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cloud_llm_service.py
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770 lines (658 loc) · 29.1 KB
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#!/usr/bin/env python3
"""
Generic Cloud LLM Service supporting multiple providers.
Supports:
- Google Gemini (via google-generativeai SDK)
- Moonshot Kimi (OpenAI-compatible API)
- OpenAI (OpenAI-compatible API)
- Anthropic Claude (OpenAI-compatible API)
- DeepSeek (OpenAI-compatible API)
- OpenRouter (aggregator with many free models)
- Custom OpenAI-compatible endpoints
"""
import json
import logging
import os
from abc import ABC, abstractmethod
from datetime import datetime
from typing import TYPE_CHECKING, Dict, Generator, List, Optional, Union
if TYPE_CHECKING:
from xray_proxy_manager import XrayProxyManager, XrayProxyManagerWithFallback
import httpx
# Gemini SDK (optional)
try:
import google.generativeai as genai
GEMINI_AVAILABLE = True
except ImportError:
GEMINI_AVAILABLE = False
genai = None
# VLESS Proxy Manager (optional)
try:
from xray_proxy_manager import (
XrayProxyManager,
XrayProxyManagerWithFallback,
validate_vless_url,
)
XRAY_AVAILABLE = True
except ImportError:
XRAY_AVAILABLE = False
XrayProxyManager = None
XrayProxyManagerWithFallback = None
validate_vless_url = None
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Provider type configuration (also defined in db/models.py)
PROVIDER_TYPES = {
"gemini": {
"name": "Google Gemini",
"default_base_url": None,
"default_models": ["gemini-2.0-flash", "gemini-2.5-flash", "gemini-2.5-pro"],
"requires_base_url": False,
},
"kimi": {
"name": "Moonshot Kimi",
"default_base_url": "https://api.moonshot.ai/v1",
"default_models": ["kimi-k2", "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"],
"requires_base_url": True,
},
"openai": {
"name": "OpenAI",
"default_base_url": "https://api.openai.com/v1",
"default_models": ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo"],
"requires_base_url": True,
},
"claude": {
"name": "Anthropic Claude",
"default_base_url": "https://api.anthropic.com/v1",
"default_models": ["claude-opus-4-5-20251101", "claude-sonnet-4-20250514"],
"requires_base_url": True,
},
"deepseek": {
"name": "DeepSeek",
"default_base_url": "https://api.deepseek.com/v1",
"default_models": ["deepseek-chat", "deepseek-coder"],
"requires_base_url": True,
},
"openrouter": {
"name": "OpenRouter",
"default_base_url": "https://openrouter.ai/api/v1",
"default_models": [
# Free models (январь 2026)
"nvidia/nemotron-3-nano-30b-a3b:free",
"nvidia/nemotron-nano-12b-v2-vl:free",
"arcee-ai/trinity-large-preview:free",
"arcee-ai/trinity-mini:free",
"upstage/solar-pro-3:free",
"liquid/lfm-2.5-1.2b-instruct:free",
"allenai/molmo-2-8b:free",
"tngtech/tng-r1t-chimera:free",
# Paid (дешёвые)
"google/gemini-2.0-flash-001",
"openai/gpt-4o-mini",
"deepseek/deepseek-chat-v3-0324",
],
"requires_base_url": True,
},
"custom": {
"name": "Custom OpenAI-Compatible",
"default_base_url": "",
"default_models": [],
"requires_base_url": True,
},
"claude_bridge": {
"name": "Claude Bridge (Local CLI)",
"default_base_url": "http://127.0.0.1:8787/v1",
"default_models": ["sonnet", "opus", "haiku"],
"requires_base_url": False,
},
}
class BaseLLMProvider(ABC):
"""Abstract base class for LLM providers."""
def __init__(self, config: dict):
self.config = config
self.api_key = config.get("api_key", "")
self.model_name = config.get("model_name", "")
self.base_url = config.get("base_url", "")
self.provider_id = config.get("id", "unknown")
self.provider_type = config.get("provider_type", "custom")
# Runtime parameters
self.runtime_params = config.get("config", {}) or {}
if not self.runtime_params:
self.runtime_params = {
"temperature": 0.7,
"max_tokens": 512,
"top_p": 0.9,
}
@abstractmethod
def generate_response(
self, user_message: str, system_prompt: str = None, history: List[Dict] = None
) -> str:
"""Generate a response synchronously."""
pass
@abstractmethod
def generate_response_stream(
self, user_message: str, system_prompt: str = None, history: List[Dict] = None
) -> Generator[str, None, None]:
"""Generate a response with streaming."""
pass
@abstractmethod
def generate_response_from_messages(
self, messages: List[Dict[str, str]], stream: bool = False
) -> Union[str, Generator[str, None, None]]:
"""Generate response from OpenAI-format messages."""
pass
@abstractmethod
def is_available(self) -> bool:
"""Check if provider is available."""
pass
def set_params(self, **kwargs):
"""Set runtime parameters."""
for key, value in kwargs.items():
if value is not None:
self.runtime_params[key] = value
logger.info(f"[{self.provider_id}] Parameters updated: {self.runtime_params}")
def get_params(self) -> Dict:
"""Get runtime parameters."""
return self.runtime_params.copy()
class OpenAICompatibleProvider(BaseLLMProvider):
"""
Provider for OpenAI-compatible APIs.
Supports: Kimi (Moonshot), OpenAI, DeepSeek, Claude*, Custom endpoints.
*Note: Claude has its own API format, but can be used via OpenAI-compatible proxy.
"""
def __init__(self, config: dict):
super().__init__(config)
# Bridge runs on localhost — must bypass global VLESS/HTTP proxy.
# GeminiProvider sets HTTP_PROXY globally for xray; httpx picks it up
# and routes localhost requests through the proxy, which fails.
# Set NO_PROXY BEFORE creating httpx.Client so it respects it.
if self.provider_type == "claude_bridge":
self._ensure_no_proxy_for_localhost()
# Claude bridge needs longer timeouts: CLI warmup (7-30s) + complex
# prompt processing can exceed 60s before first token arrives.
# Bridge itself allows 600s per-chunk; match that on client side.
if self.provider_type == "claude_bridge":
self.client = httpx.Client(
timeout=httpx.Timeout(connect=10.0, read=300.0, write=10.0, pool=10.0)
)
# Bridge/Claude can handle much longer responses than default 512
self.runtime_params.setdefault("max_tokens", 4096)
else:
self.client = httpx.Client(timeout=60.0)
# Validate required fields (bridge uses CLI auth, no API key needed)
if not self.api_key and self.provider_type != "claude_bridge":
raise ValueError(f"API key required for provider {self.provider_id}")
# Set default base URL if not provided
if not self.base_url:
default_url = PROVIDER_TYPES.get(self.provider_type, {}).get("default_base_url", "")
if default_url:
self.base_url = default_url
else:
raise ValueError(f"Base URL required for provider {self.provider_id}")
logger.info(f"[{self.provider_id}] Initialized OpenAI-compatible provider: {self.base_url}")
@staticmethod
def _ensure_no_proxy_for_localhost():
"""Add 127.0.0.1 and localhost to NO_PROXY so httpx bypasses VLESS proxy."""
bypass = {"127.0.0.1", "localhost"}
for key in ("NO_PROXY", "no_proxy"):
current = os.environ.get(key, "")
existing = {h.strip() for h in current.split(",") if h.strip()}
missing = bypass - existing
if missing:
new_val = ",".join(sorted(existing | bypass))
os.environ[key] = new_val
def _get_headers(self) -> dict:
headers: dict = {"Content-Type": "application/json"}
if self.api_key:
headers["Authorization"] = f"Bearer {self.api_key}"
return headers
def is_available(self) -> bool:
try:
response = self.client.get(
f"{self.base_url}/models", headers=self._get_headers(), timeout=10.0
)
# 200 = success, 401/403 = auth issue but API reachable
return response.status_code in [200, 401, 403]
except Exception as e:
logger.warning(f"[{self.provider_id}] Health check failed: {e}")
return False
def generate_response(
self, user_message: str, system_prompt: str = None, history: List[Dict] = None
) -> str:
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
if history:
messages.extend(history)
messages.append({"role": "user", "content": user_message})
return self._generate_non_stream(messages)
def generate_response_stream(
self, user_message: str, system_prompt: str = None, history: List[Dict] = None
) -> Generator[str, None, None]:
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
if history:
messages.extend(history)
messages.append({"role": "user", "content": user_message})
yield from self._generate_stream(messages)
def generate_response_from_messages(
self, messages: List[Dict[str, str]], stream: bool = False
) -> Union[str, Generator[str, None, None]]:
if stream:
return self._generate_stream(messages)
return self._generate_non_stream(messages)
def _generate_non_stream(self, messages: List[Dict[str, str]]) -> str:
try:
response = self.client.post(
f"{self.base_url}/chat/completions",
headers=self._get_headers(),
json={
"model": self.model_name,
"messages": messages,
"temperature": self.runtime_params.get("temperature", 0.7),
"max_tokens": self.runtime_params.get("max_tokens", 512),
"top_p": self.runtime_params.get("top_p", 0.9),
"stream": False,
},
)
response.raise_for_status()
result = response.json()
return result["choices"][0]["message"]["content"].strip()
except httpx.HTTPStatusError as e:
logger.error(
f"[{self.provider_id}] HTTP error: {e.response.status_code} - {e.response.text}"
)
error_messages = {
401: "Invalid API key",
403: "Access denied - check API key permissions",
404: f"Model '{self.model_name}' not found - check model name",
429: "Rate limit exceeded - wait or upgrade plan",
500: "Provider server error",
502: "Provider gateway error",
503: "Provider temporarily unavailable",
}
msg = error_messages.get(e.response.status_code, f"HTTP {e.response.status_code}")
return f"Error: {msg}"
except Exception as e:
logger.error(f"[{self.provider_id}] Error: {e}")
return "Извините, произошла техническая ошибка."
def _generate_stream(self, messages: List[Dict[str, str]]) -> Generator[str, None, None]:
try:
with self.client.stream(
"POST",
f"{self.base_url}/chat/completions",
headers=self._get_headers(),
json={
"model": self.model_name,
"messages": messages,
"temperature": self.runtime_params.get("temperature", 0.7),
"max_tokens": self.runtime_params.get("max_tokens", 512),
"top_p": self.runtime_params.get("top_p", 0.9),
"stream": True,
},
) as response:
response.raise_for_status()
for line in response.iter_lines():
if line.startswith("data: "):
data = line[6:]
if data == "[DONE]":
break
try:
chunk = json.loads(data)
delta = chunk["choices"][0].get("delta", {})
content = delta.get("content", "")
if content:
yield content
except json.JSONDecodeError:
continue
except Exception as e:
logger.error(f"[{self.provider_id}] Stream error: {e}")
yield "Извините, произошла техническая ошибка."
class GeminiProvider(BaseLLMProvider):
"""
Provider for Google Gemini API.
Uses the google-generativeai SDK.
Supports optional VLESS proxy via xray-core with fallback support.
"""
def __init__(self, config: dict):
super().__init__(config)
if not GEMINI_AVAILABLE:
raise ImportError(
"google-generativeai package not installed. Install with: pip install google-generativeai"
)
if not self.api_key:
raise ValueError("API key required for Gemini provider")
# Initialize VLESS proxy BEFORE configuring genai (so HTTP client uses proxy)
self.proxy_manager: Optional[Union["XrayProxyManager", "XrayProxyManagerWithFallback"]] = (
None
)
self._setup_vless_proxy()
# Start proxy and set env vars BEFORE genai.configure()
# This ensures the HTTP client is created with proxy settings
if self.proxy_manager:
self.proxy_manager.start()
proxy_url = f"http://127.0.0.1:{self.proxy_manager.http_port}"
os.environ["HTTP_PROXY"] = proxy_url
os.environ["HTTPS_PROXY"] = proxy_url
os.environ["http_proxy"] = proxy_url
os.environ["https_proxy"] = proxy_url
logger.info(f"[{self.provider_id}] Proxy env set: {proxy_url}")
genai.configure(api_key=self.api_key)
self.model = genai.GenerativeModel(model_name=self.model_name or "gemini-2.0-flash")
logger.info(f"[{self.provider_id}] Initialized Gemini provider: {self.model_name}")
def _setup_vless_proxy(self):
"""Setup VLESS proxy if configured in runtime_params.
Supports:
- vless_url: single URL (backward compatible)
- vless_urls: list of URLs with automatic fallback
"""
# Check for list of URLs first (new format with fallback)
vless_urls = self.runtime_params.get("vless_urls", [])
vless_url = self.runtime_params.get("vless_url", "")
# Normalize to list
if vless_urls:
urls = vless_urls if isinstance(vless_urls, list) else [vless_urls]
elif vless_url:
urls = [vless_url]
else:
return
if not XRAY_AVAILABLE:
logger.warning(
f"[{self.provider_id}] VLESS URL configured but xray_proxy_manager not available"
)
return
# Use fallback manager if multiple URLs, otherwise simple manager
if len(urls) > 1:
self.proxy_manager = XrayProxyManagerWithFallback()
count = self.proxy_manager.configure_proxies(urls)
if count > 0:
logger.info(
f"[{self.provider_id}] VLESS proxy configured with {count} fallback servers"
)
else:
logger.warning(f"[{self.provider_id}] No valid VLESS URLs configured")
self.proxy_manager = None
else:
# Single URL - use simple manager
is_valid, error = validate_vless_url(urls[0])
if not is_valid:
logger.error(f"[{self.provider_id}] Invalid VLESS URL: {error}")
return
self.proxy_manager = XrayProxyManager()
if self.proxy_manager.configure(urls[0]):
logger.info(f"[{self.provider_id}] VLESS proxy configured")
else:
logger.warning(f"[{self.provider_id}] Failed to configure VLESS proxy")
self.proxy_manager = None
def get_proxy_status(self) -> dict:
"""Get VLESS proxy status."""
if not self.proxy_manager:
return {
"configured": False,
"xray_available": XRAY_AVAILABLE and XrayProxyManager is not None,
"fallback_enabled": False,
}
status = self.proxy_manager.get_status()
status["fallback_enabled"] = isinstance(self.proxy_manager, XrayProxyManagerWithFallback)
return status
def test_proxy_connection(self, index: int = -1) -> dict:
"""Test VLESS proxy connection to Google API.
Args:
index: Proxy index to test (-1 for current/all)
"""
if not self.proxy_manager:
return {"success": False, "error": "No VLESS proxy configured"}
if isinstance(self.proxy_manager, XrayProxyManagerWithFallback):
if index >= 0:
return self.proxy_manager.test_proxy(index)
return {"results": self.proxy_manager.test_all_proxies()}
return self.proxy_manager.test_connection("https://generativelanguage.googleapis.com")
def mark_proxy_failed(self):
"""Mark current proxy as failed and switch to next (fallback mode only)."""
if isinstance(self.proxy_manager, XrayProxyManagerWithFallback):
self.proxy_manager.mark_current_failed()
def reset_proxies(self):
"""Reset all proxies to enabled state (fallback mode only)."""
if isinstance(self.proxy_manager, XrayProxyManagerWithFallback):
self.proxy_manager.reset_all_proxies()
def is_available(self) -> bool:
"""Check if provider is available. Proxy is already running if configured."""
try:
# Proxy env vars are set in __init__, just test the API
list(genai.list_models())
return True
except Exception as e:
logger.warning(f"[{self.provider_id}] Health check failed: {e}")
return False
def generate_response(
self, user_message: str, system_prompt: str = None, history: List[Dict] = None
) -> str:
"""Generate response. Proxy is already running if configured."""
try:
# Rebuild model with system instruction if provided
if system_prompt:
model = genai.GenerativeModel(
model_name=self.model_name or "gemini-2.0-flash",
system_instruction=system_prompt,
)
else:
model = self.model
# Convert history to Gemini format
gemini_history = []
if history:
for msg in history:
role = "model" if msg["role"] == "assistant" else msg["role"]
if role not in ["user", "model"]:
continue
gemini_history.append({"role": role, "parts": [msg["content"]]})
chat = model.start_chat(history=gemini_history)
response = chat.send_message(user_message)
return response.text.strip()
except Exception as e:
logger.error(f"[{self.provider_id}] Error: {e}")
return "Извините, произошла техническая ошибка."
def generate_response_stream(
self, user_message: str, system_prompt: str = None, history: List[Dict] = None
) -> Generator[str, None, None]:
"""Generate streaming response. Proxy is already running if configured."""
try:
if system_prompt:
model = genai.GenerativeModel(
model_name=self.model_name or "gemini-2.0-flash",
system_instruction=system_prompt,
)
else:
model = self.model
gemini_history = []
if history:
for msg in history:
role = "model" if msg["role"] == "assistant" else msg["role"]
if role not in ["user", "model"]:
continue
gemini_history.append({"role": role, "parts": [msg["content"]]})
chat = model.start_chat(history=gemini_history)
response = chat.send_message(user_message, stream=True)
for chunk in response:
if chunk.text:
yield chunk.text
except Exception as e:
logger.error(f"[{self.provider_id}] Stream error: {e}")
yield "Извините, произошла техническая ошибка."
def generate_response_from_messages(
self, messages: List[Dict[str, str]], stream: bool = False
) -> Union[str, Generator[str, None, None]]:
# Extract system prompt and convert to Gemini format
system_prompt = None
history = []
last_user = ""
for msg in messages:
if msg["role"] == "system":
system_prompt = msg["content"]
elif msg["role"] == "user":
last_user = msg["content"]
history.append({"role": "user", "content": msg["content"]})
elif msg["role"] == "assistant":
history.append({"role": "assistant", "content": msg["content"]})
# Remove last user message from history (will be sent)
if history and history[-1]["role"] == "user":
history = history[:-1]
if stream:
return self.generate_response_stream(last_user, system_prompt, history)
return self.generate_response(last_user, system_prompt, history)
class CloudLLMService:
"""
Main service class for cloud LLM providers.
Manages provider instances and provides unified interface.
Compatible with LLMService and VLLMLLMService interfaces.
"""
# Provider class mapping
PROVIDER_CLASSES = {
"gemini": GeminiProvider,
"kimi": OpenAICompatibleProvider,
"openai": OpenAICompatibleProvider,
"claude": OpenAICompatibleProvider,
"deepseek": OpenAICompatibleProvider,
"openrouter": OpenAICompatibleProvider,
"custom": OpenAICompatibleProvider,
"claude_bridge": OpenAICompatibleProvider,
}
def __init__(self, provider_config: dict):
"""
Initialize with provider configuration from database.
Args:
provider_config: Dict with id, provider_type, api_key, base_url, model_name, config
"""
self.config = provider_config
self.provider_type = provider_config.get("provider_type", "custom")
self.provider_id = provider_config.get("id", "unknown")
# Get provider class and instantiate
provider_class = self.PROVIDER_CLASSES.get(self.provider_type, OpenAICompatibleProvider)
self.provider: BaseLLMProvider = provider_class(provider_config)
# For compatibility with existing code
self.model_name = provider_config.get("model_name", "")
self.api_url = provider_config.get("base_url", "")
self.backend_type = "cloud" # Отличает от vLLM
# FAQ (загружается через reload_faq из БД)
self.faq: Dict[str, str] = {}
# Conversation history
self.conversation_history: List[Dict[str, str]] = []
# System prompt (for secretary persona)
self.system_prompt = provider_config.get("system_prompt", "")
logger.info(f"CloudLLMService initialized: {self.provider_id} ({self.provider_type})")
def _normalize_faq(self, faq_dict: Dict[str, str]) -> Dict[str, str]:
"""Нормализует ключи FAQ (lowercase, strip)"""
return {k.lower().strip(): v for k, v in faq_dict.items()}
def _check_faq(self, user_message: str) -> Optional[str]:
if not self.faq:
return None
normalized = user_message.lower().strip().rstrip("?!.,")
if normalized in self.faq:
return self._apply_faq_templates(self.faq[normalized])
for key, response in self.faq.items():
if key in normalized or normalized in key:
return self._apply_faq_templates(response)
return None
def _apply_faq_templates(self, response: str) -> str:
now = datetime.now()
replacements = {
"{current_time}": now.strftime("%H:%M"),
"{current_date}": now.strftime("%d.%m.%Y"),
"{day_of_week}": [
"понедельник",
"вторник",
"среда",
"четверг",
"пятница",
"суббота",
"воскресенье",
][now.weekday()],
}
for placeholder, value in replacements.items():
response = response.replace(placeholder, value)
return response
def reload_faq(self, faq_dict: Dict[str, str] = None):
"""
Перезагружает FAQ (hot reload).
Args:
faq_dict: FAQ словарь из БД. Если не передан, FAQ очищается.
"""
if faq_dict:
self.faq = self._normalize_faq(faq_dict)
else:
self.faq = {}
logger.info(f"🔄 FAQ перезагружен: {len(self.faq)} записей")
def get_system_prompt(self) -> str:
"""Return the system prompt configured for this provider."""
return self.system_prompt or ""
def is_available(self) -> bool:
"""Check if provider is available."""
return self.provider.is_available()
def generate_response(self, user_message: str, use_history: bool = True) -> str:
"""Generate response (compatible with LLMService/VLLMLLMService)."""
# Check FAQ first
faq_response = self._check_faq(user_message)
if faq_response:
if use_history:
self.conversation_history.append({"role": "user", "content": user_message})
self.conversation_history.append({"role": "assistant", "content": faq_response})
return faq_response
history = self.conversation_history if use_history else []
response = self.provider.generate_response(user_message, self.system_prompt, history)
if use_history:
self.conversation_history.append({"role": "user", "content": user_message})
self.conversation_history.append({"role": "assistant", "content": response})
return response
def generate_response_stream(
self, user_message: str, use_history: bool = True
) -> Generator[str, None, None]:
"""Generate streaming response (compatible with LLMService/VLLMLLMService)."""
# Check FAQ first
faq_response = self._check_faq(user_message)
if faq_response:
if use_history:
self.conversation_history.append({"role": "user", "content": user_message})
self.conversation_history.append({"role": "assistant", "content": faq_response})
yield faq_response
return
history = self.conversation_history if use_history else []
full_response = ""
for chunk in self.provider.generate_response_stream(
user_message, self.system_prompt, history
):
full_response += chunk
yield chunk
if use_history and full_response:
self.conversation_history.append({"role": "user", "content": user_message})
self.conversation_history.append({"role": "assistant", "content": full_response})
def generate_response_from_messages(
self, messages: List[Dict[str, str]], stream: bool = False
) -> Union[str, Generator[str, None, None]]:
"""Generate response from OpenAI-format messages (compatible with orchestrator)."""
# Check FAQ for single-message requests
user_messages = [m for m in messages if m.get("role") == "user"]
if len(user_messages) == 1:
faq_response = self._check_faq(user_messages[0]["content"])
if faq_response:
if stream:
def gen():
yield faq_response
return gen()
return faq_response
return self.provider.generate_response_from_messages(messages, stream)
def reset_conversation(self):
"""Clear conversation history."""
self.conversation_history = []
def get_conversation_history(self) -> List[Dict[str, str]]:
"""Get conversation history."""
return self.conversation_history
def set_params(self, **kwargs):
"""Set runtime parameters."""
self.provider.set_params(**kwargs)
def get_params(self) -> Dict:
"""Get runtime parameters."""
return self.provider.get_params()
# For compatibility with VLLMLLMService persona system
@property
def runtime_params(self) -> Dict:
return self.provider.runtime_params