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v1.2.3 - Bugfix SyntaxError bei KI-Beispiel-Generierung

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- Bugfix: Behebt einen `SyntaxError: invalid syntax` in der Funktion `_generate_ai_examples`.
- Die fehlerhafte f-String-Formatierung, die einen Backslash innerhalb eines Ausdrucks enthielt, wurde durch eine robuste String-Verkettung ersetzt.
- Dies stellt die Lauffähigkeit des Skripts auf allen Python-Versionen sicher.
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Floke
2025-09-18 13:43:56 +00:00
parent a0c7d26e9f
commit 7f3d6c603a
1 changed files with 210 additions and 154 deletions
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knowledge_base_builder.py
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@@ -1,196 +1,252 @@
# knowledge_base_builder.py
# contact_grouping.py
__version__ = "v1.2.2"
__version__ = "v1.2.3"
import logging
import json
import re
import os
import sys
from collections import Counter
import pandas as pd
from collections import defaultdict
from google_sheet_handler import GoogleSheetHandler
from helpers import create_log_filename
from helpers import create_log_filename, call_openai_chat
from config import Config
# --- Konfiguration ---
SOURCE_SHEET_NAME = "CRM_Jobtitles"
EXACT_MATCH_OUTPUT_FILE = "exact_match_map.json"
KEYWORD_RULES_OUTPUT_FILE = "keyword_rules.json"
DEPARTMENT_PRIORITIES = {
"Fuhrparkmanagement": 1,
"Legal": 1,
"Baustofflogistik": 1,
"Baustoffherstellung": 1,
"Field Service Management / Kundenservice": 2,
"IT": 3,
"Production Maintenance / Wartung Produktion": 4,
"Utility Maintenance": 5,
"Procurement / Einkauf": 6,
"Supply Chain Management": 7,
"Finanzen": 8,
"Technik": 8,
"Management / GF / C-Level": 10,
"Logistik": 11,
"Vertrieb": 12,
"Transportwesen": 13,
"Berater": 20,
"Undefined": 99
}
BRANCH_GROUP_RULES = {
"bau": [
"Baustoffhandel", "Baustoffindustrie",
"Logistiker Baustoffe", "Bauunternehmen"
],
"versorger": [
"Stadtwerke", "Verteilnetzbetreiber",
"Telekommunikation", "Gase & Mineralöl"
],
"produktion": [
"Maschinenbau", "Automobil", "Anlagenbau", "Medizintechnik",
"Chemie & Pharma", "Elektrotechnik", "Lebensmittelproduktion",
"Bürotechnik", "Automaten (Vending, Slot)", "Gebäudetechnik Allgemein",
"Braune & Weiße Ware", "Fenster / Glas", "Getränke", "Möbel", "Agrar, Pellets"
]
}
MIN_SAMPLES_FOR_BRANCH_RULE = 5
# --- MODIFIZIERT: Schwellenwert auf 60% gesenkt ---
BRANCH_SPECIFICITY_THRESHOLD = 0.6
STOP_WORDS = {
'manager', 'leiter', 'head', 'lead', 'senior', 'junior', 'direktor', 'director',
'verantwortlicher', 'beauftragter', 'referent', 'sachbearbeiter', 'mitarbeiter',
'spezialist', 'specialist', 'expert', 'experte', 'consultant', 'berater',
'assistant', 'assistenz', 'teamleiter', 'teamlead', 'abteilungsleiter',
'bereichsleiter', 'gruppenleiter', 'geschäftsführer', 'vorstand', 'ceo', 'cio',
'cfo', 'cto', 'coo', 'von', 'of', 'und', 'für', 'der', 'die', 'das', '&'
}
TARGET_SHEET_NAME = "Matching_Positions"
LEARNING_SOURCE_SHEET_NAME = "CRM_Jobtitles"
EXACT_MATCH_FILE = "exact_match_map.json"
KEYWORD_RULES_FILE = "keyword_rules.json"
DEFAULT_DEPARTMENT = "Undefined"
AI_BATCH_SIZE = 150
def setup_logging():
log_filename = create_log_filename("knowledge_base_builder")
log_filename = create_log_filename("contact_grouping")
if not log_filename:
print("KRITISCHER FEHLER: Log-Datei konnte nicht erstellt werden. Logge nur in die Konsole.")
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler()])
return
log_level = logging.DEBUG
root_logger = logging.getLogger()
if root_logger.handlers:
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
logging.basicConfig(
level=log_level,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(log_filename, encoding='utf-8'),
logging.StreamHandler()
]
)
logging.basicConfig(level=log_level, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[logging.FileHandler(log_filename, encoding='utf-8'), logging.StreamHandler()])
logging.getLogger("gspread").setLevel(logging.WARNING)
logging.getLogger("oauth2client").setLevel(logging.WARNING)
logging.info(f"Logging erfolgreich initialisiert. Log-Datei: {log_filename}")
class ContactGrouper:
def __init__(self):
self.logger = logging.getLogger(__name__ + ".ContactGrouper")
self.exact_match_map = None
self.keyword_rules = None
self.ai_example_prompt_part = ""
def build_knowledge_base():
logger = logging.getLogger(__name__)
logger.info(f"Starte Erstellung der Wissensbasis (Version {__version__})...")
def load_knowledge_base(self):
self.logger.info("Lade Wissensbasis...")
self.exact_match_map = self._load_json(EXACT_MATCH_FILE)
self.keyword_rules = self._load_json(KEYWORD_RULES_FILE)
if self.exact_match_map is None or self.keyword_rules is None:
self.logger.critical("Fehler beim Laden der Wissensbasis. Abbruch.")
return False
self._generate_ai_examples()
self.logger.info("Wissensbasis erfolgreich geladen und KI-Beispiele generiert.")
return True
gsh = GoogleSheetHandler()
df = gsh.get_sheet_as_dataframe(SOURCE_SHEET_NAME)
def _load_json(self, file_path):
if not os.path.exists(file_path):
self.logger.error(f"Wissensbasis-Datei '{file_path}' nicht gefunden.")
return None
try:
with open(file_path, 'r', encoding='utf-8') as f:
self.logger.debug(f"Lese und parse '{file_path}'...")
data = json.load(f)
self.logger.debug(f"'{file_path}' erfolgreich geparst.")
return data
except (json.JSONDecodeError, IOError) as e:
self.logger.error(f"Fehler beim Laden der Datei '{file_path}': {e}")
return None
if df is None or df.empty:
logger.critical(f"Konnte keine Daten aus '{SOURCE_SHEET_NAME}' laden. Abbruch.")
return
def _normalize_text(self, text):
if not isinstance(text, str): return ""
return text.lower().strip()
df.columns = [col.strip() for col in df.columns]
required_cols = ["Job Title", "Department", "Branche"]
if not all(col in df.columns for col in required_cols):
logger.critical(f"Benötigte Spalten {required_cols} nicht in '{SOURCE_SHEET_NAME}' gefunden. Abbruch.")
return
def _generate_ai_examples(self):
self.logger.info("Generiere KI-Beispiele aus der Wissensbasis...")
if not self.exact_match_map:
return
titles_by_dept = defaultdict(list)
for title, dept in self.exact_match_map.items():
titles_by_dept[dept].append(title)
example_lines = []
sorted_depts = sorted(self.keyword_rules.keys(), key=lambda d: self.keyword_rules.get(d, {}).get('priority', 99))
for dept in sorted_depts:
if dept == DEFAULT_DEPARTMENT or not titles_by_dept[dept]:
continue
top_titles = sorted(titles_by_dept[dept], key=len)[:5]
# --- KORREKTUR: Die fehlerhafte Zeile wurde ersetzt ---
formatted_titles = ', '.join('"' + title + '"' for title in top_titles)
example_lines.append(f"- Für '{dept}': {formatted_titles}")
self.ai_example_prompt_part = "\n".join(example_lines)
self.logger.debug(f"Generierter Beispiel-Prompt:\n{self.ai_example_prompt_part}")
logger.info(f"{len(df)} Zeilen aus '{SOURCE_SHEET_NAME}' geladen.")
df.dropna(subset=required_cols, inplace=True)
df = df[df["Job Title"].str.strip() != '']
df['normalized_title'] = df['Job Title'].str.lower().str.strip()
logger.info(f"{len(df)} Zeilen nach Bereinigung.")
def _find_best_match(self, job_title, company_branch):
normalized_title = self._normalize_text(job_title)
normalized_branch = self._normalize_text(company_branch)
if not normalized_title: return DEFAULT_DEPARTMENT
logger.info("Erstelle 'Primary Mapping' für exakte Treffer (Stufe 1)...")
exact_match_map = df.groupby('normalized_title')['Department'].apply(lambda x: x.mode()[0]).to_dict()
try:
with open(EXACT_MATCH_OUTPUT_FILE, 'w', encoding='utf-8') as f:
json.dump(exact_match_map, f, indent=4, ensure_ascii=False)
logger.info(f"-> '{EXACT_MATCH_OUTPUT_FILE}' mit {len(exact_match_map)} Titeln erstellt.")
except IOError as e:
logger.error(f"Fehler beim Schreiben der Datei '{EXACT_MATCH_OUTPUT_FILE}': {e}")
return
logger.info("Erstelle 'Keyword-Datenbank' mit automatischer Branchen-Logik (Stufe 2)...")
titles_by_department = df.groupby('Department')['normalized_title'].apply(list).to_dict()
branches_by_department = df.groupby('Department')['Branche'].apply(list).to_dict()
keyword_rules = {}
for department, titles in titles_by_department.items():
all_words = []
for title in titles:
words = re.split(r'[\s/(),-]+', title)
all_words.extend([word for word in words if word])
word_counts = Counter(all_words)
top_keywords = [word for word, count in word_counts.most_common(50) if word not in STOP_WORDS and (len(word) > 2 or word in {'it', 'edv'})]
if top_keywords:
rule = {
"priority": DEPARTMENT_PRIORITIES.get(department, 99),
"keywords": sorted(top_keywords)
}
department_branches = branches_by_department.get(department, [])
total_titles_in_dept = len(department_branches)
if total_titles_in_dept >= MIN_SAMPLES_FOR_BRANCH_RULE:
branch_group_counts = Counter()
for branch_name in department_branches:
for group_keyword, d365_names in BRANCH_GROUP_RULES.items():
if branch_name in d365_names:
branch_group_counts[group_keyword] += 1
if branch_group_counts:
most_common_group, count = branch_group_counts.most_common(1)[0]
ratio = count / total_titles_in_dept
if ratio > BRANCH_SPECIFICITY_THRESHOLD:
logger.info(f" -> Department '{department}' ist spezifisch für Branche '{most_common_group}' ({ratio:.0%}). Regel wird hinzugefügt.")
rule["required_branch_keywords"] = [most_common_group]
else:
logger.debug(f" -> Department '{department}' nicht spezifisch genug. Dominante Branche '{most_common_group}' nur bei {ratio:.0%}, benötigt >{BRANCH_SPECIFICITY_THRESHOLD:.0%}.")
exact_match = self.exact_match_map.get(normalized_title)
if exact_match:
rule = self.keyword_rules.get(exact_match, {})
required_keywords = rule.get("required_branch_keywords")
if required_keywords:
if not any(keyword in normalized_branch for keyword in required_keywords):
self.logger.debug(f"'{job_title}' -> Exakter Match '{exact_match}' verworfen (Branche: '{company_branch}')")
else:
logger.debug(f" -> Department '{department}' konnte keiner Branchen-Gruppe zugeordnet werden.")
self.logger.debug(f"'{job_title}' -> '{exact_match}' (Stufe 1, Branche OK)")
return exact_match
else:
logger.debug(f" -> Department '{department}' hat zu wenige Datenpunkte ({total_titles_in_dept} < {MIN_SAMPLES_FOR_BRANCH_RULE}) für eine Branchen-Regel.")
self.logger.debug(f"'{job_title}' -> '{exact_match}' (Stufe 1)")
return exact_match
keyword_rules[department] = rule
try:
with open(KEYWORD_RULES_OUTPUT_FILE, 'w', encoding='utf-8') as f:
json.dump(keyword_rules, f, indent=4, ensure_ascii=False)
logger.info(f"-> '{KEYWORD_RULES_OUTPUT_FILE}' mit Regeln für {len(keyword_rules)} Departments erstellt.")
except IOError as e:
logger.error(f"Fehler beim Schreiben der Datei '{KEYWORD_RULES_OUTPUT_FILE}': {e}")
return
title_tokens = set(re.split(r'[\s/(),-]+', normalized_title))
scores = {}
for department, rules in self.keyword_rules.items():
required_keywords = rules.get("required_branch_keywords")
if required_keywords:
if not any(keyword in normalized_branch for keyword in required_keywords):
self.logger.debug(f"Dept '{department}' für '{job_title}' übersprungen (Branche: '{company_branch}')")
continue
matches = title_tokens.intersection(rules.get("keywords", []))
if matches: scores[department] = len(matches)
logger.info("Wissensbasis erfolgreich erstellt.")
if not scores:
self.logger.debug(f"'{job_title}' -> '{DEFAULT_DEPARTMENT}' (Stufe 2: Keine passenden Keywords)")
return DEFAULT_DEPARTMENT
max_score = max(scores.values())
top_departments = [dept for dept, score in scores.items() if score == max_score]
if len(top_departments) == 1:
winner = top_departments[0]
self.logger.debug(f"'{job_title}' -> '{winner}' (Stufe 2: Score {max_score})")
return winner
best_priority = float('inf')
winner = top_departments[0]
for department in top_departments:
priority = self.keyword_rules.get(department, {}).get("priority", 99)
if priority < best_priority:
best_priority = priority
winner = department
self.logger.debug(f"'{job_title}' -> '{winner}' (Stufe 2: Score {max_score}, Prio {best_priority})")
return winner
def _get_ai_classification(self, contacts_to_classify):
self.logger.info(f"Sende {len(contacts_to_classify)} Titel an KI (mit Kontext)...")
if not contacts_to_classify: return {}
valid_departments = sorted([dept for dept in self.keyword_rules.keys() if dept != DEFAULT_DEPARTMENT])
prompt_parts = [
"You are a specialized data processing tool. Your SOLE function is to receive a list of job titles and classify each one into a predefined department category.",
"--- VALID DEPARTMENT CATEGORIES ---",
", ".join(valid_departments),
"\n--- EXAMPLES OF TYPICAL ROLES ---",
self.ai_example_prompt_part,
"\n--- RULES ---",
"1. You MUST use the 'company_branch' to make a context-aware decision.",
"2. For departments with branch requirements (like 'Baustofflogistik' for 'bau'), you MUST ONLY use them if the branch matches.",
"3. Your response MUST be a single, valid JSON array of objects.",
"4. Each object MUST contain the keys 'job_title' and 'department'.",
"5. Your entire response MUST start with '[' and end with ']'.",
"6. You MUST NOT add any introductory text, explanations, summaries, or markdown formatting like ```json.",
"\n--- CONTACTS TO CLASSIFY (JSON) ---",
json.dumps(contacts_to_classify, ensure_ascii=False)
]
prompt = "\n".join(prompt_parts)
response_str = ""
try:
response_str = call_openai_chat(prompt, temperature=0.0, model="gpt-4o-mini", response_format_json=True)
match = re.search(r'\[.*\]', response_str, re.DOTALL)
if not match:
self.logger.error("Kein JSON-Array in KI-Antwort gefunden.")
self.logger.debug(f"ROH-ANTWORT DER API:\n{response_str}")
return {}
json_str = match.group(0)
results_list = json.loads(json_str)
classified_map = {item['job_title']: item['department'] for item in results_list if item.get('department') in valid_departments}
self.logger.info(f"{len(classified_map)} Titel erfolgreich von KI klassifiziert.")
return classified_map
except json.JSONDecodeError as e:
self.logger.error(f"Fehler beim Parsen des extrahierten JSON: {e}")
self.logger.debug(f"EXTRAHIERTER JSON-STRING, DER FEHLER VERURSACHTE:\n{json_str}")
return {}
except Exception as e:
self.logger.error(f"Unerwarteter Fehler bei KI-Klassifizierung: {e}")
return {}
def _append_learnings_to_source(self, gsh, new_mappings_df):
if new_mappings_df.empty: return
self.logger.info(f"Lern-Mechanismus: Hänge {len(new_mappings_df)} neue KI-Erkenntnisse an '{LEARNING_SOURCE_SHEET_NAME}' an...")
rows_to_append = new_mappings_df[["Job Title", "Department"]].values.tolist()
if not gsh.append_rows(LEARNING_SOURCE_SHEET_NAME, rows_to_append):
self.logger.error("Fehler beim Anhängen der Lern-Daten.")
def process_contacts(self):
self.logger.info("Starte Kontakt-Verarbeitung...")
gsh = GoogleSheetHandler()
df = gsh.get_sheet_as_dataframe(TARGET_SHEET_NAME)
if df is None or df.empty:
self.logger.warning(f"'{TARGET_SHEET_NAME}' ist leer. Nichts zu tun.")
return
self.logger.info(f"{len(df)} Zeilen aus '{TARGET_SHEET_NAME}' geladen.")
df.columns = [col.strip() for col in df.columns]
if "Job Title" not in df.columns or "Branche" not in df.columns:
self.logger.critical(f"Benötigte Spalten 'Job Title' und/oder 'Branche' nicht gefunden. Abbruch.")
return
df['Original Job Title'] = df['Job Title']
if "Department" not in df.columns: df["Department"] = ""
self.logger.info("Starte regelbasierte Zuordnung (Stufe 1 & 2) mit Branchen-Kontext...")
df['Department'] = df.apply(lambda row: self._find_best_match(row['Job Title'], row.get('Branche', '')), axis=1)
self.logger.info("Regelbasierte Zuordnung abgeschlossen.")
undefined_df = df[df['Department'] == DEFAULT_DEPARTMENT]
if not undefined_df.empty:
self.logger.info(f"{len(undefined_df)} Jobtitel konnten nicht zugeordnet werden. Starte Stufe 3 (KI).")
contacts_to_classify = undefined_df[['Job Title', 'Branche']].drop_duplicates().to_dict('records')
contacts_to_classify = [{'job_title': c['Job Title'], 'company_branch': c.get('Branche', '')} for c in contacts_to_classify]
ai_results_map = {}
contact_chunks = [contacts_to_classify[i:i + AI_BATCH_SIZE] for i in range(0, len(contacts_to_classify), AI_BATCH_SIZE)]
self.logger.info(f"Teile KI-Anfrage in {len(contact_chunks)} Batches von max. {AI_BATCH_SIZE} Kontakten auf.")
for i, chunk in enumerate(contact_chunks):
self.logger.info(f"Verarbeite KI-Batch {i+1}/{len(contact_chunks)}...")
chunk_results = self._get_ai_classification(chunk)
ai_results_map.update(chunk_results)
df['Department'] = df.apply(lambda row: ai_results_map.get(row['Job Title'], row['Department']) if row['Department'] == DEFAULT_DEPARTMENT else row['Department'], axis=1)
new_learnings = [{'Job Title': title, 'Department': dept} for title, dept in ai_results_map.items()]
if new_learnings:
self._append_learnings_to_source(gsh, pd.DataFrame(new_learnings))
else:
self.logger.info("Alle Jobtitel durch Regeln zugeordnet. Stufe 3 wird übersprungen.")
self.logger.info("--- Zuordnungs-Statistik ---")
stats = df['Department'].value_counts()
for department, count in stats.items(): self.logger.info(f"- {department}: {count} Zuordnungen")
self.logger.info(f"GESAMT: {len(df)} Jobtitel verarbeitet.")
output_df = df.drop(columns=['Original Job Title'])
output_data = [output_df.columns.values.tolist()] + output_df.values.tolist()
if gsh.clear_and_write_data(TARGET_SHEET_NAME, output_data):
self.logger.info(f"Ergebnisse erfolgreich in '{TARGET_SHEET_NAME}' geschrieben.")
else:
self.logger.error("Fehler beim Zurückschreiben der Daten.")
if __name__ == "__main__":
setup_logging()
build_knowledge_base()
logging.info(f"Starte contact_grouping.py v{__version__}")
Config.load_api_keys()
grouper = ContactGrouper()
if not grouper.load_knowledge_base():
logging.critical("Skript-Abbruch: Wissensbasis nicht geladen.")
sys.exit(1)
grouper.process_contacts()