config.py

"""
config.py — Client LLM unificato / Unified LLM Client
======================================================
Supporta Ollama (locale), Azure OpenAI, OpenAI diretto.
Supports Ollama (local), Azure OpenAI, direct OpenAI.

Uso / Usage:
    from config import get_client, get_model, get_embedding_model, describe_config, settings
    client = get_client()
    model  = get_model()
"""

from __future__ import annotations

import os
from pathlib import Path
from dataclasses import dataclass, field

from dotenv import load_dotenv
from openai import OpenAI, AzureOpenAI

# Carica .env dalla root del repo / Load .env from repo root
_env_path = Path(__file__).resolve().parent / ".env"
load_dotenv(_env_path)


# ------------------------------------------------------------------
# Settings
# ------------------------------------------------------------------

@dataclass
class Settings:
    """Configurazione caricata da variabili d'ambiente / Configuration loaded from env vars."""

    provider: str = field(default_factory=lambda: os.getenv("LLM_PROVIDER", "ollama").lower())

    # Ollama
    ollama_base_url: str = field(default_factory=lambda: os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1"))
    ollama_model: str = field(default_factory=lambda: os.getenv("OLLAMA_MODEL", "llama3.2"))
    ollama_embedding_model: str = field(default_factory=lambda: os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"))

    # Azure OpenAI
    azure_endpoint: str = field(default_factory=lambda: os.getenv("AZURE_OPENAI_ENDPOINT", ""))
    azure_api_key: str = field(default_factory=lambda: os.getenv("AZURE_OPENAI_API_KEY", ""))
    azure_api_version: str = field(default_factory=lambda: os.getenv("AZURE_OPENAI_API_VERSION", "2024-12-01-preview"))
    azure_deployment: str = field(default_factory=lambda: os.getenv("AZURE_OPENAI_DEPLOYMENT", "gpt-4"))
    azure_embedding_deployment: str = field(default_factory=lambda: os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT", "text-embedding-3-large"))
    azure_tts_deployment: str = field(default_factory=lambda: os.getenv("AZURE_TTS_DEPLOYMENT", "tts"))
    azure_stt_deployment: str = field(default_factory=lambda: os.getenv("AZURE_STT_DEPLOYMENT", "whisper"))
    azure_realtime_deployment: str = field(default_factory=lambda: os.getenv("AZURE_REALTIME_DEPLOYMENT", "gpt-4o-realtime-preview"))

    # OpenAI diretto / Direct OpenAI
    openai_api_key: str = field(default_factory=lambda: os.getenv("OPENAI_API_KEY", ""))
    openai_model: str = field(default_factory=lambda: os.getenv("OPENAI_MODEL", "gpt-4o-mini"))
    openai_embedding_model: str = field(default_factory=lambda: os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-3-small"))


settings = Settings()


# ------------------------------------------------------------------
# Client factory
# ------------------------------------------------------------------

def get_client() -> OpenAI | AzureOpenAI:
    """Restituisce un client OpenAI configurato per il provider scelto.
    Returns an OpenAI client configured for the chosen provider."""

    if settings.provider == "ollama":
        return OpenAI(
            base_url=settings.ollama_base_url,
            api_key="ollama",  # Ollama non richiede API key / Ollama doesn't need an API key
        )

    if settings.provider == "azure":
        return AzureOpenAI(
            azure_endpoint=settings.azure_endpoint,
            api_key=settings.azure_api_key,
            api_version=settings.azure_api_version,
        )

    if settings.provider == "openai":
        return OpenAI(api_key=settings.openai_api_key)

    raise ValueError(
        f"Provider '{settings.provider}' non supportato. "
        f"Usa: ollama, azure, openai. / "
        f"Provider '{settings.provider}' not supported. "
        f"Use: ollama, azure, openai."
    )


def get_model() -> str:
    """Restituisce il nome del modello per il provider corrente.
    Returns the model name for the current provider."""

    if settings.provider == "ollama":
        return settings.ollama_model
    if settings.provider == "azure":
        return settings.azure_deployment
    if settings.provider == "openai":
        return settings.openai_model
    raise ValueError(f"Provider sconosciuto / Unknown provider: {settings.provider}")


def get_embedding_model() -> str:
    """Restituisce il modello di embedding per il provider corrente.
    Returns the embedding model for the current provider."""

    if settings.provider == "ollama":
        return settings.ollama_embedding_model
    if settings.provider == "azure":
        return settings.azure_embedding_deployment
    if settings.provider == "openai":
        return settings.openai_embedding_model
    raise ValueError(f"Provider sconosciuto / Unknown provider: {settings.provider}")


def describe_config() -> str:
    """Descrizione leggibile della configurazione corrente.
    Human-readable description of the current configuration."""

    model = get_model()
    if settings.provider == "ollama":
        return f"Ollama (local) — {model} @ {settings.ollama_base_url}"
    if settings.provider == "azure":
        return f"Azure OpenAI — {model} @ {settings.azure_endpoint}"
    if settings.provider == "openai":
        return f"OpenAI — {model}"
    return f"Unknown provider: {settings.provider}"


# ------------------------------------------------------------------
# Quick test
# ------------------------------------------------------------------

if __name__ == "__main__":
    print(f"Provider: {settings.provider}")
    print(f"Config:   {describe_config()}")
    print(f"Model:    {get_model()}")
    print(f"Embed:    {get_embedding_model()}")

    client = get_client()
    resp = client.chat.completions.create(
        model=get_model(),
        messages=[{"role": "user", "content": "Rispondi solo: ciao!"}],
        max_tokens=10,
    )
    print(f"Test LLM: {resp.choices[0].message.content}")