feat: revue de code, doc CODE_REVIEW, forfaits 2026, traduction LLM, providers avec modèle
Made-with: Cursor
This commit is contained in:
Sepehr Ramezani
670
services/providers/openai_provider.py
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670
services/providers/openai_provider.py
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@@ -0,0 +1,670 @@
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"""
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OpenAI Provider - Cloud LLM translation provider.
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Extends TranslationProvider base class with robust error handling,
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retry logic, and health monitoring for OpenAI API.
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Features:
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- Cloud LLM translation via OpenAI Chat Completions API
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- Custom system prompt support
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- Specific error codes for all OpenAI API errors
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- Retry logic with exponential backoff for transient errors
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- Timeout configuration (faster than local Ollama)
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- Health check with caching
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- Structlog-compatible logging (no document content in logs)
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"""
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import threading
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import time
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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try:
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import structlog
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logger = structlog.get_logger(__name__)
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_HAS_STRUCTLOG = True
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except ImportError:
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import logging
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logger = logging.getLogger(__name__)
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_HAS_STRUCTLOG = False
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def _log_info(event: str, **kwargs):
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"""Log info with structlog or standard logging compatibility."""
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if _HAS_STRUCTLOG:
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logger.info(event, **kwargs)
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else:
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msg = f"{event} " + " ".join(f"{k}={v}" for k, v in kwargs.items())
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logger.info(msg)
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def _log_warning(event: str, **kwargs):
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"""Log warning with structlog or standard logging compatibility."""
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if _HAS_STRUCTLOG:
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logger.warning(event, **kwargs)
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else:
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msg = f"{event} " + " ".join(f"{k}={v}" for k, v in kwargs.items())
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logger.warning(msg)
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def _log_error(event: str, **kwargs):
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"""Log error with structlog or standard logging compatibility."""
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if _HAS_STRUCTLOG:
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logger.error(event, **kwargs)
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else:
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msg = f"{event} " + " ".join(f"{k}={v}" for k, v in kwargs.items())
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logger.error(msg)
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import requests
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from requests.exceptions import Timeout, ConnectionError as RequestsConnectionError
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from .base import TranslationProvider
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from .schemas import (
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ProviderHealthStatus,
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TranslationRequest,
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TranslationResponse,
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)
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# Error codes
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OPENAI_RATE_LIMITED = "OPENAI_RATE_LIMITED"
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OPENAI_INVALID_KEY = "OPENAI_INVALID_KEY"
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OPENAI_QUOTA_EXCEEDED = "OPENAI_QUOTA_EXCEEDED"
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OPENAI_TIMEOUT = "OPENAI_TIMEOUT"
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OPENAI_SERVICE_ERROR = "OPENAI_SERVICE_ERROR"
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OPENAI_CONTEXT_TOO_LONG = "OPENAI_CONTEXT_TOO_LONG"
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_RETRYABLE_ERRORS = {OPENAI_RATE_LIMITED, OPENAI_TIMEOUT, OPENAI_SERVICE_ERROR}
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class OpenAIProviderError(Exception):
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"""Exception raised for OpenAI API errors."""
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def __init__(
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self, code: str, message: str, details: Optional[Dict[str, Any]] = None
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):
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self.code = code
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self.message = message
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self.details = details or {}
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super().__init__(message)
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def to_dict(self) -> Dict[str, Any]:
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"""Convert error to dictionary format."""
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result = {
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"error": self.code,
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"message": self.message,
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}
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if self.details:
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result["details"] = self.details
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return result
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DEFAULT_TRANSLATION_PROMPT = """You are a professional translator. Translate the following text from {source_lang} to {target_lang}.
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Rules:
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- Translate ONLY the text, do not add explanations or notes
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- Preserve the original formatting, line breaks, and structure
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- Maintain the original tone and style
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- For technical terms, use the standard translation in the target language
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- If the text contains proper nouns or brand names, keep them unchanged unless there's a well-known translation"""
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def _build_system_prompt(
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source_lang: str, target_lang: str, custom_prompt: Optional[str] = None
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) -> str:
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"""Build system prompt for translation."""
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if custom_prompt:
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return custom_prompt
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return DEFAULT_TRANSLATION_PROMPT.format(
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source_lang=source_lang, target_lang=target_lang
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)
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def _get_language_name(code: str) -> str:
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"""Convert language code to full name for better LLM understanding."""
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language_names = {
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"en": "English",
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"fr": "French",
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"es": "Spanish",
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"de": "German",
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"it": "Italian",
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"pt": "Portuguese",
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"nl": "Dutch",
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"ru": "Russian",
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"zh": "Chinese",
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"ja": "Japanese",
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"ko": "Korean",
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"ar": "Arabic",
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"hi": "Hindi",
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"tr": "Turkish",
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"pl": "Polish",
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"vi": "Vietnamese",
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"th": "Thai",
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"id": "Indonesian",
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"ms": "Malay",
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"uk": "Ukrainian",
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"cs": "Czech",
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"sv": "Swedish",
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"da": "Danish",
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"fi": "Finnish",
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"no": "Norwegian",
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"el": "Greek",
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"he": "Hebrew",
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"ro": "Romanian",
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"hu": "Hungarian",
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"bg": "Bulgarian",
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"sk": "Slovak",
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"hr": "Croatian",
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"sl": "Slovenian",
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"lt": "Lithuanian",
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"lv": "Latvian",
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"et": "Estonian",
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}
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base_code = code.split("-")[0].lower()
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return language_names.get(base_code, code)
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class OpenAITranslationProvider(TranslationProvider):
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"""
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OpenAI LLM implementation for cloud translation.
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Features:
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- Uses OpenAI Chat Completions API
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- Custom system prompt support for translation context
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- Thread-safe HTTP client
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- Robust error handling with specific error codes
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- Retry logic with exponential backoff
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- Configurable timeout (default 60s for cloud API)
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- Health check with result caching
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"""
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def __init__(
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self,
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api_key: str,
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model: str = "gpt-4o-mini",
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timeout: int = 60,
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max_retries: int = 3,
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retry_delay: float = 1.0,
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base_url: str = "https://api.openai.com/v1",
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health_check_timeout: int = 5,
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):
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"""
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Initialize OpenAI provider.
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Args:
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api_key: OpenAI API key
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model: Model name to use (default: gpt-4o-mini)
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timeout: Request timeout in seconds (default: 60)
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max_retries: Maximum retry attempts for transient errors (default: 3)
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retry_delay: Initial retry delay in seconds (default: 1.0)
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base_url: OpenAI API base URL (default: https://api.openai.com/v1)
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health_check_timeout: Timeout for health check requests in seconds (default: 5)
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"""
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if not api_key or not api_key.strip():
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raise ValueError("OpenAI API key cannot be empty")
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self._api_key = api_key
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self._model = model
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self._base_url = base_url.rstrip("/")
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self._provider_name = "openai"
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self._timeout = timeout
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self._max_retries = max_retries
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self._retry_delay = retry_delay
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self._health_check_timeout = health_check_timeout
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self._health_cache: Dict[str, Any] = {}
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self._health_cache_ttl = 60
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self._health_cache_lock = threading.Lock()
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def _make_api_request(self, text: str, system_prompt: str) -> tuple:
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"""
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Make API request to OpenAI.
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Returns:
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Tuple of (translated_content, usage_dict). usage_dict may be empty.
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Raises:
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OpenAIProviderError: For any API errors with specific codes
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"""
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if not text or not text.strip():
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return text, {}
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# Check text length (rough estimate: 1 token ~= 4 chars)
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if len(text) > 16000: # ~4000 tokens
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raise OpenAIProviderError(
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code=OPENAI_CONTEXT_TOO_LONG,
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message="Texte trop long pour le modèle (max ~4000 tokens).",
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details={"text_length": len(text), "max_tokens": 4000},
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)
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url = f"{self._base_url}/chat/completions"
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headers = {
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"Authorization": f"Bearer {self._api_key}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": self._model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": text},
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],
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"temperature": 0.3,
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"max_tokens": 4096,
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}
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try:
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response = requests.post(
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url,
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headers=headers,
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json=payload,
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timeout=self._timeout,
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)
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# Handle specific HTTP status codes
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if response.status_code == 401:
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raise OpenAIProviderError(
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code=OPENAI_INVALID_KEY,
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message="Clé API OpenAI invalide. Vérifiez votre configuration.",
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details={"status_code": 401},
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)
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if response.status_code == 429:
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try:
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error_data = response.json().get("error", {}) or {}
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except Exception:
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error_data = {}
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error_code = error_data.get("code", "")
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# Check for rate limit vs quota exceeded
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if error_code == "insufficient_quota":
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raise OpenAIProviderError(
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code=OPENAI_QUOTA_EXCEEDED,
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message="Quota OpenAI épuisé. Vérifiez votre facturation.",
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details={"status_code": 429, "error_code": error_code},
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)
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else:
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# Rate limit
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retry_after = response.headers.get("retry-after", "20")
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raise OpenAIProviderError(
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code=OPENAI_RATE_LIMITED,
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message=f"Limite de requêtes OpenAI atteinte. Réessayez dans {retry_after}s.",
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details={
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"status_code": 429,
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"retry_after_seconds": int(retry_after)
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if retry_after.isdigit()
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else 20,
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},
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)
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if response.status_code == 400:
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try:
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error_data = response.json().get("error", {}) or {}
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except Exception:
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error_data = {}
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error_code = error_data.get("code", "")
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if error_code == "context_length_exceeded":
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raise OpenAIProviderError(
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code=OPENAI_CONTEXT_TOO_LONG,
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message="Texte trop long pour le modèle (max ~4000 tokens).",
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details={"status_code": 400, "error_code": error_code},
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)
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if response.status_code >= 500:
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raise OpenAIProviderError(
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code=OPENAI_SERVICE_ERROR,
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message="Service OpenAI temporairement indisponible.",
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details={"status_code": response.status_code},
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)
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if response.status_code != 200:
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error_text = response.text[:200] if response.text else "Unknown error"
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raise OpenAIProviderError(
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code=OPENAI_SERVICE_ERROR,
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message=f"Erreur OpenAI: {error_text}",
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details={"status_code": response.status_code},
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)
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data = response.json()
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choices = data.get("choices", [])
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if not choices:
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raise OpenAIProviderError(
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code=OPENAI_SERVICE_ERROR,
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message="Erreur OpenAI: réponse vide",
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details={"response": str(data)[:200]},
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)
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content = choices[0].get("message", {}).get("content", "")
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if not content:
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raise OpenAIProviderError(
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code=OPENAI_SERVICE_ERROR,
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message="Erreur OpenAI: réponse vide",
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details={"response": str(data)[:200]},
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)
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usage = data.get("usage", {})
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return content.strip(), usage
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except Timeout:
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raise OpenAIProviderError(
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code=OPENAI_TIMEOUT,
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message="Délai d'attente OpenAI dépassé. Le service est lent.",
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details={"timeout_seconds": self._timeout},
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)
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except RequestsConnectionError:
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raise OpenAIProviderError(
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code=OPENAI_SERVICE_ERROR,
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message="Service OpenAI temporairement indisponible.",
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details={"error": "Connection failed"},
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)
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except OpenAIProviderError:
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raise
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except Exception as e:
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error_str = str(e).lower()
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if "connection" in error_str or "refused" in error_str:
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raise OpenAIProviderError(
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code=OPENAI_SERVICE_ERROR,
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message="Service OpenAI temporairement indisponible.",
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details={"original_error": str(e)[:100]},
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)
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raise OpenAIProviderError(
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code=OPENAI_SERVICE_ERROR,
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message=f"Erreur OpenAI: {str(e)[:100]}",
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details={"original_error": str(e)[:100]},
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)
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def get_name(self) -> str:
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"""Return provider name."""
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return self._provider_name
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def is_available(self) -> bool:
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"""
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Check if OpenAI API is available.
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Uses cached result if available and not expired.
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"""
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current_time = time.time()
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with self._health_cache_lock:
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if "is_available" in self._health_cache:
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cached = self._health_cache["is_available"]
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if current_time - cached["timestamp"] < self._health_cache_ttl:
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return cached["value"]
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try:
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url = f"{self._base_url}/models"
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headers = {"Authorization": f"Bearer {self._api_key}"}
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response = requests.get(
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url, headers=headers, timeout=self._health_check_timeout
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)
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available = response.status_code == 200
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except Exception as e:
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_log_warning("openai_availability_check_failed", error=str(e)[:100])
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available = False
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with self._health_cache_lock:
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self._health_cache["is_available"] = {
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"value": available,
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"timestamp": current_time,
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}
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return available
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def translate_text(self, request: TranslationRequest) -> TranslationResponse:
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"""
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Translate a single text string using OpenAI LLM.
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Supports custom system prompt via request.metadata["custom_prompt"].
|
||||
|
||||
Args:
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request: TranslationRequest with text and language info
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||||
Returns:
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TranslationResponse with translated text
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"""
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text = request.text
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||||
target_language = request.target_language
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source_language = request.source_language or "auto"
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||||
|
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if not text or not text.strip():
|
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return TranslationResponse(
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translated_text=text,
|
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provider_name=self._provider_name,
|
||||
from_cache=False,
|
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)
|
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|
||||
source_lang_name = _get_language_name(source_language)
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target_lang_name = _get_language_name(target_language)
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||||
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||||
custom_prompt = None
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if request.metadata:
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custom_prompt = request.metadata.get("custom_prompt")
|
||||
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||||
system_prompt = _build_system_prompt(
|
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source_lang_name, target_lang_name, custom_prompt
|
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)
|
||||
|
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last_error: Optional[OpenAIProviderError] = None
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retries = 0
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||||
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while retries <= self._max_retries:
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try:
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start_time = time.time()
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result, usage = self._make_api_request(text, system_prompt)
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latency = time.time() - start_time
|
||||
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log_kw: Dict[str, Any] = {
|
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"chars": len(text),
|
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"source_lang": source_language,
|
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"target_lang": target_language,
|
||||
"model": self._model,
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"latency_ms": round(latency * 1000, 2),
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"retries": retries,
|
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}
|
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if usage and isinstance(usage.get("total_tokens"), (int, float)):
|
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log_kw["tokens_used"] = usage.get("total_tokens")
|
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_log_info("openai_translation_success", **log_kw)
|
||||
|
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return TranslationResponse(
|
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translated_text=result,
|
||||
provider_name=self._provider_name,
|
||||
from_cache=False,
|
||||
source_language=source_language,
|
||||
)
|
||||
|
||||
except OpenAIProviderError as e:
|
||||
last_error = e
|
||||
|
||||
if e.code not in _RETRYABLE_ERRORS:
|
||||
break
|
||||
|
||||
retries += 1
|
||||
if retries <= self._max_retries:
|
||||
delay = self._retry_delay * (2 ** (retries - 1))
|
||||
_log_info(
|
||||
"openai_translation_retry",
|
||||
attempt=retries,
|
||||
delay_s=round(delay, 2),
|
||||
error_code=e.code,
|
||||
text_length=len(text),
|
||||
source_lang=source_language,
|
||||
target_lang=target_language,
|
||||
)
|
||||
time.sleep(delay)
|
||||
|
||||
except Exception as e:
|
||||
last_error = OpenAIProviderError(
|
||||
code=OPENAI_SERVICE_ERROR,
|
||||
message=f"Erreur OpenAI: {str(e)[:100]}",
|
||||
details={"original_error": str(e)[:100]},
|
||||
)
|
||||
retries += 1
|
||||
if retries <= self._max_retries:
|
||||
delay = self._retry_delay * (2 ** (retries - 1))
|
||||
time.sleep(delay)
|
||||
|
||||
if last_error:
|
||||
_log_error(
|
||||
"openai_translation_failed",
|
||||
error_code=last_error.code,
|
||||
text_length=len(text),
|
||||
source_lang=source_language,
|
||||
target_lang=target_language,
|
||||
retries=retries,
|
||||
)
|
||||
return TranslationResponse(
|
||||
translated_text=text,
|
||||
provider_name=self._provider_name,
|
||||
from_cache=False,
|
||||
error=last_error.message,
|
||||
error_code=last_error.code,
|
||||
error_details=last_error.details,
|
||||
)
|
||||
|
||||
return TranslationResponse(
|
||||
translated_text=text,
|
||||
provider_name=self._provider_name,
|
||||
from_cache=False,
|
||||
error="Unknown error",
|
||||
error_code=OPENAI_SERVICE_ERROR,
|
||||
)
|
||||
|
||||
def translate_batch(
|
||||
self, requests: List[TranslationRequest]
|
||||
) -> List[TranslationResponse]:
|
||||
"""
|
||||
Translate multiple texts.
|
||||
|
||||
Args:
|
||||
requests: List of TranslationRequest objects
|
||||
|
||||
Returns:
|
||||
List of TranslationResponse objects
|
||||
"""
|
||||
if not requests:
|
||||
return []
|
||||
|
||||
return [self.translate_text(req) for req in requests]
|
||||
|
||||
def health_check(self) -> ProviderHealthStatus:
|
||||
"""
|
||||
Return health status details for the provider.
|
||||
|
||||
Includes cached result for efficiency.
|
||||
|
||||
Returns:
|
||||
ProviderHealthStatus with availability, latency, and model information
|
||||
"""
|
||||
current_time = time.time()
|
||||
|
||||
with self._health_cache_lock:
|
||||
if "health_check" in self._health_cache:
|
||||
cached = self._health_cache["health_check"]
|
||||
if current_time - cached["timestamp"] < self._health_cache_ttl:
|
||||
return cached["value"]
|
||||
|
||||
start_time = time.time()
|
||||
last_check_iso = datetime.now(timezone.utc).isoformat()
|
||||
|
||||
try:
|
||||
url = f"{self._base_url}/models"
|
||||
headers = {"Authorization": f"Bearer {self._api_key}"}
|
||||
response = requests.get(
|
||||
url, headers=headers, timeout=self._health_check_timeout
|
||||
)
|
||||
latency_ms = (time.time() - start_time) * 1000
|
||||
available = response.status_code == 200
|
||||
|
||||
error_msg = None
|
||||
model_available = None
|
||||
if available:
|
||||
try:
|
||||
models_data = response.json().get("data", [])
|
||||
model_ids = [m.get("id", "") for m in models_data]
|
||||
model_available = self._model in model_ids or any(
|
||||
self._model in mid for mid in model_ids
|
||||
)
|
||||
except Exception:
|
||||
model_available = None
|
||||
else:
|
||||
if response.status_code == 401:
|
||||
error_msg = "Invalid API key"
|
||||
else:
|
||||
error_msg = f"OpenAI API returned {response.status_code}"
|
||||
|
||||
status = ProviderHealthStatus(
|
||||
name=self._provider_name,
|
||||
available=available,
|
||||
latency_ms=round(latency_ms, 2),
|
||||
error=error_msg,
|
||||
last_check=last_check_iso,
|
||||
model=self._model,
|
||||
model_available=model_available,
|
||||
)
|
||||
except Exception as e:
|
||||
latency_ms = (time.time() - start_time) * 1000
|
||||
status = ProviderHealthStatus(
|
||||
name=self._provider_name,
|
||||
available=False,
|
||||
latency_ms=round(latency_ms, 2),
|
||||
error=str(e)[:100],
|
||||
last_check=last_check_iso,
|
||||
model=self._model,
|
||||
model_available=False,
|
||||
)
|
||||
|
||||
with self._health_cache_lock:
|
||||
self._health_cache["health_check"] = {
|
||||
"value": status,
|
||||
"timestamp": current_time,
|
||||
}
|
||||
|
||||
return status
|
||||
|
||||
|
||||
def register_openai_provider():
|
||||
"""
|
||||
Register the OpenAI provider in the global registry.
|
||||
|
||||
This function should be called during module initialization
|
||||
to make the provider available through the registry.
|
||||
"""
|
||||
from .registry import registry
|
||||
|
||||
provider = get_openai_provider()
|
||||
registry.register("openai", provider)
|
||||
return provider
|
||||
|
||||
|
||||
_provider_instance: Optional[OpenAITranslationProvider] = None
|
||||
_provider_lock = threading.Lock()
|
||||
|
||||
|
||||
def get_openai_provider() -> OpenAITranslationProvider:
|
||||
"""Get or create the OpenAI provider instance (reads config from env)."""
|
||||
global _provider_instance
|
||||
if _provider_instance is None:
|
||||
with _provider_lock:
|
||||
if _provider_instance is None:
|
||||
from .config import ProvidersConfig
|
||||
|
||||
_provider_instance = OpenAITranslationProvider(
|
||||
api_key=ProvidersConfig.OPENAI_API_KEY,
|
||||
model=ProvidersConfig.OPENAI_MODEL,
|
||||
timeout=ProvidersConfig.OPENAI_TIMEOUT,
|
||||
max_retries=ProvidersConfig.OPENAI_MAX_RETRIES,
|
||||
retry_delay=ProvidersConfig.OPENAI_RETRY_DELAY,
|
||||
base_url=ProvidersConfig.OPENAI_BASE_URL,
|
||||
health_check_timeout=ProvidersConfig.OPENAI_HEALTH_CHECK_TIMEOUT,
|
||||
)
|
||||
return _provider_instance
|
||||
|
||||
|
||||
def reset_openai_provider() -> None:
|
||||
"""Reset the OpenAI provider singleton (useful when config changes)."""
|
||||
global _provider_instance
|
||||
with _provider_lock:
|
||||
_provider_instance = None
|
||||
Reference in New Issue
Block a user