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
112 lines
3.2 KiB
Python
112 lines
3.2 KiB
Python
"""DMS File 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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logger = logging.getLogger(__name__)
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class FileSearchProvider:
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"""Search provider for DMS File entities."""
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entity_type = "file"
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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 files.content_tsv."""
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sql = text(
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"""
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SELECT f.*, ts_rank(f.content_tsv, to_tsquery('pg_catalog.german', :q)) AS rank
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FROM files f
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WHERE f.tenant_id = :tid
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AND f.deleted_at IS NULL
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AND f.content_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 files.embedding."""
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sql = text(
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"""
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SELECT f.*, 1 - (f.embedding <=> cast(:emb AS vector)) AS score
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FROM files f
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WHERE f.tenant_id = :tid
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AND f.deleted_at IS NULL
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AND f.embedding IS NOT NULL
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ORDER BY f.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 name, content_text
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FROM files
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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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name = row.get("name", "") or ""
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content = row.get("content_text", "") or ""
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return f"{name} {content[:5000]}"
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def to_search_result(self, entity: object) -> dict[str, Any]:
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"""Convert file to search result dict."""
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if isinstance(entity, dict):
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name = entity.get("name", "")
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content = entity.get("content_text", "") or ""
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entity_id = str(entity.get("id", ""))
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else:
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name = getattr(entity, "name", "")
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content = getattr(entity, "content_text", "") 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": content[:200],
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"score": 0.0,
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"data": {},
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}
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