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feat(app): Add wiki re-evaluation and fix wolfra bug

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- Implemented a "Re-evaluate Wikipedia" button in the UI.

- Added a backend endpoint to trigger targeted Wikipedia metric extraction.

- Hardened the LLM metric extraction prompt to prevent hallucinations.

- Corrected several database path errors that caused data loss.

- Updated application version to 0.6.4 and documented the ongoing issue.
This commit is contained in:
Floke
2026-01-23 16:05:44 +00:00
parent d3ea4e340a
commit b4595ef974
7 changed files with 1427 additions and 791 deletions
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229
company-explorer/backend/services/classification.py
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@@ -1,6 +1,7 @@
import json
import logging
import re
from datetime import datetime
from typing import Optional, Dict, Any, List
from sqlalchemy.orm import Session
@@ -8,6 +9,7 @@ from sqlalchemy.orm import Session
from backend.database import Company, Industry, RoboticsCategory, EnrichmentData
from backend.lib.core_utils import call_gemini_flash, safe_eval_math, run_serp_search
from backend.services.scraping import scrape_website_content
from backend.lib.metric_parser import MetricParser
logger = logging.getLogger(__name__)
@@ -32,7 +34,7 @@ class ClassificationService:
if enrichment and enrichment.content:
wiki_data = enrichment.content
return wiki_data.get('text')
return wiki_data.get('full_text')
return None
def _run_llm_classification_prompt(self, website_text: str, company_name: str, industry_definitions: List[Dict[str, str]]) -> Optional[str]:
@@ -75,27 +77,33 @@ class ClassificationService:
def _run_llm_metric_extraction_prompt(self, text_content: str, search_term: str, industry_name: str) -> Optional[Dict[str, Any]]:
"""
Uses LLM to extract the specific metric value from text.
Updated to look specifically for area (m²) even if not the primary search term.
"""
prompt = r"""
Du bist ein Datenextraktions-Spezialist.
Analysiere den folgenden Text, um spezifische Metrik-Informationen zu extrahieren.
Du bist ein Datenextraktions-Spezialist für Unternehmens-Kennzahlen.
Analysiere den folgenden Text, um spezifische Werte zu extrahieren.
--- KONTEXT ---
Unternehmen ist in der Branche: {industry_name}
Gesuchter Wert (Rohdaten): '{search_term}'
Branche: {industry_name}
Primär gesuchte Metrik: '{search_term}'
--- TEXT ---
{text_content_excerpt}
--- AUFGABE ---
1. Finde den numerischen Wert für '{search_term}'.
2. Versuche auch, eine explizit genannte Gesamtfläche in Quadratmetern (m²) zu finden, falls relevant und vorhanden.
1. Finde den numerischen Wert für die primäre Metrik '{search_term}'.
2. EXTREM WICHTIG: Suche im gesamten Text nach einer Angabe zur Gesamtfläche, Nutzfläche, Grundstücksfläche oder Verkaufsfläche in Quadratmetern (m²).
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").
3. Achte auf deutsche Zahlenformate (z.B. 1.005 für tausend-fünf).
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.
Gib NUR ein JSON-Objekt zurück:
'raw_value': Der gefundene numerische Wert für '{search_term}' (als Zahl). null, falls nicht gefunden.
'raw_unit': Die Einheit des raw_value (z.B. "Betten", "Stellplätze"). null, falls nicht gefunden.
'area_value': Ein gefundener numerischer Wert für eine Gesamtfläche in m² (als Zahl). null, falls nicht gefunden.
'metric_name': Der Name der Metrik, nach der gesucht wurde (also '{search_term}').
'raw_text_segment': Das Snippet für '{search_term}' (z.B. "ca. 1.500 Besucher (2020)"). MUSS IMMER AUSGEFÜLLT SEIN WENN EIN WERT GEFUNDEN WURDE.
'raw_value': Der numerische Wert für '{search_term}'. null, falls nicht gefunden.
'raw_unit': Die Einheit (z.B. "Besucher", "Passagiere"). null, falls nicht gefunden.
'area_text_segment': Das Snippet, das eine Fläche (m²) erwähnt (z.B. "4.000 m² Gesamtfläche"). null, falls nicht gefunden.
'area_value': Der gefundene Wert der Fläche in m² (als Zahl). null, falls nicht gefunden.
'metric_name': '{search_term}'.
""".format(
industry_name=industry_name,
search_term=search_term,
@@ -112,10 +120,20 @@ class ClassificationService:
def _parse_standardization_logic(self, formula: str, raw_value: float) -> Optional[float]:
if not formula or raw_value is None:
return None
# Clean formula: Replace 'wert'/'Value' and strip area units like m² or alphanumeric noise
# that Notion sync might bring in (e.g. "wert * 25m2" -> "wert * 25")
formula_cleaned = formula.replace("wert", str(raw_value)).replace("Value", str(raw_value))
# Remove common unit strings and non-math characters (except dots and parentheses)
formula_cleaned = re.sub(r'(?i)m[²2]', '', formula_cleaned)
formula_cleaned = re.sub(r'(?i)qm', '', formula_cleaned)
# We leave the final safety check to safe_eval_math
try:
return safe_eval_math(formula_cleaned)
except:
except Exception as e:
logger.error(f"Failed to parse standardization logic '{formula}' with value {raw_value}: {e}")
return None
def _extract_and_calculate_metric_cascade(
@@ -147,18 +165,52 @@ class ClassificationService:
logger.info(f"Checking {source_name} for '{search_term}' for {company.name}")
try:
content = content_loader()
print(f"--- DEBUG: Content length for {source_name}: {len(content) if content else 0}")
if not content: continue
llm_result = self._run_llm_metric_extraction_prompt(content, search_term, industry_name)
if llm_result and (llm_result.get("raw_value") is not None or llm_result.get("area_value") is not None):
results["calculated_metric_value"] = llm_result.get("raw_value")
print(f"--- DEBUG: LLM Result for {source_name}: {llm_result}")
is_revenue = "umsatz" in search_term.lower() or "revenue" in search_term.lower()
# Hybrid Extraction Logic:
# 1. Try to parse from the text segment using our robust Python parser (prioritized for German formats)
parsed_value = None
if llm_result and llm_result.get("raw_text_segment"):
parsed_value = MetricParser.extract_numeric_value(llm_result["raw_text_segment"], is_revenue=is_revenue)
if parsed_value is not None:
logger.info(f"Successfully parsed '{llm_result['raw_text_segment']}' to {parsed_value} using MetricParser.")
# 2. Fallback to LLM's raw_value if parser failed or no segment found
# NEW: Also run MetricParser on the raw_value if it's a string, to catch errors like "802020"
final_value = parsed_value
if final_value is None and llm_result.get("raw_value"):
final_value = MetricParser.extract_numeric_value(str(llm_result["raw_value"]), is_revenue=is_revenue)
if final_value is not None:
logger.info(f"Successfully cleaned LLM raw_value '{llm_result['raw_value']}' to {final_value}")
# Ultimate fallback to original raw_value if still None (though parser is very robust)
if final_value is None:
final_value = llm_result.get("raw_value")
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")):
results["calculated_metric_value"] = final_value
results["calculated_metric_unit"] = llm_result.get("raw_unit")
results["metric_source"] = source_name
if llm_result.get("area_value") is not None:
results["standardized_metric_value"] = llm_result.get("area_value")
elif llm_result.get("raw_value") is not None and standardization_logic:
results["standardized_metric_value"] = self._parse_standardization_logic(standardization_logic, llm_result["raw_value"])
# 3. Area Extraction Logic (Cascading)
area_val = llm_result.get("area_value")
# Try to refine area_value if a segment exists
if llm_result.get("area_text_segment"):
refined_area = MetricParser.extract_numeric_value(llm_result["area_text_segment"], is_revenue=False)
if refined_area is not None:
area_val = refined_area
logger.info(f"Refined area to {area_val} from segment '{llm_result['area_text_segment']}'")
if area_val is not None:
results["standardized_metric_value"] = area_val
elif final_value is not None and standardization_logic:
results["standardized_metric_value"] = self._parse_standardization_logic(standardization_logic, final_value)
return results
except Exception as e:
@@ -166,41 +218,136 @@ class ClassificationService:
return results
def extract_metrics_for_industry(self, company: Company, db: Session, industry: Industry) -> Company:
"""
Extracts and calculates metrics for a given industry.
Splits out from classify_company_potential to allow manual overrides.
"""
if not industry or not industry.scraper_search_term:
logger.warning(f"No metric configuration for industry '{industry.name if industry else 'None'}'")
return company
# Derive standardized unit
std_unit = "m²" if "m²" in (industry.standardization_logic or "") else "Einheiten"
metrics = self._extract_and_calculate_metric_cascade(
db, company, industry.name, industry.scraper_search_term, industry.standardization_logic, std_unit
)
company.calculated_metric_name = metrics["calculated_metric_name"]
company.calculated_metric_value = metrics["calculated_metric_value"]
company.calculated_metric_unit = metrics["calculated_metric_unit"]
company.standardized_metric_value = metrics["standardized_metric_value"]
company.standardized_metric_unit = metrics["standardized_metric_unit"]
company.metric_source = metrics["metric_source"]
# Keep track of refinement
company.last_classification_at = datetime.utcnow()
db.commit()
return company
def reevaluate_wikipedia_metric(self, company: Company, db: Session, industry: Industry) -> Company:
"""
Runs the metric extraction cascade for ONLY the Wikipedia source.
"""
logger.info(f"Starting Wikipedia re-evaluation for '{company.name}'")
if not industry or not industry.scraper_search_term:
logger.warning(f"Cannot re-evaluate: No metric configuration for industry '{industry.name}'")
return company
search_term = industry.scraper_search_term
content = self._get_wikipedia_content(db, company.id)
if not content:
logger.warning("No Wikipedia content found to re-evaluate.")
return company
try:
llm_result = self._run_llm_metric_extraction_prompt(content, search_term, industry.name)
if not llm_result:
raise ValueError("LLM metric extraction returned empty result.")
is_revenue = "umsatz" in search_term.lower() or "revenue" in search_term.lower()
# Hybrid Extraction Logic (same as in cascade)
parsed_value = None
if llm_result.get("raw_text_segment"):
parsed_value = MetricParser.extract_numeric_value(llm_result["raw_text_segment"], is_revenue=is_revenue)
if parsed_value is not None:
logger.info(f"Successfully parsed '{llm_result['raw_text_segment']}' to {parsed_value} using MetricParser.")
final_value = parsed_value
if final_value is None and llm_result.get("raw_value"):
final_value = MetricParser.extract_numeric_value(str(llm_result["raw_value"]), is_revenue=is_revenue)
if final_value is not None:
logger.info(f"Successfully cleaned LLM raw_value '{llm_result['raw_value']}' to {final_value}")
if final_value is None:
final_value = llm_result.get("raw_value")
# Update company metrics if a value was found
if final_value is not None:
company.calculated_metric_name = search_term
company.calculated_metric_value = final_value
company.calculated_metric_unit = llm_result.get("raw_unit")
company.metric_source = "wikipedia_reevaluated"
# Handle standardization
std_unit = "m²" if "m²" in (industry.standardization_logic or "") else "Einheiten"
company.standardized_metric_unit = std_unit
area_val = llm_result.get("area_value")
if llm_result.get("area_text_segment"):
refined_area = MetricParser.extract_numeric_value(llm_result["area_text_segment"], is_revenue=False)
if refined_area is not None:
area_val = refined_area
if area_val is not None:
company.standardized_metric_value = area_val
elif industry.standardization_logic:
company.standardized_metric_value = self._parse_standardization_logic(industry.standardization_logic, final_value)
else:
company.standardized_metric_value = None
company.last_classification_at = datetime.utcnow()
db.commit()
logger.info(f"Successfully re-evaluated and updated metrics for {company.name} from Wikipedia.")
else:
logger.warning(f"Re-evaluation for {company.name} did not yield a metric value.")
except Exception as e:
logger.error(f"Error during Wikipedia re-evaluation for {company.name}: {e}")
return company
def classify_company_potential(self, company: Company, db: Session) -> Company:
logger.info(f"Starting classification for {company.name}")
logger.info(f"Starting complete classification for {company.name}")
# 1. Load Industries
industries = self._load_industry_definitions(db)
industry_defs = [{"name": i.name, "description": i.description} for i in industries]
# 2. Industry Classification
website_content = scrape_website_content(company.website)
if website_content:
industry_name = self._run_llm_classification_prompt(website_content, company.name, industry_defs)
company.industry_ai = industry_name if industry_name in [i.name for i in industries] else "Others"
# 2. Industry Classification (Website-based)
# STRENG: Nur wenn Branche noch auf "Others" steht oder neu ist, darf die KI klassifizieren
valid_industry_names = [i.name for i in industries]
if company.industry_ai and company.industry_ai != "Others" and company.industry_ai in valid_industry_names:
logger.info(f"KEEPING manual/existing industry '{company.industry_ai}' for {company.name}")
else:
company.industry_ai = "Others"
website_content = scrape_website_content(company.website)
if website_content:
industry_name = self._run_llm_classification_prompt(website_content, company.name, industry_defs)
company.industry_ai = industry_name if industry_name in valid_industry_names else "Others"
logger.info(f"AI CLASSIFIED {company.name} as '{company.industry_ai}'")
else:
company.industry_ai = "Others"
logger.warning(f"No website content for {company.name}, setting industry to Others")
db.commit()
# 3. Metric Extraction
if company.industry_ai != "Others":
industry = next((i for i in industries if i.name == company.industry_ai), None)
if industry and industry.scraper_search_term:
# Derive standardized unit
std_unit = "m²" if "m²" in (industry.standardization_logic or "") else "Einheiten"
metrics = self._extract_and_calculate_metric_cascade(
db, company, company.industry_ai, industry.scraper_search_term, industry.standardization_logic, std_unit
)
company.calculated_metric_name = metrics["calculated_metric_name"]
company.calculated_metric_value = metrics["calculated_metric_value"]
company.calculated_metric_unit = metrics["calculated_metric_unit"]
company.standardized_metric_value = metrics["standardized_metric_value"]
company.standardized_metric_unit = metrics["standardized_metric_unit"]
company.metric_source = metrics["metric_source"]
if industry:
self.extract_metrics_for_industry(company, db, industry)
company.last_classification_at = datetime.utcnow()
db.commit()
return company