Agent Zero
8cebb4f4e9
- unified_search: Hybride Suche (PostgreSQL FTS + pgvector + RRF Fusion) - 5 Search Providers (Contact, Company, Mail, File, Event) - KI Query Understanding (Fuzzy, Facetten via LiteLLM) - DMS Text-Extraction (PDF, DOCX, XLSX, PPTX) - Embedding Pipeline (ollama/nomic-embed-text, 768 Dim) - Background Jobs für Indexierung - Plugin-basierte Provider Registry - ai_proactive: Proaktiver KI-Agent - Context-Tracking (Frontend → Backend → Event Bus) - Proactive Engine mit LLM Suggestion-Generierung - SSE Real-time Push an Frontend - 6 AI Tools für Tool Registry - Rate-Limiting + User Settings - Deep Analysis Background Jobs - Frontend Integration: - useAIContext Hook, SuggestionSidebar, SuggestionBadge - ProactiveAISettings Page, Search API Client - Globale Suche auf neue API umgestellt - Tests: test_unified_search.py + test_ai_proactive.py (alle bestanden) - Config: Ollama Cloud DeepSeek V4 als Default, konfigurierbar - Dependencies: PyMuPDF, python-docx, python-pptx, pgvector - Bugfixes: notification type_key length, migration IF NOT EXISTS
122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
"""Contact search provider."""
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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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from app.models.contact import Contact
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logger = logging.getLogger(__name__)
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class ContactSearchProvider:
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"""Search provider for Contact entities."""
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entity_type = "contact"
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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."""
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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.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."""
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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.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 first_name, last_name, email, phone, mobile, notes
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FROM contacts
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WHERE id = :eid AND tenant_id = :tid
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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("first_name", ""),
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row.get("last_name", ""),
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row.get("email", ""),
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row.get("phone", ""),
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row.get("mobile", ""),
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row.get("notes", ""),
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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 contact to search result dict."""
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if isinstance(entity, dict):
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first = entity.get("first_name", "")
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last = entity.get("last_name", "")
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email = entity.get("email", "")
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entity_id = str(entity.get("id", ""))
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else:
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first = getattr(entity, "first_name", "")
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last = getattr(entity, "last_name", "")
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email = getattr(entity, "email", "")
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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": f"{first} {last}".strip(),
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"snippet": email or "",
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"score": 0.0,
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"data": {},
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}
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