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
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
"""KI query understanding and result aggregation via LiteLLM."""
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from __future__ import annotations
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import os
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import json
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import logging
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from typing import Any
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import litellm
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logger = logging.getLogger(__name__)
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DEFAULT_LLM_MODEL = os.environ.get('SEARCH_LLM_MODEL', 'ollama/deepseek-v4')
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OLLAMA_API_KEY = os.environ.get('API_KEY_OLLAMA_CLOUD', '')
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QUERY_ANALYZE_SYSTEM = (
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"Du bist ein Query-Analyzer fuer ein CRM. "
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"Analysiere die Suchanfrage und gib JSON zurueck: "
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'{"normalized_query": str, "entities": {"person": str|null, "company": str|null, "topic": str|null}, '
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'"intent": str, "semantic_terms": [str], "suggested_filters": {}}'
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)
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RESULT_AGGREGATE_SYSTEM = (
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"Du bist ein Result-Aggregator. Fasse Ergebnisse zusammen und generiere Facetten: "
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'{"summary": str, "facets": {"types": {}, "dates": {}, "people": []}, "suggestions": [str]}'
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)
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def _fallback_query_analysis(query: str) -> dict[str, Any]:
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return {
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"normalized_query": query,
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"entities": {},
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"intent": "search",
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"semantic_terms": [],
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"suggested_filters": {},
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}
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def _fallback_aggregate(results: list[dict], query: str) -> dict[str, Any]:
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return {
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"summary": f"{len(results)} Ergebnisse gefunden",
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"facets": {},
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"suggestions": [],
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}
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async def llm_analyze_query(query: str) -> dict[str, Any]:
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"""Analyze a search query using LLM for intent, entities, and semantic terms.
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Falls back to a simple dict if LLM fails.
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"""
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try:
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response = await litellm.acompletion(
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model=DEFAULT_LLM_MODEL,
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messages=[
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{"role": "system", "content": QUERY_ANALYZE_SYSTEM},
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{"role": "user", "content": query},
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],
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temperature=0.1,
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max_tokens=500,
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response_format={"type": "json_object"},
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api_key=OLLAMA_API_KEY,
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)
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content = response.choices[0].message.content
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return json.loads(content)
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except Exception:
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logger.warning("LLM query analysis failed, using fallback")
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return _fallback_query_analysis(query)
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async def llm_aggregate_results(results: list[dict], query: str) -> dict[str, Any]:
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"""Aggregate search results using LLM for summary, facets, and suggestions.
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Falls back to a simple dict if LLM fails.
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"""
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if not results:
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return _fallback_aggregate(results, query)
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try:
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# Truncate results to avoid token overflow
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compact = [
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{"entity_type": r.get("entity_type"), "title": r.get("title", "")[:100]}
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for r in results[:50]
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]
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user_msg = json.dumps({"query": query, "results": compact})
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response = await litellm.acompletion(
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model=DEFAULT_LLM_MODEL,
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messages=[
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{"role": "system", "content": RESULT_AGGREGATE_SYSTEM},
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{"role": "user", "content": user_msg},
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],
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temperature=0.1,
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max_tokens=1000,
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response_format={"type": "json_object"},
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api_key=OLLAMA_API_KEY,
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)
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content = response.choices[0].message.content
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return json.loads(content)
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except Exception:
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logger.warning("LLM result aggregation failed, using fallback")
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return _fallback_aggregate(results, query)
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