#!/usr/bin/env python3
"""
Neo4j Graph Service — replaces NetworkX graph builder.

Responsibilities
----------------
1. bulk_load()  — load all KG JSON files into Neo4j in batched transactions.
2. similar_cves() — find CVEs related by shared CWE / product / vendor.
3. subgraph_for_cves() — expand a set of CVE IDs by N hops for RAG context.
4. (optional) centrality_scores() — PageRank via GDS for ranked context.

Run as CLI to do the bulk load:
    python -m src.constructors.neo4j_graph_service --bulk-load
    python -m src.constructors.neo4j_graph_service --similar CVE-2021-44228
    python -m src.constructors.neo4j_graph_service --stats
"""

from __future__ import annotations

import argparse
import json
import logging
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

from neo4j import GraphDatabase, Session

project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))

from config import Config
from src.generators.rag_config import NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

_BATCH = 10_000   # rows per Neo4j transaction


class Neo4jGraphService:
    """
    Bulk-load KG JSON → Neo4j and expose graph query helpers.
    Connects on construction; call .close() when done.
    """

    def __init__(
        self,
        uri: str  = NEO4J_URI,
        user: str = NEO4J_USER,
        pwd: str  = NEO4J_PASSWORD,
    ) -> None:
        self.driver = GraphDatabase.driver(uri, auth=(user, pwd))
        self.config = Config()
        self.kg_dir = self.config.knowledge_base_dir / "knowledge_graph"

    def close(self) -> None:
        self.driver.close()

    def __enter__(self) -> "Neo4jGraphService":
        return self

    def __exit__(self, *_: Any) -> None:
        self.close()

    # ── schema ────────────────────────────────────────────────────────────────

    def create_constraints(self) -> None:
        """Unique constraints guarantee idempotent MERGE."""
        constraints = [
            ("CVE",       "id"),
            ("Product",   "name"),
            ("Vendor",    "name"),
            ("CWE",       "id"),
            ("CAPEC",     "id"),
            ("Technique", "id"),
            ("Tactic",    "id"),
        ]
        with self.driver.session() as s:
            for label, prop in constraints:
                try:
                    s.run(
                        f"CREATE CONSTRAINT IF NOT EXISTS "
                        f"FOR (n:{label}) REQUIRE n.{prop} IS UNIQUE"
                    )
                except Exception as e:
                    logger.warning(f"Constraint {label}.{prop}: {e}")
        logger.info("Constraints ensured.")

    # ── bulk loader ───────────────────────────────────────────────────────────

    def bulk_load(self) -> None:
        """Load all JSON KG files into Neo4j. Idempotent (uses MERGE)."""
        logger.info("Starting Neo4j bulk load …")
        self.create_constraints()

        self._load_cve_nodes()
        self._load_product_nodes()
        self._load_vendor_nodes()
        self._load_cwe_nodes()
        self._load_capec_nodes()
        self._load_technique_nodes()
        self._load_tactic_nodes()

        self._load_rels("cve_cwe_relationships.json",           self._upsert_cve_cwe)
        self._load_rels("cve_product_relationships.json",       self._upsert_cve_product)
        self._load_rels("cve_capec_relationships.json",         self._upsert_cve_capec)
        self._load_rels("cve_mitre_technique_relationships.json",self._upsert_cve_technique)
        self._load_rels("cve_mitre_tactic_relationships.json",  self._upsert_cve_tactic)
        self._load_rels("product_vendor_relationships.json",    self._upsert_product_vendor)

        logger.info("Bulk load complete.")
        self._log_counts()

    # ── node loaders ──────────────────────────────────────────────────────────

    def _load_cve_nodes(self) -> None:
        data = self._read_json("cves_nodes.json")
        rows = list(data.values()) if isinstance(data, dict) else data
        logger.info(f"Loading {len(rows):,} CVE nodes …")
        cypher = """
        UNWIND $rows AS r
        MERGE (c:CVE {id: r.id})
        SET c.title             = r.title,
            c.description       = r.description,
            c.source            = r.source,
            c.published_date    = r.published_date,
            c.last_modified     = r.last_modified_date,
            c.cvss_v3_score     = r.cvss_v3_score,
            c.cvss_v3_severity  = r.cvss_v3_severity,
            c.cvss_v3_vector    = r.cvss_v3_vector,
            c.attack_vector     = r.attack_vector,
            c.attack_complexity = r.attack_complexity,
            c.privileges_required = r.privileges_required,
            c.user_interaction  = r.user_interaction,
            c.scope             = r.scope,
            c.conf_impact       = r.conf_impact,
            c.integ_impact      = r.integ_impact,
            c.avail_impact      = r.avail_impact,
            c.cvss_v2_score     = r.cvss_v2_score,
            c.cvss_v2_vector    = r.cvss_v2_vector,
            c.is_in_kev         = r.is_in_kev,
            c.exploitdb_count   = r.exploitdb_count,
            c.tags              = r.tags
        """
        def _norm(r: Dict[str, Any]) -> Dict[str, Any]:
            cvss3 = r.get("cvss_v3") or {}
            cvss2 = r.get("cvss_v2") or {}
            # Use prose description (first line of content if available)
            raw_desc = r.get("content", r.get("description", "")) or ""
            prose = raw_desc.split("\n")[0].strip()
            # Build a real title from the first sentence of the description
            title = prose[:120] if prose else r.get("id", "")
            return {
                "id":                r.get("id", ""),
                "title":             title,
                "description":       prose,
                "source":            r.get("source", ""),
                "published_date":    r.get("published_date", ""),
                "last_modified_date":r.get("last_modified_date", ""),
                "cvss_v3_score":     cvss3.get("base_score"),
                "cvss_v3_severity":  cvss3.get("base_severity", ""),
                "cvss_v3_vector":    cvss3.get("vector_string", ""),
                "attack_vector":     cvss3.get("attack_vector", ""),
                "attack_complexity": cvss3.get("attack_complexity", ""),
                "privileges_required": cvss3.get("privileges_required", ""),
                "user_interaction":  cvss3.get("user_interaction", ""),
                "scope":             cvss3.get("scope", ""),
                "conf_impact":       cvss3.get("confidentiality_impact", ""),
                "integ_impact":      cvss3.get("integrity_impact", ""),
                "avail_impact":      cvss3.get("availability_impact", ""),
                "cvss_v2_score":     cvss2.get("base_score"),
                "cvss_v2_vector":    cvss2.get("vector_string", ""),
                "is_in_kev":         bool(r.get("is_in_kev", False)),
                "exploitdb_count":   r.get("exploitdb_correlations_count", 0) or 0,
                "tags":              r.get("tags") or [],
            }
        self._batch_write(cypher, [_norm(r) for r in rows])

    def _load_product_nodes(self) -> None:
        data = self._read_json("products_nodes.json")
        rows = list(data.values()) if isinstance(data, dict) else data
        logger.info(f"Loading {len(rows):,} Product nodes …")
        cypher = """
        UNWIND $rows AS r
        MERGE (p:Product {name: r.name})
        SET p.vendor            = r.vendor,
            p.display_name      = r.display_name,
            p.category          = r.category,
            p.criticality_score = r.criticality_score
        """
        self._batch_write(cypher, rows)

    def _load_vendor_nodes(self) -> None:
        data = self._read_json("vendors_nodes.json")
        rows = list(data.values()) if isinstance(data, dict) else data
        logger.info(f"Loading {len(rows):,} Vendor nodes …")
        cypher = """
        UNWIND $rows AS r
        MERGE (v:Vendor {name: r.name})
        """
        # vendors_nodes may store as {vendor_name: {name:..., ...}}
        norm_rows = []
        for r in rows:
            norm_rows.append({"name": r.get("name", r.get("vendor", ""))})
        self._batch_write(cypher, norm_rows)

    def _load_cwe_nodes(self) -> None:
        data = self._read_json("cwes_nodes.json")
        rows = list(data.values()) if isinstance(data, dict) else data
        logger.info(f"Loading {len(rows):,} CWE nodes …")

        # Enrich with real names + descriptions from cwe_chunks.json if available
        cwe_enrichment: Dict[str, Dict[str, str]] = {}
        cwe_chunks_path = (
            self.config.knowledge_base_dir / "rag_exports" / "cwe_chunks.json"
        )
        if cwe_chunks_path.exists():
            try:
                with open(cwe_chunks_path, encoding="utf-8") as f:
                    cwe_chunks = json.load(f)
                for c in cwe_chunks:
                    p = c.get("payload", {})
                    cid = p.get("cwe_id", "")
                    if cid:
                        cwe_enrichment[cid] = {
                            "name":        p.get("name", ""),
                            "description": c.get("text", "")[:500],
                        }
                logger.info(f"  CWE enrichment loaded: {len(cwe_enrichment)} entries")
            except Exception as e:
                logger.warning(f"  Could not load CWE enrichment: {e}")

        cypher = """
        UNWIND $rows AS r
        MERGE (w:CWE {id: r.id})
        SET w.name        = r.name,
            w.description = r.description,
            w.vuln_count  = r.vulnerability_count
        """

        def _norm(r: Dict[str, Any]) -> Dict[str, Any]:
            cid = r.get("id", "")
            enriched = cwe_enrichment.get(cid, {})
            raw_name = r.get("name", "")
            # Strip verbose prefix "Common Weakness Enumeration CWE-XXX" → real name
            clean_name = (
                enriched.get("name")
                or raw_name.replace("Common Weakness Enumeration ", "").strip()
                or cid
            )
            return {
                "id":               cid,
                "name":             clean_name,
                "description":      enriched.get("description", ""),
                "vulnerability_count": r.get("vulnerability_count", 0),
            }

        self._batch_write(cypher, [_norm(r) for r in rows])

    def _load_capec_nodes(self) -> None:
        data = self._read_json("capecs_nodes.json")
        rows = list(data.values()) if isinstance(data, dict) else data
        logger.info(f"Loading {len(rows):,} CAPEC nodes …")
        cypher = """
        UNWIND $rows AS r
        MERGE (a:CAPEC {id: r.id})
        SET a.name = r.name
        """
        self._batch_write(cypher, rows)

    def _load_technique_nodes(self) -> None:
        data = self._read_json("mitre_techniques_nodes.json")
        rows = list(data.values()) if isinstance(data, dict) else data
        logger.info(f"Loading {len(rows):,} Technique nodes …")

        # Load real names + descriptions from CTI technique JSON files
        cti_dir = self.config.cti_docs_dir / "attack_techniques"
        technique_enrichment: Dict[str, Dict[str, str]] = {}
        if cti_dir.exists():
            for jf in cti_dir.glob("*.json"):
                try:
                    with open(jf, encoding="utf-8") as f:
                        doc = json.load(f)
                    tid = doc.get("id", "")
                    if tid:
                        technique_enrichment[tid] = {
                            "name":        doc.get("title", ""),
                            "description": (doc.get("content") or "")[:400],
                            "tactics":     ", ".join(doc.get("tactics", [])),
                        }
                except Exception:
                    pass
            logger.info(f"  Technique enrichment loaded: {len(technique_enrichment)} entries")

        cypher = """
        UNWIND $rows AS r
        MERGE (t:Technique {id: r.id})
        SET t.name        = r.name,
            t.description = r.description,
            t.tactics     = r.tactics,
            t.vuln_count  = r.vulnerability_count
        """

        def _norm(r: Dict[str, Any]) -> Dict[str, Any]:
            tid = r.get("id", "")
            enriched = technique_enrichment.get(tid, {})
            raw_name = r.get("name", r.get("title", ""))
            clean_name = (
                enriched.get("name")
                or raw_name.replace("MITRE ATT&CK Technique ", "").strip()
                or tid
            )
            return {
                "id":               tid,
                "name":             clean_name,
                "description":      enriched.get("description", ""),
                "tactics":          enriched.get("tactics", ""),
                "vulnerability_count": r.get("vulnerability_count", 0),
            }

        self._batch_write(cypher, [_norm(r) for r in rows])

    def _load_tactic_nodes(self) -> None:
        data = self._read_json("mitre_tactics_nodes.json")
        rows = list(data.values()) if isinstance(data, dict) else data
        logger.info(f"Loading {len(rows):,} Tactic nodes …")
        cypher = """
        UNWIND $rows AS r
        MERGE (t:Tactic {id: r.id})
        SET t.name       = r.name,
            t.vuln_count = r.vulnerability_count
        """

        def _norm(r: Dict[str, Any]) -> Dict[str, Any]:
            raw_name = r.get("name", r.get("title", ""))
            # Strip verbose prefix "MITRE ATT&CK Tactic TXXX" → real name
            clean_name = raw_name.replace("MITRE ATT&CK Tactic ", "").strip() or r.get("id", "")
            return {
                "id":               r.get("id", ""),
                "name":             clean_name,
                "vulnerability_count": r.get("vulnerability_count", 0),
            }

        self._batch_write(cypher, [_norm(r) for r in rows])

    # ── relationship loaders ──────────────────────────────────────────────────

    def _load_rels(self, filename: str, handler) -> None:
        path = self.kg_dir / filename
        if not path.exists():
            logger.warning(f"Relationship file not found: {filename}")
            return
        with open(path, encoding="utf-8") as f:
            rows = json.load(f)
        logger.info(f"Loading {len(rows):,} {filename} …")
        handler(rows)

    def _upsert_cve_cwe(self, rows: List[Dict[str, Any]]) -> None:
        cypher = """
        UNWIND $rows AS r
        MATCH (c:CVE {id: r.cve_id}), (w:CWE {id: r.cwe_id})
        MERGE (c)-[:HAS_WEAKNESS]->(w)
        """
        self._batch_write(cypher, rows)

    def _upsert_cve_product(self, rows: List[Dict[str, Any]]) -> None:
        cypher = """
        UNWIND $rows AS r
        MATCH (c:CVE {id: r.cve_id}), (p:Product {name: r.product_name})
        MERGE (c)-[:AFFECTS]->(p)
        """
        self._batch_write(cypher, rows)

    def _upsert_cve_capec(self, rows: List[Dict[str, Any]]) -> None:
        cypher = """
        UNWIND $rows AS r
        MATCH (c:CVE {id: r.cve_id}), (a:CAPEC {id: r.capec_id})
        MERGE (c)-[:USES_PATTERN]->(a)
        """
        norm = [{"cve_id": r.get("cve_id", ""), "capec_id": r.get("capec_id", "")} for r in rows]
        self._batch_write(cypher, norm)

    def _upsert_cve_technique(self, rows: List[Dict[str, Any]]) -> None:
        cypher = """
        UNWIND $rows AS r
        MATCH (c:CVE {id: r.cve_id}), (t:Technique {id: r.technique_id})
        MERGE (c)-[:IMPLEMENTS_TECHNIQUE]->(t)
        """
        norm = [{"cve_id": r.get("cve_id", ""), "technique_id": r.get("technique_id", "")} for r in rows]
        self._batch_write(cypher, norm)

    def _upsert_cve_tactic(self, rows: List[Dict[str, Any]]) -> None:
        cypher = """
        UNWIND $rows AS r
        MATCH (c:CVE {id: r.cve_id}), (t:Tactic {id: r.tactic_id})
        MERGE (c)-[:MAPPED_TO_TACTIC]->(t)
        """
        norm = [{"cve_id": r.get("cve_id", ""), "tactic_id": r.get("tactic_id", "")} for r in rows]
        self._batch_write(cypher, norm)

    def _upsert_product_vendor(self, rows: List[Dict[str, Any]]) -> None:
        cypher = """
        UNWIND $rows AS r
        MATCH (p:Product {name: r.product_name}), (v:Vendor {name: r.vendor_name})
        MERGE (p)-[:MANUFACTURED_BY]->(v)
        """
        norm = [{"product_name": r.get("product_name", r.get("product", "")),
                 "vendor_name":  r.get("vendor_name",  r.get("vendor", ""))} for r in rows]
        self._batch_write(cypher, norm)

    # ── query API ─────────────────────────────────────────────────────────────

    def similar_cves(self, cve_id: str, k: int = 10) -> List[Dict[str, Any]]:
        """
        Find CVEs related to cve_id by shared CWE, product, or tactic.
        Returns list of {cve_id, shared_type, score}.
        """
        cypher = """
        MATCH (src:CVE {id: $cve_id})
        CALL {
          WITH src
          MATCH (src)-[:HAS_WEAKNESS]->(w:CWE)<-[:HAS_WEAKNESS]-(other:CVE)
          WHERE other.id <> $cve_id
          RETURN other.id AS cve_id, 'cwe' AS shared_type, count(w) AS cnt
          UNION
          WITH src
          MATCH (src)-[:AFFECTS]->(p:Product)<-[:AFFECTS]-(other:CVE)
          WHERE other.id <> $cve_id
          RETURN other.id AS cve_id, 'product' AS shared_type, count(p) AS cnt
          UNION
          WITH src
          MATCH (src)-[:MAPPED_TO_TACTIC]->(t:Tactic)<-[:MAPPED_TO_TACTIC]-(other:CVE)
          WHERE other.id <> $cve_id
          RETURN other.id AS cve_id, 'tactic' AS shared_type, count(t) AS cnt
        }
        RETURN cve_id, shared_type, sum(cnt) AS score
        ORDER BY score DESC
        LIMIT $k
        """
        with self.driver.session() as s:
            result = s.run(cypher, cve_id=cve_id, k=k)
            return [{"cve_id": r["cve_id"], "shared_type": r["shared_type"], "score": r["score"]}
                    for r in result]

    def subgraph_for_cves(self, cve_ids: List[str], hops: int = 1) -> Dict[str, Any]:
        """Backward-compat wrapper — seed subgraph from CVE IDs."""
        return self.subgraph_from_nodes(
            [{"label": "CVE", "id": cid} for cid in cve_ids], hops=hops
        )

    # ── label-agnostic KG API ─────────────────────────────────────────────────

    _LABEL_KEY: Dict[str, str] = {
        "CVE": "id", "CWE": "id", "CAPEC": "id",
        "Technique": "id", "Tactic": "id",
        "Product": "name", "Vendor": "name",
    }

    _LABEL_TO_CVE_REL: Dict[str, Optional[str]] = {
        "CWE":       "HAS_WEAKNESS",
        "CAPEC":     "USES_PATTERN",
        "Technique": "IMPLEMENTS_TECHNIQUE",
        "Tactic":    "MAPPED_TO_TACTIC",
        "Product":   "AFFECTS",
        "Vendor":    None,   # handled via Product hop
    }

    def lookup_by_label(self, label: str, query: str, k: int = 5) -> List[Dict[str, Any]]:
        """
        Fuzzy + exact match a node by label.

        Returns [{id, name, props}]. Label must be in _LABEL_KEY whitelist.
        """
        if label not in self._LABEL_KEY:
            logger.warning("lookup_by_label: unknown label %r", label)
            return []
        key = self._LABEL_KEY[label]
        cypher = f"""
        MATCH (n:{label})
        WHERE toLower(n.{key}) = toLower($q)
           OR toLower(n.{key}) CONTAINS toLower($q)
        RETURN coalesce(n.id, n.name) AS id,
               coalesce(n.name, n.id) AS name,
               properties(n)         AS props
        LIMIT $k
        """
        with self.driver.session() as s:
            result = s.run(cypher, q=query, k=k)
            return [{"id": r["id"], "name": r["name"], "props": dict(r["props"])} for r in result]

    def cves_for_node(self, label: str, node_id: str, k: int = 10) -> List[Dict[str, Any]]:
        """
        Given a non-CVE seed (Product, CWE, Technique, Tactic, CAPEC, Vendor),
        return connected CVEs ordered by CVSS score desc.
        """
        if label not in self._LABEL_TO_CVE_REL:
            return []
        key = self._LABEL_KEY.get(label, "id")
        rel  = self._LABEL_TO_CVE_REL[label]

        if label == "Vendor":
            cypher = f"""
            MATCH (v:Vendor)-[:MANUFACTURED_BY]-(p:Product)-[:AFFECTS]-(c:CVE)
            WHERE v.name = $nid
            RETURN DISTINCT c.id AS id, c.cvss_v3_score AS cvss, c.description AS desc
            ORDER BY cvss DESC LIMIT $k
            """
        elif rel:
            cypher = f"""
            MATCH (n:{label})-[:{rel}]-(c:CVE)
            WHERE n.{key} = $nid
            RETURN c.id AS id, c.cvss_v3_score AS cvss, c.description AS desc
            ORDER BY cvss DESC LIMIT $k
            """
        else:
            return []

        with self.driver.session() as s:
            return [
                {"id": r["id"], "cvss": r["cvss"], "description": r["desc"]}
                for r in s.run(cypher, nid=node_id, k=k)
            ]

    def subgraph_from_nodes(self, seeds: List[Dict[str, str]], hops: int = 1) -> Dict[str, Any]:
        """
        General subgraph expansion from heterogeneous seeds [{label, id}, ...].

        Replaces the CVE-only subgraph_for_cves. Expands each seed label
        separately and merges results. Label must be in _LABEL_KEY whitelist.
        """
        hops = max(1, int(hops))
        by_label: Dict[str, List[str]] = {}
        for s in seeds:
            lbl = s.get("label", "")
            if lbl in self._LABEL_KEY:
                by_label.setdefault(lbl, []).append(s["id"])

        all_nodes: List[Dict] = []
        all_edges: List[Dict] = []

        with self.driver.session() as session:
            for label, ids in by_label.items():
                key = self._LABEL_KEY[label]
                cypher = f"""
                MATCH path = (seed:{label})-[*1..{hops}]-(n)
                WHERE seed.{key} IN $ids
                UNWIND nodes(path) AS node
                UNWIND relationships(path) AS rel
                RETURN DISTINCT
                    labels(node)[0]                           AS node_label,
                    coalesce(node.id, node.name)              AS node_id,
                    node.name                                 AS node_name,
                    type(rel)                                 AS rel_type,
                    coalesce(startNode(rel).id, startNode(rel).name) AS rel_start,
                    coalesce(endNode(rel).id,   endNode(rel).name)   AS rel_end
                LIMIT 500
                """
                for r in session.run(cypher, ids=ids):
                    all_nodes.append({
                        "label": r["node_label"],
                        "id":    r["node_id"],
                        "name":  r["node_name"],
                    })
                    all_edges.append({
                        "type":  r["rel_type"],
                        "start": r["rel_start"],
                        "end":   r["rel_end"],
                    })

        # Dedup
        seen_n: set = set()
        seen_e: set = set()
        nodes = [n for n in all_nodes if (n["label"], n["id"]) not in seen_n
                 and not seen_n.add((n["label"], n["id"]))]   # type: ignore[func-returns-value]
        edges = [e for e in all_edges if (e["type"], e["start"], e["end"]) not in seen_e
                 and not seen_e.add((e["type"], e["start"], e["end"]))]   # type: ignore[func-returns-value]

        return {"nodes": nodes, "edges": edges}

    # ── stats ─────────────────────────────────────────────────────────────────

    def get_stats(self) -> Dict[str, int]:
        labels = ["CVE", "Product", "Vendor", "CWE", "CAPEC", "Technique", "Tactic"]
        stats: Dict[str, int] = {}
        with self.driver.session() as s:
            for label in labels:
                result = s.run(f"MATCH (n:{label}) RETURN count(n) AS cnt")
                stats[label] = result.single()["cnt"]
        return stats

    def _log_counts(self) -> None:
        stats = self.get_stats()
        for label, cnt in stats.items():
            logger.info(f"  {label}: {cnt:,}")

    # ── internal ──────────────────────────────────────────────────────────────

    def _read_json(self, filename: str) -> Any:
        path = self.kg_dir / filename
        if not path.exists():
            logger.warning(f"File not found: {path}")
            return {}
        logger.info(f"  Reading {filename} ({path.stat().st_size / 1e6:.0f} MB) …")
        with open(path, encoding="utf-8") as f:
            return json.load(f)

    def _batch_write(self, cypher: str, rows: List[Dict[str, Any]]) -> None:
        total = len(rows)
        with self.driver.session() as s:
            for start in range(0, total, _BATCH):
                batch = rows[start : start + _BATCH]
                s.run(cypher, rows=batch)
                if (start + _BATCH) % (10 * _BATCH) == 0:
                    logger.info(f"    … {start + _BATCH:,} / {total:,}")


# ─────────────────────────────────────────────────────────────────────────────
# CLI
# ─────────────────────────────────────────────────────────────────────────────

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Neo4j Graph Service")
    parser.add_argument("--bulk-load", action="store_true", help="Load KG JSON → Neo4j")
    parser.add_argument("--similar",   type=str,           help="Find CVEs similar to given CVE-ID")
    parser.add_argument("--k",         type=int, default=10)
    parser.add_argument("--stats",     action="store_true")
    args = parser.parse_args()

    with Neo4jGraphService() as svc:
        if args.bulk_load:
            svc.bulk_load()
        elif args.similar:
            results = svc.similar_cves(args.similar, k=args.k)
            print(f"\nCVEs similar to {args.similar}:")
            for r in results:
                print(f"  {r['cve_id']:25s}  ({r['shared_type']:10s})  score={r['score']}")
        elif args.stats:
            stats = svc.get_stats()
            print("\nNeo4j node counts:")
            for label, cnt in stats.items():
                print(f"  {label:12s}: {cnt:,}")
        else:
            parser.print_help()