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leocrm/app/plugins/builtins/unified_search/providers/contactperson_provider.py
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Phase 5.1-5.4: PWA, Public Plugin Endpoints, Contacts Embedding, Search Coverage
5.1 Public Plugin Endpoints:
- PluginRouteDef.is_public field in manifest.py
- main.py: public routes mounted without auth dependency
- permissions/public_routes.py: token-based share link access (info, verify, download)
- permissions/plugin.py: public share route registered with is_public=True

5.2 PWA:
- vite.config.ts: VitePWA plugin configured (autoUpdate, workbox, runtime caching)
- frontend/public/manifest.json: PWA manifest with icons
- index.html: theme-color, manifest link, apple-touch-icon, apple-mobile-web-app meta
- Build generates sw.js + workbox (90 precache entries)

5.3 Contacts Embedding:
- contact.py: embedding column (Vector(768)) added to Contact model
- Migration 0002_embeddings.sql already exists (adds embedding + HNSW index)
- ContactSearchProvider already queries embedding column

5.4 Search Coverage:
- 5 new search providers: task, contactperson, tag, conversation, user
- All providers implement FTS search with tenant_id + deleted_at filters
- TagSearchProvider also supports vector search (384-dim embedding)
- provider_registry.py: all 5 new providers auto-registered
- Total: 10 search providers (was 5)
2026-08-04 14:49:35 +02:00

162 lines
5.9 KiB
Python

"""ContactPerson search provider — queries contactpersons table."""
from __future__ import annotations
import logging
import uuid
from typing import Any
from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession
from app.plugins.builtins.unified_search.base_provider import BaseSearchProvider
logger = logging.getLogger(__name__)
class ContactPersonSearchProvider(BaseSearchProvider):
"""Search provider for ContactPerson (Ansprechpartner) entities."""
entity_type = "contactperson"
async def _search_fts_filtered(
self,
db: AsyncSession,
tsquery: str,
tenant_id: uuid.UUID,
limit: int,
visible_ids: set[uuid.UUID] | None,
) -> list[dict[str, Any]]:
"""Full-text search on contactpersons fields."""
if visible_ids is not None:
sql = text(
"""
SELECT cp.*, ts_rank(
to_tsvector('pg_catalog.german',
coalesce(cp.displayname, '') || ' ' ||
coalesce(cp.firstname, '') || ' ' ||
coalesce(cp.lastname, '') || ' ' ||
coalesce(cp.email, '') || ' ' ||
coalesce(cp.function, '') || ' ' ||
coalesce(cp.tags, '')
),
to_tsquery('pg_catalog.german', :q)
) AS rank
FROM contactpersons cp
WHERE cp.tenant_id = :tid
AND cp.deleted_at IS NULL
AND to_tsvector('pg_catalog.german',
coalesce(cp.displayname, '') || ' ' ||
coalesce(cp.firstname, '') || ' ' ||
coalesce(cp.lastname, '') || ' ' ||
coalesce(cp.email, '') || ' ' ||
coalesce(cp.function, '') || ' ' ||
coalesce(cp.tags, '')
) @@ to_tsquery('pg_catalog.german', :q)
AND cp.id = ANY(:visible_ids)
ORDER BY rank DESC
LIMIT :lim
"""
)
result = await db.execute(
sql,
{
"q": tsquery,
"tid": tenant_id,
"lim": limit,
"visible_ids": list(visible_ids),
},
)
else:
sql = text(
"""
SELECT cp.*, ts_rank(
to_tsvector('pg_catalog.german',
coalesce(cp.displayname, '') || ' ' ||
coalesce(cp.firstname, '') || ' ' ||
coalesce(cp.lastname, '') || ' ' ||
coalesce(cp.email, '') || ' ' ||
coalesce(cp.function, '') || ' ' ||
coalesce(cp.tags, '')
),
to_tsquery('pg_catalog.german', :q)
) AS rank
FROM contactpersons cp
WHERE cp.tenant_id = :tid
AND cp.deleted_at IS NULL
AND to_tsvector('pg_catalog.german',
coalesce(cp.displayname, '') || ' ' ||
coalesce(cp.firstname, '') || ' ' ||
coalesce(cp.lastname, '') || ' ' ||
coalesce(cp.email, '') || ' ' ||
coalesce(cp.function, '') || ' ' ||
coalesce(cp.tags, '')
) @@ 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_filtered(
self,
db: AsyncSession,
embedding: list[float],
tenant_id: uuid.UUID,
limit: int,
visible_ids: set[uuid.UUID] | None,
) -> list[dict[str, Any]]:
"""Semantic search — contactpersons table has no embedding column yet."""
return []
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, firstname, lastname, email, function, tags
FROM contactpersons
WHERE id = :eid AND tenant_id = :tid
"""
)
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("firstname", ""),
row.get("lastname", ""),
row.get("email", ""),
row.get("function", ""),
row.get("tags", ""),
]
return " ".join(str(p) for p in parts if p)
def to_search_result(self, entity: object) -> dict[str, Any]:
"""Convert contactperson to search result dict."""
if isinstance(entity, dict):
displayname = entity.get("displayname", "")
email = entity.get("email", "") or ""
entity_id = str(entity.get("id", ""))
contact_id = str(entity.get("contact_id", "")) if entity.get("contact_id") else ""
else:
displayname = getattr(entity, "displayname", "")
email = getattr(entity, "email", "") or ""
entity_id = str(getattr(entity, "id", ""))
contact_id = str(getattr(entity, "contact_id", "")) if getattr(entity, "contact_id", None) else ""
return {
"entity_type": self.entity_type,
"entity_id": entity_id,
"title": displayname,
"snippet": email[:200],
"score": 0.0,
"data": {"contact_id": contact_id},
}