"""
ai_client.py — Cloud AI Client for free remote deployments

Backend-first model router for mobile/no-PC usage. It prefers free or low-cost
OpenAI-compatible providers when configured and falls back across providers before
returning an error. The iPhone remains only a control surface; all model calls run
from the deployed backend/HF Space.

v2 — timeout + asyncio.to_thread per ogni chiamata sync (non blocca l'event loop),
     fallback immediato al provider successivo su timeout o errore.

S752-C: _ProviderHealth — corruption rate tracking + auto-cooldown per provider.
"""
from __future__ import annotations

import asyncio
import os
import time as _time_mod
from collections import deque
from dataclasses import dataclass, field
from typing import AsyncIterator, Optional

from openai import OpenAI
import logging
_logger = logging.getLogger("agente_ai")  # S-BUGFIX

# Gap 2.3: Distributed LLM cache — importato con guard per graceful degradation
try:
    from api.llm_cache import (
        cache_key as _lc_key,
        should_cache as _lc_should,
        get_cached as _lc_get,
        set_cached as _lc_set,
    )
    _LLM_CACHE_AVAILABLE = True
except ImportError:
    _LLM_CACHE_AVAILABLE = False

    def _lc_should(*_a, **_kw): return False   # type: ignore[misc]
    def _lc_key(*_a, **_kw): return ""         # type: ignore[misc]
    async def _lc_get(*_a, **_kw): return None  # type: ignore[misc]
    async def _lc_set(*_a, **_kw): pass         # type: ignore[misc]

# Timeout per provider — abbassabile via env per reti lente
PROVIDER_TIMEOUT: float = float(os.getenv("PROVIDER_TIMEOUT", "15"))
STREAM_TIMEOUT: float   = float(os.getenv("STREAM_TIMEOUT",   "45"))

# S391: Cerebras free tier ctx limit — ~8K token * ~4 chars/token.
CEREBRAS_CTX_LIMIT_CHARS: int = int(os.getenv("CEREBRAS_CTX_LIMIT_CHARS", "32000"))

# S416-Fix5: limiti reali output_token per modello → evita truncation silenziosa.
_MODEL_OUTPUT_LIMITS: dict[str, int] = {
    "llama3-8b-8192":                      8192,
    "gemini-2.5-flash":                       65536,
    "gemini-2.5-flash-lite":                 32768,
    "gemini-2.5-flash-preview-04-17":       65536,
    "deepseek-r1:free":                   16000,
    "deepseek/deepseek-r1:free":          16000,
    "qwen/qwen3-30b-a3b:free":            8192,
    "qwen/qwen3-235b-a22b:free":         16384,
    "meta-llama/llama-4-scout:free":          8192,
    "meta-llama/llama-4-maverick:free":        8192,
    "meta-llama/llama-4-scout":                8192,
    "meta-llama/llama-4-maverick":             8192,
    "llama3.1-8b":                             2048,
    "gemini-2.5-pro":                         65536,
    "cerebras/gpt-oss-120b":                   8192,
    "gpt-oss-120b":                           32768,   # S-LOOP1: Cerebras reasoning model (thinking tokens → alto consumo, serve spazio)
    "qwen/qwen3.6-27b":                       32768,   # S-LOOP1: Groq Qwen3.6 27B (sostituisce qwen3-32b deprecato)
    "Meta-Llama-3.3-70B-Instruct":             8192,   # S-LOOP1: SambaNova Llama 3.3 70B
    "openai/gpt-oss-20b:free":                 4096,   # S-LOOP1: OpenRouter gpt-oss-20b free
    # S-2026-06: nuovi modelli OpenRouter free verificati live 2026-06-14
    "nvidia/nemotron-3-ultra-550b-a55b:free":  16384,  # 1M ctx, ARCHITECT role
    "nvidia/nemotron-3-super-120b-a12b:free":  16384,  # 1M ctx, 120B
    "nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free": 8192,
    "nvidia/nemotron-3-nano-30b-a3b:free":      8192,
    "qwen/qwen3-coder:free":                   32768,  # 1M ctx, CODER role — 480B
    "qwen/qwen3-next-80b-a3b-instruct:free":   16384,  # 262K ctx
    "poolside/laguna-m.1:free":                16384,  # 262K ctx, coding
    "poolside/laguna-xs.2:free":               8192,   # 262K ctx, lightweight
    "nex-agi/nex-n2-pro:free":                 16384,  # 262K ctx
    "google/gemma-4-31b-it:free":              16384,  # 262K ctx
    "google/gemma-4-26b-a4b-it:free":          16384,  # 262K ctx
    "openai/gpt-oss-120b:free":                16384,  # 131K ctx
    "meta-llama/llama-3.3-70b-instruct:free":  16384,  # 131K ctx
    "meta-llama/llama-3.2-3b-instruct:free":   4096,
    # SambaNova — modelli verificati live 2026-06-14
    "DeepSeek-V3.1":                           32768,  # 131K ctx
    "DeepSeek-V3.2":                           8192,   # 32K ctx
    "MiniMax-M2.7":                            32768,  # 196K ctx!
    "gemma-4-31B-it":                          16384,  # 131K ctx
    # Gemini 3.x — disponibili live 2026-06-14 (flash = free)
    "gemini-3-flash-preview":                  32768,
    "gemini-3.5-flash":                        32768,
    "gemini-3.1-flash-lite":                   16384,
    "gemini-3.1-flash-lite-preview":           16384,
    # Groq 2026 — verificati live
    "openai/gpt-oss-120b":                      8192,  # Groq GPT-OSS 120B
    "openai/gpt-oss-20b":                       4096,  # Groq GPT-OSS 20B
    # Groq 2026 — live verificato 2026-06-14 /v1/models
    "groq/compound":                             8192,  # Groq Compound (web + reasoning)
    "groq/compound-mini":                        4096,  # Groq Compound Mini
    "openai/gpt-oss-safeguard-20b":             4096,  # Groq GPT-OSS Safeguard 20B
    # Cerebras — reasoning model con thinking tokens
    "zai-glm-4.7":                              4096,  # Cerebras GLM 4.7
}

def _safe_max_tokens(requested: int, model_id: str) -> int:
    """S416-Fix5: cappa max_tokens al limite output reale del modello (margine 512 token)."""
    limit = _MODEL_OUTPUT_LIMITS.get(model_id, requested)
    return min(requested, max(512, limit - 512))


# ── S752-C: Provider Health — corruption rate tracking + auto-cooldown ─────────
# Ogni provider ha una finestra rolling di 10 esiti ('ok'|'corrupt'|'timeout').
# Se corruption_rate >= 40% su >= 5 campioni → cooldown automatico 60s.
# Il cooldown si azzera da solo allo scadere — nessun intervento manuale necessario.

_CORRUPT_RATE_THRESHOLD: float = 0.40   # soglia per attivare cooldown
_COOLDOWN_SECONDS:       float = 60.0   # durata cooldown in secondi
_CORRUPT_MIN_LEN:        int   = 5      # risposta < 5 chars = corrupted

@dataclass
class _ProviderHealth:
    outcomes: "deque[str]" = field(default_factory=lambda: deque(maxlen=10))
    cooldown_until: float = 0.0
    call_timestamps: "deque[float]" = field(default_factory=lambda: deque(maxlen=120))  # P19-B1: RPM sliding window

_PROVIDER_HEALTH: dict[str, _ProviderHealth] = {}

# P19-B1: Limiti RPM per provider — sliding window 60s.
# Fonte: free tier ufficiali 2026. Override via env var (RPM_GEMINI, RPM_GROQ, ecc.)
# 0 = nessun limite (provider a pagamento o senza cap documentato).
_PROVIDER_RPM_LIMITS: dict[str, int] = {
    "gemini":         int(os.getenv("RPM_GEMINI",          "15")),  # Google AI free: 15 RPM
    "groq":           int(os.getenv("RPM_GROQ",            "30")),  # Groq free: 30 RPM/model
    "groq-fast":      int(os.getenv("RPM_GROQ_FAST",       "30")),
    "groq-qwen":      int(os.getenv("RPM_GROQ_QWEN",       "30")),
    "groq-scout":     int(os.getenv("RPM_GROQ_SCOUT",      "30")),
    "groq-compound":  int(os.getenv("RPM_GROQ_COMPOUND",   "20")),
    "groq-b":         int(os.getenv("RPM_GROQ_B",         "30")),  # Groq key B slot 1
    "groq-fast-b":    int(os.getenv("RPM_GROQ_FAST_B",    "30")),  # Groq key B slot 2
    "groq-c":         int(os.getenv("RPM_GROQ_C",          "30")),  # Groq key C slot 1
    "groq-fast-c":    int(os.getenv("RPM_GROQ_FAST_C",     "30")),  # Groq key C slot 2

    "cerebras":       int(os.getenv("RPM_CEREBRAS",        "30")),
    "cerebras-b":     int(os.getenv("RPM_CEREBRAS_B",       "30")),  # Cerebras key B
    "sambanova":      int(os.getenv("RPM_SAMBANOVA",       "60")),
    "sambanova-b":    int(os.getenv("RPM_SAMBANOVA_B",      "60")),  # SambaNova key B
    "nvidia":         int(os.getenv("RPM_NVIDIA",           "30")),  # NVIDIA NIM free tier
    "nvidia-b":       int(os.getenv("RPM_NVIDIA_B",          "30")),  # NVIDIA NIM key B
    "openrouter":     int(os.getenv("RPM_OPENROUTER",      "20")),  # free tier
    "huggingface":    int(os.getenv("RPM_HF",              "10")),  # HF router free: ~10 RPM
    "cloudflare":     int(os.getenv("RPM_CLOUDFLARE",     "300")),  # CF Workers AI
    "cloudflare-b":   int(os.getenv("RPM_CLOUDFLARE_B",   "300")),  # CF Workers AI Account B
    # openai_compatible: 0 = nessun limite (account a pagamento)
}


def _record_provider_outcome(name: str, outcome: str) -> None:
    """
    Registra esito ('ok' | 'corrupt' | 'timeout') e attiva cooldown automatico
    se la corruption rate supera la soglia su almeno 5 campioni.
    Mai rilancia eccezioni.
    """
    try:
        rec = _PROVIDER_HEALTH.setdefault(name, _ProviderHealth())
        rec.outcomes.append(outcome)
        if len(rec.outcomes) >= 5:
            n_corrupt = sum(1 for o in rec.outcomes if o == 'corrupt')
            rate      = n_corrupt / len(rec.outcomes)
            if rate >= _CORRUPT_RATE_THRESHOLD and rec.cooldown_until < _time_mod.monotonic():
                rec.cooldown_until = _time_mod.monotonic() + _COOLDOWN_SECONDS
                import logging as _log_cd
                _log_cd.getLogger("agente_ai").warning(
                    "S752 provider '%s' cooldown %ds — corruption_rate=%.0f%%",
                    name, int(_COOLDOWN_SECONDS), rate * 100,
                )
    except Exception:
        pass  # health tracking mai blocca il path principale


def _provider_in_cooldown(name: str) -> bool:
    """True se il provider è in cooldown e il tempo non è ancora scaduto."""
    rec = _PROVIDER_HEALTH.get(name)
    return rec is not None and rec.cooldown_until > _time_mod.monotonic()


def _classify_llm_response(text: str) -> str:
    """
    Classifica la risposta come 'ok' | 'corrupt'.
    Corrupt = vuota, troppo corta, o raw API error JSON trapelato come testo.
    """
    if not text or len(text.strip()) < _CORRUPT_MIN_LEN:
        return 'corrupt'
    s = text.strip()
    # Raw API error JSON passato come testo dal provider
    if s.startswith('{"error"') or s.startswith('{"message"'):
        return 'corrupt'
    # choices[]/delta leak — streaming non gestito correttamente
    if '"choices"' in s and '"delta"' in s:
        return 'corrupt'
    return 'ok'


def get_provider_health_snapshot() -> dict[str, dict]:
    """
    Ritorna snapshot dello stato di salute di tutti i provider.
    Usato da /api/ai/health per esporre dati di osservabilità.
    """
    now = _time_mod.monotonic()
    result: dict[str, dict] = {}
    for name, rec in _PROVIDER_HEALTH.items():
        total   = len(rec.outcomes)
        corrupt = sum(1 for o in rec.outcomes if o == 'corrupt')
        timeout = sum(1 for o in rec.outcomes if o == 'timeout')
        # P19-B1: snapshot finestra RPM corrente (ultimi 60s)
        _rpm_win = sum(1 for ts in rec.call_timestamps if (now - ts) <= 60.0)
        _rpm_lim = _PROVIDER_RPM_LIMITS.get(name, 0)
        result[name] = {
            "total_samples":    total,
            "corrupt_count":    corrupt,
            "timeout_count":    timeout,
            "corruption_rate":  round(corrupt / total, 3) if total > 0 else 0.0,
            "in_cooldown":      rec.cooldown_until > now,
            "cooldown_remaining_s": max(0.0, round(rec.cooldown_until - now, 1)),
            "rpm_window_count": _rpm_win,
            "rpm_limit":        _rpm_lim,
            "rpm_saturated":    bool(_rpm_lim) and _rpm_win >= _rpm_lim,
        }
    return result


# ── P19-B1: RPM sliding-window check/record ─────────────────────────────────────

def _rpm_allowed(name: str) -> bool:
    """
    True se il provider ha ancora capacità nella finestra 60s.
    Se il limite è 0 → sempre True. Fail-open: eccezione → True.
    """
    try:
        limit = _PROVIDER_RPM_LIMITS.get(name, 0)
        if limit == 0:
            return True
        rec = _PROVIDER_HEALTH.get(name)
        if rec is None:
            return True
        now = _time_mod.monotonic()
        # Rimuovi timestamps fuori finestra
        while rec.call_timestamps and (now - rec.call_timestamps[0]) > 60.0:
            rec.call_timestamps.popleft()
        allowed = len(rec.call_timestamps) < limit
        if not allowed:
            _logger.warning(
                "[P19-B1] provider '%s' al limite %d RPM — skip immediato verso provider successivo",
                name, limit,
            )
        return allowed
    except Exception:
        return True  # fail-open: mai blocca il path principale


def _rpm_record(name: str) -> None:
    """Registra timestamp chiamata nella sliding window. Fail-safe."""
    try:
        rec = _PROVIDER_HEALTH.setdefault(name, _ProviderHealth())
        rec.call_timestamps.append(_time_mod.monotonic())
    except Exception:
        pass


@dataclass(frozen=True)
class ProviderConfig:
    name: str
    api_key: str
    base_url: str
    default_model: str
    embedding_model: Optional[str] = None


class AIClient:
    def __init__(self) -> None:
        self.providers    = self._discover_providers()
        self._client_cache: dict[str, OpenAI] = {}
        self.provider_name = self.providers[0].name          if self.providers else "unconfigured"
        self.default_model = self.providers[0].default_model if self.providers else os.getenv("LLM_MODEL", "openrouter/auto")
        self.client        = self._client_for(self.providers[0]) if self.providers else None

    # ── Discovery ────────────────────────────────────────────────────────────

    def _discover_providers(self) -> list[ProviderConfig]:
        """
        S387 — Provider chain aggiornata al 2026-06-03 con i modelli disponibili su ogni piattaforma.

        GROQ (3 slot, bucket TPM separati per modello — stessa chiave):
          groq      → openai/gpt-oss-120b                    (benchmark #1: 100%, 290ms, ctx 131K)
          groq-qwen → qwen/qwen3.6-27b                             (Qwen3 32B, ~14K TPM,  ctx 131K — ragionamento/math)
          groq-fast → openai/gpt-oss-20b                       (~100K TPM, ctx 131K  — emergenza rate-limit)

        GEMINI (1 slot):
          gemini    → gemini-2.5-flash-lite                    (S435: 2.0 spento, 2.5-flash-lite ✓)

        OPENROUTER (1 slot, modello :free aggiornato):
          openrouter → openai/gpt-oss-20b:free                  (S435: nemotron rate-limit esaurito)

        HUGGINGFACE (opzionale, disabilitato di default: 402 free tier esaurito):
          huggingface → Qwen/Qwen2.5-Coder-32B-Instruct

        OPENAI-COMPAT (opzionale, fallback finale):
          openai_compatible → gpt-4o-mini

        S752-C: Logica bucket TPM Groq: i rate limit sono per-modello su Groq →
        anche se groq (Scout) è a 429, groq-qwen (Qwen3) e groq-fast (8b) rispondono.
        Provider in cooldown S752 vengono skippati nel sequential (non nella race).
        """
        providers: list[ProviderConfig] = []

        groq_key = os.getenv("GROQ_API_KEY")

        # ── GROQ SLOT 1: Llama 4 Scout — modello primario 2026 ───────────────
        if groq_key:
            providers.append(ProviderConfig(
                name="groq",
                api_key=groq_key,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_MODEL", "openai/gpt-oss-120b"),
            ))

        # ── GROQ SLOT 2: Llama 3.1 8B Instant — fast race partner ─────────────
        if groq_key and not os.getenv("DISABLE_GROQ_FAST"):
            providers.append(ProviderConfig(
                name="groq-fast",
                api_key=groq_key,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_FAST_MODEL", "openai/gpt-oss-20b"),
            ))

        # ── GROQ SLOT 3: Qwen3-32B — fallback qualità ragionamento/math ──────
        if groq_key and not os.getenv("DISABLE_GROQ_QWEN"):
            providers.append(ProviderConfig(
                name="groq-qwen",
                api_key=groq_key,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_QWEN_MODEL", "qwen/qwen3.6-27b"),
            ))

        # ── GROQ SLOT 4: Llama 4 Scout — 10M ctx, emergenza rate-limit versatile ─────
        if groq_key and not os.getenv("DISABLE_GROQ_SCOUT"):
            providers.append(ProviderConfig(
                name="groq-scout",
                api_key=groq_key,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_SCOUT_MODEL", "openai/gpt-oss-120b"),
            ))

        # ── GROQ SLOT 5: Compound — web search + reasoning integrati ─────────────────
        if groq_key and not os.getenv("DISABLE_GROQ_COMPOUND"):
            providers.append(ProviderConfig(
                name="groq-compound",
                api_key=groq_key,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_COMPOUND_MODEL", "groq/compound"),
            ))


        # ── GROQ KEY B — slot 1: llama-3.3-70b con chiave secondaria ─────────────
        # GAP-API-LB: seconda chiave Groq per raddoppiare il rate-limit giornaliero.
        # Attivato da GROQ_API_KEY_B env var. DISABLE_GROQ_B per disabilitare.
        groq_key_b = os.getenv("GROQ_API_KEY_B")
        if groq_key_b and not os.getenv("DISABLE_GROQ_B"):
            providers.append(ProviderConfig(
                name="groq-b",
                api_key=groq_key_b,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_MODEL", "openai/gpt-oss-120b"),
            ))

        # ── GROQ KEY B — slot 2: openai/gpt-oss-20b con chiave secondaria ────
        if groq_key_b and not os.getenv("DISABLE_GROQ_B"):
            providers.append(ProviderConfig(
                name="groq-fast-b",
                api_key=groq_key_b,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_FAST_MODEL", "openai/gpt-oss-20b"),
            ))

        # ── GROQ KEY C — slot 1: terza chiave, raddoppia ancora il budget giornaliero ─
        groq_key_c = os.getenv("GROQ_API_KEY_C")
        if groq_key_c and not os.getenv("DISABLE_GROQ_C"):
            providers.append(ProviderConfig(
                name="groq-c",
                api_key=groq_key_c,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_MODEL", "openai/gpt-oss-120b"),
            ))

        # ── GROQ KEY C — slot 2: fast model con chiave C ─────────────────────────
        if groq_key_c and not os.getenv("DISABLE_GROQ_C"):
            providers.append(ProviderConfig(
                name="groq-fast-c",
                api_key=groq_key_c,
                base_url="https://api.groq.com/openai/v1",
                default_model=os.getenv("GROQ_FAST_MODEL", "openai/gpt-oss-20b"),
            ))

        # ── CEREBRAS: gpt-oss-120b — reasoning model con "reasoning" field ──────────
        # IMPORTANTE: gpt-oss-120b usa thinking tokens interni → max_tokens≥500 richiesto
        # Con max_tokens<200 il content è "" (reasoning consuma tutti i token)
        cerebras_key = os.getenv("CEREBRAS_API_KEY")
        if cerebras_key:
            providers.append(ProviderConfig(
                name="cerebras",
                api_key=cerebras_key,
                base_url="https://api.cerebras.ai/v1",
                default_model=os.getenv("CEREBRAS_MODEL", "gpt-oss-120b"),
            ))

        # ── CEREBRAS KEY B — seconda chiave, raddoppia quota oraria ──────────────
        cerebras_key_b = os.getenv("CEREBRAS_API_KEY_B")
        if cerebras_key_b and not os.getenv("DISABLE_CEREBRAS_B"):
            providers.append(ProviderConfig(
                name="cerebras-b",
                api_key=cerebras_key_b,
                base_url="https://api.cerebras.ai/v1",
                default_model=os.getenv("CEREBRAS_MODEL", "gpt-oss-120b"),
            ))

        # ── SAMBANOVA: Llama 3.3 70B — 2200+ tok/s, 60 req/min, no daily cap ────
        sn_key = os.getenv("SAMBANOVA_API_KEY", "")
        if sn_key:
            providers.append(ProviderConfig(
                name="sambanova",
                api_key=sn_key,
                base_url="https://api.sambanova.ai/v1",
                default_model=os.getenv("SAMBANOVA_MODEL", "DeepSeek-V3.1"),
            ))

        # ── SAMBANOVA KEY B — seconda chiave, raddoppia 60 req/min ───────────────
        sn_key_b = os.getenv("SAMBANOVA_API_KEY_B", "")
        if sn_key_b and not os.getenv("DISABLE_SAMBANOVA_B"):
            providers.append(ProviderConfig(
                name="sambanova-b",
                api_key=sn_key_b,
                base_url="https://api.sambanova.ai/v1",
                default_model=os.getenv("SAMBANOVA_MODEL", "DeepSeek-V3.1"),
            ))

        # ── NVIDIA NIM: nemotron-3-ultra-550b — 1M ctx, 550B params, gratuito ──────
        # endpoint OpenAI-compatible: integrate.api.nvidia.com/v1
        # modello primario: nvidia/nemotron-3-ultra-550b-a55b (ARCHITECT, 16K output)
        nvidia_key = os.getenv("NVIDIA_API_KEY")
        if nvidia_key:
            providers.append(ProviderConfig(
                name="nvidia",
                api_key=nvidia_key,
                base_url="https://integrate.api.nvidia.com/v1",
                default_model=os.getenv("NVIDIA_MODEL", "nvidia/nemotron-3-super-120b-a12b"),
            ))

        # ── NVIDIA NIM key B — slot secondario (raddoppia rate-limit gratuito NIM) ────────────
        # Modello B: meta/llama-3.3-70b-instruct (veloce, stabile) — complementare a nvidia (120B)
        nvidia_key_b = os.getenv("NVIDIA_API_KEY_B")
        if nvidia_key_b and not os.getenv("DISABLE_NVIDIA_B"):
            providers.append(ProviderConfig(
                name="nvidia-b",
                api_key=nvidia_key_b,
                base_url="https://integrate.api.nvidia.com/v1",
                default_model=os.getenv("NVIDIA_B_MODEL", "meta/llama-3.3-70b-instruct"),
            ))

        # ── GEMINI: gemini-2.5-flash-lite — S435: 2.0-flash-lite spento dal 1-giu-2026 ────────
        gemini_key = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")
        if gemini_key:
            providers.append(ProviderConfig(
                name="gemini",
                api_key=gemini_key,
                base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                default_model=os.getenv("GEMINI_MODEL", "gemini-2.5-flash"),
            ))
        # GEMINI B-E (Failover A-E)
        for suffix in ['B', 'C', 'E']:
            g_key = os.getenv(f'GEMINI_API_KEY_{suffix}')
            if g_key:
                providers.append(ProviderConfig(
                    name=f'gemini-{suffix.lower()}',
                    api_key=g_key,
                    base_url='https://generativelanguage.googleapis.com/v1beta/openai/',
                    default_model=os.getenv('GEMINI_MODEL', 'gemini-2.5-flash'),
                ))

        # ── OPENROUTER: gpt-oss-20b:free — S435: nemotron rate-limit esaurito ─────────
        openrouter_key = os.getenv("OPENROUTER_API_KEY")
        if openrouter_key:
            providers.append(ProviderConfig(
                name="openrouter",
                api_key=openrouter_key,
                base_url="https://openrouter.ai/api/v1",
                default_model=os.getenv("OPENROUTER_MODEL", "openai/gpt-oss-120b:free"),
            ))
        # OPENROUTER B-E (Failover A-E)
        for suffix in ['B', 'C', 'D', 'E']:
            or_key = os.getenv(f'OPENROUTER_API_KEY_{suffix}')
            if or_key:
                providers.append(ProviderConfig(
                    name=f'openrouter-{suffix.lower()}',
                    api_key=or_key,
                    base_url='https://openrouter.ai/api/v1',
                    default_model=os.getenv('OPENROUTER_MODEL', 'openai/gpt-oss-120b:free'),
                ))

        # ── HUGGINGFACE: Qwen2.5-Coder-32B ────────────────────────────────────
        hf_key = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_API_KEY") or os.getenv("HUGGINGFACE_TOKEN")
        # RF-3: rimosso gate ENABLE_HF_PROVIDER — si attiva automaticamente se HF_TOKEN presente
        if hf_key:
            providers.append(ProviderConfig(
                name="huggingface",
                api_key=hf_key,
                base_url=os.getenv("HF_OPENAI_BASE_URL", "https://router.huggingface.co/v1"),
                default_model=os.getenv("HF_MODEL", "Qwen/Qwen2.5-Coder-32B-Instruct"),
            ))

        # 5. OpenAI compat last
        openai_key = os.getenv("OPENAI_API_KEY")
        if openai_key:
            providers.append(ProviderConfig(
                name="openai_compatible",
                api_key=openai_key,
                base_url=os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
                default_model=os.getenv("OPENAI_MODEL", os.getenv("LLM_MODEL", "gpt-4o-mini")),
                embedding_model=os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"),
            ))

                # ── CLOUDFLARE WORKERS AI: llama-3.3-70b-instruct-fp8-fast ─────────────────
        # RF-3: attivo se CF_API_TOKEN + CF_ACCOUNT_ID entrambi presenti.
        # Endpoint OpenAI-compatible: /accounts/{id}/ai/v1
        # Free: 10K req/giorno, nessun account separato — usa il token Cloudflare.
        cf_token = os.getenv("CF_API_TOKEN")
        cf_account = os.getenv("CF_ACCOUNT_ID")
        if cf_token and cf_account and not os.getenv("DISABLE_CF_PROVIDER"):
            providers.append(ProviderConfig(
                name="cloudflare",
                api_key=cf_token,
                base_url=f"https://api.cloudflare.com/client/v4/accounts/{cf_account}/ai/v1",
                default_model=os.getenv("CF_AI_MODEL", "@cf/meta/llama-3.3-70b-instruct-fp8-fast"),
            ))

        # ── CLOUDFLARE WORKERS AI (Account B): Fallback secondario ─────────────────
        # S408-DUAL: attivo se CF_API_TOKEN_B + CF_ACCOUNT_ID_B presenti.
        # Raddoppia la quota giornaliera a 20k request totali.
        cf_token_b = os.getenv("CF_API_TOKEN_B")
        cf_account_b = os.getenv("CF_ACCOUNT_ID_B")
        if cf_token_b and cf_account_b and not os.getenv("DISABLE_CF_B"):
            providers.append(ProviderConfig(
                name="cloudflare-b",
                api_key=cf_token_b,
                base_url=f"https://api.cloudflare.com/client/v4/accounts/{cf_account_b}/ai/v1",
                default_model=os.getenv("CF_AI_MODEL_B", "@cf/meta/llama-3.3-70b-instruct-fp8-fast"),
            ))
        return providers

    # ── Client factory ────────────────────────────────────────────────────────

    def _client_for(self, provider: ProviderConfig) -> OpenAI:
        """B11: Cache client per provider — evita re-istanziazione OpenAI() a ogni LLM call."""
        if provider.name not in self._client_cache:
            self._client_cache[provider.name] = OpenAI(
                api_key=provider.api_key,
                base_url=provider.base_url,
                timeout=PROVIDER_TIMEOUT,
                max_retries=0,
            )
        return self._client_cache[provider.name]

    def _model_for(self, provider: ProviderConfig, requested: Optional[str]) -> str:
        """
        S195-Robust: resolve correct model ID for this provider.
        1. Strips litellm provider prefix (e.g. 'groq/openai/gpt-oss-20b' -> 'openai/gpt-oss-20b')
        2. Blocks Groq-only model IDs from being sent to other providers.
        """
        if not requested:
            return provider.default_model
        _KNOWN_PREFIXES = {
            "groq", "openrouter", "openai", "anthropic", "cohere", "mistral",
            "huggingface", "gemini", "azure", "bedrock", "vertex_ai",
        }
        _segs = requested.split("/", 1)
        bare = _segs[1] if len(_segs) > 1 and _segs[0].lower() in _KNOWN_PREFIXES else requested
        _GROQ_ONLY: set[str] = {
            "openai/gpt-oss-20b",
            "openai/gpt-oss-120b",
            "openai/gpt-oss-120b",
            "qwen/qwen3.6-27b",
            "groq/compound",
            "groq/compound-mini",
            "openai/gpt-oss-20b",
            "openai/gpt-oss-120b",
            "openai/gpt-4o-mini",
            "groq/compound",
            "groq/compound-mini",
            "openai/gpt-oss-safeguard-20b",
            "allam-2-7b",
            "whisper-large-v3",
            "whisper-large-v3-turbo",
        }
        if not provider.name.startswith("groq") and bare in _GROQ_ONLY:
            return provider.default_model
        return bare

    # ── Qwen3 /no_think helper ───────────────────────────────────────────────

    @staticmethod
    def _inject_no_think(messages: list) -> list:
        """
        S436: Qwen3 thinking disable via system message prefix /no_think.
        Ritorna una NUOVA lista — non muta messages originale.
        """
        NO_THINK = "/no_think"
        msgs = list(messages)
        for i, m in enumerate(msgs):
            if m.get("role") == "system":
                content = m.get("content") or ""
                if not content.startswith(NO_THINK):
                    msgs[i] = {**m, "content": f"{NO_THINK}\n{content}"}
                return msgs
        msgs.insert(0, {"role": "system", "content": NO_THINK})
        return msgs

    # ── Race-to-first helper ─────────────────────────────────────────────────

    async def _try_chat_once(
        self,
        provider: ProviderConfig,
        messages: list,
        model: Optional[str],
        temperature: float,
        max_tokens: int,
        timeout: float,
    ) -> str:
        """
        S356: Tenta una singola chiamata a un provider con timeout fisso.
        S752-C: Registra esito ('ok'|'corrupt'|'timeout') nel health tracker.
        """
        client = self._client_for(provider)
        if provider.name == "cerebras":
            _est = sum(len(str(m.get("content", "") or "")) for m in messages)
            if _est > CEREBRAS_CTX_LIMIT_CHARS:
                raise ValueError(f"cerebras: ctx troppo lungo ({_est} chars > {CEREBRAS_CTX_LIMIT_CHARS})")
        _model = model
        _msgs = self._inject_no_think(messages) if provider.name == "groq-qwen" else messages
        for attempt in range(2):
            try:
                _extra = {}
                if provider.name == "gemini":
                    _extra = {"extra_body": {"thinking": {"type": "disabled"}}}
                _resolved_model = self._model_for(provider, _model)
                response = await asyncio.wait_for(
                    asyncio.to_thread(
                        client.chat.completions.create,
                        model=_resolved_model,
                        messages=_msgs,
                        temperature=temperature,
                        max_tokens=_safe_max_tokens(max_tokens, _resolved_model),
                        stream=False,
                        **_extra,
                    ),
                    timeout=timeout,
                )
                result = response.choices[0].message.content or ""
                # P16-B4: cattura finish_reason per segnalare truncation al caller
                self._last_finish_reason: str = (
                    getattr(response.choices[0], "finish_reason", None) or "stop"
                )
                # S752-C: registra esito nel health tracker
                _record_provider_outcome(provider.name, _classify_llm_response(result))
                return result
            except asyncio.TimeoutError:
                _record_provider_outcome(provider.name, 'timeout')
                raise TimeoutError(f"{provider.name} timeout {timeout:.0f}s")
            except Exception as exc:
                exc_str = str(exc)
                is_bad_model = (
                    "not a valid model" in exc_str or
                    ("400" in exc_str and "BadRequest" in exc_str)
                )
                if is_bad_model and _model is not None and attempt == 0:
                    _model = None
                    continue
                raise
        raise RuntimeError(f"{provider.name}: tentativi esauriti")

    # ── Health ────────────────────────────────────────────────────────────────

    async def health(self) -> dict:
        if not self.providers:
            return {
                "available": False,
                "provider": "none",
                "error": "No remote provider key configured. Set OPENROUTER_API_KEY, GEMINI_API_KEY, GROQ_API_KEY, HF_TOKEN or OPENAI_API_KEY.",
                "models": [],
            }

        checks: list[dict] = []
        for provider in self.providers:
            client = self._client_for(provider)
            try:
                await asyncio.wait_for(
                    asyncio.to_thread(
                        client.chat.completions.create,
                        model=provider.default_model,
                        messages=[{"role": "user", "content": "ping"}],
                        max_tokens=1,
                        temperature=0,
                    ),
                    timeout=PROVIDER_TIMEOUT,
                )
                checks.append({"provider": provider.name, "available": True, "model": provider.default_model})
                return {
                    "available": True,
                    "provider": provider.name,
                    "models": [p.default_model for p in self.providers],
                    "default": provider.default_model,
                    "checks": checks,
                    # S752-C: include health snapshot nell'endpoint /api/ai/health
                    "provider_health": get_provider_health_snapshot(),
                }
            except Exception as exc:
                checks.append({"provider": provider.name, "available": False, "model": provider.default_model, "error": str(exc)})

        return {
            "available": False,
            "provider": "configured_but_unavailable",
            "models": [p.default_model for p in self.providers],
            "checks": checks,
            "provider_health": get_provider_health_snapshot(),
        }

    # ── Chat (non-stream) ─────────────────────────────────────────────────────

    async def chat(
        self,
        messages: list,
        *,
        model: Optional[str] = None,
        temperature: float = 0.7,
        max_tokens: int = 4096,
        timeout: Optional[float] = None,
    ) -> str:
        """
        S356: Race-to-first sui primi 2 provider con timeout aggressivo (5s).
        Se entrambi falliscono, fallback sequenziale completo con timeout pieno.

        S752-C: nel sequential, skippa provider in cooldown (se almeno 1 altro
        non è in cooldown). Registra 'ok'/'corrupt'/'timeout' sul health tracker.
        """
        per_provider_timeout = timeout or PROVIDER_TIMEOUT
        last_error: Exception | None = None

        # Gap 2.3: Distributed LLM cache — check prima di qualsiasi chiamata provider.
        # Solo chat() non-streaming, temperature ≤ 0.5, senza tool results.
        # get_cached() timeout 2s → miss trasparente se Upstash non risponde.
        _lc_ck: str | None = None
        if _LLM_CACHE_AVAILABLE and _lc_should(messages, temperature):
            _lc_ck = _lc_key(model or "", messages)
            _lc_cached = await _lc_get(_lc_ck)
            if _lc_cached is not None:
                return _lc_cached

        # ── Fase 1: Race-to-first sui primi 2 provider ───────────────────────
        # Race NON controlla cooldown — è già fast (5s) e cancella il perdente.
        RACE_TIMEOUT = min(per_provider_timeout * 0.4, 5.0)
        _race_hard_fail: set[str] = set()
        if len(self.providers) >= 2:
            _race_n = min(2, len(self.providers))
            # P19-B1: preferisce provider con capacità RPM disponibile
            _race_cands = [p for p in self.providers[:_race_n] if _rpm_allowed(p.name)]
            _race_providers = _race_cands if _race_cands else self.providers[:1]
            for _rp in _race_providers:
                _rpm_record(_rp.name)
            race_tasks = [
                asyncio.create_task(
                    self._try_chat_once(p, messages, model, temperature, max_tokens, RACE_TIMEOUT)
                )
                for p in _race_providers
            ]
            done, pending = await asyncio.wait(race_tasks, return_when=asyncio.FIRST_COMPLETED)
            for t in pending:
                t.cancel()
                try:
                    await t
                except (asyncio.CancelledError, Exception) as _exc:
                    _logger.debug("[ai_client] silenced %s", type(_exc).__name__)  # noqa: BLE001
            for p, t in zip(_race_providers, race_tasks):
                if t.done() and not t.cancelled():
                    _exc = t.exception()
                    if _exc is not None:
                        _es = str(_exc)
                        if "429" in _es or "402" in _es or "rate_limit" in _es.lower() or "depleted" in _es.lower():
                            _race_hard_fail.add(p.name)
            for t in sorted(done, key=lambda x: len(x.result()) if x.exception() is None else 0, reverse=True):
                exc = t.exception()
                if exc is None:
                    result = t.result()
                    if result and len(result.strip()) >= 30:
                        if _lc_ck:
                            asyncio.create_task(_lc_set(_lc_ck, result))
                        return result
                    last_error = RuntimeError(f"risposta troppo corta ({len(result)} chars)")
                else:
                    last_error = exc

        # ── Fase 2: Fallback sequenziale su tutti i provider ─────────────────
        # S752-C: skippa provider in cooldown — a meno che TUTTI siano in cooldown
        _all_in_cooldown = all(_provider_in_cooldown(p.name) for p in self.providers)
        # P19-B1: fallback se TUTTI sono al limite RPM → non skippiamo nessuno (fail-open)
        _all_at_rpm = all(not _rpm_allowed(p.name) for p in self.providers)

        for provider in self.providers:
            if provider.name in _race_hard_fail:
                last_error = RuntimeError(f"{provider.name}: rate-limit/no-credits (già fallito in race)")
                continue

            # S752-C: cooldown check — bypass se tutti i provider sono in cooldown
            if not _all_in_cooldown and _provider_in_cooldown(provider.name):
                last_error = RuntimeError(f"{provider.name}: in cooldown (corruption_rate >= 40%)")
                continue

            # P19-B1: RPM limit — skip immediato, nessuna HTTP call, nessun timeout wait
            if not _all_at_rpm and not _rpm_allowed(provider.name):
                last_error = RuntimeError(
                    f"{provider.name}: RPM limit {_PROVIDER_RPM_LIMITS.get(provider.name, '?')}/min — attendi 60s"
                )
                continue
            _rpm_record(provider.name)

            client = self._client_for(provider)
            _model = model
            _msgs = self._inject_no_think(messages) if provider.name == "groq-qwen" else messages
            for attempt in range(3):
                try:
                    _extra: dict = {}
                    if provider.name == "gemini":
                        _extra = {"extra_body": {"thinking": {"type": "disabled"}}}
                    _resolved_model_seq = self._model_for(provider, _model)
                    response = await asyncio.wait_for(
                        asyncio.to_thread(
                            client.chat.completions.create,
                            model=_resolved_model_seq,
                            messages=_msgs,
                            temperature=temperature,
                            max_tokens=_safe_max_tokens(max_tokens, _resolved_model_seq),
                            stream=False,
                            **_extra,
                        ),
                        timeout=per_provider_timeout,
                    )
                    result = response.choices[0].message.content or ""
                    # S752-C: registra esito nel sequential path
                    _record_provider_outcome(provider.name, _classify_llm_response(result))
                    if _lc_ck and result:
                        asyncio.create_task(_lc_set(_lc_ck, result))
                    return result
                except asyncio.TimeoutError:
                    _record_provider_outcome(provider.name, 'timeout')
                    last_error = TimeoutError(f"{provider.name} non ha risposto entro {per_provider_timeout}s")
                    break
                except Exception as exc:
                    last_error = exc
                    exc_str = str(exc)
                    is_rate_limit = "429" in exc_str or "rate_limit" in exc_str.lower()
                    is_no_credits = "402" in exc_str or "depleted" in exc_str.lower()
                    is_bad_model  = (
                        "not a valid model" in exc_str or
                        ("400" in exc_str and "BadRequest" in exc_str)
                    )
                    if is_no_credits:
                        _record_provider_outcome(provider.name, "timeout")   # 402/no-credits
                        break
                    if is_bad_model and _model is not None and attempt == 0:
                        _model = None
                        continue
                    if is_rate_limit:
                        _record_provider_outcome(provider.name, "timeout")   # S-LOOP4: registra rate-limit (era dead code dopo break)
                        break  # S-LOOP1: 429/rate-limit -> passa subito al provider successivo (no sleep+retry)
                    _record_provider_outcome(provider.name, "corrupt")  # errore generico non classificato
                    break

        # GAP-FALLBACK-GRACEFUL: Se TUTTI i provider falliscono, restituisci un messaggio utile
        # invece di crashare. Questo permette al frontend di mostrare un errore chiaro.
        _logger.error("[ai_client] All providers exhausted: %s", last_error)
        raise RuntimeError(
            f"🔴 Tutti i provider AI sono temporaneamente non disponibili. "
            f"Ultimo errore: {str(last_error)[:100]}. "
            f"Riprova tra qualche minuto o contatta il supporto."
        )

    # ── Stream chat ───────────────────────────────────────────────────────────

    async def stream_chat(
        self,
        messages: list,
        *,
        model: Optional[str] = None,
        temperature: float = 0.7,
        max_tokens: int = 4096,
    ) -> AsyncIterator[str]:
        """
        Streaming con fallback automatico tra provider.
        S752-C: skippa provider in cooldown (se almeno 1 altro non è in cooldown).
        """
        last_error: Exception | None = None
        _all_in_cooldown = all(_provider_in_cooldown(p.name) for p in self.providers)
        _all_at_rpm_s = all(not _rpm_allowed(p.name) for p in self.providers)  # P19-B1

        for provider in self.providers:
            # S752-C: cooldown check nel path streaming
            if not _all_in_cooldown and _provider_in_cooldown(provider.name):
                last_error = RuntimeError(f"{provider.name}: in cooldown")
                continue

            # P19-B1: RPM limit check — skip immediato nel path streaming
            if not _all_at_rpm_s and not _rpm_allowed(provider.name):
                last_error = RuntimeError(
                    f"{provider.name}: RPM limit {_PROVIDER_RPM_LIMITS.get(provider.name, '?')}/min"
                )
                continue
            _rpm_record(provider.name)

            client = self._client_for(provider)
            if provider.name == "cerebras":
                _est = sum(len(str(m.get("content", "") or "")) for m in messages)
                if _est > CEREBRAS_CTX_LIMIT_CHARS:
                    last_error = ValueError(f"cerebras: ctx troppo lungo ({_est} chars)")
                    continue
            try:
                q: asyncio.Queue[str | None] = asyncio.Queue()
                _loop = asyncio.get_running_loop()
                _msgs = self._inject_no_think(messages) if provider.name == "groq-qwen" else messages
                _stream_model     = self._model_for(provider, model)
                _stream_max_tokens = _safe_max_tokens(max_tokens, _stream_model)

                _finish_reason_holder: list[str] = ['stop']  # P16-B4: thread→coroutine handoff

                def _stream_to_queue() -> None:
                    try:
                        _stream_extra: dict = {}
                        if provider.name == "gemini":
                            _stream_extra = {"extra_body": {"thinking": {"type": "disabled"}}}
                        stream = client.chat.completions.create(
                            model=_stream_model,
                            messages=_msgs,
                            temperature=temperature,
                            max_tokens=_stream_max_tokens,
                            stream=True,
                            **_stream_extra,
                        )
                        for chunk in stream:
                            if chunk.choices and chunk.choices[0].delta.content:
                                _loop.call_soon_threadsafe(
                                    q.put_nowait, chunk.choices[0].delta.content
                                )
                            # P16-B4: cattura finish_reason dall'ultimo chunk streaming
                            if chunk.choices and chunk.choices[0].finish_reason:
                                _finish_reason_holder[0] = chunk.choices[0].finish_reason
                    finally:
                        _loop.call_soon_threadsafe(q.put_nowait, None)

                import threading
                t = threading.Thread(target=_stream_to_queue, daemon=True)
                t.start()

                deadline = _loop.time() + STREAM_TIMEOUT
                _stream_buf: list[str] = []
                while True:
                    remaining = deadline - _loop.time()
                    if remaining <= 0:
                        _record_provider_outcome(provider.name, 'timeout')
                        raise TimeoutError(f"stream timeout {STREAM_TIMEOUT}s")
                    try:
                        token = await asyncio.wait_for(q.get(), timeout=min(remaining, 5.0))
                    except asyncio.TimeoutError:
                        _record_provider_outcome(provider.name, 'timeout')
                        raise TimeoutError(f"stream timeout {STREAM_TIMEOUT}s")
                    if token is None:
                        break
                    _stream_buf.append(token)
                    yield token
                # S752-C: registra esito stream completato
                _record_provider_outcome(
                    provider.name,
                    _classify_llm_response(''.join(_stream_buf))
                )
                # P16-B4: espone finish_reason al caller (unified_loop.py legge _last_finish_reason)
                self._last_finish_reason = _finish_reason_holder[0]
                return
            except asyncio.TimeoutError:
                last_error = TimeoutError(f"{provider.name} stream timeout dopo {STREAM_TIMEOUT}s")
                continue
            except Exception as exc:
                last_error = exc
                exc_str = str(exc)
                is_bad_model_stream = (
                    "not a valid model" in exc_str or
                    ("400" in exc_str and "BadRequest" in exc_str)
                )
                if is_bad_model_stream and model is not None:
                    try:
                        _fallback_model = self._model_for(provider, None)
                        _fallback_msgs  = self._inject_no_think(messages) if provider.name == "groq-qwen" else messages
                        _fallback_max   = _safe_max_tokens(max_tokens, _fallback_model)
                        _extra_fb: dict = {}
                        if provider.name == "gemini":
                            _extra_fb = {"extra_body": {"thinking": {"type": "disabled"}}}
                        _fb_resp = await asyncio.wait_for(
                            asyncio.to_thread(
                                client.chat.completions.create,
                                model=_fallback_model,
                                messages=_fallback_msgs,
                                temperature=temperature,
                                max_tokens=_fallback_max,
                                stream=False,
                                **_extra_fb,
                            ),
                            timeout=STREAM_TIMEOUT,
                        )
                        _fb_text = _fb_resp.choices[0].message.content or ""
                        if _fb_text:
                            _record_provider_outcome(provider.name, _classify_llm_response(_fb_text))
                            yield _fb_text
                            return
                    except Exception as _exc:
                        _logger.debug("[ai_client] silenced %s", type(_exc).__name__)  # noqa: BLE001
                # S-LOOP3: registra esito streaming nel health tracker (era mancante nel path Exception)
                # 429/rate_limit → timeout, altri errori → corrupt
                _sl3_exc_str = str(exc)
                _sl3_is_rl = "429" in _sl3_exc_str or "rate_limit" in _sl3_exc_str.lower() or "402" in _sl3_exc_str
                _sl3_outcome = "timeout" if _sl3_is_rl or "timeout" in _sl3_exc_str.lower() else "corrupt"
                _record_provider_outcome(provider.name, _sl3_outcome)
                continue

        raise RuntimeError(f"Nessun provider streaming disponibile: {last_error}")

    # ── Embeddings ────────────────────────────────────────────────────────────

    async def embed(self, text: str, model: str = "text-embedding-3-small") -> list[float]:
        for provider in self.providers:
            embedding_model = provider.embedding_model or os.getenv("EMBEDDING_MODEL")
            if not embedding_model:
                continue
            client = self._client_for(provider)
            try:
                response = await asyncio.wait_for(
                    asyncio.to_thread(
                        client.embeddings.create,
                        input=[text],
                        model=embedding_model or model,
                    ),
                    timeout=PROVIDER_TIMEOUT,
                )
                return response.data[0].embedding
            except Exception:
                continue
        return []