.py @@ -0,0 +1,43 @@ +"""Genera la seccion del system prompt que describe los entity types disponibles para extraccion.""" + + +def build_entity_schema_prompt(entity_presets: list[dict]) -> str: + """Genera texto legible para el LLM describiendo los entity types disponibles. + + Formatea los presets del registry en una seccion del system prompt que indica + al LLM que tipos de entidades puede extraer y que atributos tiene cada uno. + + Args: + entity_presets: Lista de presets con campos 'label', 'type_ref' y + opcionalmente 'metadata_fields'. Ejemplo: + [{"type_ref": "osint_person_go_cybersecurity", + "label": "Person", + "metadata_fields": ["full_name", "alias"]}] + + Returns: + String formateado con la seccion del prompt. Retorna string vacio si + la lista de presets esta vacia. + """ + if not entity_presets: + return "" + + lines = ["Entity types available for extraction:", ""] + + for i, preset in enumerate(entity_presets, start=1): + label = preset.get("label", "Unknown") + type_ref = preset.get("type_ref", "") + metadata_fields = preset.get("metadata_fields", []) + + lines.append(f"{i}. {label} (type_ref: {type_ref})") + + if metadata_fields: + attrs = ", ".join(metadata_fields) + lines.append(f" Attributes: {attrs}") + + lines.append("") + + # Remove trailing blank line + if lines and lines[-1] == "": + lines.pop() + + return "\n".join(lines) diff --git a/lib/build_relation_schema_prompt.py b/lib/build_relation_schema_prompt.py new file mode 100644 index 0000000..83ba526 --- /dev/null +++ b/lib/build_relation_schema_prompt.py @@ -0,0 +1,22 @@ +"""Genera la seccion del system prompt con los tipos de relacion permitidos.""" + + +def build_relation_schema_prompt(relation_types: list[str]) -> str: + """Genera texto legible para el LLM describiendo los tipos de relacion permitidos. + + Formatea la lista de tipos de relacion en una seccion del system prompt que + indica al LLM que relaciones puede extraer entre entidades. + + Args: + relation_types: Lista de strings con los tipos de relacion permitidos. + Ejemplo: ["funds", "employs", "communicates_with"] + + Returns: + String formateado con la seccion del prompt. Retorna string vacio si + la lista esta vacia. + """ + if not relation_types: + return "" + + joined = ", ".join(relation_types) + return f"Allowed relation types:\n{joined}" diff --git a/lib/core_functions.py b/lib/core_functions.py new file mode 100644 index 0000000..30d0c63 --- /dev/null +++ b/lib/core_functions.py @@ -0,0 +1,814 @@ +"""Core functional programming utilities — pure functions for list/collection operations.""" + +import hashlib +import re +from functools import reduce as _reduce +from typing import Any, Callable, Dict, List, Optional, Tuple + + +def filter_list(xs: list, pred: Callable) -> list: + """Filter list by predicate. Does not mutate the original.""" + return [x for x in xs if pred(x)] + + +def map_list(xs: list, fn: Callable) -> list: + """Map function over list. Does not mutate the original.""" + return [fn(x) for x in xs] + + +def reduce_list(xs: list, initial: Any, fn: Callable) -> Any: + """Reduce list with accumulator. fn(acc, x) -> acc.""" + return _reduce(fn, xs, initial) + + +def flat_map(xs: list, fn: Callable) -> list: + """Map function over list then flatten one level.""" + result = [] + for x in xs: + result.extend(fn(x)) + return result + + +def flatten(xss: list) -> list: + """Flatten a list of lists one level.""" + result = [] + for xs in xss: + result.extend(xs) + return result + + +def chunk(xs: list, size: int) -> list: + """Split list into chunks of given size. Last chunk may be smaller.""" + if size <= 0: + return [] + return [xs[i : i + size] for i in range(0, len(xs), size)] + + +def take(xs: list, n: int) -> list: + """Take first n elements from list.""" + return xs[:n] + + +def drop(xs: list, n: int) -> list: + """Drop first n elements from list.""" + return xs[n:] + + +def unique(xs: list) -> list: + """Remove duplicates preserving order. Uses identity for hashable elements.""" + seen = set() + result = [] + for x in xs: + if x not in seen: + seen.add(x) + result.append(x) + return result + + +def group_by(xs: list, key_fn: Callable) -> Dict: + """Group elements by key function. Returns dict of key -> list.""" + groups: Dict = {} + for x in xs: + k = key_fn(x) + if k not in groups: + groups[k] = [] + groups[k].append(x) + return groups + + +def partition(xs: list, pred: Callable) -> Tuple[list, list]: + """Split list into (matches, non_matches) based on predicate.""" + matches = [] + non_matches = [] + for x in xs: + if pred(x): + matches.append(x) + else: + non_matches.append(x) + return (matches, non_matches) + + +def find(xs: list, pred: Callable) -> Any: + """Find first element matching predicate. Returns None if not found.""" + for x in xs: + if pred(x): + return x + return None + + +def find_index(xs: list, pred: Callable) -> int: + """Find index of first element matching predicate. Returns -1 if not found.""" + for i, x in enumerate(xs): + if pred(x): + return i + return -1 + + +def zip_with(xs: list, ys: list, fn: Callable) -> list: + """Zip two lists with a combining function. Stops at shorter list.""" + return [fn(x, y) for x, y in zip(xs, ys)] + + +def all_of(xs: list, pred: Callable) -> bool: + """Return True if all elements match predicate.""" + return all(pred(x) for x in xs) + + +def any_of(xs: list, pred: Callable) -> bool: + """Return True if any element matches predicate.""" + return any(pred(x) for x in xs) + + +def pipe(value: Any, *fns: Callable) -> Any: + """Pipe a value through a sequence of functions left-to-right.""" + result = value + for fn in fns: + result = fn(result) + return result + + +def compose(*fns: Callable) -> Callable: + """Compose functions right-to-left. compose(f, g)(x) == f(g(x)).""" + def composed(x: Any) -> Any: + result = x + for fn in reversed(fns): + result = fn(result) + return result + return composed + + +# ── Tree manipulation ──────────────────────────────────────────────────────── + + +def flatten_tree(structure: Any) -> List[Dict]: + """Flatten a hierarchical tree (dict with 'nodes') to a list without children.""" + import copy + if isinstance(structure, dict): + node = copy.deepcopy(structure) + node.pop('nodes', None) + nodes = [node] + for key in list(structure.keys()): + if 'nodes' in key: + nodes.extend(flatten_tree(structure[key])) + return nodes + elif isinstance(structure, list): + nodes = [] + for item in structure: + nodes.extend(flatten_tree(item)) + return nodes + return [] + + +def tree_to_flat_list(structure: Any) -> List[Dict]: + """Convert hierarchical tree to flat list preserving DFS order (keeps internal nodes).""" + if isinstance(structure, dict): + nodes = [structure] + if 'nodes' in structure: + nodes.extend(tree_to_flat_list(structure['nodes'])) + return nodes + elif isinstance(structure, list): + nodes = [] + for item in structure: + nodes.extend(tree_to_flat_list(item)) + return nodes + return [] + + +def get_leaf_nodes(structure: Any) -> List[Dict]: + """Extract only leaf nodes (no children) from a hierarchical tree.""" + import copy + if isinstance(structure, dict): + if not structure.get('nodes'): + node = copy.deepcopy(structure) + node.pop('nodes', None) + return [node] + leaf_nodes = [] + for key in list(structure.keys()): + if 'nodes' in key: + leaf_nodes.extend(get_leaf_nodes(structure[key])) + return leaf_nodes + elif isinstance(structure, list): + leaf_nodes = [] + for item in structure: + leaf_nodes.extend(get_leaf_nodes(item)) + return leaf_nodes + return [] + + +def write_node_ids(data: Any, node_id: int = 0) -> int: + """Assign sequential zero-padded IDs (0001, 0002...) to all nodes in a tree. Returns next counter.""" + if isinstance(data, dict): + data['node_id'] = str(node_id).zfill(4) + node_id += 1 + for key in list(data.keys()): + if 'nodes' in key: + node_id = write_node_ids(data[key], node_id) + elif isinstance(data, list): + for item in data: + node_id = write_node_ids(item, node_id) + return node_id + + +def list_to_tree(data: List[Dict]) -> List[Dict]: + """Convert flat list with structure codes ('1.2.3') to nested tree.""" + def get_parent_structure(structure): + if not structure: + return None + parts = str(structure).split('.') + return '.'.join(parts[:-1]) if len(parts) > 1 else None + + nodes = {} + root_nodes = [] + + for item in data: + structure = item.get('structure') + node = { + 'title': item.get('title'), + 'start_index': item.get('start_index'), + 'end_index': item.get('end_index'), + 'nodes': [] + } + nodes[structure] = node + parent_structure = get_parent_structure(structure) + + if parent_structure and parent_structure in nodes: + nodes[parent_structure]['nodes'].append(node) + else: + root_nodes.append(node) + + def clean_node(node): + if not node['nodes']: + del node['nodes'] + else: + for child in node['nodes']: + clean_node(child) + return node + + return [clean_node(node) for node in root_nodes] + + +def remove_tree_fields(data: Any, fields: List[str] = None) -> Any: + """Recursively remove specified fields from a tree (dict/list).""" + if fields is None: + fields = ['text'] + if isinstance(data, dict): + return {k: remove_tree_fields(v, fields) for k, v in data.items() if k not in fields} + elif isinstance(data, list): + return [remove_tree_fields(item, fields) for item in data] + return data + + +def format_tree_structure(structure: Any, order: List[str] = None) -> Any: + """Reorder fields of each node in a tree according to specified key order.""" + if not order: + return structure + if isinstance(structure, dict): + if 'nodes' in structure: + structure['nodes'] = format_tree_structure(structure['nodes'], order) + if not structure.get('nodes'): + structure.pop('nodes', None) + return {key: structure[key] for key in order if key in structure} + elif isinstance(structure, list): + return [format_tree_structure(item, order) for item in structure] + return structure + + +def create_node_mapping(tree: List[Dict]) -> Dict[str, Dict]: + """Create flat dict mapping node_id to node for O(1) lookup.""" + mapping = {} + def _traverse(nodes): + for node in nodes: + if node.get('node_id'): + mapping[node['node_id']] = node + if node.get('nodes'): + _traverse(node['nodes']) + _traverse(tree) + return mapping + + +# ── Text / JSON extraction ─────────────────────────────────────────────────── + + +def extract_json_from_llm(content: str) -> Dict: + """Extract and parse JSON from LLM responses. Handles ```json blocks, trailing commas, None->null.""" + import json + try: + start_idx = content.find("```json") + if start_idx != -1: + start_idx += 7 + end_idx = content.rfind("```") + json_content = content[start_idx:end_idx].strip() + else: + json_content = content.strip() + + json_content = json_content.replace('None', 'null') + json_content = json_content.replace('\n', ' ').replace('\r', ' ') + json_content = ' '.join(json_content.split()) + + return json.loads(json_content) + except (json.JSONDecodeError, Exception): + try: + json_content = json_content.replace(',]', ']').replace(',}', '}') + return json.loads(json_content) + except Exception: + return {} + + +def parse_page_range(pages: str) -> List[int]: + """Parse page range string ('5-7', '3,8', '12') into sorted list of unique ints.""" + result = [] + for part in pages.split(','): + part = part.strip() + if '-' in part: + start, end = int(part.split('-', 1)[0].strip()), int(part.split('-', 1)[1].strip()) + if start > end: + raise ValueError(f"Invalid range '{part}': start must be <= end") + result.extend(range(start, end + 1)) + else: + result.append(int(part)) + return sorted(set(result)) + + +# ── Markdown parsing ───────────────────────────────────────────────────────── + + +def extract_markdown_headers(markdown_content: str) -> Tuple[List[Dict], List[str]]: + """Extract all headers (h1-h6) from markdown with line numbers, skipping code blocks.""" + import re + header_pattern = r'^(#{1,6})\s+(.+)$' + code_block_pattern = r'^```' + node_list = [] + lines = markdown_content.split('\n') + in_code_block = False + + for line_num, line in enumerate(lines, 1): + stripped_line = line.strip() + if re.match(code_block_pattern, stripped_line): + in_code_block = not in_code_block + continue + if not stripped_line: + continue + if not in_code_block: + match = re.match(header_pattern, stripped_line) + if match: + level = len(match.group(1)) + title = match.group(2).strip() + node_list.append({'title': title, 'level': level, 'line_num': line_num}) + + return node_list, lines + + +def build_tree_from_headers(node_list: List[Dict]) -> List[Dict]: + """Build nested tree from flat list of headers with levels (h1>h2>h3).""" + if not node_list: + return [] + + stack = [] + root_nodes = [] + node_counter = 1 + + for node in node_list: + current_level = node['level'] + tree_node = { + 'title': node['title'], + 'node_id': str(node_counter).zfill(4), + 'line_num': node['line_num'], + 'nodes': [] + } + node_counter += 1 + + while stack and stack[-1][1] >= current_level: + stack.pop() + + if not stack: + root_nodes.append(tree_node) + else: + parent_node, _ = stack[-1] + parent_node['nodes'].append(tree_node) + + stack.append((tree_node, current_level)) + + def clean_empty_nodes(nodes): + for n in nodes: + if n['nodes']: + clean_empty_nodes(n['nodes']) + else: + del n['nodes'] + return nodes + + return clean_empty_nodes(root_nodes) + + +# ── Pagination / chunking ──────────────────────────────────────────────────── + + +def page_list_to_groups(page_contents: List[str], token_lengths: List[int], + max_tokens: int = 20000, overlap_pages: int = 1) -> List[str]: + """Group pages into text chunks respecting token limit with configurable overlap.""" + import math + num_tokens = sum(token_lengths) + + if num_tokens <= max_tokens: + return ["".join(page_contents)] + + subsets = [] + current_subset = [] + current_token_count = 0 + + expected_parts = math.ceil(num_tokens / max_tokens) + avg_tokens = math.ceil(((num_tokens / expected_parts) + max_tokens) / 2) + + for i, (page_content, page_tokens) in enumerate(zip(page_contents, token_lengths)): + if current_token_count + page_tokens > avg_tokens: + subsets.append(''.join(current_subset)) + overlap_start = max(i - overlap_pages, 0) + current_subset = list(page_contents[overlap_start:i]) + current_token_count = sum(token_lengths[overlap_start:i]) + + current_subset.append(page_content) + current_token_count += page_tokens + + if current_subset: + subsets.append(''.join(current_subset)) + + return subsets + + +def calculate_page_offset(pairs: List[Dict]) -> int: + """Calculate offset between logical page numbers and physical indices using reference pairs.""" + differences = [] + for pair in pairs: + try: + difference = pair['physical_index'] - pair['page'] + differences.append(difference) + except (KeyError, TypeError): + continue + + if not differences: + return 0 + + counts: Dict[int, int] = {} + for diff in differences: + counts[diff] = counts.get(diff, 0) + 1 + + return max(counts.items(), key=lambda x: x[1])[0] + + +# ── Text preprocessing ─────────────────────────────────────────────────────── + + +def preprocess_text(text: str) -> str: + """Normalize whitespace and newlines in raw text. + + Args: + text: Raw text to normalize. + + Returns: + Normalized text with consistent newlines, stripped lines, and no + excessive blank lines. + """ + # Normalize line endings: \r\n and \r -> \n + text = text.replace('\r\n', '\n').replace('\r', '\n') + # Reduce 3+ consecutive newlines to at most 2 + text = re.sub(r'\n{3,}', '\n\n', text) + # Strip whitespace from each line + text = '\n'.join(line.strip() for line in text.split('\n')) + # Strip globally + return text.strip() + + +def get_text_stats(text: str) -> dict: + """Compute basic statistics of a text: characters, lines, words. + + Args: + text: Input text to analyze. + + Returns: + Dict with keys total_chars (int), total_lines (int), total_words (int). + """ + return { + 'total_chars': len(text), + 'total_lines': text.count('\n') + 1, + 'total_words': len(text.split()), + } + + +# ── Git URL parsing ────────────────────────────────────────────────────────── + +_DEFAULT_GIT_HOSTS = ["github.com", "gitlab.com"] + + +def _sanitize_git_segment(segment: str) -> str: + """Strip .git suffix then keep only [a-zA-Z0-9_-] chars.""" + if segment.endswith(".git"): + segment = segment[:-4] + return re.sub(r"[^a-zA-Z0-9_\-]", "", segment) + + +def parse_git_url(url: str, known_hosts: Optional[List[str]] = None) -> Optional[str]: + """Parse a code-hosting URL and return the 'org/repo' path component. + + Supports HTTPS, HTTP, git://, ssh:// and SSH shorthand (git@host:path). + Returns None if the URL does not match any known host or is malformed. + + Args: + url: Repository URL in any supported format. + known_hosts: List of accepted hostnames. Defaults to github.com and gitlab.com. + + Returns: + 'org/repo' string or None. + """ + from urllib.parse import urlparse + + hosts = known_hosts if known_hosts is not None else _DEFAULT_GIT_HOSTS + url = url.strip() + + if url.startswith("git@"): + # git@github.com:org/repo.git + rest = url[len("git@"):] + if ":" not in rest: + return None + host, path = rest.split(":", 1) + if host not in hosts: + return None + segments = [s for s in path.split("/") if s] + if len(segments) < 2: + return None + org = _sanitize_git_segment(segments[0]) + repo = _sanitize_git_segment(segments[1]) + if not org or not repo: + return None + return f"{org}/{repo}" + + for prefix in ("http://", "https://", "git://", "ssh://"): + if url.startswith(prefix): + parsed = urlparse(url) + netloc = parsed.hostname or "" + if netloc not in hosts: + return None + segments = [s for s in parsed.path.split("/") if s] + if len(segments) < 2: + return None + org = _sanitize_git_segment(segments[0]) + repo = _sanitize_git_segment(segments[1]) + if not org or not repo: + return None + return f"{org}/{repo}" + + return None + + +def is_git_repo_url(url: str, known_hosts: Optional[List[str]] = None) -> bool: + """Return True only if url points to a clonable git repository. + + Accepts org/repo and org/repo/tree/ paths. + Rejects paths that navigate to sub-resources (issues, blobs, PRs, etc.). + + Args: + url: URL to verify. + known_hosts: Accepted hostnames. Defaults to github.com and gitlab.com. + + Returns: + True if url is a clonable repository URL. + """ + from urllib.parse import urlparse + + hosts = known_hosts if known_hosts is not None else _DEFAULT_GIT_HOSTS + url = url.strip() + + # SSH shorthand — always repo-level if host matches + if url.startswith("git@"): + rest = url[len("git@"):] + if ":" not in rest: + return False + host, _ = rest.split(":", 1) + return host in hosts + + # git:// and ssh:// — always repo-level if host matches + for prefix in ("ssh://", "git://"): + if url.startswith(prefix): + parsed = urlparse(url) + return (parsed.hostname or "") in hosts + + # http:// and https:// — must have exactly org/repo or org/repo/tree/ + for prefix in ("http://", "https://"): + if url.startswith(prefix): + parsed = urlparse(url) + if (parsed.hostname or "") not in hosts: + return False + segments = [s for s in parsed.path.split("/") if s] + if len(segments) == 2: + return True + if len(segments) == 4 and segments[2] == "tree": + return True + return False + + return False + + +def validate_git_ssh_uri(url: str) -> None: + """Validate a git SSH URI of the form git@host:path. + + Raises ValueError with a descriptive message if the URI is malformed. + + Args: + url: URI string to validate. + + Raises: + ValueError: If the URI does not conform to git SSH format. + """ + if not url.startswith("git@"): + raise ValueError(f"git SSH URI must start with 'git@', got: {url!r}") + rest = url[len("git@"):] + if ":" not in rest: + raise ValueError(f"git SSH URI must contain ':', got: {url!r}") + _, path = rest.split(":", 1) + if not path: + raise ValueError(f"git SSH URI must have a non-empty path after ':', got: {url!r}") + + +# --------------------------------------------------------------------------- +# Markdown parsing utilities +# --------------------------------------------------------------------------- + + +def extract_frontmatter(content: str) -> Tuple[str, Optional[Dict]]: + """Extract YAML frontmatter delimited by '---' from the start of a markdown string. + + Args: + content: Raw markdown string, optionally starting with YAML frontmatter. + + Returns: + Tuple of (content_without_frontmatter, frontmatter_dict). + frontmatter_dict is None when no frontmatter is found. + """ + pattern = re.compile(r'^---\n(.*?)\n---\n', re.DOTALL) + match = pattern.match(content) + if not match: + return content, None + + raw = match.group(1) + remaining = content[match.end():] + + try: + import yaml # type: ignore + data = yaml.safe_load(raw) + if not isinstance(data, dict): + data = None + except Exception: + # Fallback: simple key: value parser (no yaml dependency) + data = {} + for line in raw.splitlines(): + if ':' in line: + key, _, value = line.partition(':') + data[key.strip()] = value.strip() + + return remaining, data + + +def find_headings(content: str) -> List[Tuple[int, int, str, int]]: + """Find all markdown headings (# to ######), excluding those inside code blocks, + HTML comments, and indented blocks. + + Args: + content: Markdown text to search. + + Returns: + List of (start_pos, end_pos, title, level) for each heading found. + """ + excluded: List[Tuple[int, int]] = [] + + # Code blocks (triple backtick) + for m in re.finditer(r'```.*?```', content, re.DOTALL): + excluded.append((m.start(), m.end())) + + # HTML comments + for m in re.finditer(r'', content, re.DOTALL): + excluded.append((m.start(), m.end())) + + # Indented blocks (lines starting with 4 spaces or a tab) + for m in re.finditer(r'^( |\t).+$', content, re.MULTILINE): + excluded.append((m.start(), m.end())) + + def is_excluded(pos: int) -> bool: + return any(start <= pos < end for start, end in excluded) + + results: List[Tuple[int, int, str, int]] = [] + for m in re.finditer(r'^(#{1,6})\s+(.+)$', content, re.MULTILINE): + # Skip escaped headings (\#) + before = content[m.start() - 1] if m.start() > 0 else '' + if before == '\\': + continue + if is_excluded(m.start()): + continue + level = len(m.group(1)) + title = m.group(2).strip() + results.append((m.start(), m.end(), title, level)) + + return results + + +def estimate_token_count(content: str) -> int: + """Estimate token count without a tokenizer. + + CJK characters count as ~0.7 tokens each; other non-whitespace characters + count as ~0.3 tokens each. + + Args: + content: Text to estimate. + + Returns: + Estimated integer token count. + """ + cjk = re.findall(r'[\u4e00-\u9fff\u3040-\u30ff\uac00-\ud7af]', content) + without_cjk = re.sub(r'[\u4e00-\u9fff\u3040-\u30ff\uac00-\ud7af]', '', content) + others = re.findall(r'\S', without_cjk) + return int(len(cjk) * 0.7 + len(others) * 0.3) + + +def smart_split_content( + content: str, + max_tokens: int = 1024, + max_chars: int = 8000, +) -> List[str]: + """Split large content into parts respecting token and character limits. + + Splits by paragraphs (double newline). If a single paragraph exceeds the + limit it is force-cut into chunks of max_chars. + + Args: + content: Text to split. + max_tokens: Maximum estimated tokens per part. + max_chars: Maximum characters per part. + + Returns: + List of string parts. + """ + paragraphs = content.split('\n\n') + parts: List[str] = [] + current_parts: List[str] = [] + current_tokens = 0 + current_chars = 0 + + def flush() -> None: + if current_parts: + parts.append('\n\n'.join(current_parts)) + current_parts.clear() + + for para in paragraphs: + para_tokens = estimate_token_count(para) + para_chars = len(para) + + # Single paragraph exceeds limits — force-cut it + if para_tokens > max_tokens or para_chars > max_chars: + flush() + current_tokens = 0 + current_chars = 0 + for i in range(0, len(para), max_chars): + parts.append(para[i:i + max_chars]) + continue + + # Would exceed limits if added — flush first + if (current_tokens + para_tokens > max_tokens or + current_chars + para_chars > max_chars): + flush() + current_tokens = 0 + current_chars = 0 + + current_parts.append(para) + current_tokens += para_tokens + current_chars += para_chars + + flush() + return parts if parts else [content] + + +def sanitize_for_path(text: str, max_length: int = 50) -> str: + """Convert text to a safe string for use in file paths. + + Keeps word characters, CJK characters, spaces and hyphens. Replaces spaces + with underscores. Truncates with a sha256 suffix if the result exceeds + max_length. + + Args: + text: Input text to sanitize. + max_length: Maximum length of the returned string. + + Returns: + Safe path-friendly string. + """ + cleaned = re.sub( + r'[^\w\u4e00-\u9fff\u3040-\u30ff\uac00-\ud7af \-]', + '', + text, + ) + cleaned = cleaned.replace(' ', '_').strip('_') + + if not cleaned: + return 'section' + + if len(cleaned) <= max_length: + return cleaned + + suffix = '_' + hashlib.sha256(text.encode()).hexdigest()[:8] + return cleaned[:max_length - len(suffix)] + suffix diff --git a/lib/deduplicate_entities.py b/lib/deduplicate_entities.py new file mode 100644 index 0000000..9e1db25 --- /dev/null +++ b/lib/deduplicate_entities.py @@ -0,0 +1,283 @@ +"""Deduplica entidades candidatas usando fuzzy matching de nombres.""" + +from __future__ import annotations + +import sys +import os +import uuid + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "..")) + +from entity_candidate import EntityCandidate +from deduplication_result import DeduplicationResult +from normalize_entity_name import normalize_entity_name +from merge_entity_attributes import merge_entity_attributes + + +# ── Similitud helpers ────────────────────────────────────────────────────────── + +def _levenshtein(a: str, b: str) -> int: + """Distancia de edicion Levenshtein entre dos strings.""" + if a == b: + return 0 + if not a: + return len(b) + if not b: + return len(a) + prev = list(range(len(b) + 1)) + for i, ca in enumerate(a, 1): + curr = [i] + for j, cb in enumerate(b, 1): + cost = 0 if ca == cb else 1 + curr.append(min(prev[j] + 1, curr[j - 1] + 1, prev[j - 1] + cost)) + prev = curr + return prev[-1] + + +def _jaccard(tokens_a: list[str], tokens_b: list[str]) -> float: + """Similitud de Jaccard entre dos conjuntos de tokens.""" + set_a = set(tokens_a) + set_b = set(tokens_b) + if not set_a and not set_b: + return 1.0 + inter = len(set_a & set_b) + union = len(set_a | set_b) + return inter / union if union else 0.0 + + +def _name_similarity(a: str, b: str) -> float: + """Score de similitud entre dos nombres normalizados. + + Combina similitud de Levenshtein y Jaccard sobre tokens. + Aplica bonus de contencion (+0.3) y deteccion de acronimos. + """ + if a == b: + return 1.0 + + # Similitud Levenshtein + max_len = max(len(a), len(b)) + lev_sim = 1.0 - (_levenshtein(a, b) / max_len) if max_len else 1.0 + + # Similitud Jaccard sobre tokens + tokens_a = a.split() + tokens_b = b.split() + jac_sim = _jaccard(tokens_a, tokens_b) + + score = max(lev_sim, jac_sim) + + # Bonus de contencion: un nombre contiene al otro + if a in b or b in a: + score = min(1.0, score + 0.3) + + # Deteccion de acronimo: "FBI" ~ "Federal Bureau of Investigation" + if _is_acronym_of(a, tokens_b) or _is_acronym_of(b, tokens_a): + score = min(1.0, score + 0.3) + + return score + + +def _is_acronym_of(candidate: str, tokens: list[str]) -> bool: + """Comprueba si candidate es un acronimo formado por las iniciales de tokens.""" + if not candidate or not tokens: + return False + initials = "".join(t[0] for t in tokens if t).upper() + return candidate.upper() == initials + + +_EXACT_TYPES = {"ip", "email", "domain", "crypto_wallet", "phone"} + + +def _is_exact_type(entity_type: str) -> bool: + """Tipos tecnicos donde solo se acepta matching exacto.""" + return entity_type.lower() in _EXACT_TYPES + + +# ── Union-Find ───────────────────────────────────────────────────────────────── + +class _UnionFind: + def __init__(self, n: int) -> None: + self._parent = list(range(n)) + self._rank = [0] * n + + def find(self, x: int) -> int: + while self._parent[x] != x: + self._parent[x] = self._parent[self._parent[x]] + x = self._parent[x] + return x + + def union(self, x: int, y: int) -> None: + rx, ry = self.find(x), self.find(y) + if rx == ry: + return + if self._rank[rx] < self._rank[ry]: + rx, ry = ry, rx + self._parent[ry] = rx + if self._rank[rx] == self._rank[ry]: + self._rank[rx] += 1 + + +# ── Implementacion principal ──────────────────────────────────────────────────── + +def deduplicate_entities( + candidates: list[EntityCandidate], + name_threshold: float = 0.85, + same_type_only: bool = True, +) -> DeduplicationResult: + """Agrupa entidades candidatas que refieren a la misma entidad real. + + Usa fuzzy matching de nombres (Levenshtein + Jaccard) y Union-Find para + detectar clusters transitivos. Por cada cluster genera una entidad canonica + mergeando atributos de todos sus miembros. + + Para tipos tecnicos (ip, email, domain, crypto_wallet, phone) solo se + acepta matching exacto normalizado, ignorando el umbral de nombre. + + Args: + candidates: lista de EntityCandidate a deduplicar. + name_threshold: score minimo para considerar dos nombres iguales (0-1). + same_type_only: si True, solo compara entidades del mismo type_ref. + + Returns: + DeduplicationResult con entidades deduplicadas, mapas de resolucion + e historial de merges. + """ + if not candidates: + return DeduplicationResult( + entities=[], + entity_id_map={}, + name_to_id={}, + merge_log=[], + total_before=0, + total_after=0, + ) + + n = len(candidates) + + # Paso 1: normalizar nombres + normalized: list[str] = [] + for c in candidates: + norm = normalize_entity_name(c.name, c.type_ref) + normalized.append(norm) + + # Paso 2: Union-Find sobre todos los indices + uf = _UnionFind(n) + + # Paso 3: comparacion pairwise (con agrupacion por tipo si same_type_only) + merge_pairs: list[tuple[int, int, float]] = [] + + for i in range(n): + for j in range(i + 1, n): + if same_type_only and candidates[i].type_ref != candidates[j].type_ref: + continue + + ni, nj = normalized[i], normalized[j] + et = candidates[i].type_ref.lower() + + if _is_exact_type(et): + if ni == nj: + uf.union(i, j) + merge_pairs.append((i, j, 1.0)) + continue + + score = _name_similarity(ni, nj) + if score >= name_threshold: + uf.union(i, j) + merge_pairs.append((i, j, score)) + + # Paso 4: agrupar indices por raiz del Union-Find + clusters: dict[int, list[int]] = {} + for i in range(n): + root = uf.find(i) + clusters.setdefault(root, []).append(i) + + # Paso 5: merge por cluster + merged_entities: list[EntityCandidate] = [] + entity_id_map: dict[str, str] = {} + name_to_id: dict[str, str] = {} + merge_log: list[dict] = [] + + # Pares mergeados para construir el log + merged_pairs_by_root: dict[int, list[tuple[int, int, float]]] = {} + for i, j, score in merge_pairs: + root = uf.find(i) + merged_pairs_by_root.setdefault(root, []).append((i, j, score)) + + for root, indices in clusters.items(): + cluster_candidates = [candidates[idx] for idx in indices] + + if len(cluster_candidates) == 1: + c = cluster_candidates[0] + canonical_name = c.name + canonical_norm = normalized[indices[0]] + merged_attrs = c.attributes + merged_confidence = c.confidence + merged_chunks = list(c.source_chunk_indices) + merged_from = list(c.merged_from) if c.merged_from else [c.name] + else: + # Candidato con mayor confidence es el canonico + best = max(cluster_candidates, key=lambda c: c.confidence) + canonical_name = best.name + canonical_norm = normalize_entity_name(best.name, best.type_ref) + + merged_attrs = merge_entity_attributes( + [c.attributes for c in cluster_candidates] + ) + merged_confidence = max(c.confidence for c in cluster_candidates) + + merged_chunks: list[int] = [] + seen_chunks: set[int] = set() + for c in cluster_candidates: + for idx in c.source_chunk_indices: + if idx not in seen_chunks: + merged_chunks.append(idx) + seen_chunks.add(idx) + + merged_from: list[str] = [] + seen_names: set[str] = set() + for c in cluster_candidates: + names_to_add = c.merged_from if c.merged_from else [c.name] + for nm in names_to_add: + if nm not in seen_names: + merged_from.append(nm) + seen_names.add(nm) + + # Log de merge + other_names = [c.name for c in cluster_candidates if c is not best] + pairs = merged_pairs_by_root.get(root, []) + max_score = max((s for _, _, s in pairs), default=1.0) + merge_log.append( + { + "canonical": canonical_name, + "merged": other_names, + "score": round(max_score, 4), + "reason": "fuzzy_name", + } + ) + + ent_id = str(uuid.uuid4()) + entity = EntityCandidate( + name=canonical_name, + name_normalized=canonical_norm, + type_ref=cluster_candidates[0].type_ref, + type_label=cluster_candidates[0].type_label, + attributes=merged_attrs, + confidence=merged_confidence, + source_chunk_indices=merged_chunks, + merged_from=merged_from, + ) + merged_entities.append(entity) + + # Poblar mapas de resolucion + entity_id_map[canonical_norm] = ent_id + for orig_name in merged_from: + name_to_id[orig_name] = ent_id + name_to_id[canonical_norm] = ent_id + + return DeduplicationResult( + entities=merged_entities, + entity_id_map=entity_id_map, + name_to_id=name_to_id, + merge_log=merge_log, + total_before=n, + total_after=len(merged_entities), + ) diff --git a/lib/deduplicate_relations.py b/lib/deduplicate_relations.py new file mode 100644 index 0000000..37e53f9 --- /dev/null +++ b/lib/deduplicate_relations.py @@ -0,0 +1,189 @@ +"""Deduplica RelationCandidate resolviendo nombres a IDs y colapsando duplicados.""" + +import logging +import os +import sys + +logger = logging.getLogger(__name__) + +# --- Importar levenshtein_distance desde cybersecurity --- +# Soporta dos contextos: +# 1. Ejecutado desde python/functions/datascience/ (pytest local) +# 2. Ejecutado desde la raiz del registry (fn run) +def _levenshtein_distance(a: str, b: str) -> int: + """Calcula la distancia de edicion de Levenshtein entre dos strings.""" + if len(a) < len(b): + return _levenshtein_distance(b, a) + if len(b) == 0: + return len(a) + prev_row = list(range(len(b) + 1)) + for i, ca in enumerate(a): + curr_row = [i + 1] + for j, cb in enumerate(b): + cost = 0 if ca == cb else 1 + curr_row.append( + min(curr_row[j] + 1, prev_row[j + 1] + 1, prev_row[j] + cost) + ) + prev_row = curr_row + return prev_row[-1] + + +try: + _here = os.path.dirname(os.path.abspath(__file__)) + _cyber_path = os.path.join(_here, "..", "cybersecurity") + if _cyber_path not in sys.path: + sys.path.insert(0, _cyber_path) + from cybersecurity import levenshtein_distance as _lev +except ImportError: + _lev = None # type: ignore + +levenshtein_distance = _lev if _lev is not None else _levenshtein_distance + + +def _fuzzy_resolve(name: str, entity_id_map: dict[str, str], threshold: int = 3) -> str: + """Intenta resolver un nombre contra las claves del mapa por fuzzy match. + + Recorre todas las claves de entity_id_map y busca la mas cercana segun + distancia de Levenshtein. Retorna el entity_id si la distancia es <= + threshold, o '' si no hay match aceptable. + + Args: + name: nombre a resolver (ya en lowercase strip). + entity_id_map: mapa nombre_normalizado -> entity_id. + threshold: distancia maxima de edicion para considerar match (default 3). + + Returns: + entity_id del mejor match o '' si no hay match. + """ + best_id = "" + best_dist = threshold + 1 + for key, entity_id in entity_id_map.items(): + dist = levenshtein_distance(name, key) + if dist < best_dist: + best_dist = dist + best_id = entity_id + return best_id if best_dist <= threshold else "" + + +def deduplicate_relations( + relations: list, + entity_id_map: dict[str, str], +) -> list: + """Deduplica relaciones candidatas resolviendo nombres a IDs de entidad finales. + + Algoritmo: + 1. Para cada RelationCandidate, intentar resolver from_name y to_name al + entity_id via entity_id_map (lookup exacto primero, ignorando mayusculas). + Si no hay match exacto, intentar fuzzy match con levenshtein_distance. + Si sigue sin match, descartar la relacion con warning. + 2. Descartar self-loops (from_id == to_id). + 3. Deduplicar por (from_id, to_id, relation_type): + - description: concatenar descripciones unicas separadas por '; ' + - confidence: max del grupo + 4. Retornar lista limpia de RelationCandidate con from_id y to_id resueltos. + + Args: + relations: lista de RelationCandidate con from_name/to_name originales. + entity_id_map: mapa nombre_normalizado -> entity_id (output de + deduplicate_entities). Permite resolver nombres que fueron mergeados. + + Returns: + Lista deduplicada de RelationCandidate con from_id y to_id resueltos. + """ + # Importar tipo — funciona tanto desde datascience/ como desde raiz del registry + try: + _types_path = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "..", "..", "..", "python", "types", "datascience", + ) + if _types_path not in sys.path: + sys.path.insert(0, _types_path) + from relation_candidate import RelationCandidate + except ImportError: + from relation_candidate import RelationCandidate # type: ignore + + resolved: list = [] + + for rel in relations: + # --- Resolver from_name --- + from_key = rel.from_name.lower().strip() + from_id = entity_id_map.get(from_key, "") + if not from_id: + from_id = _fuzzy_resolve(from_key, entity_id_map) + if not from_id: + logger.warning( + "deduplicate_relations: no se pudo resolver from_name=%r — descartando", + rel.from_name, + ) + continue + + # --- Resolver to_name --- + to_key = rel.to_name.lower().strip() + to_id = entity_id_map.get(to_key, "") + if not to_id: + to_id = _fuzzy_resolve(to_key, entity_id_map) + if not to_id: + logger.warning( + "deduplicate_relations: no se pudo resolver to_name=%r — descartando", + rel.to_name, + ) + continue + + # --- Descartar self-loops --- + if from_id == to_id: + logger.debug( + "deduplicate_relations: self-loop descartado (from=%r, to=%r, type=%r)", + rel.from_name, + rel.to_name, + rel.relation_type, + ) + continue + + resolved.append( + RelationCandidate( + from_name=rel.from_name, + to_name=rel.to_name, + from_id=from_id, + to_id=to_id, + relation_type=rel.relation_type, + description=rel.description, + confidence=rel.confidence, + source_chunk_index=rel.source_chunk_index, + ) + ) + + # --- Deduplicar por (from_id, to_id, relation_type) --- + groups: dict[tuple, list] = {} + for rel in resolved: + key = (rel.from_id, rel.to_id, rel.relation_type) + groups.setdefault(key, []).append(rel) + + result: list = [] + for (from_id, to_id, rel_type), group in groups.items(): + if len(group) == 1: + result.append(group[0]) + continue + + # Mergear: max confidence + union de descripciones unicas + best_confidence = max(r.confidence for r in group) + seen_desc: set[str] = set() + descriptions: list[str] = [] + for r in group: + if r.description and r.description not in seen_desc: + descriptions.append(r.description) + seen_desc.add(r.description) + + result.append( + RelationCandidate( + from_name=group[0].from_name, + to_name=group[0].to_name, + from_id=from_id, + to_id=to_id, + relation_type=rel_type, + description="; ".join(descriptions), + confidence=best_confidence, + source_chunk_index=group[0].source_chunk_index, + ) + ) + + return result diff --git a/lib/deduplication_result.py b/lib/deduplication_result.py new file mode 100644 index 0000000..f02522b --- /dev/null +++ b/lib/deduplication_result.py @@ -0,0 +1,22 @@ +"""DeduplicationResult — resultado del proceso de deduplicacion de entidades.""" + +from dataclasses import dataclass, field + +from entity_candidate import EntityCandidate + + +@dataclass +class DeduplicationResult: + """Resultado de deduplicacion de entidades. + + El `name_to_id` mapea TODOS los nombres originales (incluyendo los + mergeados) a su ID final, permitiendo resolver relaciones que usan + cualquier variante del nombre. + """ + + entities: list[EntityCandidate] + entity_id_map: dict[str, str] + name_to_id: dict[str, str] + merge_log: list[dict] = field(default_factory=list) + total_before: int = 0 + total_after: int = 0 diff --git a/lib/entity_candidate.py b/lib/entity_candidate.py new file mode 100644 index 0000000..eaa9db4 --- /dev/null +++ b/lib/entity_candidate.py @@ -0,0 +1,34 @@ +"""EntityCandidate — candidato de entidad extraido por el LLM.""" + +from dataclasses import dataclass, field + + +@dataclass +class EntityCandidate: + """Candidato de entidad extraido por el LLM. + + Puede venir de un solo chunk o ser el resultado de mergear multiples + extracciones. `merged_from` rastrea los nombres originales para debugging. + """ + + name: str + name_normalized: str = "" + type_ref: str = "" + type_label: str = "" + attributes: dict = field(default_factory=dict) + confidence: float = 0.0 + source_chunk_indices: list[int] = field(default_factory=list) + merged_from: list[str] = field(default_factory=list) + + def to_dict(self) -> dict: + """Serializa el candidato a un diccionario.""" + return { + "name": self.name, + "name_normalized": self.name_normalized, + "type_ref": self.type_ref, + "type_label": self.type_label, + "attributes": self.attributes, + "confidence": self.confidence, + "source_chunk_indices": self.source_chunk_indices, + "merged_from": self.merged_from, + } diff --git a/lib/extract_entities_llm.py b/lib/extract_entities_llm.py new file mode 100644 index 0000000..b0dad86 --- /dev/null +++ b/lib/extract_entities_llm.py @@ -0,0 +1,145 @@ +"""Extrae entidades de un chunk de texto usando un LLM inyectado.""" + +import sys +import os +import warnings +from typing import Callable + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "..")) + +from entity_candidate import EntityCandidate + + +def _build_system_prompt(entity_schema: list[dict], language_instruction: str) -> str: + """Construye el system prompt para extraccion de entidades.""" + lines = [ + "You are an entity extraction expert. Given text, extract all entities", + "matching these types. For each entity, provide: name, type_ref,", + "attributes (matching the metadata_fields for that type), and a", + "confidence score (0.0-1.0).", + "", + "Entity types:", + ] + + for schema_entry in entity_schema: + label = schema_entry.get("label", "Unknown") + type_ref = schema_entry.get("type_ref", "") + metadata_fields = schema_entry.get("metadata_fields", []) + lines.append(f"- {label} (type_ref: {type_ref})") + if metadata_fields: + lines.append(f" fields: {', '.join(metadata_fields)}") + + lines += [ + "", + 'Output JSON: {"entities": [{"name": "...", "type_ref": "...", "attributes": {...}, "confidence": 0.9}]}', + "", + "Rules:", + "- Only extract entities explicitly mentioned in the text", + "- Use the exact type_ref from the schema", + "- Leave unknown attributes as null", + "- Confidence: 1.0 = explicitly named, 0.7 = strongly implied, 0.5 = weakly implied", + f"- {language_instruction}", + ] + + return "\n".join(lines) + + +def extract_entities_llm( + text: str, + entity_schema: list[dict], + llm_chat_json: Callable[[list[dict]], dict], + language_instruction: str = "Respond in English.", +) -> list[EntityCandidate]: + """Extrae entidades de un chunk de texto usando un LLM inyectado. + + Construye un system prompt con el schema de entity types, llama al LLM + y valida la respuesta retornando una lista de EntityCandidate. + + Args: + text: Chunk de texto a analizar. + entity_schema: Lista de tipos con metadata fields. Cada entrada es un + dict con las claves 'type_ref', 'label' y opcionalmente + 'metadata_fields'. Ejemplo: + [{"type_ref": "osint_person_go_cybersecurity", "label": "Person", + "metadata_fields": ["full_name", "alias"]}] + llm_chat_json: Funcion que recibe una lista de mensajes OpenAI-style + y retorna un dict con la respuesta JSON del LLM. Interfaz: + llm_chat_json([{"role": "system", "content": "..."}, ...]) -> dict + language_instruction: Instruccion de idioma para el LLM. Por defecto + "Respond in English." + + Returns: + Lista de EntityCandidate extraidos. Retorna lista vacia si el LLM + no retorna JSON valido o si no se encuentran entidades. + + Raises: + ValueError: Si entity_schema esta vacio. + """ + if not entity_schema: + raise ValueError("entity_schema no puede estar vacio") + + valid_type_refs = {entry.get("type_ref", "") for entry in entity_schema} + type_ref_to_label = { + entry.get("type_ref", ""): entry.get("label", "") for entry in entity_schema + } + + system_prompt = _build_system_prompt(entity_schema, language_instruction) + + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": text}, + ] + + try: + response = llm_chat_json(messages) + except Exception as exc: + warnings.warn(f"extract_entities_llm: error llamando al LLM: {exc}", stacklevel=2) + return [] + + raw_entities = response.get("entities", []) + if not isinstance(raw_entities, list): + warnings.warn( + "extract_entities_llm: la respuesta del LLM no contiene 'entities' como lista", + stacklevel=2, + ) + return [] + + candidates: list[EntityCandidate] = [] + for item in raw_entities: + if not isinstance(item, dict): + continue + + name = item.get("name", "") + if not name: + continue + + type_ref = item.get("type_ref", "") + if type_ref not in valid_type_refs: + warnings.warn( + f"extract_entities_llm: type_ref '{type_ref}' no esta en el schema, descartando entidad '{name}'", + stacklevel=2, + ) + continue + + attributes = item.get("attributes", {}) + if not isinstance(attributes, dict): + attributes = {} + # Normalizar null values a None + attributes = {k: v for k, v in attributes.items() if v is not None} + + confidence = item.get("confidence", 0.0) + if not isinstance(confidence, (int, float)): + confidence = 0.0 + confidence = float(max(0.0, min(1.0, confidence))) + + candidates.append( + EntityCandidate( + name=name, + type_ref=type_ref, + type_label=type_ref_to_label.get(type_ref, ""), + attributes=attributes, + confidence=confidence, + ) + ) + + return candidates diff --git a/lib/extract_relations_llm.py b/lib/extract_relations_llm.py new file mode 100644 index 0000000..65bb2c1 --- /dev/null +++ b/lib/extract_relations_llm.py @@ -0,0 +1,141 @@ +"""extract_relations_llm — extrae relaciones entre entidades usando un LLM.""" + +import logging +import sys +import os +from typing import Callable + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "..", "")) + +from entity_candidate import EntityCandidate +from relation_candidate import RelationCandidate + +logger = logging.getLogger(__name__) + + +def extract_relations_llm( + text: str, + entities: list[EntityCandidate], + relation_types: list[str], + llm_chat_json: Callable[[list[dict]], dict], + language_instruction: str = "Respond in English.", +) -> list[RelationCandidate]: + """Extrae relaciones entre entidades de un chunk de texto usando un LLM. + + Dado el texto original y las entidades ya extraidas, pide al LLM que + identifique relaciones entre pares de entidades. Las relaciones cuyo + from_name o to_name no coincidan con ninguna entidad existente se descartan. + Los tipos de relacion no permitidos se reemplazan por "related_to". + + Args: + text: chunk de texto (el mismo que se uso para extraer las entidades). + entities: entidades ya extraidas del chunk. + relation_types: tipos de relacion permitidos, ej: ["funds", "employs", + "communicates_with", "owns", "related_to"]. + llm_chat_json: funcion inyectada que recibe una lista de mensajes + (dicts con "role" y "content") y retorna un dict con la respuesta + JSON del LLM. + language_instruction: instruccion de idioma para el LLM. + + Returns: + Lista de RelationCandidate validados. Vacia si hay menos de 2 entidades + o si el LLM no encuentra relaciones. + """ + if len(entities) < 2: + return [] + + entity_names = {e.name for e in entities} + relation_types_set = set(relation_types) + + # Construir lista de entidades para el prompt + entity_lines = "\n".join( + f'- "{e.name}" ({e.type_label or e.type_ref or "Entity"})' for e in entities + ) + + # Construir tipos de relacion para el prompt + relation_types_str = ", ".join(relation_types) + + system_prompt = f"""\ +You are a relation extraction expert. Given text and a list of entities already \ +extracted, identify relationships between them. + +Entities found in this text: +{entity_lines} + +Allowed relation types: {relation_types_str} + +Output JSON: {{"relations": [ + {{"from_name": "Entity A", "to_name": "Entity B", + "relation_type": "employs", "description": "...", "confidence": 0.8}} +]}} + +Rules: +- Only extract relations explicitly stated or strongly implied in the text +- from_name and to_name must match entity names exactly as listed above +- relation_type must be one of the allowed types +- Confidence: 1.0 = explicitly stated, 0.7 = strongly implied, 0.5 = weakly implied +- Do not invent entities not in the list above +- {language_instruction}""" + + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": text}, + ] + + try: + response = llm_chat_json(messages) + except Exception as exc: + logger.warning("extract_relations_llm: LLM call failed: %s", exc) + return [] + + raw_relations = response.get("relations", []) + if not isinstance(raw_relations, list): + logger.warning("extract_relations_llm: 'relations' is not a list in LLM response") + return [] + + results: list[RelationCandidate] = [] + for item in raw_relations: + if not isinstance(item, dict): + continue + + from_name = item.get("from_name", "") + to_name = item.get("to_name", "") + + # Validar que ambos nombres corresponden a entidades existentes + if from_name not in entity_names: + logger.debug( + "extract_relations_llm: from_name '%s' no coincide con ninguna entidad — descartando", + from_name, + ) + continue + if to_name not in entity_names: + logger.debug( + "extract_relations_llm: to_name '%s' no coincide con ninguna entidad — descartando", + to_name, + ) + continue + + relation_type = item.get("relation_type", "") + if relation_type not in relation_types_set: + logger.debug( + "extract_relations_llm: tipo '%s' no permitido — usando 'related_to'", + relation_type, + ) + relation_type = "related_to" + + confidence = item.get("confidence", 0.0) + if not isinstance(confidence, (int, float)): + confidence = 0.0 + confidence = float(max(0.0, min(1.0, confidence))) + + results.append( + RelationCandidate( + from_name=from_name, + to_name=to_name, + relation_type=relation_type, + description=item.get("description", ""), + confidence=confidence, + ) + ) + + return results diff --git a/lib/extract_text_from_file.py b/lib/extract_text_from_file.py new file mode 100644 index 0000000..747c354 --- /dev/null +++ b/lib/extract_text_from_file.py @@ -0,0 +1,92 @@ +"""Extract plain text from PDF, Markdown, or TXT files.""" + + +SUPPORTED_EXTENSIONS = {".pdf", ".md", ".markdown", ".txt"} + + +def _detect_encoding(data: bytes) -> str: + """Detect encoding of raw bytes using multiple fallback strategies.""" + # Strategy 1: UTF-8 + try: + data.decode("utf-8") + return "utf-8" + except UnicodeDecodeError: + pass + + # Strategy 2: charset_normalizer + try: + from charset_normalizer import from_bytes + + result = from_bytes(data).best() + if result is not None and result.encoding: + return result.encoding + except ImportError: + pass + + # Strategy 3: chardet + try: + import chardet + + detected = chardet.detect(data) + if detected and detected.get("encoding"): + return detected["encoding"] + except ImportError: + pass + + # Last resort: UTF-8 with replacement + return "utf-8" + + +def extract_text_from_file(file_path: str) -> str: + """Extract plain text from a file. Supports PDF, Markdown and TXT. + + For PDF files uses PyMuPDF (fitz) to extract text from each page, + joining them with double newlines. For text-based files (.md, .markdown, + .txt) reads the file with automatic encoding detection. + + Args: + file_path: Absolute or relative path to the file. + + Returns: + str: Extracted plain text content. + + Raises: + FileNotFoundError: If the file does not exist. + ValueError: If the file extension is not supported. + ImportError: If PyMuPDF is not installed and a PDF is provided. + """ + import os + + if not os.path.exists(file_path): + raise FileNotFoundError(f"File not found: {file_path}") + + _, ext = os.path.splitext(file_path.lower()) + + if ext == ".pdf": + try: + import fitz # PyMuPDF + except ImportError as e: + raise ImportError( + "PyMuPDF is required for PDF extraction. " + "Install it with: pip install PyMuPDF" + ) from e + + doc = fitz.open(file_path) + pages = [page.get_text() for page in doc] + return "\n\n".join(pages) + + elif ext in {".md", ".markdown", ".txt"}: + with open(file_path, "rb") as f: + raw = f.read() + + encoding = _detect_encoding(raw) + try: + return raw.decode(encoding) + except (UnicodeDecodeError, LookupError): + return raw.decode("utf-8", errors="replace") + + else: + raise ValueError( + f"Unsupported file extension: '{ext}'. " + f"Supported: {', '.join(sorted(SUPPORTED_EXTENSIONS))}" + ) diff --git a/lib/extraction_pipeline.py b/lib/extraction_pipeline.py new file mode 100644 index 0000000..769a4dd --- /dev/null +++ b/lib/extraction_pipeline.py @@ -0,0 +1,208 @@ +"""Pipeline de extraccion de entidades y relaciones desde un documento.""" + +from __future__ import annotations + +import sys +import os +import time +import warnings +from typing import Callable + +# Soporte para ejecucion desde la raiz del registry o desde el directorio del archivo + +from extract_text_from_file import extract_text_from_file +from core_functions import preprocess_text +from split_text_into_chunks import split_text_into_chunks +from build_entity_schema_prompt import build_entity_schema_prompt +from build_relation_schema_prompt import build_relation_schema_prompt +from extract_entities_llm import extract_entities_llm +from extract_relations_llm import extract_relations_llm +from deduplicate_entities import deduplicate_entities +from deduplicate_relations import deduplicate_relations +from entity_candidate import EntityCandidate +from extraction_result import ExtractionResult +from extraction_stats import ExtractionStats + + +def extraction_pipeline( + file_path: str, + entity_presets: list[dict], + relation_types: list[str], + llm_chat_json: Callable[[list[dict]], dict], + chunk_size: int = 500, + chunk_overlap: int = 50, + confidence_threshold: float = 0.5, + dedup_threshold: float = 0.85, + on_progress: Callable[[str, float], None] | None = None, +) -> ExtractionResult: + """Pipeline completa de extraccion de entidades y relaciones desde un documento. + + Orquesta extract_text_from_file -> preprocess_text -> split_text_into_chunks + -> extract_entities_llm por chunk -> deduplicate_entities -> + extract_relations_llm por chunk -> deduplicate_relations. + + Args: + file_path: ruta al archivo a procesar (PDF, Markdown, TXT). + entity_presets: lista de dicts con type_ref, label y metadata_fields. + Ejemplo: [{"type_ref": "osint_person_go_cybersecurity", + "label": "Person", + "metadata_fields": ["full_name", "nationality"]}] + relation_types: tipos de relacion permitidos para extraccion. + Ejemplo: ["funds", "employs", "communicates_with", "owns"] + llm_chat_json: funcion inyectada que recibe messages OpenAI y retorna dict + con la respuesta JSON ya parseada. Sin acoplamiento a ningun proveedor. + chunk_size: numero de caracteres por chunk (default 500). + chunk_overlap: overlap entre chunks consecutivos (default 50). + confidence_threshold: umbral minimo de confidence para aceptar entidades + candidatas antes de deduplicar (default 0.5). + dedup_threshold: score minimo de similitud para mergear entidades (default 0.85). + on_progress: callback opcional de progreso (message: str, pct: float 0-1). + 0-40%: extraccion de entidades, 40-80%: extraccion de relaciones, + 80-100%: deduplicacion. + + Returns: + ExtractionResult con entidades y relaciones deduplicadas y stats del proceso. + + Raises: + FileNotFoundError: si file_path no existe. + ValueError: si entity_presets esta vacio. + """ + if not entity_presets: + raise ValueError("entity_presets no puede estar vacio") + + if not os.path.exists(file_path): + raise FileNotFoundError(f"Archivo no encontrado: {file_path}") + + def _progress(msg: str, pct: float) -> None: + if on_progress is not None: + try: + on_progress(msg, pct) + except Exception: + pass + + start_time = time.monotonic() + stats = ExtractionStats() + + # ── Paso 1: Extraer texto ────────────────────────────────────────────────── + _progress("Extracting text from file...", 0.0) + try: + raw_text = extract_text_from_file(file_path) + except Exception as exc: + warnings.warn(f"extraction_pipeline: error al extraer texto: {exc}") + raw_text = "" + + # ── Paso 2: Preprocesar ──────────────────────────────────────────────────── + clean_text = preprocess_text(raw_text) + stats.total_chars = len(clean_text) + + # ── Paso 3: Dividir en chunks ────────────────────────────────────────────── + chunks = split_text_into_chunks(clean_text, chunk_size=chunk_size, overlap=chunk_overlap) + n = len(chunks) + stats.total_chunks = n + + if n == 0: + stats.processing_time_seconds = time.monotonic() - start_time + return ExtractionResult(entities=[], relations=[], stats=stats) + + # ── Paso 4: Extraer entidades por chunk ──────────────────────────────────── + all_raw_entities: list[EntityCandidate] = [] + + for i, chunk in enumerate(chunks): + _progress(f"Extracting entities from chunk {i + 1}/{n}", (i / n) * 0.4) + try: + candidates = extract_entities_llm( + text=chunk, + entity_schema=entity_presets, + llm_chat_json=llm_chat_json, + ) + except Exception as exc: + warnings.warn( + f"extraction_pipeline: error en extract_entities_llm chunk {i}: {exc}" + ) + candidates = [] + + for candidate in candidates: + # Anotar el chunk de origen + if i not in candidate.source_chunk_indices: + candidate.source_chunk_indices.append(i) + all_raw_entities.append(candidate) + + # ── Paso 5: Filtrar por confidence ───────────────────────────────────────── + filtered_entities = [ + e for e in all_raw_entities if e.confidence >= confidence_threshold + ] + stats.raw_entities_count = len(filtered_entities) + + # Actualizar stats de tipos + for ent in filtered_entities: + stats.entity_types_found[ent.type_ref] = ( + stats.entity_types_found.get(ent.type_ref, 0) + 1 + ) + + # ── Paso 6: Deduplicar entidades ─────────────────────────────────────────── + _progress("Deduplicating entities...", 0.4) + dedup_result = deduplicate_entities(filtered_entities, name_threshold=dedup_threshold) + + stats.final_entities_count = dedup_result.total_after + stats.entities_merged = dedup_result.total_before - dedup_result.total_after + + final_entities = dedup_result.entities + entity_id_map = dedup_result.name_to_id # nombre_original -> entity_id + + # ── Paso 7: Extraer relaciones por chunk ─────────────────────────────────── + all_raw_relations = [] + + for i, chunk in enumerate(chunks): + _progress(f"Extracting relations...", 0.4 + (i / n) * 0.4) + + # Obtener entidades relevantes de este chunk + chunk_entities = [ + e for e in final_entities if i in e.source_chunk_indices + ] + # Si no hay entidades en este chunk especifico, usar todas + if not chunk_entities: + chunk_entities = final_entities + + if len(chunk_entities) < 2: + continue + + try: + chunk_relations = extract_relations_llm( + text=chunk, + entities=chunk_entities, + relation_types=relation_types, + llm_chat_json=llm_chat_json, + ) + except Exception as exc: + warnings.warn( + f"extraction_pipeline: error en extract_relations_llm chunk {i}: {exc}" + ) + chunk_relations = [] + + for rel in chunk_relations: + rel.source_chunk_index = i + all_raw_relations.extend(chunk_relations) + + stats.raw_relations_count = len(all_raw_relations) + + # Actualizar stats de tipos de relacion + for rel in all_raw_relations: + stats.relation_types_found[rel.relation_type] = ( + stats.relation_types_found.get(rel.relation_type, 0) + 1 + ) + + # ── Paso 8: Deduplicar relaciones ────────────────────────────────────────── + _progress("Deduplicating relations...", 0.8) + final_relations = deduplicate_relations(all_raw_relations, entity_id_map) + + stats.final_relations_count = len(final_relations) + stats.relations_merged = stats.raw_relations_count - len(final_relations) + stats.processing_time_seconds = time.monotonic() - start_time + + _progress("Done", 1.0) + + return ExtractionResult( + entities=final_entities, + relations=final_relations, + stats=stats, + ) diff --git a/lib/extraction_result.py b/lib/extraction_result.py new file mode 100644 index 0000000..65aec74 --- /dev/null +++ b/lib/extraction_result.py @@ -0,0 +1,20 @@ +"""ExtractionResult — resultado final del pipeline de extraccion.""" + +from dataclasses import dataclass, field + +from entity_candidate import EntityCandidate +from extraction_stats import ExtractionStats +from relation_candidate import RelationCandidate + + +@dataclass +class ExtractionResult: + """Resultado final del pipeline de extraccion de entidades y relaciones. + + Contiene las listas deduplicadas de entidades y relaciones junto con + las estadisticas del proceso completo. + """ + + entities: list[EntityCandidate] + relations: list[RelationCandidate] + stats: ExtractionStats = field(default_factory=ExtractionStats) diff --git a/lib/extraction_stats.py b/lib/extraction_stats.py new file mode 100644 index 0000000..107ac5c --- /dev/null +++ b/lib/extraction_stats.py @@ -0,0 +1,25 @@ +"""ExtractionStats — estadisticas del proceso de extraccion.""" + +from dataclasses import dataclass, field + + +@dataclass +class ExtractionStats: + """Estadisticas del proceso de extraccion. + + Util para reporting y debugging. Registra conteos antes y despues de + deduplicacion, tiempo de procesamiento y distribucion de tipos encontrados. + """ + + total_chunks: int = 0 + total_chars: int = 0 + raw_entities_count: int = 0 + final_entities_count: int = 0 + entities_merged: int = 0 + raw_relations_count: int = 0 + final_relations_count: int = 0 + relations_merged: int = 0 + relations_discarded: int = 0 + entity_types_found: dict[str, int] = field(default_factory=dict) + relation_types_found: dict[str, int] = field(default_factory=dict) + processing_time_seconds: float = 0.0 diff --git a/lib/merge_entity_attributes.py b/lib/merge_entity_attributes.py new file mode 100644 index 0000000..6c86458 --- /dev/null +++ b/lib/merge_entity_attributes.py @@ -0,0 +1,78 @@ +"""Combina atributos de multiples candidatos de la misma entidad.""" + +from __future__ import annotations + +_NUMERIC_FIELDS = {"risk_score", "balance", "cvss"} +_DATE_MIN_FIELDS = {"first_seen", "created_date"} +_DATE_MAX_FIELDS = {"last_seen", "expires_date"} +_BOOL_FIELDS = {"verified", "exploited"} + + +def merge_entity_attributes(attr_list: list[dict]) -> dict: + """Combina atributos de multiples candidatos de la misma entidad. + + Para cada campo presente en cualquier candidato recopila todos los valores + non-null y aplica heuristicas de resolucion por tipo de campo: + - Numerico (risk_score, balance, cvss): max + - Fecha min (first_seen, created_date): min (mas antigua) + - Fecha max (last_seen, expires_date): max (mas reciente) + - Lista (cualquier valor de tipo list): union sin duplicados + - Boolean (verified, exploited): OR logico + - String: el mas largo + + Args: + attr_list: Lista de dicts con los atributos de cada candidato. + + Returns: + Dict con los atributos fusionados. + """ + if not attr_list: + return {} + + # Recopilar todas las claves presentes en cualquier candidato + all_keys: set[str] = set() + for attrs in attr_list: + all_keys.update(attrs.keys()) + + merged: dict = {} + + for key in all_keys: + # Recopilar valores non-null + values = [attrs[key] for attrs in attr_list if key in attrs and attrs[key] is not None] + + if not values: + merged[key] = None + continue + + if len(values) == 1: + merged[key] = values[0] + continue + + # Todos iguales + if all(v == values[0] for v in values): + merged[key] = values[0] + continue + + # Resolver conflicto segun tipo de campo + if key in _NUMERIC_FIELDS: + merged[key] = max(values) + elif key in _DATE_MIN_FIELDS: + merged[key] = min(values) + elif key in _DATE_MAX_FIELDS: + merged[key] = max(values) + elif key in _BOOL_FIELDS: + merged[key] = any(values) + elif isinstance(values[0], list): + # Union de listas sin duplicados, preservando orden de aparicion + seen: list = [] + for lst in values: + for item in lst: + if item not in seen: + seen.append(item) + merged[key] = seen + else: + # String u otro: usar el mas largo + str_values = [str(v) for v in values] + merged[key] = max(str_values, key=len) + + return merged diff --git a/lib/normalize_entity_name.py b/lib/normalize_entity_name.py new file mode 100644 index 0000000..a01b14a --- /dev/null +++ b/lib/normalize_entity_name.py @@ -0,0 +1,81 @@ +"""Normaliza el nombre de una entidad para comparacion y deduplicacion.""" + +import re + + +_TITLES = re.compile( + r"^\b(?:Dr|Mr|Mrs|Ms|Miss|Prof|Sr|Jr|Ing|Lic|Gen|Col|Maj|Capt|Sgt|Rev|Hon)\.?\s+", + re.IGNORECASE, +) + +_LEGAL_SUFFIXES = re.compile( + r"\b(?:Inc|LLC|Ltd|Corp|Co|S\.?A|GmbH|B\.?V|N\.?V|PLC|AG|SRL|S\.?L|Pty|" + r"LP|LLP|LLLP|PC|PA|PLLC|Foundation|Group|Holdings|Enterprises?|" + r"International|Industries|Services?|Solutions?|Systems?|Technologies?)\.?\s*$", + re.IGNORECASE, +) + +_MULTI_SPACE = re.compile(r"\s+") + + +def normalize_entity_name(name: str, entity_type: str = "") -> str: + """Normaliza el nombre de una entidad para comparacion y deduplicacion. + + Aplica reglas diferentes segun el tipo de entidad: + - ip / email / domain / crypto_wallet / phone: normalizacion tecnica + - person: normalizacion de nombre humano (titulos, formato apellido-nombre) + - organization: normalizacion corporativa (sufijos legales) + - default: lower + strip + colapsar espacios + + Args: + name: nombre de la entidad a normalizar. + entity_type: tipo de entidad (ip, email, domain, crypto_wallet, phone, + person, organization). Vacio = default. + + Returns: + nombre normalizado como string. + """ + name = name.strip() + et = entity_type.lower().strip() + + if et == "ip": + return name.lower() + + if et == "email": + return name.lower() + + if et == "domain": + result = name.lower().rstrip(".") + if result.startswith("www."): + result = result[4:] + return result + + if et == "crypto_wallet": + # Bitcoin addresses son case-sensitive — solo strip + return name + + if et == "phone": + # Mantener solo digitos y el signo + + return re.sub(r"[^\d+]", "", name) + + if et == "person": + # Remover titulos al inicio + result = _TITLES.sub("", name).strip() + # Detectar formato "Apellido, Nombre" + if "," in result: + parts = result.split(",", 1) + last = parts[0].strip() + first = parts[1].strip() + result = f"{first} {last}" + # Colapsar espacios y title case + result = _MULTI_SPACE.sub(" ", result).strip() + return result.title() + + if et == "organization": + result = _LEGAL_SUFFIXES.sub("", name).strip() + result = _MULTI_SPACE.sub(" ", result).strip() + # Title case para consistencia + return result.title() + + # Default: lower, strip, colapsar espacios + return _MULTI_SPACE.sub(" ", name.lower()).strip() diff --git a/lib/relation_candidate.py b/lib/relation_candidate.py new file mode 100644 index 0000000..524d546 --- /dev/null +++ b/lib/relation_candidate.py @@ -0,0 +1,35 @@ +"""RelationCandidate — candidato de relacion extraido por el LLM.""" + +from dataclasses import dataclass + + +@dataclass +class RelationCandidate: + """Candidato de relacion entre dos entidades extraido por el LLM. + + `from_name` y `to_name` contienen los nombres crudos del texto. `from_id` + y `to_id` se llenan durante la fase de deduplicacion cuando se resuelven + contra los EntityCandidate finales. + """ + + from_name: str + to_name: str + from_id: str = "" + to_id: str = "" + relation_type: str = "" + description: str = "" + confidence: float = 0.0 + source_chunk_index: int = -1 + + def to_dict(self) -> dict: + """Serializa el candidato a un diccionario.""" + return { + "from_name": self.from_name, + "to_name": self.to_name, + "from_id": self.from_id, + "to_id": self.to_id, + "relation_type": self.relation_type, + "description": self.description, + "confidence": self.confidence, + "source_chunk_index": self.source_chunk_index, + } diff --git a/lib/render_sigma_html.py b/lib/render_sigma_html.py new file mode 100644 index 0000000..74ea911 --- /dev/null +++ b/lib/render_sigma_html.py @@ -0,0 +1,234 @@ +"""Renderiza un grafo sigma.js como HTML standalone con dark theme y layout ForceAtlas2.""" + +import json +import os + + +_HTML_TEMPLATE = """\ + + + + + + {title} + + + + + + +
+
+

{title}

+
+
+
+
+ + + + +""" + + +def render_sigma_html( + graph_data: dict, + output_path: str, + title: str = "OSINT Graph", +) -> str: + """Genera un HTML standalone con sigma.js que visualiza el grafo OSINT. + + Recibe el dict producido por ops_to_sigma_json, embebe los datos como JSON + en el HTML, aplica ForceAtlas2 (500 iteraciones sincrono) y renderiza con + sigma.js v2.4. Incluye dark theme, panel de filtros por tipo de nodo y + tooltip con metadata al hacer hover. + + Args: + graph_data: Dict con claves 'nodes' y 'edges' en formato graphology/sigma. + output_path: Ruta del archivo HTML a escribir. + title: Titulo del grafo mostrado en el panel y la pestana. + + Returns: + Ruta absoluta del archivo HTML escrito. + + Raises: + Exception: Si no se puede escribir el archivo en output_path. + """ + json_data = json.dumps(graph_data, ensure_ascii=False) + + html = _HTML_TEMPLATE.format( + title=title, + json_data=json_data, + ) + + abs_path = os.path.abspath(output_path) + os.makedirs(os.path.dirname(abs_path) or ".", exist_ok=True) + + try: + with open(abs_path, "w", encoding="utf-8") as f: + f.write(html) + except OSError as exc: + raise Exception(f"render_sigma_html: no se pudo escribir '{abs_path}': {exc}") from exc + + return abs_path diff --git a/lib/split_text_into_chunks.py b/lib/split_text_into_chunks.py new file mode 100644 index 0000000..bbdc572 --- /dev/null +++ b/lib/split_text_into_chunks.py @@ -0,0 +1,66 @@ +"""Split text into overlapping chunks with sentence-boundary awareness.""" + + +def split_text_into_chunks( + text: str, chunk_size: int = 500, overlap: int = 50 +) -> list[str]: + """Divide texto en chunks de tamaño fijo con overlap, cortando en límites de oración. + + Args: + text: Texto a dividir. + chunk_size: Tamaño máximo de cada chunk en caracteres. + overlap: Número de caracteres de solapamiento entre chunks consecutivos. + + Returns: + Lista de chunks. Vacía si el texto es vacío. + """ + if not text: + return [] + + if len(text) <= chunk_size: + stripped = text.strip() + return [stripped] if stripped else [] + + # Separadores en orden de prioridad (más específicos primero) + separators = ["。", "！", "？", ".\n", "!\n", "?\n", "\n\n", ". ", "! ", "? "] + + chunks: list[str] = [] + start = 0 + text_len = len(text) + + while start < text_len: + end = start + chunk_size + + if end < text_len: + # Buscar el último separador de oración dentro de text[start:end] + # Solo aceptar si está después del 30% del chunk + min_pos = start + int(chunk_size * 0.30) + best_end = None + + for sep in separators: + sep_len = len(sep) + # Buscar la última ocurrencia del separador en text[start:end] + search_region = text[start:end] + pos = search_region.rfind(sep) + if pos == -1: + continue + abs_pos = start + pos + sep_len + if abs_pos > min_pos: + # Usar este separador solo si produce un corte más tarde que el mínimo + # y más temprano que chunk_size (ya garantizado por rfind en [start:end]) + if best_end is None or abs_pos > best_end: + best_end = abs_pos + + if best_end is not None: + end = best_end + + chunk = text[start:end].strip() + if chunk: + chunks.append(chunk) + + start = end - overlap + # Protección contra bucle infinito si overlap >= chunk_size o end no avanza + if start >= end: + start = end + + return chunks diff --git a/main.py b/main.py new file mode 100644 index 0000000..f682e5f --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from ontology-graph!") + + +if __name__ == "__main__": + main() diff --git a/notebooks/01_extraction.ipynb b/notebooks/01_extraction.ipynb new file mode 100644 index 0000000..0424535 --- /dev/null +++ b/notebooks/01_extraction.ipynb @@ -0,0 +1,935 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ontology Graph Extraction\n", + "\n", + "Extrae entidades y relaciones de cualquier documento usando funciones del registry.\n", + "- LLM: `claude -p --model haiku`\n", + "- Tipos: OSINT del registry + genéricos (concept, url, date, quantity, text_fragment, coordinates)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'python.functions.core.extract_json_from_llm'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 3\u001b[39m ROOT = \u001b[33m'/home/lucas/fn_registry'\u001b[39m\n\u001b[32m 4\u001b[39m os.environ[\u001b[33m'FN_REGISTRY_ROOT'\u001b[39m] = ROOT\n\u001b[32m 5\u001b[39m sys.path.insert(\u001b[32m0\u001b[39m, ROOT)\n\u001b[32m 6\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m python.functions.core.extract_json_from_llm \u001b[38;5;28;01mimport\u001b[39;00m extract_json_from_llm\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m python.functions.datascience.extraction_pipeline \u001b[38;5;28;01mimport\u001b[39;00m extraction_pipeline\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m python.functions.datascience.render_sigma_html \u001b[38;5;28;01mimport\u001b[39;00m render_sigma_html\n\u001b[32m 10\u001b[39m \n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'python.functions.core.extract_json_from_llm'" + ] + } + ], + "source": [ + "import sys, os, json, subprocess\n", + "\n", + "ROOT = '/home/lucas/fn_registry'\n", + "os.environ['FN_REGISTRY_ROOT'] = ROOT\n", + "sys.path.insert(0, ROOT)\n", + "\n", + "from python.functions.core.extract_json_from_llm import extract_json_from_llm\n", + "from python.functions.datascience.extraction_pipeline import extraction_pipeline\n", + "from python.functions.datascience.render_sigma_html import render_sigma_html\n", + "\n", + "print('Registry root:', ROOT)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'FN_REGISTRY_ROOT'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m sys, os, json, subprocess\n\u001b[32m 2\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m ROOT = os.environ[\u001b[33m'FN_REGISTRY_ROOT'\u001b[39m]\n\u001b[32m 4\u001b[39m sys.path.insert(\u001b[32m0\u001b[39m, ROOT)\n\u001b[32m 5\u001b[39m \n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m python.functions.core.extract_json_from_llm \u001b[38;5;28;01mimport\u001b[39;00m extract_json_from_llm\n", + "\u001b[36mFile \u001b[39m\u001b[32m:717\u001b[39m, in \u001b[36m_Environ.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n", + "\u001b[31mKeyError\u001b[39m: 'FN_REGISTRY_ROOT'" + ] + } + ], + "source": [ + "import sys, os, json, subprocess\n", + "\n", + "ROOT = os.environ['FN_REGISTRY_ROOT']\n", + "sys.path.insert(0, ROOT)\n", + "\n", + "from python.functions.core.extract_json_from_llm import extract_json_from_llm\n", + "from python.functions.datascience.extraction_pipeline import extraction_pipeline\n", + "from python.functions.datascience.render_sigma_html import render_sigma_html\n", + "\n", + "print('Registry root:', ROOT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LLM wrapper: claude -p + haiku" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def claude_haiku_json(messages: list[dict]) -> dict:\n", + " \"\"\"Wrapper que convierte messages OpenAI-style a claude -p --model haiku.\"\"\"\n", + " # Construir prompt desde messages\n", + " parts = []\n", + " for msg in messages:\n", + " role = msg['role']\n", + " content = msg['content']\n", + " if role == 'system':\n", + " parts.append(f\"[SYSTEM]\\n{content}\")\n", + " elif role == 'user':\n", + " parts.append(f\"[USER]\\n{content}\")\n", + " prompt = \"\\n\\n\".join(parts)\n", + " \n", + " result = subprocess.run(\n", + " ['claude', '-p', '--model', 'haiku', '--output-format', 'json', prompt],\n", + " capture_output=True, text=True, timeout=120\n", + " )\n", + " \n", + " if result.returncode != 0:\n", + " raise RuntimeError(f\"claude -p failed: {result.stderr}\")\n", + " \n", + " # Extraer el campo 'result' del JSON envelope de claude\n", + " envelope = json.loads(result.stdout)\n", + " raw_text = envelope.get('result', '')\n", + " \n", + " # Parsear JSON del LLM (maneja codeblocks, trailing commas, etc.)\n", + " return extract_json_from_llm(raw_text)\n", + "\n", + "# Test rapido\n", + "test = claude_haiku_json([{'role': 'user', 'content': 'Return JSON: {\"ok\": true}'}])\n", + "print('LLM wrapper OK:', test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Entity presets: OSINT + genéricos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# --- Presets OSINT (del registry) ---\n", + "OSINT_PRESETS = [\n", + " {\"type_ref\": \"osint_person_go_cybersecurity\", \"label\": \"Person\",\n", + " \"metadata_fields\": [\"full_name\", \"alias\", \"nationality\", \"dob\", \"gender\", \"risk_score\"]},\n", + " {\"type_ref\": \"osint_organization_go_cybersecurity\", \"label\": \"Organization\",\n", + " \"metadata_fields\": [\"legal_name\", \"country\", \"sector\", \"founded\", \"risk_score\"]},\n", + " {\"type_ref\": \"osint_location_go_cybersecurity\", \"label\": \"Location\",\n", + " \"metadata_fields\": [\"lat\", \"lon\", \"address\", \"country\", \"city\"]},\n", + " {\"type_ref\": \"osint_event_go_cybersecurity\", \"label\": \"Event\",\n", + " \"metadata_fields\": [\"event_type\", \"date\", \"location\", \"description\", \"severity\"]},\n", + " {\"type_ref\": \"osint_email_go_cybersecurity\", \"label\": \"Email\",\n", + " \"metadata_fields\": [\"address\", \"provider\", \"verified\", \"breached\"]},\n", + " {\"type_ref\": \"osint_domain_go_cybersecurity\", \"label\": \"Domain\",\n", + " \"metadata_fields\": [\"fqdn\", \"registrar\", \"created_date\", \"expires_date\"]},\n", + " {\"type_ref\": \"osint_ip_address_go_cybersecurity\", \"label\": \"IP Address\",\n", + " \"metadata_fields\": [\"ip\", \"asn\", \"country\", \"isp\", \"geolocation\"]},\n", + " {\"type_ref\": \"osint_phone_go_cybersecurity\", \"label\": \"Phone\",\n", + " \"metadata_fields\": [\"number\", \"country_code\", \"carrier\", \"phone_type\"]},\n", + " {\"type_ref\": \"osint_social_media_go_cybersecurity\", \"label\": \"Social Media Account\",\n", + " \"metadata_fields\": [\"platform\", \"username\", \"url\", \"followers\", \"verified\"]},\n", + " {\"type_ref\": \"osint_document_go_cybersecurity\", \"label\": \"Document\",\n", + " \"metadata_fields\": [\"title\", \"format\", \"classification\", \"source\"]},\n", + " {\"type_ref\": \"osint_crypto_wallet_go_cybersecurity\", \"label\": \"Crypto Wallet\",\n", + " \"metadata_fields\": [\"address\", \"blockchain\", \"balance\"]},\n", + " {\"type_ref\": \"osint_malware_go_cybersecurity\", \"label\": \"Malware\",\n", + " \"metadata_fields\": [\"family\", \"hash_sha256\", \"threat_level\"]},\n", + " {\"type_ref\": \"osint_vulnerability_go_cybersecurity\", \"label\": \"Vulnerability\",\n", + " \"metadata_fields\": [\"cve_id\", \"cvss\", \"affected_product\", \"exploited\"]},\n", + "]\n", + "\n", + "# --- Presets genéricos (sin tipo Go, inline) ---\n", + "GENERIC_PRESETS = [\n", + " {\"type_ref\": \"concept\", \"label\": \"Concept\",\n", + " \"metadata_fields\": [\"name\", \"category\", \"definition\"]},\n", + " {\"type_ref\": \"url\", \"label\": \"URL/Link\",\n", + " \"metadata_fields\": [\"url\", \"domain\", \"context\"]},\n", + " {\"type_ref\": \"date_reference\", \"label\": \"Date/Time\",\n", + " \"metadata_fields\": [\"date\", \"precision\", \"context\"]},\n", + " {\"type_ref\": \"quantity\", \"label\": \"Quantity/Amount\",\n", + " \"metadata_fields\": [\"value\", \"unit\", \"context\"]},\n", + " {\"type_ref\": \"coordinates\", \"label\": \"Coordinates\",\n", + " \"metadata_fields\": [\"lat\", \"lon\", \"label\"]},\n", + " {\"type_ref\": \"text_fragment\", \"label\": \"Key Text Fragment\",\n", + " \"metadata_fields\": [\"text\", \"category\", \"relevance\"]},\n", + "]\n", + "\n", + "ALL_PRESETS = OSINT_PRESETS + GENERIC_PRESETS\n", + "print(f'{len(ALL_PRESETS)} entity presets loaded ({len(OSINT_PRESETS)} OSINT + {len(GENERIC_PRESETS)} generic)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Relation types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "RELATION_TYPES = [\n", + " # Personas / orgs\n", + " \"employs\", \"works_for\", \"founded\", \"owns\", \"controls\",\n", + " \"member_of\", \"affiliated_with\", \"collaborates_with\",\n", + " # Comunicacion\n", + " \"communicates_with\", \"sent_to\", \"received_from\",\n", + " # Ubicacion\n", + " \"located_in\", \"headquartered_in\", \"traveled_to\", \"operates_in\",\n", + " # Eventos\n", + " \"participated_in\", \"caused\", \"occurred_at\", \"occurred_on\",\n", + " # Documentos / conceptos\n", + " \"mentions\", \"references\", \"describes\", \"authored\", \"published\",\n", + " # Financiero\n", + " \"funds\", \"transacted_with\", \"invested_in\",\n", + " # Tecnico\n", + " \"hosts\", \"resolves_to\", \"exploits\", \"targets\",\n", + " # Generico\n", + " \"related_to\", \"part_of\", \"instance_of\", \"has_attribute\",\n", + "]\n", + "\n", + "print(f'{len(RELATION_TYPES)} relation types')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extraer documento\n", + "\n", + "Pon tu documento en `data/` y cambia el path." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DOC_PATH = os.path.join(os.path.dirname(os.getcwd()), 'data', 'document.pdf') # <-- cambiar\n", + "\n", + "# Progreso visible\n", + "def on_progress(msg, pct):\n", + " print(f' [{pct*100:5.1f}%] {msg}')\n", + "\n", + "result = extraction_pipeline(\n", + " file_path=DOC_PATH,\n", + " entity_presets=ALL_PRESETS,\n", + " relation_types=RELATION_TYPES,\n", + " llm_chat_json=claude_haiku_json,\n", + " chunk_size=800,\n", + " chunk_overlap=100,\n", + " confidence_threshold=0.5,\n", + " dedup_threshold=0.85,\n", + " on_progress=on_progress,\n", + ")\n", + "\n", + "print(f'\\nEntities: {result.stats.final_entities_count}')\n", + "print(f'Relations: {result.stats.final_relations_count}')\n", + "print(f'Chunks: {result.stats.total_chunks}')\n", + "print(f'Time: {result.stats.processing_time_seconds:.1f}s')\n", + "print(f'Entity types: {result.stats.entity_types_found}')\n", + "print(f'Relation types: {result.stats.relation_types_found}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explorar resultados" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Entities\n", + "ent_rows = []\n", + "for e in result.entities:\n", + " ent_rows.append({\n", + " 'id': e.id,\n", + " 'name': e.name,\n", + " 'type': e.type_ref,\n", + " 'confidence': e.confidence,\n", + " 'attributes': e.attributes,\n", + " })\n", + "df_entities = pd.DataFrame(ent_rows)\n", + "print(f'=== Entities ({len(df_entities)}) ===')\n", + "df_entities.sort_values('type')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Relations\n", + "rel_rows = []\n", + "for r in result.relations:\n", + " rel_rows.append({\n", + " 'from_name': r.from_name,\n", + " 'relation': r.relation_type,\n", + " 'to_name': r.to_name,\n", + " 'confidence': r.confidence,\n", + " 'description': r.description,\n", + " })\n", + "df_relations = pd.DataFrame(rel_rows)\n", + "print(f'=== Relations ({len(df_relations)}) ===')\n", + "df_relations.sort_values('relation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizar grafo con sigma.js" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Colores por tipo de entidad\n", + "TYPE_COLORS = {\n", + " 'osint_person_go_cybersecurity': '#e74c3c',\n", + " 'osint_organization_go_cybersecurity': '#3498db',\n", + " 'osint_location_go_cybersecurity': '#2ecc71',\n", + " 'osint_event_go_cybersecurity': '#f39c12',\n", + " 'osint_email_go_cybersecurity': '#9b59b6',\n", + " 'osint_domain_go_cybersecurity': '#1abc9c',\n", + " 'osint_ip_address_go_cybersecurity': '#e67e22',\n", + " 'osint_phone_go_cybersecurity': '#95a5a6',\n", + " 'osint_social_media_go_cybersecurity': '#e91e63',\n", + " 'osint_document_go_cybersecurity': '#607d8b',\n", + " 'osint_crypto_wallet_go_cybersecurity': '#ff9800',\n", + " 'osint_malware_go_cybersecurity': '#f44336',\n", + " 'osint_vulnerability_go_cybersecurity': '#ff5722',\n", + " 'concept': '#00bcd4',\n", + " 'url': '#8bc34a',\n", + " 'date_reference': '#cddc39',\n", + " 'quantity': '#ffc107',\n", + " 'coordinates': '#4caf50',\n", + " 'text_fragment': '#78909c',\n", + "}\n", + "DEFAULT_COLOR = '#aaaaaa'\n", + "\n", + "def extraction_to_sigma(result) -> dict:\n", + " \"\"\"Convierte ExtractionResult a formato sigma.js/graphology.\"\"\"\n", + " # Contar degree para tamaño de nodo\n", + " degree = {}\n", + " for r in result.relations:\n", + " from_id = r.from_id or r.from_name\n", + " to_id = r.to_id or r.to_name\n", + " degree[from_id] = degree.get(from_id, 0) + 1\n", + " degree[to_id] = degree.get(to_id, 0) + 1\n", + "\n", + " nodes = []\n", + " for e in result.entities:\n", + " eid = e.id or e.name\n", + " nodes.append({\n", + " 'key': eid,\n", + " 'attributes': {\n", + " 'label': e.name,\n", + " 'color': TYPE_COLORS.get(e.type_ref, DEFAULT_COLOR),\n", + " 'size': 4 + min(degree.get(eid, 0) * 2, 20),\n", + " 'type': e.type_ref,\n", + " **{k: str(v) for k, v in (e.attributes or {}).items() if v is not None},\n", + " }\n", + " })\n", + "\n", + " edges = []\n", + " node_keys = {n['key'] for n in nodes}\n", + " for i, r in enumerate(result.relations):\n", + " from_id = r.from_id or r.from_name\n", + " to_id = r.to_id or r.to_name\n", + " if from_id in node_keys and to_id in node_keys:\n", + " edges.append({\n", + " 'key': f'e{i}',\n", + " 'source': from_id,\n", + " 'target': to_id,\n", + " 'attributes': {\n", + " 'label': r.relation_type,\n", + " 'type': r.relation_type,\n", + " }\n", + " })\n", + "\n", + " return {'nodes': nodes, 'edges': edges}\n", + "\n", + "graph_data = extraction_to_sigma(result)\n", + "print(f'Graph: {len(graph_data[\"nodes\"])} nodes, {len(graph_data[\"edges\"])} edges')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output_dir = os.path.join(os.path.dirname(os.getcwd()), 'data')\n", + "html_path = render_sigma_html(\n", + " graph_data=graph_data,\n", + " output_path=os.path.join(output_dir, 'ontology_graph.html'),\n", + " title='Ontology Graph',\n", + ")\n", + "print(f'Graph saved: {html_path}')\n", + "print(f'Open in browser: file://{html_path}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Auto-discovery de nuevos tipos\n", + "\n", + "Si el documento contiene entidades que no encajan en los presets, haiku las detecta y sugiere nuevos presets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def discover_new_types(result, existing_presets: list[dict]) -> list[dict]:\n", + " \"\"\"Pide a haiku que sugiera tipos nuevos basandose en entidades de baja confianza o genericas.\"\"\"\n", + " # Recopilar entidades clasificadas como concept/text_fragment (genéricos fallback)\n", + " generic_entities = [\n", + " {'name': e.name, 'type': e.type_ref, 'attributes': e.attributes}\n", + " for e in result.entities\n", + " if e.type_ref in ('concept', 'text_fragment', 'related_to')\n", + " ]\n", + " \n", + " if not generic_entities:\n", + " print('No hay entidades genéricas — los presets cubren todo.')\n", + " return []\n", + "\n", + " existing_labels = [p['label'] for p in existing_presets]\n", + " \n", + " prompt_msg = [\n", + " {'role': 'system', 'content': (\n", + " 'You analyze entities extracted from a document and suggest new entity type presets. '\n", + " 'Existing types: ' + ', '.join(existing_labels) + '. '\n", + " 'For entities that dont fit existing types, suggest new type presets. '\n", + " 'Output JSON: {\"new_presets\": [{\"type_ref\": \"snake_case_id\", \"label\": \"Human Label\", '\n", + " '\"metadata_fields\": [\"field1\", \"field2\", ...]}]}. '\n", + " 'Only suggest types that are genuinely different from existing ones. '\n", + " 'Return {\"new_presets\": []} if no new types are needed.'\n", + " )},\n", + " {'role': 'user', 'content': (\n", + " 'These entities were classified as generic (concept/text_fragment) '\n", + " 'because they didnt fit existing types:\\n\\n'\n", + " + json.dumps(generic_entities[:30], ensure_ascii=False, indent=2)\n", + " )}\n", + " ]\n", + " \n", + " resp = claude_haiku_json(prompt_msg)\n", + " new_presets = resp.get('new_presets', [])\n", + " \n", + " if new_presets:\n", + " print(f'Discovered {len(new_presets)} new types:')\n", + " for p in new_presets:\n", + " print(f\" - {p['label']} ({p['type_ref']}): {p['metadata_fields']}\")\n", + " else:\n", + " print('No new types needed.')\n", + " \n", + " return new_presets\n", + "\n", + "new_types = discover_new_types(result, ALL_PRESETS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Si se descubrieron tipos nuevos, re-extraer con presets ampliados\n", + "if new_types:\n", + " EXPANDED_PRESETS = ALL_PRESETS + new_types\n", + " print(f'Re-extracting with {len(EXPANDED_PRESETS)} presets...')\n", + " \n", + " result = extraction_pipeline(\n", + " file_path=DOC_PATH,\n", + " entity_presets=EXPANDED_PRESETS,\n", + " relation_types=RELATION_TYPES,\n", + " llm_chat_json=claude_haiku_json,\n", + " chunk_size=800,\n", + " chunk_overlap=100,\n", + " confidence_threshold=0.5,\n", + " dedup_threshold=0.85,\n", + " on_progress=on_progress,\n", + " )\n", + " \n", + " print(f'\\nEntities: {result.stats.final_entities_count}')\n", + " print(f'Relations: {result.stats.final_relations_count}')\n", + " \n", + " # Re-generar grafo\n", + " graph_data = extraction_to_sigma(result)\n", + " html_path = render_sigma_html(\n", + " graph_data=graph_data,\n", + " output_path=os.path.join(output_dir, 'ontology_graph.html'),\n", + " title='Ontology Graph (expanded)',\n", + " )\n", + " print(f'Updated graph: file://{html_path}')\n", + "else:\n", + " print('No re-extraction needed.')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'python.functions.core.extract_json_from_llm'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 5\u001b[39m os.environ[\u001b[33m'FN_REGISTRY_ROOT'\u001b[39m] = ROOT\n\u001b[32m 6\u001b[39m sys.path.insert(\u001b[32m0\u001b[39m, ROOT)\n\u001b[32m 7\u001b[39m sys.path.insert(\u001b[32m0\u001b[39m, os.path.join(ROOT, \u001b[33m'python'\u001b[39m, \u001b[33m'functions'\u001b[39m))\n\u001b[32m 8\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m python.functions.core.extract_json_from_llm \u001b[38;5;28;01mimport\u001b[39;00m extract_json_from_llm\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m python.functions.datascience.extraction_pipeline \u001b[38;5;28;01mimport\u001b[39;00m extraction_pipeline\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m python.functions.datascience.render_sigma_html \u001b[38;5;28;01mimport\u001b[39;00m render_sigma_html\n\u001b[32m 12\u001b[39m \n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'python.functions.core.extract_json_from_llm'" + ] + } + ], + "source": [ + "import sys, os, json, subprocess\n", + "from pathlib import Path\n", + "\n", + "ROOT = '/home/lucas/fn_registry'\n", + "os.environ['FN_REGISTRY_ROOT'] = ROOT\n", + "sys.path.insert(0, ROOT)\n", + "sys.path.insert(0, os.path.join(ROOT, 'python', 'functions'))\n", + "\n", + "from python.functions.core.extract_json_from_llm import extract_json_from_llm\n", + "from python.functions.datascience.extraction_pipeline import extraction_pipeline\n", + "from python.functions.datascience.render_sigma_html import render_sigma_html\n", + "\n", + "print('OK: imports loaded')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imports OK\n" + ] + } + ], + "source": [ + "import sys, os, json, subprocess\n", + "\n", + "# Añadir lib/ al path\n", + "sys.path.insert(0, '/home/lucas/fn_registry/analysis/ontology_graph/lib')\n", + "\n", + "from core_functions import extract_json_from_llm\n", + "from extraction_pipeline import extraction_pipeline\n", + "from render_sigma_html import render_sigma_html\n", + "\n", + "print('imports OK')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LLM wrapper OK: {'ok': True}\n" + ] + } + ], + "source": [ + "def claude_haiku_json(messages: list[dict]) -> dict:\n", + " \"\"\"Wrapper: messages OpenAI-style -> claude -p --model haiku -> dict.\"\"\"\n", + " parts = []\n", + " for msg in messages:\n", + " role = msg['role']\n", + " content = msg['content']\n", + " if role == 'system':\n", + " parts.append(f'[SYSTEM]\\n{content}')\n", + " elif role == 'user':\n", + " parts.append(f'[USER]\\n{content}')\n", + " prompt = '\\n\\n'.join(parts)\n", + " \n", + " result = subprocess.run(\n", + " ['claude', '-p', '--model', 'haiku', '--output-format', 'json', prompt],\n", + " capture_output=True, text=True, timeout=120\n", + " )\n", + " if result.returncode != 0:\n", + " raise RuntimeError(f'claude -p failed: {result.stderr}')\n", + " \n", + " envelope = json.loads(result.stdout)\n", + " raw_text = envelope.get('result', '')\n", + " return extract_json_from_llm(raw_text)\n", + "\n", + "# Test\n", + "test = claude_haiku_json([{'role': 'user', 'content': 'Return JSON: {\"ok\": true}'}])\n", + "print('LLM wrapper OK:', test)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19 presets, 35 relation types\n" + ] + } + ], + "source": [ + "OSINT_PRESETS = [\n", + " {'type_ref': 'osint_person_go_cybersecurity', 'label': 'Person',\n", + " 'metadata_fields': ['full_name', 'alias', 'nationality', 'dob', 'gender', 'risk_score']},\n", + " {'type_ref': 'osint_organization_go_cybersecurity', 'label': 'Organization',\n", + " 'metadata_fields': ['legal_name', 'country', 'sector', 'founded', 'risk_score']},\n", + " {'type_ref': 'osint_location_go_cybersecurity', 'label': 'Location',\n", + " 'metadata_fields': ['lat', 'lon', 'address', 'country', 'city']},\n", + " {'type_ref': 'osint_event_go_cybersecurity', 'label': 'Event',\n", + " 'metadata_fields': ['event_type', 'date', 'location', 'description', 'severity']},\n", + " {'type_ref': 'osint_email_go_cybersecurity', 'label': 'Email',\n", + " 'metadata_fields': ['address', 'provider', 'verified', 'breached']},\n", + " {'type_ref': 'osint_domain_go_cybersecurity', 'label': 'Domain',\n", + " 'metadata_fields': ['fqdn', 'registrar', 'created_date', 'expires_date']},\n", + " {'type_ref': 'osint_ip_address_go_cybersecurity', 'label': 'IP Address',\n", + " 'metadata_fields': ['ip', 'asn', 'country', 'isp', 'geolocation']},\n", + " {'type_ref': 'osint_phone_go_cybersecurity', 'label': 'Phone',\n", + " 'metadata_fields': ['number', 'country_code', 'carrier', 'phone_type']},\n", + " {'type_ref': 'osint_social_media_go_cybersecurity', 'label': 'Social Media Account',\n", + " 'metadata_fields': ['platform', 'username', 'url', 'followers', 'verified']},\n", + " {'type_ref': 'osint_document_go_cybersecurity', 'label': 'Document',\n", + " 'metadata_fields': ['title', 'format', 'classification', 'source']},\n", + " {'type_ref': 'osint_crypto_wallet_go_cybersecurity', 'label': 'Crypto Wallet',\n", + " 'metadata_fields': ['address', 'blockchain', 'balance']},\n", + " {'type_ref': 'osint_malware_go_cybersecurity', 'label': 'Malware',\n", + " 'metadata_fields': ['family', 'hash_sha256', 'threat_level']},\n", + " {'type_ref': 'osint_vulnerability_go_cybersecurity', 'label': 'Vulnerability',\n", + " 'metadata_fields': ['cve_id', 'cvss', 'affected_product', 'exploited']},\n", + "]\n", + "\n", + "GENERIC_PRESETS = [\n", + " {'type_ref': 'concept', 'label': 'Concept',\n", + " 'metadata_fields': ['name', 'category', 'definition']},\n", + " {'type_ref': 'url', 'label': 'URL/Link',\n", + " 'metadata_fields': ['url', 'domain', 'context']},\n", + " {'type_ref': 'date_reference', 'label': 'Date/Time',\n", + " 'metadata_fields': ['date', 'precision', 'context']},\n", + " {'type_ref': 'quantity', 'label': 'Quantity/Amount',\n", + " 'metadata_fields': ['value', 'unit', 'context']},\n", + " {'type_ref': 'coordinates', 'label': 'Coordinates',\n", + " 'metadata_fields': ['lat', 'lon', 'label']},\n", + " {'type_ref': 'text_fragment', 'label': 'Key Text Fragment',\n", + " 'metadata_fields': ['text', 'category', 'relevance']},\n", + "]\n", + "\n", + "ALL_PRESETS = OSINT_PRESETS + GENERIC_PRESETS\n", + "\n", + "RELATION_TYPES = [\n", + " 'employs', 'works_for', 'founded', 'owns', 'controls',\n", + " 'member_of', 'affiliated_with', 'collaborates_with',\n", + " 'communicates_with', 'sent_to', 'received_from',\n", + " 'located_in', 'headquartered_in', 'traveled_to', 'operates_in',\n", + " 'participated_in', 'caused', 'occurred_at', 'occurred_on',\n", + " 'mentions', 'references', 'describes', 'authored', 'published',\n", + " 'funds', 'transacted_with', 'invested_in',\n", + " 'hosts', 'resolves_to', 'exploits', 'targets',\n", + " 'related_to', 'part_of', 'instance_of', 'has_attribute',\n", + "]\n", + "\n", + "print(f'{len(ALL_PRESETS)} presets, {len(RELATION_TYPES)} relation types')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 0.0%] Extracting text from file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 0.0%] Extracting entities from chunk 1/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 0.7%] Extracting entities from chunk 2/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 1.5%] Extracting entities from chunk 3/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 2.2%] Extracting entities from chunk 4/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 3.0%] Extracting entities from chunk 5/54\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lucas/fn_registry/analysis/ontology_graph/lib/extraction_pipeline.py:113: UserWarning: extract_entities_llm: type_ref 'osint_service_go_cybersecurity' no esta en el schema, descartando entidad 'Bizum'\n", + " candidates = extract_entities_llm(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 3.7%] Extracting entities from chunk 6/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 4.4%] Extracting entities from chunk 7/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 5.2%] Extracting entities from chunk 8/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 5.9%] Extracting entities from chunk 9/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 6.7%] Extracting entities from chunk 10/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 7.4%] Extracting entities from chunk 11/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 8.1%] Extracting entities from chunk 12/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 8.9%] Extracting entities from chunk 13/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 9.6%] Extracting entities from chunk 14/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 10.4%] Extracting entities from chunk 15/54\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " [ 11.1%] Extracting entities from chunk 16/54\n" + ] + } + ], + "source": [ + "DOC_PATH = '/home/lucas/fn_registry/analysis/ontology_graph/data/condiciones-generales-bizum.pdf'\n", + "\n", + "def on_progress(msg, pct):\n", + " print(f' [{pct*100:5.1f}%] {msg}')\n", + "\n", + "result = extraction_pipeline(\n", + " file_path=DOC_PATH,\n", + " entity_presets=ALL_PRESETS,\n", + " relation_types=RELATION_TYPES,\n", + " llm_chat_json=claude_haiku_json,\n", + " chunk_size=800,\n", + " chunk_overlap=100,\n", + " confidence_threshold=0.5,\n", + " dedup_threshold=0.85,\n", + " on_progress=on_progress,\n", + ")\n", + "\n", + "print(f'\\nEntities: {result.stats.final_entities_count}')\n", + "print(f'Relations: {result.stats.final_relations_count}')\n", + "print(f'Chunks: {result.stats.total_chunks}')\n", + "print(f'Time: {result.stats.processing_time_seconds:.1f}s')\n", + "print(f'Entity types: {result.stats.entity_types_found}')\n", + "print(f'Relation types: {result.stats.relation_types_found}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipeline optimizado\n", + "\n", + "- 1 sola llamada LLM por chunk (entities + relations + tipos nuevos)\n", + "- Chunks de 2000 chars\n", + "- Paralelizado con ThreadPoolExecutor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "from extract_text_from_file import extract_text_from_file\n", + "from core_functions import preprocess_text\n", + "from split_text_into_chunks import split_text_into_chunks\n", + "from deduplicate_entities import deduplicate_entities\n", + "from deduplicate_relations import deduplicate_relations\n", + "from entity_candidate import EntityCandidate\n", + "from relation_candidate import RelationCandidate\n", + "\n", + "def build_unified_prompt(entity_presets, relation_types):\n", + " \"\"\"System prompt que pide entities + relations + tipos nuevos en 1 sola llamada.\"\"\"\n", + " type_lines = []\n", + " for p in entity_presets:\n", + " fields = ', '.join(p.get('metadata_fields', []))\n", + " type_lines.append(f\"- {p['label']} (type_ref: {p['type_ref']}): [{fields}]\")\n", + "\n", + " return f'''You are an entity and relation extraction expert. Given text, extract ALL entities and relations in a single pass.\n", + "\n", + "ENTITY TYPES:\n", + "{chr(10).join(type_lines)}\n", + "\n", + "RELATION TYPES: {', '.join(relation_types)}\n", + "\n", + "OUTPUT FORMAT (strict JSON):\n", + "{{\n", + " \"entities\": [\n", + " {{\"name\": \"...\", \"type_ref\": \"...\", \"attributes\": {{...}}, \"confidence\": 0.9}}\n", + " ],\n", + " \"relations\": [\n", + " {{\"from_name\": \"...\", \"to_name\": \"...\", \"relation_type\": \"...\", \"confidence\": 0.8, \"description\": \"...\"}}\n", + " ],\n", + " \"suggested_types\": [\n", + " {{\"type_ref\": \"snake_case_id\", \"label\": \"Human Label\", \"metadata_fields\": [\"field1\", \"field2\"], \"reason\": \"why this type is needed\"}}\n", + " ]\n", + "}}\n", + "\n", + "RULES:\n", + "- Extract ALL entities explicitly mentioned in the text\n", + "- Use exact type_ref from the schema. Leave unknown attributes as null\n", + "- Confidence: 1.0 = explicitly named, 0.7 = strongly implied, 0.5 = weakly implied\n", + "- Relations: from_name and to_name MUST match extracted entity names exactly\n", + "- suggested_types: if you find important entities that do NOT fit any existing type, suggest a new type with its fields. Use these suggested types for those entities in the entities array.\n", + "- If no suggested types are needed, return \"suggested_types\": []\n", + "- Respond in the same language as the text for descriptions'''\n", + "\n", + "UNIFIED_PROMPT = build_unified_prompt(ALL_PRESETS, RELATION_TYPES)\n", + "print(f'Prompt length: {len(UNIFIED_PROMPT)} chars')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c7a0ea9 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "ontology-graph" +version = "0.1.0" 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