Initial implementation of the chat application with MariaDB and ArangoDB integration, including Docker setup and web interface.

worker/worker.py

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+import os
+import mysql.connector
+import requests
+from arango import ArangoClient
+import schedule
+import time
+
+# MariaDB
+def get_db():
+    return mysql.connector.connect(
+        host=os.environ['DB_HOST'],
+        user=os.environ['DB_USER'],
+        password=os.environ['DB_PASSWORD'],
+        database=os.environ['DB_NAME']
+    )
+
+# ArangoDB
+def get_arango():
+    client = ArangoClient(hosts=os.environ['ARANGO_URL'])
+    db = client.db('llm_facts', username=os.environ['ARANGO_USER'], password=os.environ['ARANGO_PASSWORD'])
+    return db.collection('facts')
+
+# Analysiere neue Chats und überführe sie in ArangoDB
+def analyze_chats():
+    db = get_db()
+    cursor = db.cursor(dictionary=True)
+    cursor.execute("SELECT * FROM chats WHERE llm_output IS NULL")
+    chats = cursor.fetchall()
+
+    arango = get_arango()
+
+    for chat in chats:
+        # Anfrage an LLM zur Analyse / Fakt-Extraktion
+        resp = requests.post(f"{os.environ['LM_API_URL']}/generate", json={"prompt": f"Extrahiere Fakten und Kontext aus diesem Text:\n{chat['user_input']}"})
+        fact = resp.json()
+
+        # Speichern in ArangoDB
+        arango.insert({
+            "_raw": chat['user_input'],
+            "_source": chat['project'],
+            "content": fact,
+            "tags": ["auto"],
+            "relations": []
+        })
+
+        # Update llm_output in MariaDB
+        cursor.execute("UPDATE chats SET llm_output=%s WHERE id=%s", (fact, chat['id']))
+
+    db.commit()
+    cursor.close()
+    db.close()
+
+# Alle 30 Sekunden ausführen
+schedule.every(30).seconds.do(analyze_chats)
+
+while True:
+    schedule.run_pending()
+    time.sleep(1)