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* feat(models): add declarative reasoning capability contract (#5073) Introduce an optional per-model `reasoning:` block beside the legacy `supports_thinking` / `supports_reasoning_effort` booleans. It declares thinking availability (unsupported | optional | required), the accepted effort values with aliases, default, and serialization path, the payload dialect, and the reasoning-history requirement. When present the booleans are derived from it and contradictory profiles fail at config load. `deerflow.models.reasoning` normalizes legacy and declared profiles into one immutable contract and resolves every caller's generic request against it. `create_chat_model` is the single enforcement point: required-thinking models never receive a synthesized disable payload, unsupported effort values never reach the provider, and the dialect synthesizes the on/off payload when no template exists. Legacy profiles keep the historical path byte-for-byte. The lead agent and subagent descriptor resolve the same policy first so run metadata reports effective values. `/api/models` and `DeerFlowClient` project the contract as `reasoning`; the frontend derives mode clamping and effort choices from it through `core/models/reasoning.ts`, and user preferences accept provider-specific effort tokens. The setup wizard's Z.AI GLM-5.3-Flash profile migrates to the contract, restoring its low/high/max effort control. * docs(models): move the reasoning contract guidance into models/AGENTS.md backend/AGENTS.md sits within 46 bytes of the 96 KiB effective-chain hard limit for the middlewares, runtime, sandbox, and subagents guides, so the new section pushed scripts/check_agent_guidance.py over it. The models guide is the nearest file for the factory and the contract module, and its chain has ample room. * fix(models): address review on the reasoning contract - vLLM dialect: the enable path now mirrors whichever chat_template_kwargs switch the template declares, like the disable path already did, so a template using the older `thinking` key no longer also grows `enable_thinking`. - Validate operator-supplied effort values at `effort.path` inside `when_thinking_enabled`, `when_thinking_disabled`, and the `thinking` shortcut against the contract, not only the profile-level value; they are forwarded when the caller chooses nothing, so the "unknown values never reach the provider" guarantee now holds for them too. - Constrain `effort.path` to dotted identifiers and reject single-segment paths that would replace a whole mapping (`extra_body`, `thinking`, `model_kwargs`, `default_headers`, `default_query`). - Z.AI GLM-5.3-Flash profile: `default: high` instead of `max`, because the default also governs summarization, title generation, and subagent calls that never choose an effort; `max` stays selectable. Documented in the config example, the configuration manual, and the design note. * fix(models): close reasoning contract review gaps * fix(models): preserve native Ollama reasoning config * fix(models): keep native reasoning strings and project reasoning into the descriptor - `ModelConfig.reasoning` also accepts the level string ChatOllama takes for gpt-oss style models (`low` / `medium` / `high`); the factory forwards both the boolean and the string on the legacy path, so profiles that passed `reasoning: high` through before the contract field existed keep loading and keep sending it. - The assembly descriptor no longer excludes `reasoning` from `model_parameters`: the native ChatOllama value and a declared contract's dialect/history/effort change the request payload, so they now move the assembly fingerprint. - e2e: the sidecar spec pinned the pre-contract body where a model without effort control still received `reasoning_effort: minimal`; the preset is now dropped for such models (the Gateway discarded it anyway), so the assertion expects the key to be absent.
203 lines
8.1 KiB
Python
203 lines
8.1 KiB
Python
import logging
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from typing import Literal
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from fastapi import APIRouter, Depends, HTTPException, Request
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from pydantic import BaseModel, Field
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from app.gateway.authz import (
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_AuthorizationUnavailable,
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_is_internal_caller,
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authorize_model_use,
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resolve_model_authorization,
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)
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from app.gateway.deps import get_config, get_optional_user_from_request
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from deerflow.config.app_config import AppConfig
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from deerflow.config.model_config import ModelConfig
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from deerflow.models.reasoning import reasoning_capabilities_payload, resolve_reasoning_contract
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api", tags=["models"])
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class ReasoningEffortCapabilitiesResponse(BaseModel):
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"""Effort values a model accepts, in display order."""
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values: list[str] = Field(..., description="Accepted effort values (provider vocabulary)")
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default: str | None = Field(default=None, description="Effort applied when the caller does not choose one")
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aliases: dict[str, str] = Field(default_factory=dict, description="Generic DeerFlow value -> provider value")
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class ReasoningCapabilitiesResponse(BaseModel):
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"""Normalized reasoning contract (issue #5073), derived for legacy profiles."""
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thinking: Literal["unsupported", "optional", "required"] = Field(..., description="Whether thinking can be toggled, is always on, or is unavailable")
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effort: ReasoningEffortCapabilitiesResponse | None = Field(default=None, description="Effort control; null when the model exposes none")
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history: Literal["preserve", "clear"] | None = Field(default=None, description="Reasoning-history requirement, when declared")
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source: Literal["legacy", "contract"] = Field(..., description="Whether the profile declared a contract or the booleans were projected")
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class ModelResponse(BaseModel):
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"""Response model for model information."""
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name: str = Field(..., description="Unique identifier for the model")
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model: str = Field(..., description="Actual provider model identifier")
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display_name: str | None = Field(None, description="Human-readable name")
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description: str | None = Field(None, description="Model description")
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supports_thinking: bool = Field(default=False, description="Whether model supports thinking mode (deprecated: derived from `reasoning`)")
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supports_reasoning_effort: bool = Field(default=False, description="Whether model supports reasoning effort (deprecated: derived from `reasoning`)")
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reasoning: ReasoningCapabilitiesResponse = Field(..., description="Normalized reasoning capability contract")
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def _model_response(model: ModelConfig) -> ModelResponse:
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return ModelResponse(
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name=model.name,
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model=model.model,
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display_name=model.display_name,
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description=model.description,
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supports_thinking=model.supports_thinking,
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supports_reasoning_effort=model.supports_reasoning_effort,
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reasoning=ReasoningCapabilitiesResponse(**reasoning_capabilities_payload(resolve_reasoning_contract(model))),
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)
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class TokenUsageResponse(BaseModel):
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"""Token usage display configuration."""
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enabled: bool = Field(default=False, description="Whether token usage display is enabled")
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class ModelsListResponse(BaseModel):
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"""Response model for listing all models."""
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models: list[ModelResponse]
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token_usage: TokenUsageResponse
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@router.get(
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"/models",
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response_model=ModelsListResponse,
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summary="List All Models",
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description="Retrieve a list of all available AI models configured in the system.",
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)
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async def list_models(
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request: Request,
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config: AppConfig = Depends(get_config),
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) -> ModelsListResponse:
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"""List all available models from configuration.
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Returns model information suitable for frontend display,
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excluding sensitive fields like API keys and internal configuration.
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When ``authorization.enabled`` is true, only models the caller's role may
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``list`` are returned (filtered via ``provider.filter_resources``). A
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provider error yields an empty list (fail-closed) or all models (fail-open).
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Returns:
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A list of all configured models with their metadata and token usage display settings.
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Example Response:
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```json
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{
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"models": [
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{
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"name": "gpt-4",
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"model": "gpt-4",
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"display_name": "GPT-4",
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"description": "OpenAI GPT-4 model",
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"supports_thinking": false,
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"supports_reasoning_effort": false,
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"reasoning": {"thinking": "unsupported", "effort": null, "history": null, "source": "legacy"}
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},
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{
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"name": "claude-3-opus",
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"model": "claude-3-opus",
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"display_name": "Claude 3 Opus",
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"description": "Anthropic Claude 3 Opus model",
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"supports_thinking": true,
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"supports_reasoning_effort": false,
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"reasoning": {"thinking": "optional", "effort": null, "history": null, "source": "legacy"}
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}
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],
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"token_usage": {
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"enabled": true
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}
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}
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```
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"""
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visible_models = config.models
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fail_closed = config.authorization.fail_closed
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user = await get_optional_user_from_request(request)
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if user is not None:
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try:
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provider, principal = resolve_model_authorization(user, is_internal=_is_internal_caller(request, user))
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except _AuthorizationUnavailable as exc:
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if exc.fail_closed:
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visible_models = []
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else:
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if provider is not None and principal is not None:
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try:
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allowed_names = provider.filter_resources(principal, "model", [m.name for m in config.models])
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if not isinstance(allowed_names, list) or any(not isinstance(n, str) for n in allowed_names):
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raise TypeError("AuthorizationProvider.filter_resources must return list[str]")
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allowed_set = set(allowed_names)
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visible_models = [m for m in config.models if m.name in allowed_set]
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except Exception:
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logger.warning("Authorization provider failed while filtering models", exc_info=True)
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visible_models = [] if fail_closed else config.models
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models = [_model_response(model) for model in visible_models]
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return ModelsListResponse(
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models=models,
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token_usage=TokenUsageResponse(enabled=config.token_usage.enabled),
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)
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@router.get(
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"/models/{model_name}",
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response_model=ModelResponse,
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summary="Get Model Details",
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description="Retrieve detailed information about a specific AI model by its name.",
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)
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async def get_model(
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model_name: str,
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request: Request,
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config: AppConfig = Depends(get_config),
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) -> ModelResponse:
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"""Get a specific model by name.
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Args:
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model_name: The unique name of the model to retrieve.
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Returns:
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Model information if found.
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Raises:
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HTTPException: 404 if model not found; 403 if the caller's role may not
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``use`` the model (only when ``authorization.enabled`` is true). A
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provider resolution error yields 403 (fail-closed) or allows the request
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(fail-open), mirroring ``list_models``'s provider-error semantics.
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Example Response:
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```json
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{
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"name": "gpt-4",
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"display_name": "GPT-4",
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"description": "OpenAI GPT-4 model",
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"supports_thinking": false
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}
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```
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"""
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model = config.get_model_config(model_name)
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if model is None:
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raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
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# Phase 3: enforce model:use authorization (deny → 403, not 404, since the
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# model exists but the role lacks permission to use it).
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user = await get_optional_user_from_request(request)
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if user is not None:
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authorize_model_use(user, model_name, is_internal=_is_internal_caller(request, user), app_config=config)
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return _model_response(model)
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