f3fc86de07
Remove the airllm provider block from providers.yaml, the airllm availability check from cascade.py, and associated tests. Renumber remaining provider priorities (OpenAI → 2, Anthropic → 3). Fixes #459 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
957 lines
32 KiB
Python
957 lines
32 KiB
Python
"""Cascade LLM Router — Automatic failover between providers.
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Routes requests through an ordered list of LLM providers,
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automatically failing over on rate limits or errors.
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Tracks metrics for latency, errors, and cost.
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Now with multi-modal support — automatically selects vision-capable
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models for image inputs and falls back through capability chains.
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"""
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import asyncio
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import base64
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import logging
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import time
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from dataclasses import dataclass, field
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from datetime import UTC, datetime
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from enum import Enum
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from pathlib import Path
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from typing import Any
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try:
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import yaml
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except ImportError:
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yaml = None # type: ignore
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try:
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import requests
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except ImportError:
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requests = None # type: ignore
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logger = logging.getLogger(__name__)
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class ProviderStatus(Enum):
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"""Health status of a provider."""
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HEALTHY = "healthy"
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DEGRADED = "degraded" # Working but slow or occasional errors
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UNHEALTHY = "unhealthy" # Circuit breaker open
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DISABLED = "disabled"
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class CircuitState(Enum):
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"""Circuit breaker state."""
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CLOSED = "closed" # Normal operation
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OPEN = "open" # Failing, rejecting requests
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HALF_OPEN = "half_open" # Testing if recovered
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class ContentType(Enum):
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"""Type of content in the request."""
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TEXT = "text"
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VISION = "vision" # Contains images
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AUDIO = "audio" # Contains audio
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MULTIMODAL = "multimodal" # Multiple content types
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@dataclass
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class ProviderMetrics:
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"""Metrics for a single provider."""
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total_requests: int = 0
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successful_requests: int = 0
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failed_requests: int = 0
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total_latency_ms: float = 0.0
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last_request_time: str | None = None
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last_error_time: str | None = None
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consecutive_failures: int = 0
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@property
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def avg_latency_ms(self) -> float:
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if self.total_requests == 0:
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return 0.0
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return self.total_latency_ms / self.total_requests
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@property
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def error_rate(self) -> float:
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if self.total_requests == 0:
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return 0.0
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return self.failed_requests / self.total_requests
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@dataclass
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class ModelCapability:
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"""Capabilities a model supports."""
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name: str
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supports_vision: bool = False
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supports_audio: bool = False
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supports_tools: bool = False
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supports_json: bool = False
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supports_streaming: bool = True
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context_window: int = 4096
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@dataclass
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class Provider:
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"""LLM provider configuration and state."""
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name: str
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type: str # ollama, openai, anthropic
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enabled: bool
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priority: int
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url: str | None = None
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api_key: str | None = None
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base_url: str | None = None
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models: list[dict] = field(default_factory=list)
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# Runtime state
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status: ProviderStatus = ProviderStatus.HEALTHY
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metrics: ProviderMetrics = field(default_factory=ProviderMetrics)
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circuit_state: CircuitState = CircuitState.CLOSED
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circuit_opened_at: float | None = None
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half_open_calls: int = 0
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def get_default_model(self) -> str | None:
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"""Get the default model for this provider."""
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for model in self.models:
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if model.get("default"):
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return model["name"]
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if self.models:
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return self.models[0]["name"]
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return None
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def get_model_with_capability(self, capability: str) -> str | None:
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"""Get a model that supports the given capability."""
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for model in self.models:
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capabilities = model.get("capabilities", [])
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if capability in capabilities:
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return model["name"]
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# Fall back to default
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return self.get_default_model()
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def model_has_capability(self, model_name: str, capability: str) -> bool:
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"""Check if a specific model has a capability."""
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for model in self.models:
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if model["name"] == model_name:
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capabilities = model.get("capabilities", [])
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return capability in capabilities
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return False
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@dataclass
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class RouterConfig:
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"""Cascade router configuration."""
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timeout_seconds: int = 30
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max_retries_per_provider: int = 2
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retry_delay_seconds: int = 1
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circuit_breaker_failure_threshold: int = 5
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circuit_breaker_recovery_timeout: int = 60
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circuit_breaker_half_open_max_calls: int = 2
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cost_tracking_enabled: bool = True
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budget_daily_usd: float = 10.0
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# Multi-modal settings
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auto_pull_models: bool = True
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fallback_chains: dict = field(default_factory=dict)
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class CascadeRouter:
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"""Routes LLM requests with automatic failover.
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Now with multi-modal support:
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- Automatically detects content type (text, vision, audio)
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- Selects appropriate models based on capabilities
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- Falls back through capability-specific model chains
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- Supports image URLs and base64 encoding
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Usage:
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router = CascadeRouter()
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# Text request
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response = await router.complete(
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messages=[{"role": "user", "content": "Hello"}],
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model="llama3.2"
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)
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# Vision request (automatically detects and selects vision model)
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response = await router.complete(
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messages=[{
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"role": "user",
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"content": "What's in this image?",
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"images": ["path/to/image.jpg"]
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}],
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model="llava:7b"
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)
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# Check metrics
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metrics = router.get_metrics()
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"""
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def __init__(self, config_path: Path | None = None) -> None:
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self.config_path = config_path or Path("config/providers.yaml")
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self.providers: list[Provider] = []
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self.config: RouterConfig = RouterConfig()
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self._load_config()
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# Initialize multi-modal manager if available
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self._mm_manager: Any | None = None
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try:
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from infrastructure.models.multimodal import get_multimodal_manager
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self._mm_manager = get_multimodal_manager()
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except Exception as exc:
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logger.debug("Multi-modal manager not available: %s", exc)
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logger.info("CascadeRouter initialized with %d providers", len(self.providers))
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def _load_config(self) -> None:
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"""Load configuration from YAML."""
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if not self.config_path.exists():
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logger.warning("Config not found: %s, using defaults", self.config_path)
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return
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try:
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if yaml is None:
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raise RuntimeError("PyYAML not installed")
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content = self.config_path.read_text()
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# Expand environment variables
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content = self._expand_env_vars(content)
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data = yaml.safe_load(content)
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# Load cascade settings
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cascade = data.get("cascade", {})
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# Load fallback chains
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fallback_chains = data.get("fallback_chains", {})
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# Load multi-modal settings
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multimodal = data.get("multimodal", {})
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self.config = RouterConfig(
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timeout_seconds=cascade.get("timeout_seconds", 30),
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max_retries_per_provider=cascade.get("max_retries_per_provider", 2),
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retry_delay_seconds=cascade.get("retry_delay_seconds", 1),
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circuit_breaker_failure_threshold=cascade.get("circuit_breaker", {}).get(
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"failure_threshold", 5
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),
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circuit_breaker_recovery_timeout=cascade.get("circuit_breaker", {}).get(
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"recovery_timeout", 60
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),
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circuit_breaker_half_open_max_calls=cascade.get("circuit_breaker", {}).get(
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"half_open_max_calls", 2
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),
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auto_pull_models=multimodal.get("auto_pull", True),
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fallback_chains=fallback_chains,
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)
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# Load providers
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for p_data in data.get("providers", []):
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# Skip disabled providers
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if not p_data.get("enabled", False):
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continue
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provider = Provider(
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name=p_data["name"],
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type=p_data["type"],
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enabled=p_data.get("enabled", True),
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priority=p_data.get("priority", 99),
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url=p_data.get("url"),
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api_key=p_data.get("api_key"),
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base_url=p_data.get("base_url"),
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models=p_data.get("models", []),
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)
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# Check if provider is actually available
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if self._check_provider_available(provider):
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self.providers.append(provider)
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else:
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logger.warning("Provider %s not available, skipping", provider.name)
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# Sort by priority
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self.providers.sort(key=lambda p: p.priority)
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except Exception as exc:
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logger.error("Failed to load config: %s", exc)
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def _expand_env_vars(self, content: str) -> str:
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"""Expand ${VAR} syntax in YAML content.
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Uses os.environ directly (not settings) because this is a generic
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YAML config loader that must expand arbitrary variable references.
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"""
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import os
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import re
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def replace_var(match: "re.Match[str]") -> str:
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var_name = match.group(1)
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return os.environ.get(var_name, match.group(0))
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return re.sub(r"\$\{(\w+)\}", replace_var, content)
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def _check_provider_available(self, provider: Provider) -> bool:
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"""Check if a provider is actually available."""
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if provider.type == "ollama":
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# Check if Ollama is running
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if requests is None:
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# Can't check without requests, assume available
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return True
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try:
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url = provider.url or "http://localhost:11434"
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response = requests.get(f"{url}/api/tags", timeout=5)
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return response.status_code == 200
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except Exception as exc:
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logger.debug("Ollama provider check error: %s", exc)
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return False
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elif provider.type in ("openai", "anthropic", "grok"):
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# Check if API key is set
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return provider.api_key is not None and provider.api_key != ""
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return True
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def _detect_content_type(self, messages: list[dict]) -> ContentType:
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"""Detect the type of content in the messages.
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Checks for images, audio, etc. in the message content.
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"""
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has_image = False
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has_audio = False
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for msg in messages:
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content = msg.get("content", "")
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# Check for image URLs/paths
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if msg.get("images"):
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has_image = True
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# Check for image URLs in content
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if isinstance(content, str):
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image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp")
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if any(ext in content.lower() for ext in image_extensions):
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has_image = True
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if content.startswith("data:image/"):
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has_image = True
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# Check for audio
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if msg.get("audio"):
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has_audio = True
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# Check for multimodal content structure
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if isinstance(content, list):
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for item in content:
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if isinstance(item, dict):
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if item.get("type") == "image_url":
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has_image = True
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elif item.get("type") == "audio":
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has_audio = True
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if has_image and has_audio:
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return ContentType.MULTIMODAL
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elif has_image:
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return ContentType.VISION
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elif has_audio:
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return ContentType.AUDIO
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return ContentType.TEXT
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def _get_fallback_model(
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self, provider: Provider, original_model: str, content_type: ContentType
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) -> str | None:
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"""Get a fallback model for the given content type."""
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# Map content type to capability
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capability_map = {
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ContentType.VISION: "vision",
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ContentType.AUDIO: "audio",
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ContentType.MULTIMODAL: "vision", # Vision models often do both
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}
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capability = capability_map.get(content_type)
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if not capability:
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return None
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# Check provider's models for capability
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fallback_model = provider.get_model_with_capability(capability)
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if fallback_model and fallback_model != original_model:
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return fallback_model
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# Use fallback chains from config
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fallback_chain = self.config.fallback_chains.get(capability, [])
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for model_name in fallback_chain:
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if provider.model_has_capability(model_name, capability):
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return model_name
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return None
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async def complete(
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self,
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messages: list[dict],
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model: str | None = None,
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temperature: float = 0.7,
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max_tokens: int | None = None,
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) -> dict:
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"""Complete a chat conversation with automatic failover.
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Multi-modal support:
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- Automatically detects if messages contain images
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- Falls back to vision-capable models when needed
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- Supports image URLs, paths, and base64 encoding
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Args:
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messages: List of message dicts with role and content
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model: Preferred model (tries this first, then provider defaults)
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temperature: Sampling temperature
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max_tokens: Maximum tokens to generate
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Returns:
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Dict with content, provider_used, and metrics
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Raises:
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RuntimeError: If all providers fail
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"""
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# Detect content type for multi-modal routing
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content_type = self._detect_content_type(messages)
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if content_type != ContentType.TEXT:
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logger.debug("Detected %s content, selecting appropriate model", content_type.value)
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errors = []
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for provider in self.providers:
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# Skip disabled providers
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if not provider.enabled:
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logger.debug("Skipping %s (disabled)", provider.name)
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continue
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# Skip unhealthy providers (circuit breaker)
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if provider.status == ProviderStatus.UNHEALTHY:
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# Check if circuit breaker can close
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if self._can_close_circuit(provider):
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provider.circuit_state = CircuitState.HALF_OPEN
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provider.half_open_calls = 0
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logger.info("Circuit breaker half-open for %s", provider.name)
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else:
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logger.debug("Skipping %s (circuit open)", provider.name)
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continue
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# Determine which model to use
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selected_model = model or provider.get_default_model()
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is_fallback_model = False
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# For non-text content, check if model supports it
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if content_type != ContentType.TEXT and selected_model:
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if provider.type == "ollama" and self._mm_manager:
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from infrastructure.models.multimodal import ModelCapability
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# Check if selected model supports the required capability
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if content_type == ContentType.VISION:
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supports = self._mm_manager.model_supports(
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selected_model, ModelCapability.VISION
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)
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if not supports:
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# Find fallback model
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fallback = self._get_fallback_model(
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provider, selected_model, content_type
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)
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if fallback:
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logger.info(
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"Model %s doesn't support vision, falling back to %s",
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selected_model,
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fallback,
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)
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selected_model = fallback
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is_fallback_model = True
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else:
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logger.warning(
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"No vision-capable model found on %s, trying anyway",
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provider.name,
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)
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# Try this provider
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for attempt in range(self.config.max_retries_per_provider):
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try:
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result = await self._try_provider(
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provider=provider,
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messages=messages,
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model=selected_model,
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temperature=temperature,
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max_tokens=max_tokens,
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content_type=content_type,
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)
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# Success! Update metrics and return
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self._record_success(provider, result.get("latency_ms", 0))
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return {
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"content": result["content"],
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"provider": provider.name,
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"model": result.get(
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"model", selected_model or provider.get_default_model()
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),
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"latency_ms": result.get("latency_ms", 0),
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"is_fallback_model": is_fallback_model,
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}
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except Exception as exc:
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error_msg = str(exc)
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logger.warning(
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"Provider %s attempt %d failed: %s", provider.name, attempt + 1, error_msg
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)
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errors.append(f"{provider.name}: {error_msg}")
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if attempt < self.config.max_retries_per_provider - 1:
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await asyncio.sleep(self.config.retry_delay_seconds)
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# All retries failed for this provider
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self._record_failure(provider)
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# All providers failed
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raise RuntimeError(f"All providers failed: {'; '.join(errors)}")
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async def _try_provider(
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self,
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provider: Provider,
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messages: list[dict],
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model: str,
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temperature: float,
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max_tokens: int | None,
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content_type: ContentType = ContentType.TEXT,
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) -> dict:
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"""Try a single provider request."""
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start_time = time.time()
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if provider.type == "ollama":
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result = await self._call_ollama(
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provider=provider,
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messages=messages,
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model=model or provider.get_default_model(),
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temperature=temperature,
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content_type=content_type,
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)
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elif provider.type == "openai":
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result = await self._call_openai(
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provider=provider,
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messages=messages,
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model=model or provider.get_default_model(),
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temperature=temperature,
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max_tokens=max_tokens,
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)
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elif provider.type == "anthropic":
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result = await self._call_anthropic(
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provider=provider,
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messages=messages,
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model=model or provider.get_default_model(),
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temperature=temperature,
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max_tokens=max_tokens,
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)
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elif provider.type == "grok":
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result = await self._call_grok(
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provider=provider,
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messages=messages,
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model=model or provider.get_default_model(),
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temperature=temperature,
|
|
max_tokens=max_tokens,
|
|
)
|
|
else:
|
|
raise ValueError(f"Unknown provider type: {provider.type}")
|
|
|
|
latency_ms = (time.time() - start_time) * 1000
|
|
result["latency_ms"] = latency_ms
|
|
|
|
return result
|
|
|
|
async def _call_ollama(
|
|
self,
|
|
provider: Provider,
|
|
messages: list[dict],
|
|
model: str,
|
|
temperature: float,
|
|
content_type: ContentType = ContentType.TEXT,
|
|
) -> dict:
|
|
"""Call Ollama API with multi-modal support."""
|
|
import aiohttp
|
|
|
|
url = f"{provider.url}/api/chat"
|
|
|
|
# Transform messages for Ollama format (including images)
|
|
transformed_messages = self._transform_messages_for_ollama(messages)
|
|
|
|
payload = {
|
|
"model": model,
|
|
"messages": transformed_messages,
|
|
"stream": False,
|
|
"options": {
|
|
"temperature": temperature,
|
|
},
|
|
}
|
|
|
|
timeout = aiohttp.ClientTimeout(total=self.config.timeout_seconds)
|
|
|
|
async with aiohttp.ClientSession(timeout=timeout) as session:
|
|
async with session.post(url, json=payload) as response:
|
|
if response.status != 200:
|
|
text = await response.text()
|
|
raise RuntimeError(f"Ollama error {response.status}: {text}")
|
|
|
|
data = await response.json()
|
|
return {
|
|
"content": data["message"]["content"],
|
|
"model": model,
|
|
}
|
|
|
|
def _transform_messages_for_ollama(self, messages: list[dict]) -> list[dict]:
|
|
"""Transform messages to Ollama format, handling images."""
|
|
transformed = []
|
|
|
|
for msg in messages:
|
|
new_msg = {
|
|
"role": msg.get("role", "user"),
|
|
"content": msg.get("content", ""),
|
|
}
|
|
|
|
# Handle images
|
|
images = msg.get("images", [])
|
|
if images:
|
|
new_msg["images"] = []
|
|
for img in images:
|
|
if isinstance(img, str):
|
|
if img.startswith("data:image/"):
|
|
# Base64 encoded image
|
|
new_msg["images"].append(img.split(",")[1])
|
|
elif img.startswith("http://") or img.startswith("https://"):
|
|
# URL - would need to download, skip for now
|
|
logger.warning("Image URLs not yet supported, skipping: %s", img)
|
|
elif Path(img).exists():
|
|
# Local file path - read and encode
|
|
try:
|
|
with open(img, "rb") as f:
|
|
img_data = base64.b64encode(f.read()).decode()
|
|
new_msg["images"].append(img_data)
|
|
except Exception as exc:
|
|
logger.error("Failed to read image %s: %s", img, exc)
|
|
|
|
transformed.append(new_msg)
|
|
|
|
return transformed
|
|
|
|
async def _call_openai(
|
|
self,
|
|
provider: Provider,
|
|
messages: list[dict],
|
|
model: str,
|
|
temperature: float,
|
|
max_tokens: int | None,
|
|
) -> dict:
|
|
"""Call OpenAI API."""
|
|
import openai
|
|
|
|
client = openai.AsyncOpenAI(
|
|
api_key=provider.api_key,
|
|
base_url=provider.base_url,
|
|
timeout=self.config.timeout_seconds,
|
|
)
|
|
|
|
kwargs = {
|
|
"model": model,
|
|
"messages": messages,
|
|
"temperature": temperature,
|
|
}
|
|
if max_tokens:
|
|
kwargs["max_tokens"] = max_tokens
|
|
|
|
response = await client.chat.completions.create(**kwargs)
|
|
|
|
return {
|
|
"content": response.choices[0].message.content,
|
|
"model": response.model,
|
|
}
|
|
|
|
async def _call_anthropic(
|
|
self,
|
|
provider: Provider,
|
|
messages: list[dict],
|
|
model: str,
|
|
temperature: float,
|
|
max_tokens: int | None,
|
|
) -> dict:
|
|
"""Call Anthropic API."""
|
|
import anthropic
|
|
|
|
client = anthropic.AsyncAnthropic(
|
|
api_key=provider.api_key,
|
|
timeout=self.config.timeout_seconds,
|
|
)
|
|
|
|
# Convert messages to Anthropic format
|
|
system_msg = None
|
|
conversation = []
|
|
for msg in messages:
|
|
if msg["role"] == "system":
|
|
system_msg = msg["content"]
|
|
else:
|
|
conversation.append(
|
|
{
|
|
"role": msg["role"],
|
|
"content": msg["content"],
|
|
}
|
|
)
|
|
|
|
kwargs = {
|
|
"model": model,
|
|
"messages": conversation,
|
|
"temperature": temperature,
|
|
"max_tokens": max_tokens or 1024,
|
|
}
|
|
if system_msg:
|
|
kwargs["system"] = system_msg
|
|
|
|
response = await client.messages.create(**kwargs)
|
|
|
|
return {
|
|
"content": response.content[0].text,
|
|
"model": response.model,
|
|
}
|
|
|
|
async def _call_grok(
|
|
self,
|
|
provider: Provider,
|
|
messages: list[dict],
|
|
model: str,
|
|
temperature: float,
|
|
max_tokens: int | None,
|
|
) -> dict:
|
|
"""Call xAI Grok API via OpenAI-compatible SDK."""
|
|
import httpx
|
|
import openai
|
|
|
|
client = openai.AsyncOpenAI(
|
|
api_key=provider.api_key,
|
|
base_url=provider.base_url or "https://api.x.ai/v1",
|
|
timeout=httpx.Timeout(300.0),
|
|
)
|
|
|
|
kwargs = {
|
|
"model": model,
|
|
"messages": messages,
|
|
"temperature": temperature,
|
|
}
|
|
if max_tokens:
|
|
kwargs["max_tokens"] = max_tokens
|
|
|
|
response = await client.chat.completions.create(**kwargs)
|
|
|
|
return {
|
|
"content": response.choices[0].message.content,
|
|
"model": response.model,
|
|
}
|
|
|
|
def _record_success(self, provider: Provider, latency_ms: float) -> None:
|
|
"""Record a successful request."""
|
|
provider.metrics.total_requests += 1
|
|
provider.metrics.successful_requests += 1
|
|
provider.metrics.total_latency_ms += latency_ms
|
|
provider.metrics.last_request_time = datetime.now(UTC).isoformat()
|
|
provider.metrics.consecutive_failures = 0
|
|
|
|
# Close circuit breaker if half-open
|
|
if provider.circuit_state == CircuitState.HALF_OPEN:
|
|
provider.half_open_calls += 1
|
|
if provider.half_open_calls >= self.config.circuit_breaker_half_open_max_calls:
|
|
self._close_circuit(provider)
|
|
|
|
# Update status based on error rate
|
|
if provider.metrics.error_rate < 0.1:
|
|
provider.status = ProviderStatus.HEALTHY
|
|
elif provider.metrics.error_rate < 0.3:
|
|
provider.status = ProviderStatus.DEGRADED
|
|
|
|
def _record_failure(self, provider: Provider) -> None:
|
|
"""Record a failed request."""
|
|
provider.metrics.total_requests += 1
|
|
provider.metrics.failed_requests += 1
|
|
provider.metrics.last_error_time = datetime.now(UTC).isoformat()
|
|
provider.metrics.consecutive_failures += 1
|
|
|
|
# Check if we should open circuit breaker
|
|
if provider.metrics.consecutive_failures >= self.config.circuit_breaker_failure_threshold:
|
|
self._open_circuit(provider)
|
|
|
|
# Update status
|
|
if provider.metrics.error_rate > 0.3:
|
|
provider.status = ProviderStatus.DEGRADED
|
|
if provider.metrics.error_rate > 0.5:
|
|
provider.status = ProviderStatus.UNHEALTHY
|
|
|
|
def _open_circuit(self, provider: Provider) -> None:
|
|
"""Open the circuit breaker for a provider."""
|
|
provider.circuit_state = CircuitState.OPEN
|
|
provider.circuit_opened_at = time.time()
|
|
provider.status = ProviderStatus.UNHEALTHY
|
|
logger.warning("Circuit breaker OPEN for %s", provider.name)
|
|
|
|
def _can_close_circuit(self, provider: Provider) -> bool:
|
|
"""Check if circuit breaker can transition to half-open."""
|
|
if provider.circuit_opened_at is None:
|
|
return False
|
|
elapsed = time.time() - provider.circuit_opened_at
|
|
return elapsed >= self.config.circuit_breaker_recovery_timeout
|
|
|
|
def _close_circuit(self, provider: Provider) -> None:
|
|
"""Close the circuit breaker (provider healthy again)."""
|
|
provider.circuit_state = CircuitState.CLOSED
|
|
provider.circuit_opened_at = None
|
|
provider.half_open_calls = 0
|
|
provider.metrics.consecutive_failures = 0
|
|
provider.status = ProviderStatus.HEALTHY
|
|
logger.info("Circuit breaker CLOSED for %s", provider.name)
|
|
|
|
def reload_config(self) -> dict:
|
|
"""Hot-reload providers.yaml, preserving runtime state.
|
|
|
|
Re-reads the config file, rebuilds the provider list, and
|
|
preserves circuit breaker state and metrics for providers
|
|
that still exist after reload.
|
|
|
|
Returns:
|
|
Summary dict with added/removed/preserved counts.
|
|
"""
|
|
# Snapshot current runtime state keyed by provider name
|
|
old_state: dict[
|
|
str, tuple[ProviderMetrics, CircuitState, float | None, int, ProviderStatus]
|
|
] = {}
|
|
for p in self.providers:
|
|
old_state[p.name] = (
|
|
p.metrics,
|
|
p.circuit_state,
|
|
p.circuit_opened_at,
|
|
p.half_open_calls,
|
|
p.status,
|
|
)
|
|
|
|
old_names = set(old_state.keys())
|
|
|
|
# Reload from disk
|
|
self.providers = []
|
|
self._load_config()
|
|
|
|
# Restore preserved state
|
|
new_names = {p.name for p in self.providers}
|
|
preserved = 0
|
|
for p in self.providers:
|
|
if p.name in old_state:
|
|
metrics, circuit, opened_at, half_open, status = old_state[p.name]
|
|
p.metrics = metrics
|
|
p.circuit_state = circuit
|
|
p.circuit_opened_at = opened_at
|
|
p.half_open_calls = half_open
|
|
p.status = status
|
|
preserved += 1
|
|
|
|
added = new_names - old_names
|
|
removed = old_names - new_names
|
|
|
|
logger.info(
|
|
"Config reloaded: %d providers (%d preserved, %d added, %d removed)",
|
|
len(self.providers),
|
|
preserved,
|
|
len(added),
|
|
len(removed),
|
|
)
|
|
|
|
return {
|
|
"total_providers": len(self.providers),
|
|
"preserved": preserved,
|
|
"added": sorted(added),
|
|
"removed": sorted(removed),
|
|
}
|
|
|
|
def get_metrics(self) -> dict:
|
|
"""Get metrics for all providers."""
|
|
return {
|
|
"providers": [
|
|
{
|
|
"name": p.name,
|
|
"type": p.type,
|
|
"status": p.status.value,
|
|
"circuit_state": p.circuit_state.value,
|
|
"metrics": {
|
|
"total_requests": p.metrics.total_requests,
|
|
"successful": p.metrics.successful_requests,
|
|
"failed": p.metrics.failed_requests,
|
|
"error_rate": round(p.metrics.error_rate, 3),
|
|
"avg_latency_ms": round(p.metrics.avg_latency_ms, 2),
|
|
},
|
|
}
|
|
for p in self.providers
|
|
]
|
|
}
|
|
|
|
def get_status(self) -> dict:
|
|
"""Get current router status."""
|
|
healthy = sum(1 for p in self.providers if p.status == ProviderStatus.HEALTHY)
|
|
|
|
return {
|
|
"total_providers": len(self.providers),
|
|
"healthy_providers": healthy,
|
|
"degraded_providers": sum(
|
|
1 for p in self.providers if p.status == ProviderStatus.DEGRADED
|
|
),
|
|
"unhealthy_providers": sum(
|
|
1 for p in self.providers if p.status == ProviderStatus.UNHEALTHY
|
|
),
|
|
"providers": [
|
|
{
|
|
"name": p.name,
|
|
"type": p.type,
|
|
"status": p.status.value,
|
|
"priority": p.priority,
|
|
"default_model": p.get_default_model(),
|
|
}
|
|
for p in self.providers
|
|
],
|
|
}
|
|
|
|
async def generate_with_image(
|
|
self,
|
|
prompt: str,
|
|
image_path: str,
|
|
model: str | None = None,
|
|
temperature: float = 0.7,
|
|
) -> dict:
|
|
"""Convenience method for vision requests.
|
|
|
|
Args:
|
|
prompt: Text prompt about the image
|
|
image_path: Path to image file
|
|
model: Vision-capable model (auto-selected if not provided)
|
|
temperature: Sampling temperature
|
|
|
|
Returns:
|
|
Response dict with content and metadata
|
|
"""
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": prompt,
|
|
"images": [image_path],
|
|
}
|
|
]
|
|
return await self.complete(
|
|
messages=messages,
|
|
model=model,
|
|
temperature=temperature,
|
|
)
|
|
|
|
|
|
# Module-level singleton
|
|
cascade_router: CascadeRouter | None = None
|
|
|
|
|
|
def get_router() -> CascadeRouter:
|
|
"""Get or create the cascade router singleton."""
|
|
global cascade_router
|
|
if cascade_router is None:
|
|
cascade_router = CascadeRouter()
|
|
return cascade_router
|