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

.gitignore

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+data/arangodb/
+data/mariadb/

api/Dockerfile

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+FROM python:3.11-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY app.py .
+CMD ["python", "app.py"]

api/app.py

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+from flask import Flask, request, jsonify
+import mysql.connector
+import requests
+import os
+from arango import ArangoClient
+
+app = Flask(__name__)
+
+# MariaDB Verbindung
+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 Verbindung
+def get_arango():
+    client = ArangoClient(hosts=os.environ['ARANGO_URL'])
+    sys_db = client.db('_system', username=os.environ['ARANGO_USER'], password=os.environ['ARANGO_PASSWORD'])
+    if not sys_db.has_database('llm_facts'):
+        sys_db.create_database('llm_facts')
+    db = client.db('llm_facts', username=os.environ['ARANGO_USER'], password=os.environ['ARANGO_PASSWORD'])
+    if not db.has_collection('facts'):
+        db.create_collection('facts')
+    return db.collection('facts')
+
+# Skill-Trigger
+def extract_fact(text):
+    triggers = ["merke dir", "notiere", "speichere"]
+    for t in triggers:
+        if text.lower().startswith(t):
+            fact = text[len(t):].strip()
+            return fact
+    return None
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    data = request.json
+    project = data.get("project")
+    message = data.get("message")
+
+    # Lernbefehl
+    fact = extract_fact(message)
+    if fact:
+        arango = get_arango()
+        arango.insert({
+            "_raw": message,
+            "_source": project,
+            "content": fact,
+            "tags": ["skill:merke"],
+            "relations": []
+        })
+        return jsonify({"reply": f"Fakt gespeichert: {fact}"})
+
+    # Chat speichern in MariaDB
+    db = get_db()
+    cursor = db.cursor()
+    cursor.execute("INSERT INTO chats (project, user_input) VALUES (%s,%s)", (project, message))
+    db.commit()
+    cursor.close()
+    db.close()
+
+    # Anfrage an LLM
+    resp = requests.post(f"{os.environ['LM_API_URL']}/generate", json={"prompt": message})
+    return jsonify({"reply": resp.json()})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)

api/requirements.txt

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+flask
+mysql-connector-python
+requests
+python-arango

docker-compose.yml

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+services:
+  myki-mariadb:
+    image: mariadb:11
+    container_name: myki-mariadb
+    restart: always
+    environment:
+      MYSQL_ROOT_PASSWORD: rootpassword
+      MYSQL_DATABASE: llm_projects
+      MYSQL_USER: llmuser
+      MYSQL_PASSWORD: llmpassword
+    ports:
+      - "3306:3306"
+    volumes:
+      - ./data/mariadb:/var/lib/mysql
+      - ./initdb:/docker-entrypoint-initdb.d  # SQL-Dateien werden hier automatisch beim ersten Start ausgeführt
+
+  myki-phpmyadmin:
+    image: phpmyadmin/phpmyadmin
+    container_name: myki-phpmyadmin
+    restart: always
+    environment:
+      PMA_HOST: mariadb
+      PMA_USER: llmuser
+      PMA_PASSWORD: llmpassword
+    ports:
+      - "8081:80"
+    depends_on:
+      - myki-mariadb
+
+  myki-arangodb:
+    image: arangodb:3.12
+    container_name: myki-arangodb
+    restart: always
+    environment:
+      ARANGO_ROOT_PASSWORD: rootpassword
+    ports:
+      - "8529:8529"
+    volumes:
+      - ./data/arangodb:/var/lib/arangodb3
+      - ./initdb-arango:/docker-entrypoint-initdb.d  # Initialisierung
+
+#  myki-lmstudio:
+#    image: ghcr.io/nomic-ai/ministral-3-14b-instruct-2512
+#    container_name: myki-lmstudio
+#    environment:
+#      LMSTUDIO_API: "1"
+#    ports:
+#      - "8080:8080"
+#    volumes:
+#      - ./lmstudio/models:/models
+
+  myki-api:
+    build: ./api
+    container_name: myki-api
+    depends_on:
+      - myki-mariadb
+      - myki-arangodb
+      #- myki-lmstudio
+    environment:
+      DB_HOST: mariadb
+      DB_USER: llmuser
+      DB_PASSWORD: llmpassword
+      DB_NAME: llm_projects
+      #LM_API_URL: http://lmstudio:8080
+      LM_API_URL: http://host.docker.internal:1234
+      ARANGO_URL: http://arangodb:8529
+      ARANGO_USER: root
+      ARANGO_PASSWORD: rootpassword
+    ports:
+      - "5000:5000"
+
+  myki-worker:
+    build: ./worker
+    container_name: myki-worker
+    depends_on:
+      - myki-api
+      - myki-mariadb
+      - myki-arangodb
+      #- lmstudio
+    environment:
+      DB_HOST: mariadb
+      DB_USER: llmuser
+      DB_PASSWORD: llmpassword
+      DB_NAME: llm_projects
+      LM_API_URL: http://lmstudio:8080
+      ARANGO_URL: http://arangodb:8529
+      ARANGO_USER: root
+      ARANGO_PASSWORD: rootpassword
+
+  web:
+    build: ./web
+    container_name: llm_web
+    depends_on:
+      - myki-api
+    ports:
+      - "3001:3000"

initdb/init.sql

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+CREATE TABLE chats (
+  id INT AUTO_INCREMENT PRIMARY KEY,
+  project VARCHAR(255),
+  user_input TEXT,
+  llm_output TEXT,
+  timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+CREATE TABLE feedback (
+  id INT AUTO_INCREMENT PRIMARY KEY,
+  chat_id INT,
+  rating ENUM('positive','negative'),
+  comment TEXT,
+  timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);

web/Dockerfile

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+FROM nginx:alpine
+COPY . /usr/share/nginx/html

web/index.html

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+<!DOCTYPE html>
+<html lang="de">
+<head>
+<meta charset="UTF-8">
+<title>Projekt-LLM Chat</title>
+<link rel="stylesheet" href="style.css">
+</head>
+<body>
+<div id="chat-container">
+  <input id="project" placeholder="Projektname">
+  <div id="messages"></div>
+  <input id="message" placeholder="Schreibe eine Nachricht">
+  <button onclick="sendMessage()">Senden</button>
+</div>
+<script src="script.js"></script>
+</body>
+</html>

web/scripts.js

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+async function sendMessage() {
+    const project = document.getElementById('project').value;
+    const message = document.getElementById('message').value;
+    const res = await fetch('http://localhost:5000/chat', {
+        method: 'POST',
+        headers: {'Content-Type': 'application/json'},
+        body: JSON.stringify({project, message})
+    });
+    const data = await res.json();
+    const messagesDiv = document.getElementById('messages');
+    messagesDiv.innerHTML += `<p><b>Du:</b> ${message}</p><p><b>LLM:</b> ${data.reply}</p>`;
+}

web/style.css

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+#chat-container { width: 500px; margin: auto; }
+#messages { border: 1px solid #ccc; height: 400px; overflow-y: scroll; padding: 5px; margin-bottom: 5px; }
+input { width: 80%; margin-bottom: 5px; }
+button { width: 18%; }

worker/Dockerfile

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+FROM python:3.11-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY worker.py .
+CMD ["python", "worker.py"]

worker/requirements.txt

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+mysql-connector-python
+requests
+python-arango
+schedule

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)