f9dd55cfd3
- Add missing base64 import in providers/base.py - Remove unused base64 import from providers/openai_compatible.py - All tests now pass (19/19 image validation tests) - Code quality checks pass 100%
875 lines
35 KiB
Python
875 lines
35 KiB
Python
"""Base class for OpenAI-compatible API providers."""
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import copy
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import ipaddress
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import logging
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import os
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import time
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from abc import abstractmethod
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from typing import Optional
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from urllib.parse import urlparse
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from openai import OpenAI
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from .base import (
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ModelCapabilities,
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ModelProvider,
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ModelResponse,
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ProviderType,
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)
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class OpenAICompatibleProvider(ModelProvider):
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"""Base class for any provider using an OpenAI-compatible API.
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This includes:
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- Direct OpenAI API
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- OpenRouter
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- Any other OpenAI-compatible endpoint
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"""
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DEFAULT_HEADERS = {}
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FRIENDLY_NAME = "OpenAI Compatible"
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def __init__(self, api_key: str, base_url: str = None, **kwargs):
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"""Initialize the provider with API key and optional base URL.
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Args:
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api_key: API key for authentication
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base_url: Base URL for the API endpoint
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**kwargs: Additional configuration options including timeout
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"""
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super().__init__(api_key, **kwargs)
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self._client = None
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self.base_url = base_url
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self.organization = kwargs.get("organization")
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self.allowed_models = self._parse_allowed_models()
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# Configure timeouts - especially important for custom/local endpoints
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self.timeout_config = self._configure_timeouts(**kwargs)
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# Validate base URL for security
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if self.base_url:
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self._validate_base_url()
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# Warn if using external URL without authentication
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if self.base_url and not self._is_localhost_url() and not api_key:
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logging.warning(
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f"Using external URL '{self.base_url}' without API key. "
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"This may be insecure. Consider setting an API key for authentication."
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)
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def _parse_allowed_models(self) -> Optional[set[str]]:
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"""Parse allowed models from environment variable.
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Returns:
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Set of allowed model names (lowercase) or None if not configured
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"""
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# Get provider-specific allowed models
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provider_type = self.get_provider_type().value.upper()
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env_var = f"{provider_type}_ALLOWED_MODELS"
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models_str = os.getenv(env_var, "")
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if models_str:
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# Parse and normalize to lowercase for case-insensitive comparison
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models = {m.strip().lower() for m in models_str.split(",") if m.strip()}
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if models:
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logging.info(f"Configured allowed models for {self.FRIENDLY_NAME}: {sorted(models)}")
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return models
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# Log info if no allow-list configured for proxy providers
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if self.get_provider_type() not in [ProviderType.GOOGLE, ProviderType.OPENAI]:
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logging.info(
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f"Model allow-list not configured for {self.FRIENDLY_NAME} - all models permitted. "
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f"To restrict access, set {env_var} with comma-separated model names."
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)
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return None
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def _configure_timeouts(self, **kwargs):
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"""Configure timeout settings based on provider type and custom settings.
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Custom URLs and local models often need longer timeouts due to:
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- Network latency on local networks
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- Extended thinking models taking longer to respond
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- Local inference being slower than cloud APIs
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Returns:
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httpx.Timeout object with appropriate timeout settings
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"""
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import httpx
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# Default timeouts - more generous for custom/local endpoints
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default_connect = 30.0 # 30 seconds for connection (vs OpenAI's 5s)
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default_read = 600.0 # 10 minutes for reading (same as OpenAI default)
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default_write = 600.0 # 10 minutes for writing
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default_pool = 600.0 # 10 minutes for pool
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# For custom/local URLs, use even longer timeouts
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if self.base_url and self._is_localhost_url():
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default_connect = 60.0 # 1 minute for local connections
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default_read = 1800.0 # 30 minutes for local models (extended thinking)
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default_write = 1800.0 # 30 minutes for local models
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default_pool = 1800.0 # 30 minutes for local models
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logging.info(f"Using extended timeouts for local endpoint: {self.base_url}")
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elif self.base_url:
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default_connect = 45.0 # 45 seconds for custom remote endpoints
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default_read = 900.0 # 15 minutes for custom remote endpoints
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default_write = 900.0 # 15 minutes for custom remote endpoints
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default_pool = 900.0 # 15 minutes for custom remote endpoints
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logging.info(f"Using extended timeouts for custom endpoint: {self.base_url}")
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# Allow override via kwargs or environment variables in future, for now...
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connect_timeout = kwargs.get("connect_timeout", float(os.getenv("CUSTOM_CONNECT_TIMEOUT", default_connect)))
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read_timeout = kwargs.get("read_timeout", float(os.getenv("CUSTOM_READ_TIMEOUT", default_read)))
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write_timeout = kwargs.get("write_timeout", float(os.getenv("CUSTOM_WRITE_TIMEOUT", default_write)))
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pool_timeout = kwargs.get("pool_timeout", float(os.getenv("CUSTOM_POOL_TIMEOUT", default_pool)))
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timeout = httpx.Timeout(connect=connect_timeout, read=read_timeout, write=write_timeout, pool=pool_timeout)
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logging.debug(
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f"Configured timeouts - Connect: {connect_timeout}s, Read: {read_timeout}s, "
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f"Write: {write_timeout}s, Pool: {pool_timeout}s"
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)
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return timeout
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def _is_localhost_url(self) -> bool:
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"""Check if the base URL points to localhost or local network.
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Returns:
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True if URL is localhost or local network, False otherwise
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"""
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if not self.base_url:
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return False
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try:
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parsed = urlparse(self.base_url)
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hostname = parsed.hostname
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# Check for common localhost patterns
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if hostname in ["localhost", "127.0.0.1", "::1"]:
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return True
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# Check for private network ranges (local network)
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if hostname:
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try:
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ip = ipaddress.ip_address(hostname)
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return ip.is_private or ip.is_loopback
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except ValueError:
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# Not an IP address, might be a hostname
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pass
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return False
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except Exception:
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return False
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def _validate_base_url(self) -> None:
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"""Validate base URL for security (SSRF protection).
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Raises:
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ValueError: If URL is invalid or potentially unsafe
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"""
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if not self.base_url:
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return
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try:
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parsed = urlparse(self.base_url)
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# Check URL scheme - only allow http/https
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if parsed.scheme not in ("http", "https"):
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raise ValueError(f"Invalid URL scheme: {parsed.scheme}. Only http/https allowed.")
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# Check hostname exists
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if not parsed.hostname:
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raise ValueError("URL must include a hostname")
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# Check port is valid (if specified)
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port = parsed.port
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if port is not None and (port < 1 or port > 65535):
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raise ValueError(f"Invalid port number: {port}. Must be between 1 and 65535.")
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except Exception as e:
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if isinstance(e, ValueError):
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raise
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raise ValueError(f"Invalid base URL '{self.base_url}': {str(e)}")
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@property
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def client(self):
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"""Lazy initialization of OpenAI client with security checks and timeout configuration."""
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if self._client is None:
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import os
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import httpx
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# Temporarily disable proxy environment variables to prevent httpx from detecting them
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original_env = {}
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proxy_env_vars = ["HTTP_PROXY", "HTTPS_PROXY", "ALL_PROXY", "http_proxy", "https_proxy", "all_proxy"]
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for var in proxy_env_vars:
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if var in os.environ:
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original_env[var] = os.environ[var]
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del os.environ[var]
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try:
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# Create a custom httpx client that explicitly avoids proxy parameters
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timeout_config = (
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self.timeout_config
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if hasattr(self, "timeout_config") and self.timeout_config
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else httpx.Timeout(30.0)
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)
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# Create httpx client with minimal config to avoid proxy conflicts
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# Note: proxies parameter was removed in httpx 0.28.0
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# Check for test transport injection
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if hasattr(self, "_test_transport"):
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# Use custom transport for testing (HTTP recording/replay)
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http_client = httpx.Client(
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transport=self._test_transport,
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timeout=timeout_config,
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follow_redirects=True,
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)
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else:
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# Normal production client
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http_client = httpx.Client(
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timeout=timeout_config,
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follow_redirects=True,
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)
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# Keep client initialization minimal to avoid proxy parameter conflicts
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client_kwargs = {
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"api_key": self.api_key,
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"http_client": http_client,
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}
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if self.base_url:
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client_kwargs["base_url"] = self.base_url
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if self.organization:
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client_kwargs["organization"] = self.organization
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# Add default headers if any
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if self.DEFAULT_HEADERS:
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client_kwargs["default_headers"] = self.DEFAULT_HEADERS.copy()
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logging.debug(f"OpenAI client initialized with custom httpx client and timeout: {timeout_config}")
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# Create OpenAI client with custom httpx client
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self._client = OpenAI(**client_kwargs)
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except Exception as e:
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# If all else fails, try absolute minimal client without custom httpx
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logging.warning(f"Failed to create client with custom httpx, falling back to minimal config: {e}")
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try:
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minimal_kwargs = {"api_key": self.api_key}
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if self.base_url:
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minimal_kwargs["base_url"] = self.base_url
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self._client = OpenAI(**minimal_kwargs)
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except Exception as fallback_error:
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logging.error(f"Even minimal OpenAI client creation failed: {fallback_error}")
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raise
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finally:
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# Restore original proxy environment variables
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for var, value in original_env.items():
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os.environ[var] = value
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return self._client
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def _sanitize_for_logging(self, params: dict) -> dict:
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"""Sanitize sensitive data from parameters before logging.
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Args:
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params: Dictionary of API parameters
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Returns:
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dict: Sanitized copy of parameters safe for logging
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"""
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sanitized = copy.deepcopy(params)
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# Sanitize messages content
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if "input" in sanitized:
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for msg in sanitized.get("input", []):
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if isinstance(msg, dict) and "content" in msg:
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for content_item in msg.get("content", []):
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if isinstance(content_item, dict) and "text" in content_item:
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# Truncate long text and add ellipsis
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text = content_item["text"]
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if len(text) > 100:
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content_item["text"] = text[:100] + "... [truncated]"
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# Remove any API keys that might be in headers/auth
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sanitized.pop("api_key", None)
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sanitized.pop("authorization", None)
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return sanitized
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def _safe_extract_output_text(self, response) -> str:
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"""Safely extract output_text from o3-pro response with validation.
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Args:
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response: Response object from OpenAI SDK
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Returns:
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str: The output text content
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Raises:
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ValueError: If output_text is missing, None, or not a string
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"""
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logging.debug(f"Response object type: {type(response)}")
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logging.debug(f"Response attributes: {dir(response)}")
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if not hasattr(response, "output_text"):
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raise ValueError(f"o3-pro response missing output_text field. Response type: {type(response).__name__}")
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content = response.output_text
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logging.debug(f"Extracted output_text: '{content}' (type: {type(content)})")
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if content is None:
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raise ValueError("o3-pro returned None for output_text")
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if not isinstance(content, str):
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raise ValueError(f"o3-pro output_text is not a string. Got type: {type(content).__name__}")
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return content
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def _generate_with_responses_endpoint(
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self,
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model_name: str,
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messages: list,
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temperature: float,
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max_output_tokens: Optional[int] = None,
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**kwargs,
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) -> ModelResponse:
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"""Generate content using the /v1/responses endpoint for o3-pro via OpenAI library."""
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# Convert messages to the correct format for responses endpoint
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input_messages = []
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for message in messages:
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role = message.get("role", "")
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content = message.get("content", "")
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if role == "system":
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# For o3-pro, system messages should be handled carefully to avoid policy violations
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# Instead of prefixing with "System:", we'll include the system content naturally
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input_messages.append({"role": "user", "content": [{"type": "input_text", "text": content}]})
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elif role == "user":
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input_messages.append({"role": "user", "content": [{"type": "input_text", "text": content}]})
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elif role == "assistant":
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input_messages.append({"role": "assistant", "content": [{"type": "output_text", "text": content}]})
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# Prepare completion parameters for responses endpoint
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# Based on OpenAI documentation, use nested reasoning object for responses endpoint
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completion_params = {
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"model": model_name,
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"input": input_messages,
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"reasoning": {"effort": "medium"}, # Use nested object for responses endpoint
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"store": True,
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}
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# Add max tokens if specified (using max_completion_tokens for responses endpoint)
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if max_output_tokens:
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completion_params["max_completion_tokens"] = max_output_tokens
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# For responses endpoint, we only add parameters that are explicitly supported
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# Remove unsupported chat completion parameters that may cause API errors
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# Retry logic with progressive delays
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max_retries = 4
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retry_delays = [1, 3, 5, 8]
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last_exception = None
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actual_attempts = 0
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for attempt in range(max_retries):
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try: # Log sanitized payload for debugging
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import json
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sanitized_params = self._sanitize_for_logging(completion_params)
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logging.info(
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f"o3-pro API request (sanitized): {json.dumps(sanitized_params, indent=2, ensure_ascii=False)}"
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)
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# Use OpenAI client's responses endpoint
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response = self.client.responses.create(**completion_params)
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# Extract content from responses endpoint format
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# Use validation helper to safely extract output_text
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content = self._safe_extract_output_text(response)
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# Try to extract usage information
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usage = None
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if hasattr(response, "usage"):
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usage = self._extract_usage(response)
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elif hasattr(response, "input_tokens") and hasattr(response, "output_tokens"):
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# Safely extract token counts with None handling
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input_tokens = getattr(response, "input_tokens", 0) or 0
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output_tokens = getattr(response, "output_tokens", 0) or 0
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usage = {
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"input_tokens": input_tokens,
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"output_tokens": output_tokens,
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"total_tokens": input_tokens + output_tokens,
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}
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return ModelResponse(
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content=content,
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usage=usage,
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model_name=model_name,
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friendly_name=self.FRIENDLY_NAME,
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provider=self.get_provider_type(),
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metadata={
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"model": getattr(response, "model", model_name),
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"id": getattr(response, "id", ""),
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"created": getattr(response, "created_at", 0),
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"endpoint": "responses",
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},
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)
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except Exception as e:
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last_exception = e
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# Check if this is a retryable error using structured error codes
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is_retryable = self._is_error_retryable(e)
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if is_retryable and attempt < max_retries - 1:
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delay = retry_delays[attempt]
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logging.warning(
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f"Retryable error for o3-pro responses endpoint, attempt {actual_attempts}/{max_retries}: {str(e)}. Retrying in {delay}s..."
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)
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time.sleep(delay)
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else:
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break
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# If we get here, all retries failed
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error_msg = f"o3-pro responses endpoint error after {actual_attempts} attempt{'s' if actual_attempts > 1 else ''}: {str(last_exception)}"
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logging.error(error_msg)
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raise RuntimeError(error_msg) from last_exception
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def generate_content(
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self,
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prompt: str,
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model_name: str,
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system_prompt: Optional[str] = None,
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temperature: float = 0.3,
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max_output_tokens: Optional[int] = None,
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images: Optional[list[str]] = None,
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**kwargs,
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) -> ModelResponse:
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"""Generate content using the OpenAI-compatible API.
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Args:
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prompt: User prompt to send to the model
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model_name: Name of the model to use
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system_prompt: Optional system prompt for model behavior
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temperature: Sampling temperature
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max_output_tokens: Maximum tokens to generate
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**kwargs: Additional provider-specific parameters
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Returns:
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ModelResponse with generated content and metadata
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"""
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# Validate model name against allow-list
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if not self.validate_model_name(model_name):
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raise ValueError(f"Model '{model_name}' not in allowed models list. Allowed models: {self.allowed_models}")
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# Get effective temperature for this model
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effective_temperature = self.get_effective_temperature(model_name, temperature)
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# Only validate if temperature is not None (meaning the model supports it)
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if effective_temperature is not None:
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# Validate parameters with the effective temperature
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self.validate_parameters(model_name, effective_temperature)
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# Prepare messages
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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# Prepare user message with text and potentially images
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user_content = []
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user_content.append({"type": "text", "text": prompt})
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# Add images if provided and model supports vision
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if images and self._supports_vision(model_name):
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for image_path in images:
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try:
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image_content = self._process_image(image_path)
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if image_content:
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user_content.append(image_content)
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except Exception as e:
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logging.warning(f"Failed to process image {image_path}: {e}")
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# Continue with other images and text
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continue
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elif images and not self._supports_vision(model_name):
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logging.warning(f"Model {model_name} does not support images, ignoring {len(images)} image(s)")
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# Add user message
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if len(user_content) == 1:
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# Only text content, use simple string format for compatibility
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messages.append({"role": "user", "content": prompt})
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else:
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# Text + images, use content array format
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messages.append({"role": "user", "content": user_content})
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# Prepare completion parameters
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completion_params = {
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"model": model_name,
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"messages": messages,
|
|
}
|
|
|
|
# Check model capabilities once to determine parameter support
|
|
resolved_model = self._resolve_model_name(model_name)
|
|
|
|
# Use the effective temperature we calculated earlier
|
|
if effective_temperature is not None:
|
|
completion_params["temperature"] = effective_temperature
|
|
supports_temperature = True
|
|
else:
|
|
# Model doesn't support temperature
|
|
supports_temperature = False
|
|
|
|
# Add max tokens if specified and model supports it
|
|
# O3/O4 models that don't support temperature also don't support max_tokens
|
|
if max_output_tokens and supports_temperature:
|
|
completion_params["max_tokens"] = max_output_tokens
|
|
|
|
# Add any additional OpenAI-specific parameters
|
|
# Use capabilities to filter parameters for reasoning models
|
|
for key, value in kwargs.items():
|
|
if key in ["top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "stream"]:
|
|
# Reasoning models (those that don't support temperature) also don't support these parameters
|
|
if not supports_temperature and key in ["top_p", "frequency_penalty", "presence_penalty"]:
|
|
continue # Skip unsupported parameters for reasoning models
|
|
completion_params[key] = value
|
|
|
|
# Check if this is o3-pro and needs the responses endpoint
|
|
if resolved_model == "o3-pro":
|
|
# This model requires the /v1/responses endpoint
|
|
# If it fails, we should not fall back to chat/completions
|
|
return self._generate_with_responses_endpoint(
|
|
model_name=resolved_model,
|
|
messages=messages,
|
|
temperature=temperature,
|
|
max_output_tokens=max_output_tokens,
|
|
**kwargs,
|
|
)
|
|
|
|
# Retry logic with progressive delays
|
|
max_retries = 4 # Total of 4 attempts
|
|
retry_delays = [1, 3, 5, 8] # Progressive delays: 1s, 3s, 5s, 8s
|
|
|
|
last_exception = None
|
|
actual_attempts = 0
|
|
|
|
for attempt in range(max_retries):
|
|
actual_attempts = attempt + 1 # Convert from 0-based index to human-readable count
|
|
try:
|
|
# Generate completion
|
|
response = self.client.chat.completions.create(**completion_params)
|
|
|
|
# Extract content and usage
|
|
content = response.choices[0].message.content
|
|
usage = self._extract_usage(response)
|
|
|
|
return ModelResponse(
|
|
content=content,
|
|
usage=usage,
|
|
model_name=model_name,
|
|
friendly_name=self.FRIENDLY_NAME,
|
|
provider=self.get_provider_type(),
|
|
metadata={
|
|
"finish_reason": response.choices[0].finish_reason,
|
|
"model": response.model, # Actual model used
|
|
"id": response.id,
|
|
"created": response.created,
|
|
},
|
|
)
|
|
|
|
except Exception as e:
|
|
last_exception = e
|
|
|
|
# Check if this is a retryable error using structured error codes
|
|
is_retryable = self._is_error_retryable(e)
|
|
|
|
# If this is the last attempt or not retryable, give up
|
|
if attempt == max_retries - 1 or not is_retryable:
|
|
break
|
|
|
|
# Get progressive delay
|
|
delay = retry_delays[attempt]
|
|
|
|
# Log retry attempt
|
|
logging.warning(
|
|
f"{self.FRIENDLY_NAME} error for model {model_name}, attempt {actual_attempts}/{max_retries}: {str(e)}. Retrying in {delay}s..."
|
|
)
|
|
time.sleep(delay)
|
|
|
|
# If we get here, all retries failed
|
|
error_msg = f"{self.FRIENDLY_NAME} API error for model {model_name} after {actual_attempts} attempt{'s' if actual_attempts > 1 else ''}: {str(last_exception)}"
|
|
logging.error(error_msg)
|
|
raise RuntimeError(error_msg) from last_exception
|
|
|
|
def count_tokens(self, text: str, model_name: str) -> int:
|
|
"""Count tokens for the given text.
|
|
|
|
Uses a layered approach:
|
|
1. Try provider-specific token counting endpoint
|
|
2. Try tiktoken for known model families
|
|
3. Fall back to character-based estimation
|
|
|
|
Args:
|
|
text: Text to count tokens for
|
|
model_name: Model name for tokenizer selection
|
|
|
|
Returns:
|
|
Estimated token count
|
|
"""
|
|
# 1. Check if provider has a remote token counting endpoint
|
|
if hasattr(self, "count_tokens_remote"):
|
|
try:
|
|
return self.count_tokens_remote(text, model_name)
|
|
except Exception as e:
|
|
logging.debug(f"Remote token counting failed: {e}")
|
|
|
|
# 2. Try tiktoken for known models
|
|
try:
|
|
import tiktoken
|
|
|
|
# Try to get encoding for the specific model
|
|
try:
|
|
encoding = tiktoken.encoding_for_model(model_name)
|
|
except KeyError:
|
|
encoding = tiktoken.get_encoding("cl100k_base")
|
|
|
|
return len(encoding.encode(text))
|
|
|
|
except (ImportError, Exception) as e:
|
|
logging.debug(f"Tiktoken not available or failed: {e}")
|
|
|
|
# 3. Fall back to character-based estimation
|
|
logging.warning(
|
|
f"No specific tokenizer available for '{model_name}'. "
|
|
"Using character-based estimation (~4 chars per token)."
|
|
)
|
|
return len(text) // 4
|
|
|
|
def validate_parameters(self, model_name: str, temperature: float, **kwargs) -> None:
|
|
"""Validate model parameters.
|
|
|
|
For proxy providers, this may use generic capabilities.
|
|
|
|
Args:
|
|
model_name: Model to validate for
|
|
temperature: Temperature to validate
|
|
**kwargs: Additional parameters to validate
|
|
"""
|
|
try:
|
|
capabilities = self.get_capabilities(model_name)
|
|
|
|
# Check if we're using generic capabilities
|
|
if hasattr(capabilities, "_is_generic"):
|
|
logging.debug(
|
|
f"Using generic parameter validation for {model_name}. Actual model constraints may differ."
|
|
)
|
|
|
|
# Validate temperature using parent class method
|
|
super().validate_parameters(model_name, temperature, **kwargs)
|
|
|
|
except Exception as e:
|
|
# For proxy providers, we might not have accurate capabilities
|
|
# Log warning but don't fail
|
|
logging.warning(f"Parameter validation limited for {model_name}: {e}")
|
|
|
|
def _extract_usage(self, response) -> dict[str, int]:
|
|
"""Extract token usage from OpenAI response.
|
|
|
|
Args:
|
|
response: OpenAI API response object
|
|
|
|
Returns:
|
|
Dictionary with usage statistics
|
|
"""
|
|
usage = {}
|
|
|
|
if hasattr(response, "usage") and response.usage:
|
|
# Safely extract token counts with None handling
|
|
usage["input_tokens"] = getattr(response.usage, "prompt_tokens", 0) or 0
|
|
usage["output_tokens"] = getattr(response.usage, "completion_tokens", 0) or 0
|
|
usage["total_tokens"] = getattr(response.usage, "total_tokens", 0) or 0
|
|
|
|
return usage
|
|
|
|
@abstractmethod
|
|
def get_capabilities(self, model_name: str) -> ModelCapabilities:
|
|
"""Get capabilities for a specific model.
|
|
|
|
Must be implemented by subclasses.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def get_provider_type(self) -> ProviderType:
|
|
"""Get the provider type.
|
|
|
|
Must be implemented by subclasses.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def validate_model_name(self, model_name: str) -> bool:
|
|
"""Validate if the model name is supported.
|
|
|
|
Must be implemented by subclasses.
|
|
"""
|
|
pass
|
|
|
|
def supports_thinking_mode(self, model_name: str) -> bool:
|
|
"""Check if the model supports extended thinking mode.
|
|
|
|
Default is False for OpenAI-compatible providers.
|
|
"""
|
|
return False
|
|
|
|
def _supports_vision(self, model_name: str) -> bool:
|
|
"""Check if the model supports vision (image processing).
|
|
|
|
Default implementation for OpenAI-compatible providers.
|
|
Subclasses should override with specific model support.
|
|
"""
|
|
# Common vision-capable models - only include models that actually support images
|
|
vision_models = {
|
|
"gpt-5",
|
|
"gpt-5-mini",
|
|
"gpt-4o",
|
|
"gpt-4o-mini",
|
|
"gpt-4-turbo",
|
|
"gpt-4-vision-preview",
|
|
"gpt-4.1-2025-04-14",
|
|
"o3",
|
|
"o3-mini",
|
|
"o3-pro",
|
|
"o4-mini",
|
|
# Note: Claude models would be handled by a separate provider
|
|
}
|
|
supports = model_name.lower() in vision_models
|
|
logging.debug(f"Model '{model_name}' vision support: {supports}")
|
|
return supports
|
|
|
|
def _is_error_retryable(self, error: Exception) -> bool:
|
|
"""Determine if an error should be retried based on structured error codes.
|
|
|
|
Uses OpenAI API error structure instead of text pattern matching for reliability.
|
|
|
|
Args:
|
|
error: Exception from OpenAI API call
|
|
|
|
Returns:
|
|
True if error should be retried, False otherwise
|
|
"""
|
|
error_str = str(error).lower()
|
|
|
|
# Check for 429 errors first - these need special handling
|
|
if "429" in error_str:
|
|
# Try to extract structured error information
|
|
error_type = None
|
|
error_code = None
|
|
|
|
# Parse structured error from OpenAI API response
|
|
# Format: "Error code: 429 - {'error': {'type': 'tokens', 'code': 'rate_limit_exceeded', ...}}"
|
|
try:
|
|
import ast
|
|
import json
|
|
import re
|
|
|
|
# Extract JSON part from error string using regex
|
|
# Look for pattern: {...} (from first { to last })
|
|
json_match = re.search(r"\{.*\}", str(error))
|
|
if json_match:
|
|
json_like_str = json_match.group(0)
|
|
|
|
# First try: parse as Python literal (handles single quotes safely)
|
|
try:
|
|
error_data = ast.literal_eval(json_like_str)
|
|
except (ValueError, SyntaxError):
|
|
# Fallback: try JSON parsing with simple quote replacement
|
|
# (for cases where it's already valid JSON or simple replacements work)
|
|
json_str = json_like_str.replace("'", '"')
|
|
error_data = json.loads(json_str)
|
|
|
|
if "error" in error_data:
|
|
error_info = error_data["error"]
|
|
error_type = error_info.get("type")
|
|
error_code = error_info.get("code")
|
|
|
|
except (json.JSONDecodeError, ValueError, SyntaxError, AttributeError):
|
|
# Fall back to checking hasattr for OpenAI SDK exception objects
|
|
if hasattr(error, "response") and hasattr(error.response, "json"):
|
|
try:
|
|
response_data = error.response.json()
|
|
if "error" in response_data:
|
|
error_info = response_data["error"]
|
|
error_type = error_info.get("type")
|
|
error_code = error_info.get("code")
|
|
except Exception:
|
|
pass
|
|
|
|
# Determine if 429 is retryable based on structured error codes
|
|
if error_type == "tokens":
|
|
# Token-related 429s are typically non-retryable (request too large)
|
|
logging.debug(f"Non-retryable 429: token-related error (type={error_type}, code={error_code})")
|
|
return False
|
|
elif error_code in ["invalid_request_error", "context_length_exceeded"]:
|
|
# These are permanent failures
|
|
logging.debug(f"Non-retryable 429: permanent failure (type={error_type}, code={error_code})")
|
|
return False
|
|
else:
|
|
# Other 429s (like requests per minute) are retryable
|
|
logging.debug(f"Retryable 429: rate limiting (type={error_type}, code={error_code})")
|
|
return True
|
|
|
|
# For non-429 errors, check if they're retryable
|
|
retryable_indicators = [
|
|
"timeout",
|
|
"connection",
|
|
"network",
|
|
"temporary",
|
|
"unavailable",
|
|
"retry",
|
|
"408", # Request timeout
|
|
"500", # Internal server error
|
|
"502", # Bad gateway
|
|
"503", # Service unavailable
|
|
"504", # Gateway timeout
|
|
"ssl", # SSL errors
|
|
"handshake", # Handshake failures
|
|
]
|
|
|
|
return any(indicator in error_str for indicator in retryable_indicators)
|
|
|
|
def _process_image(self, image_path: str) -> Optional[dict]:
|
|
"""Process an image for OpenAI-compatible API."""
|
|
try:
|
|
if image_path.startswith("data:"):
|
|
# Validate the data URL
|
|
self.validate_image(image_path)
|
|
# Handle data URL: data:image/png;base64,iVBORw0...
|
|
return {"type": "image_url", "image_url": {"url": image_path}}
|
|
else:
|
|
# Use base class validation
|
|
image_bytes, mime_type = self.validate_image(image_path)
|
|
|
|
# Read and encode the image
|
|
import base64
|
|
|
|
image_data = base64.b64encode(image_bytes).decode()
|
|
logging.debug(f"Processing image '{image_path}' as MIME type '{mime_type}'")
|
|
|
|
# Create data URL for OpenAI API
|
|
data_url = f"data:{mime_type};base64,{image_data}"
|
|
|
|
return {"type": "image_url", "image_url": {"url": data_url}}
|
|
|
|
except ValueError as e:
|
|
logging.warning(str(e))
|
|
return None
|
|
except Exception as e:
|
|
logging.error(f"Error processing image {image_path}: {e}")
|
|
return None
|