"""Company search provider — queries contacts with type='company'."""

from __future__ import annotations

import logging
import uuid
from typing import Any

from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession

logger = logging.getLogger(__name__)


class CompanySearchProvider:
    """Search provider for Company entities (contacts with type='company')."""

    entity_type = "company"

    async def search_fts(
        self,
        db: AsyncSession,
        tsquery: str,
        tenant_id: uuid.UUID,
        limit: int,
    ) -> list[dict[str, Any]]:
        """Full-text search on contacts.search_tsv where type='company'."""
        sql = text(
            """
            SELECT c.*, ts_rank(c.search_tsv, to_tsquery('pg_catalog.german', :q)) AS rank
            FROM contacts c
            WHERE c.tenant_id = :tid
              AND c.deleted_at IS NULL
              AND c.type = 'company'
              AND c.search_tsv @@ to_tsquery('pg_catalog.german', :q)
            ORDER BY rank DESC
            LIMIT :lim
            """
        )
        result = await db.execute(
            sql,
            {"q": tsquery, "tid": tenant_id, "lim": limit},
        )
        rows = result.mappings().all()
        return [dict(r) for r in rows]

    async def search_vector(
        self,
        db: AsyncSession,
        embedding: list[float],
        tenant_id: uuid.UUID,
        limit: int,
    ) -> list[dict[str, Any]]:
        """Semantic search on contacts.embedding where type='company'."""
        sql = text(
            """
            SELECT c.*, 1 - (c.embedding <=> cast(:emb AS vector)) AS score
            FROM contacts c
            WHERE c.tenant_id = :tid
              AND c.deleted_at IS NULL
              AND c.type = 'company'
              AND c.embedding IS NOT NULL
            ORDER BY c.embedding <=> cast(:emb AS vector)
            LIMIT :lim
            """
        )
        result = await db.execute(
            sql,
            {"emb": str(embedding), "tid": tenant_id, "lim": limit},
        )
        rows = result.mappings().all()
        return [dict(r) for r in rows]

    async def get_embedding_text(
        self,
        db: AsyncSession,
        entity_id: uuid.UUID,
        tenant_id: uuid.UUID,
    ) -> str:
        """Get text for embedding generation."""
        sql = text(
            """
            SELECT displayname, name, email_1, email_2, phone_1, website,
                   mailing_city, tags, vat_code
            FROM contacts
            WHERE id = :eid AND tenant_id = :tid AND type = 'company'
            """
        )
        result = await db.execute(sql, {"eid": entity_id, "tid": tenant_id})
        row = result.mappings().first()
        if not row:
            return ""
        parts = [
            row.get("displayname", ""),
            row.get("name", ""),
            row.get("email_1", ""),
            row.get("phone_1", ""),
            row.get("website", ""),
            row.get("mailing_city", ""),
            row.get("tags", ""),
        ]
        return " ".join(str(p) for p in parts if p)

    def to_search_result(self, entity: object) -> dict[str, Any]:
        """Convert company to search result dict."""
        if isinstance(entity, dict):
            name = entity.get("name", "") or entity.get("displayname", "")
            email = entity.get("email_1", "") or ""
            entity_id = str(entity.get("id", ""))
        else:
            name = getattr(entity, "name", "") or getattr(entity, "displayname", "")
            email = getattr(entity, "email_1", "") or ""
            entity_id = str(getattr(entity, "id", ""))
        return {
            "entity_type": self.entity_type,
            "entity_id": entity_id,
            "title": name,
            "snippet": email[:200],
            "score": 0.0,
            "data": {},
        }