Agent Zero
122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
"""Company search provider — queries contacts with type='company'."""
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from __future__ import annotations
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import logging
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import uuid
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from typing import Any
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession
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logger = logging.getLogger(__name__)
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class CompanySearchProvider:
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"""Search provider for Company entities (contacts with type='company')."""
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entity_type = "company"
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async def search_fts(
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self,
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db: AsyncSession,
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tsquery: str,
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tenant_id: uuid.UUID,
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limit: int,
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) -> list[dict[str, Any]]:
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"""Full-text search on contacts.search_tsv where type='company'."""
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sql = text(
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"""
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SELECT c.*, ts_rank(c.search_tsv, to_tsquery('pg_catalog.german', :q)) AS rank
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FROM contacts c
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WHERE c.tenant_id = :tid
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AND c.deleted_at IS NULL
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AND c.type = 'company'
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AND c.search_tsv @@ to_tsquery('pg_catalog.german', :q)
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ORDER BY rank DESC
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LIMIT :lim
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"""
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)
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result = await db.execute(
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sql,
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{"q": tsquery, "tid": tenant_id, "lim": limit},
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)
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rows = result.mappings().all()
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return [dict(r) for r in rows]
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async def search_vector(
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self,
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db: AsyncSession,
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embedding: list[float],
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tenant_id: uuid.UUID,
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limit: int,
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) -> list[dict[str, Any]]:
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"""Semantic search on contacts.embedding where type='company'."""
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sql = text(
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"""
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SELECT c.*, 1 - (c.embedding <=> cast(:emb AS vector)) AS score
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FROM contacts c
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WHERE c.tenant_id = :tid
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AND c.deleted_at IS NULL
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AND c.type = 'company'
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AND c.embedding IS NOT NULL
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ORDER BY c.embedding <=> cast(:emb AS vector)
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LIMIT :lim
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"""
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)
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result = await db.execute(
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sql,
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{"emb": str(embedding), "tid": tenant_id, "lim": limit},
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)
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rows = result.mappings().all()
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return [dict(r) for r in rows]
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async def get_embedding_text(
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self,
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db: AsyncSession,
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entity_id: uuid.UUID,
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tenant_id: uuid.UUID,
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) -> str:
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"""Get text for embedding generation."""
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sql = text(
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"""
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SELECT displayname, name, email_1, email_2, phone_1, website,
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mailing_city, tags, vat_code
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FROM contacts
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WHERE id = :eid AND tenant_id = :tid AND type = 'company'
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"""
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)
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result = await db.execute(sql, {"eid": entity_id, "tid": tenant_id})
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row = result.mappings().first()
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if not row:
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return ""
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parts = [
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row.get("displayname", ""),
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row.get("name", ""),
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row.get("email_1", ""),
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row.get("phone_1", ""),
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row.get("website", ""),
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row.get("mailing_city", ""),
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row.get("tags", ""),
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]
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return " ".join(str(p) for p in parts if p)
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def to_search_result(self, entity: object) -> dict[str, Any]:
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"""Convert company to search result dict."""
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if isinstance(entity, dict):
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name = entity.get("name", "") or entity.get("displayname", "")
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email = entity.get("email_1", "") or ""
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entity_id = str(entity.get("id", ""))
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else:
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name = getattr(entity, "name", "") or getattr(entity, "displayname", "")
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email = getattr(entity, "email_1", "") or ""
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entity_id = str(getattr(entity, "id", ""))
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return {
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"entity_type": self.entity_type,
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"entity_id": entity_id,
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"title": name,
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"snippet": email[:200],
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"score": 0.0,
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"data": {},
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}
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