knowledge_base_builder.py aktualisiert

knowledge_base_builder.py

@@ -1,252 +1,196 @@
-# contact_grouping.py
+# knowledge_base_builder.py
 
-__version__ = "v1.2.3"
+__version__ = "v1.2.2"
 
 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, call_openai_chat
+from helpers import create_log_filename
 from config import Config
 
 # --- Konfiguration ---
-TARGET_SHEET_NAME = "Matching_Positions"
+SOURCE_SHEET_NAME = "CRM_Jobtitles"
-LEARNING_SOURCE_SHEET_NAME = "CRM_Jobtitles"
+EXACT_MATCH_OUTPUT_FILE = "exact_match_map.json"
-EXACT_MATCH_FILE = "exact_match_map.json"
+KEYWORD_RULES_OUTPUT_FILE = "keyword_rules.json"
-KEYWORD_RULES_FILE = "keyword_rules.json"
+
-DEFAULT_DEPARTMENT = "Undefined"
+DEPARTMENT_PRIORITIES = {
-AI_BATCH_SIZE = 150
+    "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', '&'
+}
 
 def setup_logging():
-    log_filename = create_log_filename("contact_grouping")
+    log_filename = create_log_filename("knowledge_base_builder")
     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 load_knowledge_base(self):
+def build_knowledge_base():
-        self.logger.info("Lade Wissensbasis...")
+    logger = logging.getLogger(__name__)
-        self.exact_match_map = self._load_json(EXACT_MATCH_FILE)
+    logger.info(f"Starte Erstellung der Wissensbasis (Version {__version__})...")
-        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
 
-    def _load_json(self, file_path):
+    gsh = GoogleSheetHandler()
-        if not os.path.exists(file_path):
+    df = gsh.get_sheet_as_dataframe(SOURCE_SHEET_NAME)
-            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
 
-    def _normalize_text(self, text):
+    if df is None or df.empty:
-        if not isinstance(text, str): return ""
+        logger.critical(f"Konnte keine Daten aus '{SOURCE_SHEET_NAME}' laden. Abbruch.")
-        return text.lower().strip()
+        return
         
-    def _generate_ai_examples(self):
+    df.columns = [col.strip() for col in df.columns]
-        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}")
     
-    def _find_best_match(self, job_title, company_branch):
+    required_cols = ["Job Title", "Department", "Branche"]
-        normalized_title = self._normalize_text(job_title)
+    if not all(col in df.columns for col in required_cols):
-        normalized_branch = self._normalize_text(company_branch)
+        logger.critical(f"Benötigte Spalten {required_cols} nicht in '{SOURCE_SHEET_NAME}' gefunden. Abbruch.")
-        if not normalized_title: return DEFAULT_DEPARTMENT
+        return
 
-        exact_match = self.exact_match_map.get(normalized_title)
+    logger.info(f"{len(df)} Zeilen aus '{SOURCE_SHEET_NAME}' geladen.")
-        if exact_match:
+    
-            rule = self.keyword_rules.get(exact_match, {})
+    df.dropna(subset=required_cols, inplace=True)
-            required_keywords = rule.get("required_branch_keywords")
+    df = df[df["Job Title"].str.strip() != '']
-            if required_keywords:
+    df['normalized_title'] = df['Job Title'].str.lower().str.strip()
-                if not any(keyword in normalized_branch for keyword in required_keywords):
+    logger.info(f"{len(df)} Zeilen nach Bereinigung.")
-                    self.logger.debug(f"'{job_title}' -> Exakter Match '{exact_match}' verworfen (Branche: '{company_branch}')")
+
+    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%}.")
                 else:
-                    self.logger.debug(f"'{job_title}' -> '{exact_match}' (Stufe 1, Branche OK)")
+                    logger.debug(f"  -> Department '{department}' konnte keiner Branchen-Gruppe zugeordnet werden.")
-                    return exact_match
             else:
-                self.logger.debug(f"'{job_title}' -> '{exact_match}' (Stufe 1)")
+                 logger.debug(f"  -> Department '{department}' hat zu wenige Datenpunkte ({total_titles_in_dept} < {MIN_SAMPLES_FOR_BRANCH_RULE}) für eine Branchen-Regel.")
-                return exact_match
 
-        title_tokens = set(re.split(r'[\s/(),-]+', normalized_title))
+            keyword_rules[department] = rule
-        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)
 
-        if not scores:
+    try:
-            self.logger.debug(f"'{job_title}' -> '{DEFAULT_DEPARTMENT}' (Stufe 2: Keine passenden Keywords)")
+        with open(KEYWORD_RULES_OUTPUT_FILE, 'w', encoding='utf-8') as f:
-            return DEFAULT_DEPARTMENT
+            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
         
-        max_score = max(scores.values())
+    logger.info("Wissensbasis erfolgreich erstellt.")
-        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()
-    logging.info(f"Starte contact_grouping.py v{__version__}")
+    build_knowledge_base()
-    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()