app/config.py

import os
from dotenv import load_dotenv
from enum import Enum
from openai import OpenAI
from pathlib import Path



# Carica le variabili d'ambiente dal file .env
load_dotenv()

# Configurazione del modello
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY non trovata. Verifica il file .env")

class LLMType(Enum):
    OPENAI_GPT_4O_MINI = "openai - GPT-4o-mini"
    LOCAL_QWEN = "local - Qwen 7B"
    LOCAL_PHI = "local - Phi-3 Mini"

# Configurazione modelli
LLM_CONFIGS = {
    LLMType.OPENAI_GPT_4O_MINI: {
        "client": lambda: OpenAI(api_key=OPENAI_API_KEY),
        "model": "gpt-4o-mini",
        "base_url": None
    },
    LLMType.LOCAL_QWEN: {
        "client": lambda: OpenAI(base_url="http://192.168.43.199:1234/v1", api_key="not-needed"),
        "model": "qwen2.5-coder-7b-instruct",
        "base_url": "http://192.168.43.199:1234/v1"
    },
    LLMType.LOCAL_PHI: {
        "client": lambda: OpenAI(base_url="http://192.168.43.199:1234/v1", api_key="not-needed"),
        "model": "phi-3.5-mini-ita",
        "base_url": "http://192.168.43.199:1234/v1"
    }
}

EMBEDDING_CONFIG = {
    "model_name": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
    "chunk_size": 2000,
    "chunk_overlap": 100,
    "k_documents": 5,
    "min_similarity": 0.7
}

# Aggiungi questa costante
EMBEDDING_MODEL = "sentence-transformers/multi-qa-mpnet-base-dot-v1"

# Definisci il percorso base per i database
BASE_DB_PATH = "db"

# Voci italiane edge-tts
VOICE_USER = "it-IT-DiegoNeural"      # Voce maschile utente
VOICE_ASSISTANT = "it-IT-ElsaNeural"   # Voce femminile assistente