Floke
e1ce63a7d1
- Bugfix: Behebt das Problem, bei dem die OpenAI API eine textuelle Antwort anstelle eines JSON-Arrays zurückgab. - Der Prompt für die KI-Klassifizierung (Stufe 3) wurde grundlegend überarbeitet, um Missverständnisse durch die KI zu verhindern. - Die KI wird nun als "Datenverarbeitungstool" instruiert und erhält einen unmissverständlichen Befehl mit strikten Ausgabe-Formatierungsregeln. - Dies erhöht die Zuverlässigkeit der KI-gestützten Klassifizierung erheblich und stellt sicher, dass die Antwort maschinell verarbeitbar ist.
231 lines
11 KiB
Python
231 lines
11 KiB
Python
# contact_grouping.py
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__version__ = "v1.1.7" # Versionsnummer hochgezählt
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import logging
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import json
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import re
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import os
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import sys
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import pandas as pd
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from google_sheet_handler import GoogleSheetHandler
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from helpers import create_log_filename, call_openai_chat
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from config import LOG_DIR, Config
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# --- Konfiguration ---
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TARGET_SHEET_NAME = "Matching_Positions"
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LEARNING_SOURCE_SHEET_NAME = "CRM_Jobtitles"
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EXACT_MATCH_FILE = "exact_match_map.json"
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KEYWORD_RULES_FILE = "keyword_rules.json"
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DEFAULT_DEPARTMENT = "Undefined"
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def setup_logging():
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log_filename = create_log_filename("contact_grouping")
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if not log_filename:
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print("KRITISCHER FEHLER: Log-Datei konnte nicht erstellt werden. Logge nur in die Konsole.")
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logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler()])
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return
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log_level = logging.DEBUG
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root_logger = logging.getLogger()
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if root_logger.handlers:
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for handler in root_logger.handlers[:]:
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root_logger.removeHandler(handler)
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logging.basicConfig(level=log_level, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[logging.FileHandler(log_filename, encoding='utf-8'), logging.StreamHandler()])
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logging.getLogger("gspread").setLevel(logging.WARNING)
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logging.getLogger("oauth2client").setLevel(logging.WARNING)
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logging.info(f"Logging erfolgreich initialisiert. Log-Datei: {log_filename}")
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class ContactGrouper:
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def __init__(self):
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self.logger = logging.getLogger(__name__ + ".ContactGrouper")
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self.exact_match_map = None
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self.keyword_rules = None
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def load_knowledge_base(self):
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self.logger.info("Lade Wissensbasis...")
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self.exact_match_map = self._load_json(EXACT_MATCH_FILE)
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self.keyword_rules = self._load_json(KEYWORD_RULES_FILE)
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if self.exact_match_map is None or self.keyword_rules is None:
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self.logger.critical("Eine oder mehrere Wissensbasis-Dateien konnten nicht geladen werden. Abbruch.")
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return False
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self.logger.info("Wissensbasis erfolgreich geladen.")
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return True
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def _load_json(self, file_path):
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if not os.path.exists(file_path):
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self.logger.error(f"Wissensbasis-Datei '{file_path}' nicht gefunden.")
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return None
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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self.logger.debug(f"Lese und parse '{file_path}'...")
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data = json.load(f)
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self.logger.debug(f"'{file_path}' erfolgreich geparst.")
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return data
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except (json.JSONDecodeError, IOError) as e:
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self.logger.error(f"Fehler beim Laden der Datei '{file_path}': {e}")
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return None
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def _normalize_job_title(self, job_title):
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if not isinstance(job_title, str): return ""
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return job_title.lower().strip()
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def _find_best_match(self, job_title):
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normalized_title = self._normalize_job_title(job_title)
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if not normalized_title: return DEFAULT_DEPARTMENT
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exact_match = self.exact_match_map.get(normalized_title)
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if exact_match:
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self.logger.debug(f"'{job_title}' -> '{exact_match}' (Stufe 1: Exakter Match)")
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return exact_match
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title_tokens = set(re.split(r'[\s/(),-]+', normalized_title))
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scores = {}
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for department, rules in self.keyword_rules.items():
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matches = title_tokens.intersection(rules.get("keywords", []))
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if matches: scores[department] = len(matches)
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if not scores:
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self.logger.debug(f"'{job_title}' -> '{DEFAULT_DEPARTMENT}' (Stufe 2: Keine Keywords gefunden)")
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return DEFAULT_DEPARTMENT
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max_score = max(scores.values())
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top_departments = [dept for dept, score in scores.items() if score == max_score]
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if len(top_departments) == 1:
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winner = top_departments[0]
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self.logger.debug(f"'{job_title}' -> '{winner}' (Stufe 2: Keyword Match, Score {max_score})")
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return winner
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best_priority = float('inf')
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winner = top_departments[0]
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for department in top_departments:
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priority = self.keyword_rules[department].get("priority", 99)
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if priority < best_priority:
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best_priority = priority
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winner = department
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self.logger.debug(f"'{job_title}' -> '{winner}' (Stufe 2: Keyword Match, Score {max_score}, Prio-Tiebreak: {best_priority})")
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return winner
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def _get_ai_classification(self, job_titles_to_classify):
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self.logger.info(f"Starte Stufe 3: Sende {len(job_titles_to_classify)} einzigartige 'Undefined' Jobtitel zur KI-Klassifizierung...")
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self.logger.debug(f"Beispiel-Titel für KI: {job_titles_to_classify[:3]}")
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if not job_titles_to_classify: return {}
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valid_departments = sorted([dept for dept in self.keyword_rules.keys() if dept != DEFAULT_DEPARTMENT])
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# --- NEUER, ROBUSTERER PROMPT ---
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prompt_parts = [
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"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.",
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"--- VALID DEPARTMENT CATEGORIES ---",
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", ".join(valid_departments),
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"\n--- RULES ---",
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"1. You MUST classify EVERY job title into ONE of the valid categories.",
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"2. Your response MUST be a single, valid JSON array of objects.",
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"3. Each object MUST contain the keys 'job_title' and 'department'.",
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"4. Your entire response MUST start with '[' and end with ']'.",
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"5. You MUST NOT add any introductory text, explanations, summaries, or markdown formatting like ```json.",
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"\n--- JOB TITLES TO CLASSIFY ---",
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json.dumps(job_titles_to_classify, ensure_ascii=False)
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]
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prompt = "\n".join(prompt_parts)
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response_str = ""
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try:
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response_str = call_openai_chat(prompt, temperature=0.0, model="gpt-4o-mini", response_format_json=True)
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match = re.search(r'\[.*\]', response_str, re.DOTALL)
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if not match:
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self.logger.error("Konnte kein JSON-Array in der KI-Antwort finden.")
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self.logger.debug(f"--- VOLLSTÄNDIGE ROH-ANTWORT DER API ---\n{response_str}\n------------------------------------")
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return {}
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json_str = match.group(0)
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results_list = json.loads(json_str)
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classified_map = {item['job_title']: item['department'] for item in results_list if item.get('department') in valid_departments}
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self.logger.info(f"{len(classified_map)} Jobtitel erfolgreich von der KI klassifiziert.")
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return classified_map
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except json.JSONDecodeError as e:
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self.logger.error(f"Fehler beim Parsen des extrahierten JSON: {e}")
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self.logger.debug(f"--- EXTRAHIERTER JSON-STRING, DER FEHLER VERURSACHTE ---\n{json_str}\n------------------------------------")
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return {}
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except Exception as e:
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self.logger.error(f"Unerwarteter Fehler bei KI-Klassifizierung: {e}")
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return {}
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def _append_learnings_to_source(self, gsh, new_mappings_df):
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if new_mappings_df.empty: return
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self.logger.info(f"Lern-Mechanismus: Hänge {len(new_mappings_df)} neue KI-Erkenntnisse an '{LEARNING_SOURCE_SHEET_NAME}' an...")
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rows_to_append = new_mappings_df[["Job Title", "Department"]].values.tolist()
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success = gsh.append_rows(LEARNING_SOURCE_SHEET_NAME, rows_to_append)
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if success: self.logger.info("Lern-Daten erfolgreich angehängt.")
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else: self.logger.error("Fehler beim Anhängen der Lern-Daten.")
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def process_contacts(self):
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self.logger.info("Starte Kontakt-Verarbeitung...")
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gsh = GoogleSheetHandler()
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df = gsh.get_sheet_as_dataframe(TARGET_SHEET_NAME)
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if df is None or df.empty:
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self.logger.warning(f"'{TARGET_SHEET_NAME}' ist leer. Nichts zu tun.")
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return
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self.logger.info(f"{len(df)} Zeilen aus '{TARGET_SHEET_NAME}' erfolgreich geladen.")
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df.columns = [col.strip() for col in df.columns]
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if "Job Title" not in df.columns:
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self.logger.critical(f"Benötigte Spalte 'Job Title' nicht gefunden. Abbruch.")
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return
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df['Original Job Title'] = df['Job Title']
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if "Department" not in df.columns: df["Department"] = ""
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self.logger.info("Starte regelbasierte Zuordnung (Stufe 1 & 2)...")
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df['Department'] = df['Job Title'].apply(self._find_best_match)
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self.logger.info("Regelbasierte Zuordnung abgeschlossen.")
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undefined_df = df[df['Department'] == DEFAULT_DEPARTMENT]
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if not undefined_df.empty:
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self.logger.info(f"{len(undefined_df)} Jobtitel konnten nicht durch Regeln zugeordnet werden. Starte Stufe 3 (KI).")
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titles_to_classify = undefined_df['Job Title'].unique().tolist()
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ai_results_map = self._get_ai_classification(titles_to_classify)
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df['Department'] = df.apply(
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lambda row: ai_results_map.get(row['Job Title'], row['Department']) if row['Department'] == DEFAULT_DEPARTMENT else row['Department'],
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axis=1
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)
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new_learnings = [{'Job Title': title, 'Department': dept} for title, dept in ai_results_map.items()]
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if new_learnings:
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self._append_learnings_to_source(gsh, pd.DataFrame(new_learnings))
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else:
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self.logger.info("Alle Jobtitel durch Regeln (Stufe 1 & 2) zugeordnet. Stufe 3 wird übersprungen.")
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self.logger.info("--- Zuordnungs-Statistik ---")
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stats = df['Department'].value_counts()
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for department, count in stats.items(): self.logger.info(f"- {department}: {count} Zuordnungen")
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self.logger.info(f"GESAMT: {len(df)} Jobtitel verarbeitet.")
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output_df = df.drop(columns=['Original Job Title'])
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output_data = [output_df.columns.values.tolist()] + output_df.values.tolist()
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success = gsh.clear_and_write_data(TARGET_SHEET_NAME, output_data)
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if success: self.logger.info(f"Ergebnisse erfolgreich in '{TARGET_SHEET_NAME}' geschrieben.")
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else: self.logger.error("Fehler beim Zurückschreiben der Daten.")
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if __name__ == "__main__":
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setup_logging()
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logging.info(f"Starte contact_grouping.py v{__version__}")
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Config.load_api_keys()
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grouper = ContactGrouper()
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if not grouper.load_knowledge_base():
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logging.critical("Skript-Abbruch: Fehler beim Laden der Wissensbasis.")
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sys.exit(1)
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grouper.process_contacts() |