Floke
e43e129771
- fixed Year-Prefix Bug in MetricParser - added metric_confidence and metric_proof_text to database - added Entity-Check and Annual-Priority to LLM prompt - improved UI: added confidence traffic light and mouse-over proof tooltip - restored missing API endpoints (create, bulk, wiki-override)
396 lines
20 KiB
Python
396 lines
20 KiB
Python
import json
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import logging
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import re
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from datetime import datetime
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from typing import Optional, Dict, Any, List
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from sqlalchemy.orm import Session
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from backend.database import Company, Industry, RoboticsCategory, EnrichmentData
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from backend.lib.core_utils import call_gemini_flash, safe_eval_math, run_serp_search
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from backend.services.scraping import scrape_website_content
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from backend.lib.metric_parser import MetricParser
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logger = logging.getLogger(__name__)
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class ClassificationService:
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def __init__(self):
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# We no longer load industries in init because we don't have a DB session here
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pass
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def _load_industry_definitions(self, db: Session) -> List[Industry]:
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"""Loads all industry definitions from the database."""
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industries = db.query(Industry).all()
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if not industries:
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logger.warning("No industry definitions found in DB. Classification might be limited.")
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return industries
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def _get_wikipedia_content(self, db: Session, company_id: int) -> Optional[str]:
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"""Fetches Wikipedia content from enrichment_data for a given company."""
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enrichment = db.query(EnrichmentData).filter(
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EnrichmentData.company_id == company_id,
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EnrichmentData.source_type == "wikipedia"
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).order_by(EnrichmentData.created_at.desc()).first()
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if enrichment and enrichment.content:
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wiki_data = enrichment.content
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return wiki_data.get('full_text')
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return None
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def _run_llm_classification_prompt(self, website_text: str, company_name: str, industry_definitions: List[Dict[str, str]]) -> Optional[str]:
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"""
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Uses LLM to classify the company into one of the predefined industries.
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"""
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prompt = r"""
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Du bist ein präziser Branchen-Klassifizierer für Unternehmen.
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Deine Aufgabe ist es, das vorliegende Unternehmen basierend auf seinem Website-Inhalt
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einer der untenstehenden Branchen zuzuordnen.
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--- UNTERNEHMEN ---
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Name: {company_name}
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Website-Inhalt (Auszug):
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{website_text_excerpt}
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--- ZU VERWENDENDE BRANCHEN-DEFINITIONEN (STRIKT) ---
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Wähle EINE der folgenden Branchen. Jede Branche hat eine Definition.
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{industry_definitions_json}
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--- AUFGABE ---
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Analysiere den Website-Inhalt. Wähle die Branchen-Definition, die am besten zum Unternehmen passt.
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Wenn keine der Definitionen zutrifft oder du unsicher bist, wähle "Others".
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Gib NUR den Namen der zugeordneten Branche zurück, als reinen String, nichts anderes.
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Beispiel Output: Hotellerie
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""".format(
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company_name=company_name,
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website_text_excerpt=website_text[:10000],
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industry_definitions_json=json.dumps(industry_definitions, ensure_ascii=False)
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)
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try:
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response = call_gemini_flash(prompt, temperature=0.1, json_mode=False)
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return response.strip()
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except Exception as e:
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logger.error(f"LLM classification failed for {company_name}: {e}")
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return None
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def _run_llm_metric_extraction_prompt(self, text_content: str, search_term: str, industry_name: str) -> Optional[Dict[str, Any]]:
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"""
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Uses LLM to extract the specific metric value from text.
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Updated to look specifically for area (m²) even if not the primary search term.
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"""
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prompt = r"""
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Du bist ein Datenextraktions-Spezialist für Unternehmens-Kennzahlen.
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Analysiere den folgenden Text, um spezifische Werte zu extrahieren.
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--- KONTEXT ---
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Branche: {industry_name}
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Primär gesuchte Metrik: '{search_term}'
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--- TEXT ---
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{text_content_excerpt}
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--- AUFGABE ---
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1. Finde den numerischen Wert für die primäre Metrik '{search_term}'.
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2. EXTREM WICHTIG: Suche im gesamten Text nach einer Angabe zur Gesamtfläche, Nutzfläche, Grundstücksfläche oder Verkaufsfläche in Quadratmetern (m²).
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In Branchen wie Freizeitparks, Flughäfen oder Thermen ist dies oft separat im Fließtext versteckt (z.B. "Die Therme verfügt über eine Gesamtfläche von 4.000 m²").
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3. Achte auf deutsche Zahlenformate (z.B. 1.005 für tausend-fünf).
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4. Regel: Extrahiere IMMER den umgebenden Satz oder die Zeile in 'raw_text_segment'. Rate NIEMALS einen numerischen Wert, ohne den Beweis dafür zu liefern.
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5. WICHTIG: Jahreszahlen in Klammern oder direkt dahinter (z.B. "80 (2020)" oder "80 Stand 2021") dürfen NICHT Teil von 'raw_value' sein. "80 (2020)" -> raw_value: 80.
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6. WICHTIG: Zitations-Nummern wie "[3]" müssen entfernt werden. "80[3]" -> raw_value: 80.
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7. ENTITÄTS-CHECK: Stelle sicher, dass sich die Zahl wirklich auf '{search_term}' für das Unternehmen bezieht und nicht auf einen Wettbewerber.
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8. ZEITRAUM-CHECK: Wir suchen JÄHRLICHE Werte. Wenn du "500 Besucher am Tag" und "150.000 im Jahr" findest, nimm IMMER den JÄHRLICHEN Wert. Ignoriere Tages- oder Monatswerte, es sei denn, es gibt gar keine anderen.
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Bewerte deine Zuversicht (confidence_score) zwischen 0.0 und 1.0:
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- 0.9 - 1.0: Exakter, aktueller Jahreswert aus zuverlässiger Quelle.
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- 0.6 - 0.8: Wahrscheinlich korrekt, aber evtl. etwas älter (vor 2022) oder leicht gerundet ("rund 200.000").
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- 0.1 - 0.5: Unsicher, ob es sich auf das richtige Unternehmen bezieht, oder nur Tages-/Monatswerte gefunden.
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Gib NUR ein JSON-Objekt zurück:
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'raw_text_segment': Das Snippet für '{search_term}' (z.B. "ca. 1.500 Besucher (2020)"). MUSS IMMER AUSGEFÜLLT SEIN WENN EIN WERT GEFUNDEN WURDE.
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'raw_value': Der numerische Wert für '{search_term}'. null, falls nicht gefunden.
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'raw_unit': Die Einheit (z.B. "Besucher", "Passagiere"). null, falls nicht gefunden.
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'area_text_segment': Das Snippet, das eine Fläche (m²) erwähnt (z.B. "4.000 m² Gesamtfläche"). null, falls nicht gefunden.
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'area_value': Der gefundene Wert der Fläche in m² (als Zahl). null, falls nicht gefunden.
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'metric_name': '{search_term}'.
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'confidence_score': Float zwischen 0.0 und 1.0.
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'confidence_reason': Kurze Begründung (z.B. "Klarer Jahreswert 2023").
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""".format(
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industry_name=industry_name,
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search_term=search_term,
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text_content_excerpt=text_content[:15000]
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)
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try:
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response = call_gemini_flash(prompt, temperature=0.05, json_mode=True)
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return json.loads(response)
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except Exception as e:
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logger.error(f"LLM metric extraction failed for '{search_term}': {e}")
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return None
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def _parse_standardization_logic(self, formula: str, raw_value: float) -> Optional[float]:
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if not formula or raw_value is None:
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return None
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# Clean formula: Replace 'wert'/'Value' and strip area units like m² or alphanumeric noise
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# that Notion sync might bring in (e.g. "wert * 25m2" -> "wert * 25")
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formula_cleaned = formula.replace("wert", str(raw_value)).replace("Value", str(raw_value))
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# Remove common unit strings and non-math characters (except dots and parentheses)
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formula_cleaned = re.sub(r'(?i)m[²2]', '', formula_cleaned)
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formula_cleaned = re.sub(r'(?i)qm', '', formula_cleaned)
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# We leave the final safety check to safe_eval_math
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try:
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return safe_eval_math(formula_cleaned)
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except Exception as e:
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logger.error(f"Failed to parse standardization logic '{formula}' with value {raw_value}: {e}")
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return None
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def _extract_and_calculate_metric_cascade(
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self,
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db: Session,
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company: Company,
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industry_name: str,
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search_term: str,
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standardization_logic: Optional[str],
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standardized_unit: Optional[str]
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) -> Dict[str, Any]:
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results = {
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"calculated_metric_name": search_term,
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"calculated_metric_value": None,
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"calculated_metric_unit": None,
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"standardized_metric_value": None,
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"standardized_metric_unit": standardized_unit,
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"metric_source": None,
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"metric_proof_text": None,
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"metric_confidence": 0.0,
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"metric_confidence_reason": None
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}
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# CASCADE: Website -> Wikipedia -> SerpAPI
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sources = [
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("website", lambda: scrape_website_content(company.website)),
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("wikipedia", lambda: self._get_wikipedia_content(db, company.id)),
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("serpapi", lambda: " ".join([res.get("snippet", "") for res in run_serp_search(f"{company.name} {company.city or ''} {search_term}").get("organic_results", [])]) if run_serp_search(f"{company.name} {company.city or ''} {search_term}") else None)
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]
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for source_name, content_loader in sources:
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logger.info(f"Checking {source_name} for '{search_term}' for {company.name}")
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try:
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content = content_loader()
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print(f"--- DEBUG: Content length for {source_name}: {len(content) if content else 0}")
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if not content: continue
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llm_result = self._run_llm_metric_extraction_prompt(content, search_term, industry_name)
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# Handle List response (multiple candidates) -> Take best (first)
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if isinstance(llm_result, list):
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llm_result = llm_result[0] if llm_result else None
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print(f"--- DEBUG: LLM Result for {source_name}: {llm_result}")
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is_revenue = "umsatz" in search_term.lower() or "revenue" in search_term.lower()
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# Hybrid Extraction Logic:
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# 1. Try to parse from the text segment using our robust Python parser (prioritized for German formats)
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parsed_value = None
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if llm_result and llm_result.get("raw_text_segment"):
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# PASS RAW_VALUE AS EXPECTED HINT
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parsed_value = MetricParser.extract_numeric_value(
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llm_result["raw_text_segment"],
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is_revenue=is_revenue,
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expected_value=str(llm_result.get("raw_value", "")) if llm_result.get("raw_value") else None
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)
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if parsed_value is not None:
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logger.info(f"Successfully parsed '{llm_result['raw_text_segment']}' to {parsed_value} using MetricParser.")
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# 2. Fallback to LLM's raw_value if parser failed or no segment found
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# NEW: Also run MetricParser on the raw_value if it's a string, to catch errors like "802020"
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final_value = parsed_value
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if final_value is None and llm_result.get("raw_value"):
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final_value = MetricParser.extract_numeric_value(str(llm_result["raw_value"]), is_revenue=is_revenue)
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if final_value is not None:
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logger.info(f"Successfully cleaned LLM raw_value '{llm_result['raw_value']}' to {final_value}")
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# Ultimate fallback to original raw_value if still None (though parser is very robust)
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if final_value is None:
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final_value = llm_result.get("raw_value")
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if llm_result and (final_value is not None or llm_result.get("area_value") is not None or llm_result.get("area_text_segment")):
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results["calculated_metric_value"] = final_value
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results["calculated_metric_unit"] = llm_result.get("raw_unit")
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results["metric_source"] = source_name
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results["metric_proof_text"] = llm_result.get("raw_text_segment")
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results["metric_confidence"] = llm_result.get("confidence_score")
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results["metric_confidence_reason"] = llm_result.get("confidence_reason")
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# 3. Area Extraction Logic (Cascading)
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area_val = llm_result.get("area_value")
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# Try to refine area_value if a segment exists
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if llm_result.get("area_text_segment"):
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refined_area = MetricParser.extract_numeric_value(llm_result["area_text_segment"], is_revenue=False)
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if refined_area is not None:
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area_val = refined_area
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logger.info(f"Refined area to {area_val} from segment '{llm_result['area_text_segment']}'")
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if area_val is not None:
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results["standardized_metric_value"] = area_val
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elif final_value is not None and standardization_logic:
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results["standardized_metric_value"] = self._parse_standardization_logic(standardization_logic, final_value)
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return results
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except Exception as e:
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logger.error(f"Error in {source_name} stage: {e}")
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return results
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def extract_metrics_for_industry(self, company: Company, db: Session, industry: Industry) -> Company:
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"""
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Extracts and calculates metrics for a given industry.
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Splits out from classify_company_potential to allow manual overrides.
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"""
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if not industry or not industry.scraper_search_term:
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logger.warning(f"No metric configuration for industry '{industry.name if industry else 'None'}'")
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return company
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# Derive standardized unit
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std_unit = "m²" if "m²" in (industry.standardization_logic or "") else "Einheiten"
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metrics = self._extract_and_calculate_metric_cascade(
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db, company, industry.name, industry.scraper_search_term, industry.standardization_logic, std_unit
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)
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company.calculated_metric_name = metrics["calculated_metric_name"]
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company.calculated_metric_value = metrics["calculated_metric_value"]
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company.calculated_metric_unit = metrics["calculated_metric_unit"]
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company.standardized_metric_value = metrics["standardized_metric_value"]
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company.standardized_metric_unit = metrics["standardized_metric_unit"]
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company.metric_source = metrics["metric_source"]
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company.metric_proof_text = metrics["metric_proof_text"]
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company.metric_confidence = metrics["metric_confidence"]
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company.metric_confidence_reason = metrics["metric_confidence_reason"]
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# Keep track of refinement
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company.last_classification_at = datetime.utcnow()
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db.commit()
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return company
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def reevaluate_wikipedia_metric(self, company: Company, db: Session, industry: Industry) -> Company:
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"""
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Runs the metric extraction cascade for ONLY the Wikipedia source.
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"""
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logger.info(f"Starting Wikipedia re-evaluation for '{company.name}'")
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if not industry or not industry.scraper_search_term:
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logger.warning(f"Cannot re-evaluate: No metric configuration for industry '{industry.name}'")
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return company
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search_term = industry.scraper_search_term
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content = self._get_wikipedia_content(db, company.id)
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if not content:
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logger.warning("No Wikipedia content found to re-evaluate.")
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return company
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try:
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llm_result = self._run_llm_metric_extraction_prompt(content, search_term, industry.name)
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# Handle List response (multiple candidates) -> Take best (first)
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if isinstance(llm_result, list):
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llm_result = llm_result[0] if llm_result else None
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if not llm_result:
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raise ValueError("LLM metric extraction returned empty result.")
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is_revenue = "umsatz" in search_term.lower() or "revenue" in search_term.lower()
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# Hybrid Extraction Logic (same as in cascade)
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parsed_value = None
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if llm_result.get("raw_text_segment"):
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parsed_value = MetricParser.extract_numeric_value(
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llm_result["raw_text_segment"],
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is_revenue=is_revenue,
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expected_value=str(llm_result.get("raw_value", "")) if llm_result.get("raw_value") else None
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)
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if parsed_value is not None:
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logger.info(f"Successfully parsed '{llm_result['raw_text_segment']}' to {parsed_value} using MetricParser.")
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final_value = parsed_value
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if final_value is None and llm_result.get("raw_value"):
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final_value = MetricParser.extract_numeric_value(str(llm_result["raw_value"]), is_revenue=is_revenue)
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if final_value is not None:
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logger.info(f"Successfully cleaned LLM raw_value '{llm_result['raw_value']}' to {final_value}")
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if final_value is None:
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final_value = llm_result.get("raw_value")
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# Update company metrics if a value was found
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if final_value is not None:
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company.calculated_metric_name = search_term
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company.calculated_metric_value = final_value
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company.calculated_metric_unit = llm_result.get("raw_unit")
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company.metric_source = "wikipedia_reevaluated"
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company.metric_proof_text = llm_result.get("raw_text_segment")
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company.metric_confidence = llm_result.get("confidence_score")
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company.metric_confidence_reason = llm_result.get("confidence_reason")
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# Handle standardization
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std_unit = "m²" if "m²" in (industry.standardization_logic or "") else "Einheiten"
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company.standardized_metric_unit = std_unit
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area_val = llm_result.get("area_value")
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if llm_result.get("area_text_segment"):
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refined_area = MetricParser.extract_numeric_value(llm_result["area_text_segment"], is_revenue=False)
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if refined_area is not None:
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area_val = refined_area
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if area_val is not None:
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company.standardized_metric_value = area_val
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elif industry.standardization_logic:
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company.standardized_metric_value = self._parse_standardization_logic(industry.standardization_logic, final_value)
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else:
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company.standardized_metric_value = None
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company.last_classification_at = datetime.utcnow()
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db.commit()
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logger.info(f"Successfully re-evaluated and updated metrics for {company.name} from Wikipedia.")
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else:
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logger.warning(f"Re-evaluation for {company.name} did not yield a metric value.")
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except Exception as e:
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logger.error(f"Error during Wikipedia re-evaluation for {company.name}: {e}")
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return company
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def classify_company_potential(self, company: Company, db: Session) -> Company:
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logger.info(f"Starting complete classification for {company.name}")
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# 1. Load Industries
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industries = self._load_industry_definitions(db)
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industry_defs = [{"name": i.name, "description": i.description} for i in industries]
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# 2. Industry Classification (Website-based)
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# STRENG: Nur wenn Branche noch auf "Others" steht oder neu ist, darf die KI klassifizieren
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valid_industry_names = [i.name for i in industries]
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if company.industry_ai and company.industry_ai != "Others" and company.industry_ai in valid_industry_names:
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logger.info(f"KEEPING manual/existing industry '{company.industry_ai}' for {company.name}")
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else:
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website_content = scrape_website_content(company.website)
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if website_content:
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industry_name = self._run_llm_classification_prompt(website_content, company.name, industry_defs)
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company.industry_ai = industry_name if industry_name in valid_industry_names else "Others"
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logger.info(f"AI CLASSIFIED {company.name} as '{company.industry_ai}'")
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else:
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company.industry_ai = "Others"
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logger.warning(f"No website content for {company.name}, setting industry to Others")
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db.commit()
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# 3. Metric Extraction
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if company.industry_ai != "Others":
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industry = next((i for i in industries if i.name == company.industry_ai), None)
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if industry:
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self.extract_metrics_for_industry(company, db, industry)
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return company
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