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)