mirror of
https://github.com/Routstr/routstr-core.git
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162 lines
6.3 KiB
Python
162 lines
6.3 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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import httpx
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from .base import BaseUpstreamProvider
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if TYPE_CHECKING:
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from ..payment.models import Model
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from ..core.logging import get_logger
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logger = get_logger(__name__)
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class GenericUpstreamProvider(BaseUpstreamProvider):
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"""Generic upstream provider that can fetch models from any OpenAI-compatible API."""
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def __init__(
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self,
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base_url: str,
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api_key: str = "",
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provider_fee: float = 1.01,
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upstream_name: str | None = None,
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):
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"""Initialize generic provider.
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Args:
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base_url: Base URL of the upstream API endpoint
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api_key: Optional API key for authentication
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provider_fee: Provider fee multiplier (default 1.01 for 1% fee)
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upstream_name: Optional name for the upstream provider
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"""
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self.upstream_name = upstream_name or "generic"
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super().__init__(
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base_url=base_url,
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api_key=api_key,
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provider_fee=provider_fee,
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)
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async def fetch_models(self) -> list[Model]:
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"""Fetch models from upstream API using /models endpoint."""
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from ..payment.models import Architecture, Model, Pricing, TopProvider
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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headers = {}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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response = await client.get(f"{self.base_url}/models", headers=headers)
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response.raise_for_status()
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data = response.json()
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models_list = []
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for model_data in data.get("data", []):
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model_id = model_data.get("id", "")
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if not model_id:
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continue
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model_name = model_data.get("name", model_id)
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created = model_data.get("created", 0)
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owned_by = model_data.get("owned_by", "unknown")
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model_spec = model_data.get("model_spec", {})
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context_length = 4096
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if model_spec.get("availableContextTokens"):
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context_length = model_spec["availableContextTokens"]
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elif any(
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pattern in model_id.lower() for pattern in ["32k", "32000"]
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):
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context_length = 32768
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elif any(
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pattern in model_id.lower() for pattern in ["16k", "16000"]
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):
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context_length = 16384
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elif any(pattern in model_id.lower() for pattern in ["8k", "8000"]):
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context_length = 8192
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elif "gpt-4" in model_id.lower():
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context_length = 8192
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elif "claude" in model_id.lower():
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context_length = 200000
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pricing_info = model_spec.get("pricing", {})
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input_pricing = pricing_info.get("input", {})
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output_pricing = pricing_info.get("output", {})
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prompt_price = input_pricing.get("usd", 0.001) / 1000000
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completion_price = output_pricing.get("usd", 0.001) / 1000000
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capabilities = model_spec.get("capabilities", {})
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input_modalities = ["text"]
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output_modalities = ["text"]
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if capabilities.get("supportsVision", False):
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input_modalities.append("image")
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modality = "text"
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if capabilities.get("supportsVision", False):
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modality = "text->text"
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spec_name = model_spec.get("name", model_name)
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description = f"{spec_name}"
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if owned_by != "unknown":
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description += f" via {owned_by}"
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models_list.append(
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Model(
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id=model_id,
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name=spec_name,
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created=created,
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description=description,
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context_length=context_length,
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architecture=Architecture(
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modality=modality,
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input_modalities=input_modalities,
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output_modalities=output_modalities,
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tokenizer="unknown",
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instruct_type=None,
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),
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pricing=Pricing(
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prompt=prompt_price,
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completion=completion_price,
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request=0.0,
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image=0.0,
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web_search=0.0,
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internal_reasoning=0.0,
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max_prompt_cost=0.001,
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max_completion_cost=0.001,
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max_cost=0.001,
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),
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sats_pricing=None,
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per_request_limits=None,
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top_provider=TopProvider(
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context_length=context_length,
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max_completion_tokens=context_length // 2,
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is_moderated=False,
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),
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enabled=True,
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upstream_provider_id=None,
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canonical_slug=None,
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)
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)
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logger.info(
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f"Fetched {len(models_list)} models from {self.upstream_name}",
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extra={"model_count": len(models_list), "base_url": self.base_url},
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)
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return models_list
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except Exception as e:
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logger.error(
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f"Failed to fetch models from {self.upstream_name} API: {e}",
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extra={
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"error": str(e),
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"error_type": type(e).__name__,
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"base_url": self.base_url,
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},
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)
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return []
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