417 lines
15 KiB
Python
417 lines
15 KiB
Python
"""Base class for OpenAI-compatible API providers."""
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import logging
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import os
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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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import ipaddress
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import socket
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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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RangeTemperatureConstraint,
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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
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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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# 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 = set(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 warning 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.warning(
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f"No model allow-list configured for {self.FRIENDLY_NAME}. "
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f"Set {env_var} to restrict model access and control costs."
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)
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return None
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def _is_localhost_url(self) -> bool:
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"""Check if the base URL points to localhost.
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Returns:
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True if URL is localhost, 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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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 - allow only standard HTTP/HTTPS ports
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port = parsed.port
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if port is None:
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port = 443 if parsed.scheme == 'https' else 80
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# Allow common HTTP ports and some alternative ports
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allowed_ports = {80, 443, 8080, 8443, 4000, 3000} # Common API ports
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if port not in allowed_ports:
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raise ValueError(
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f"Port {port} not allowed. Allowed ports: {sorted(allowed_ports)}"
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)
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# Check against allowed domains if configured
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allowed_domains = os.getenv("ALLOWED_BASE_DOMAINS", "").split(",")
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allowed_domains = [d.strip().lower() for d in allowed_domains if d.strip()]
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if allowed_domains:
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hostname_lower = parsed.hostname.lower()
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if not any(
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hostname_lower == domain or
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hostname_lower.endswith('.' + domain)
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for domain in allowed_domains
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):
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raise ValueError(
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f"Domain not in allow-list: {parsed.hostname}. "
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f"Allowed domains: {allowed_domains}"
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)
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# Try to resolve hostname and check if it's a private IP
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# Skip for localhost addresses which are commonly used for development
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if parsed.hostname not in ['localhost', '127.0.0.1', '::1']:
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try:
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# Get all IP addresses for the hostname
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addr_info = socket.getaddrinfo(parsed.hostname, port, proto=socket.IPPROTO_TCP)
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for family, _, _, _, sockaddr in addr_info:
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ip_str = sockaddr[0]
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try:
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ip = ipaddress.ip_address(ip_str)
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# Check for dangerous IP ranges
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if (ip.is_private or ip.is_loopback or ip.is_link_local or
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ip.is_multicast or ip.is_reserved or ip.is_unspecified):
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raise ValueError(
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f"URL resolves to restricted IP address: {ip_str}. "
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"This could be a security risk (SSRF)."
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)
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except ValueError as ve:
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# Invalid IP address format or restricted IP - re-raise if it's our security error
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if "restricted IP address" in str(ve):
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raise
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continue
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except socket.gaierror as e:
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# If we can't resolve the hostname, it's suspicious
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raise ValueError(f"Cannot resolve hostname '{parsed.hostname}': {e}")
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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."""
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if self._client is None:
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client_kwargs = {
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"api_key": self.api_key,
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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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self._client = OpenAI(**client_kwargs)
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return self._client
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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 = 0.7,
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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 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(
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f"Model '{model_name}' not in allowed models list. "
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f"Allowed models: {self.allowed_models}"
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)
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# Validate parameters
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self.validate_parameters(model_name, 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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messages.append({"role": "user", "content": prompt})
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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,
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"temperature": temperature,
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}
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# Add max tokens if specified
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if max_output_tokens:
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completion_params["max_tokens"] = max_output_tokens
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# Add any additional OpenAI-specific parameters
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for key, value in kwargs.items():
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if key in ["top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "stream"]:
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completion_params[key] = value
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try:
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# Generate completion
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response = self.client.chat.completions.create(**completion_params)
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# Extract content and usage
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content = response.choices[0].message.content
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usage = self._extract_usage(response)
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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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"finish_reason": response.choices[0].finish_reason,
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"model": response.model, # Actual model used
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"id": response.id,
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"created": response.created,
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},
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)
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except Exception as e:
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# Log error and re-raise with more context
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error_msg = f"{self.FRIENDLY_NAME} API error for model {model_name}: {str(e)}"
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logging.error(error_msg)
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raise RuntimeError(error_msg) from e
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def count_tokens(self, text: str, model_name: str) -> int:
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"""Count tokens for the given text.
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Uses a layered approach:
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1. Try provider-specific token counting endpoint
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2. Try tiktoken for known model families
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3. Fall back to character-based estimation
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Args:
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text: Text to count tokens for
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model_name: Model name for tokenizer selection
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Returns:
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Estimated token count
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"""
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# 1. Check if provider has a remote token counting endpoint
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if hasattr(self, 'count_tokens_remote'):
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try:
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return self.count_tokens_remote(text, model_name)
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except Exception as e:
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logging.debug(f"Remote token counting failed: {e}")
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# 2. Try tiktoken for known models
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try:
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import tiktoken
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# Try to get encoding for the specific model
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try:
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encoding = tiktoken.encoding_for_model(model_name)
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except KeyError:
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# Try common encodings based on model patterns
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if "gpt-4" in model_name or "gpt-3.5" in model_name:
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encoding = tiktoken.get_encoding("cl100k_base")
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else:
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encoding = tiktoken.get_encoding("cl100k_base") # Default
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return len(encoding.encode(text))
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except (ImportError, Exception) as e:
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logging.debug(f"Tiktoken not available or failed: {e}")
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# 3. Fall back to character-based estimation
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logging.warning(
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f"No specific tokenizer available for '{model_name}'. "
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"Using character-based estimation (~4 chars per token)."
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)
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return len(text) // 4
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def validate_parameters(self, model_name: str, temperature: float, **kwargs) -> None:
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"""Validate model parameters.
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For proxy providers, this may use generic capabilities.
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Args:
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model_name: Model to validate for
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temperature: Temperature to validate
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**kwargs: Additional parameters to validate
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"""
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try:
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capabilities = self.get_capabilities(model_name)
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# Check if we're using generic capabilities
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if hasattr(capabilities, '_is_generic'):
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logging.debug(
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f"Using generic parameter validation for {model_name}. "
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"Actual model constraints may differ."
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)
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# Validate temperature using parent class method
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super().validate_parameters(model_name, temperature, **kwargs)
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except Exception as e:
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# For proxy providers, we might not have accurate capabilities
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# Log warning but don't fail
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logging.warning(f"Parameter validation limited for {model_name}: {e}")
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def _extract_usage(self, response) -> dict[str, int]:
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"""Extract token usage from OpenAI response.
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Args:
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response: OpenAI API response object
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Returns:
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Dictionary with usage statistics
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"""
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usage = {}
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if hasattr(response, "usage") and response.usage:
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usage["input_tokens"] = getattr(response.usage, "prompt_tokens", 0)
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usage["output_tokens"] = getattr(response.usage, "completion_tokens", 0)
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usage["total_tokens"] = getattr(response.usage, "total_tokens", 0)
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return usage
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@abstractmethod
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def get_capabilities(self, model_name: str) -> ModelCapabilities:
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"""Get capabilities for a specific model.
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Must be implemented by subclasses.
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"""
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pass
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@abstractmethod
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def get_provider_type(self) -> ProviderType:
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"""Get the provider type.
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Must be implemented by subclasses.
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"""
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pass
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@abstractmethod
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def validate_model_name(self, model_name: str) -> bool:
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"""Validate if the model name is supported.
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Must be implemented by subclasses.
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"""
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pass
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def supports_thinking_mode(self, model_name: str) -> bool:
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"""Check if the model supports extended thinking mode.
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Default is False for OpenAI-compatible providers.
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"""
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return False |