mirror of
https://github.com/Routstr/routstr-core.git
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redshift
c1c6c81108
Keep upstream reasoning metadata (supported_efforts, default_effort, mandatory) on the catalog instead of dropping it on ingest, and map client reasoning_effort / reasoning.effort / thinking onto each model's allowlist before forwarding so unsupported levels are not sent upstream.
244 lines
8.6 KiB
Python
244 lines
8.6 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 ..core.db import UpstreamProviderRow
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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 OllamaUpstreamProvider(BaseUpstreamProvider):
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"""Upstream provider specifically configured for Ollama API."""
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provider_type = "ollama"
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default_base_url = "http://localhost:11434"
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platform_url = None
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litellm_provider_prefix = "ollama_chat/"
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def __init__(
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self,
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base_url: str = "http://localhost:11434",
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api_key: str = "",
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provider_fee: float = 1.01,
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):
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"""Initialize Ollama provider.
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Args:
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base_url: Ollama API base URL (default http://localhost:11434)
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api_key: Optional API key (Ollama typically doesn't require one)
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provider_fee: Provider fee multiplier (default 1.01 for 1% fee)
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"""
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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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@classmethod
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def _build_from_row(
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cls, provider_row: "UpstreamProviderRow"
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) -> "OllamaUpstreamProvider":
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return cls(
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base_url=provider_row.base_url,
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api_key=provider_row.api_key,
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provider_fee=provider_row.provider_fee,
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)
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@classmethod
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def get_provider_metadata(cls) -> dict[str, object]:
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return {
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"id": cls.provider_type,
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"name": "Ollama",
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"default_base_url": cls.default_base_url,
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"fixed_base_url": False,
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"platform_url": cls.platform_url,
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}
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def transform_model_name(self, model_id: str) -> str:
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"""Strip 'ollama/' prefix for Ollama API compatibility."""
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return model_id.removeprefix("ollama/")
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def get_request_base_url(self, path: str, model_obj: Model | None = None) -> str:
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"""Route proxy traffic through Ollama's OpenAI-compatible /v1 endpoint."""
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return f"{self.base_url.rstrip('/')}/v1"
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async def fetch_models(self) -> list[Model]:
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"""Fetch models from Ollama API using /api/tags 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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response = await client.get(f"{self.base_url}/api/tags")
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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("models", []):
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model_name = model_data.get("name", "")
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if not model_name:
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continue
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details = model_data.get("details", {})
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parameter_size = details.get("parameter_size", "")
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context_length = 4096
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if (
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"70b" in parameter_size.lower()
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or "72b" in parameter_size.lower()
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):
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context_length = 8192
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elif "13b" in parameter_size.lower():
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context_length = 4096
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elif "7b" in parameter_size.lower():
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context_length = 4096
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elif "3b" in parameter_size.lower():
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context_length = 2048
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elif "1b" in parameter_size.lower():
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context_length = 2048
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model_family = details.get("family", "unknown")
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model_format = details.get("format", "unknown")
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description = f"Ollama {model_family} model"
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if parameter_size:
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description += f" ({parameter_size})"
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models_list.append(
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Model(
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id=model_name,
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name=model_name.replace(":", " "),
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created=0,
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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="text",
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input_modalities=["text"],
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output_modalities=["text"],
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tokenizer=model_format,
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instruct_type=None,
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),
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pricing=Pricing(
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prompt=0.000003,
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completion=0.000003,
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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 Ollama",
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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 Ollama 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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async def refresh_models_cache(self) -> None:
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"""Refresh the in-memory models cache from upstream API."""
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try:
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from ..payment.models import _update_model_sats_pricing
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from ..payment.price import sats_usd_price
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models = await self.fetch_models()
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models_with_fees = [self._apply_provider_fee_to_model(m) for m in models]
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try:
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sats_to_usd = sats_usd_price()
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self._models_cache = [
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_update_model_sats_pricing(m, sats_to_usd) for m in models_with_fees
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]
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except Exception:
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self._models_cache = models_with_fees
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self._models_by_id = {
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m.forwarded_model_id or m.id: m for m in self._models_cache
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}
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logger.info(
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f"Refreshed models cache for {self.base_url}",
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extra={"model_count": len(models)},
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)
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except Exception as e:
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logger.error(
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f"Failed to refresh models cache for {self.base_url}",
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extra={"error": str(e), "error_type": type(e).__name__},
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)
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def get_cached_models(self) -> list[Model]:
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"""Get cached models for this provider.
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Returns:
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List of cached Model objects
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"""
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return self._models_cache
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def get_cached_model_by_id(self, model_id: str) -> Model | None:
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"""Get a specific cached model by ID.
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Args:
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model_id: Model identifier
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Returns:
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Model object or None if not found
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"""
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return self._models_by_id.get(model_id)
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def _apply_provider_fee_to_model(self, model: Model) -> Model:
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"""Apply provider fee to model's USD pricing and calculate max costs.
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Args:
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model: Model object to update
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Returns:
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Model with provider fee applied to pricing and max costs calculated
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"""
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from ..payment.models import Pricing, _calculate_usd_max_costs
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adjusted_pricing = Pricing.parse_obj(
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{k: v * self.provider_fee for k, v in model.pricing.dict().items()}
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)
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temp_model = model.copy(
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update={"pricing": adjusted_pricing, "sats_pricing": None}
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)
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(
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adjusted_pricing.max_prompt_cost,
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adjusted_pricing.max_completion_cost,
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adjusted_pricing.max_cost,
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) = _calculate_usd_max_costs(temp_model)
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return model.copy(update={"pricing": adjusted_pricing})
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