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* feat: switch model providers to AWS Bedrock (Claude) and Fireworks (non-Claude)
Migrate off direct provider APIs: AWS Bedrock for Anthropic/Claude via the
cross-region inference profile us.anthropic.claude-opus-4-8, Fireworks AI for
all non-Claude models. Drop OpenAI (gpt-5.5) and Google (gemini-3.5-flash)
entirely. DEFAULT_MODEL_ID is now Bedrock Claude; all Fireworks models stay
freely selectable for the agent and reviewer graphs and via team/profile
defaults.
- pyproject: add langchain-aws (ChatBedrockConverse + boto3)
- options.py: Bedrock Claude entry + default; remove openai/google entries
- model.py: bedrock_converse provider_model_kwargs (effort -> thinking budget),
region pin in make_model, bedrock<->fireworks fallback pairing, AWS_REGION/
FIREWORKS_API_KEY local-dev validation
- server.py: provider-aware fallback kwargs build
- sanitize_thinking_blocks: also sanitize ChatBedrockConverse thinking blocks
- model_fallback: treat transient botocore ClientError codes as fallback-worthy
- eval_jobs: repoint hardcoded eval model id to Bedrock Claude
- tests: repoint dropped model ids; drop obsolete google test module
* fix(bedrock): use adaptive thinking + output_config.effort for Opus 4.8
The handoff spec wired Bedrock Converse thinking as
{type: enabled, budget_tokens: N}, but Opus 4.7+ rejects that with a
ValidationException: thinking.type "enabled" is not supported; it requires
thinking.type "adaptive" plus output_config.effort. Verified by live invoke
against us.anthropic.claude-opus-4-8 (account 328440206208, us-east-1):
the enabled+budget shape 400s, adaptive+effort returns normally.
Map profile effort to additional_model_request_fields:
{thinking: {type: adaptive, display: summarized},
output_config: {effort: <low|medium|high|xhigh|max>}}
reusing anthropic_thinking_for/anthropic_effort_for. Update the two
subagent-model tests asserting the old shape.
* fix(deploy): seed Bedrock/Fireworks models, not the dropped anthropic:/openai: ids
Model selection is store-driven, so seed_store.sh's team_settings/default seed is
what runs in prod. It still seeded the removed providers, which would fail at runtime
after the migration:
- agent/builder: anthropic:claude-opus-4-8 -> bedrock_converse:us.anthropic.claude-opus-4-8
- reviewer: openai:gpt-5.5 (dropped) -> bedrock_converse:us.anthropic.claude-opus-4-8
(set SEED_REVIEWER_MODEL to a Fireworks model for a cross-family reviewer)
- fetch-config REQUIRED_PROVIDER_KEYS default ANTHROPIC_API_KEY,OPENAI_API_KEY ->
FIREWORKS_API_KEY (Bedrock auths via host IAM role; dropping the old keys would
otherwise fail-fast at boot)
- docs (DEPLOYMENT/ROTATION/put-config) updated to match.
Surfaced by the cross-family review + verified against deploy/.
* fix(bedrock): security-review NITs — region resolution, error sanitization, reasoning-block strip
From /sh-security-review (all confirmed-low):
- model.py: resolve region from AWS_REGION OR AWS_DEFAULT_REGION (matches
validate_local_dev_llm_config) so the validated region is the one actually used.
- model_fallback.py: sanitize Bedrock AccessDenied/ResourceNotFound errors to the
error code only, so the role ARN + account id in the raw botocore message never
reach logs or the user channel (CWE-209).
- sanitize_thinking_blocks.py: also strip empty Bedrock reasoning_content blocks
(Converse emits reasoning_content, not thinking) so the middleware is not a no-op
on Bedrock; + unit tests. (Empty blocks replay fine today; defensive.)
* deploy(bedrock): grant instance-role Bedrock invoke + repoint LLM_MODEL_ID / eval model ids
Deployment-readiness for the Bedrock migration (PR #62):
- instance-role.ts: least-privilege bedrock:InvokeModel[WithResponseStream] on the
us.anthropic.claude-opus-4-8 inference-profile ARN + the foundation-model ARN in
each routed region (us-east-1/2, us-west-2). The model runs in the server process
on the box, so the EC2 instance role is the principal. Simulator-verified (allowed
for opus-4-8, implicitDeny for other models) and synth-verified. Passed the
mandatory GPT-4.1 IAM cross-review (no blockers, least-privilege confirmed).
- config-store.ts: IaC SSM LLM_MODEL_ID anthropic:claude-opus-4-8 ->
bedrock_converse:us.anthropic.claude-opus-4-8. This SSM value overrides
seed_store.sh's default via pick precedence, so the seed-script fix alone was
insufficient — both sources now point at the supported Bedrock id.
- infra/README.md + evals/reviewer/config.toml: repoint stale anthropic:/google_genai:
ids to the Bedrock id (config.toml's model_id was an active, now-broken value).
AWS_REGION is already wired via user-data.sh (IMDS -> boot.env), so no change needed there.
* chore(secrets): drop OPENAI/GOOGLE/GROQ key shells (revoked, providers removed)
Those three providers were dropped in the Bedrock/Fireworks migration and their keys
revoked; the live Secrets Manager objects (open-swe-{dev,prod}/{OPENAI,GOOGLE,GROQ}_API_KEY)
were deleted (7-day recovery). Remove them from the IaC so a future cdk deploy does not
recreate the shells, and from fetch-config's mirror array so boot stops requesting them:
- config-store.ts SECRET_VARS + descriptions (28 -> 25 shells)
- fetch-config.sh SECRET_VARS array (kept in lockstep)
- put-config.sh: drop the put_secret lines; ANTHROPIC_API_KEY re-labelled optional
(eval judge only — Bedrock builder/reviewer auth via the host IAM role).
REQUIRED_PROVIDER_KEYS is not set in SSM, so it uses the FIREWORKS_API_KEY default.
168 lines
6.6 KiB
Python
168 lines
6.6 KiB
Python
"""Middleware that falls back to a secondary model when the primary fails transiently.
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Wraps the model call. When the primary model raises a transient provider error
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(5xx, 429, connection/timeout), the same request is retried once against the
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configured fallback model. The fallback is bound to tools by the agent factory
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on the second call, so swapping ``request.model`` is sufficient.
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Bidirectional: if the primary is Anthropic the fallback is typically OpenAI,
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and vice versa. The middleware itself is provider-agnostic — it inspects the
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exception type/status code to decide whether to fall over.
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"""
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from __future__ import annotations
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import logging
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from collections.abc import Awaitable, Callable
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from typing import Any
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import anthropic
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import openai
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from botocore.exceptions import ClientError
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from langchain.agents.middleware import AgentMiddleware
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from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import AIMessage
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logger = logging.getLogger(__name__)
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_RETRYABLE_STATUS_CODES = {408, 409, 425, 429, 500, 502, 503, 504, 529}
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_TRANSIENT_EXCEPTIONS: tuple[type[BaseException], ...] = (
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anthropic.APIConnectionError,
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anthropic.APITimeoutError,
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anthropic.RateLimitError,
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anthropic.InternalServerError,
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openai.APIConnectionError,
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openai.APITimeoutError,
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openai.RateLimitError,
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openai.InternalServerError,
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)
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_RETRYABLE_BEDROCK_ERROR_CODES = {
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"ThrottlingException",
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"ServiceUnavailableException",
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"ModelTimeoutException",
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"InternalServerException",
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}
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def _should_fallback(exc: BaseException) -> bool:
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if isinstance(exc, _TRANSIENT_EXCEPTIONS):
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return True
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# Catches OverloadedError (529) and other 5xx/429 surfaced as APIStatusError.
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if isinstance(exc, (anthropic.APIStatusError, openai.APIStatusError)):
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status = getattr(exc, "status_code", None)
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if isinstance(status, int) and status in _RETRYABLE_STATUS_CODES:
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return True
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# Bedrock (Claude) raises botocore ClientError for transient throttling/5xx.
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if isinstance(exc, ClientError):
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code = exc.response.get("Error", {}).get("Code", "")
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if code in _RETRYABLE_BEDROCK_ERROR_CODES:
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return True
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return False
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def _error_body(exc: BaseException) -> dict[str, Any]:
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body = getattr(exc, "body", None)
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return body if isinstance(body, dict) else {}
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def _nested_str(data: dict[str, Any], *keys: str) -> str | None:
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current: Any = data
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for key in keys:
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if not isinstance(current, dict):
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return None
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current = current.get(key)
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return current if isinstance(current, str) and current else None
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def _provider_access_error_message(exc: BaseException) -> str | None:
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if isinstance(exc, anthropic.BadRequestError):
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body = _error_body(exc)
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error_code = _nested_str(body, "error", "details", "error_code")
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if error_code == "model_not_available":
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provider_message = _nested_str(body, "error", "message") or str(exc)
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return (
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"The selected Anthropic model is not available to this workspace. "
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f"Anthropic returned: {provider_message} "
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"Choose a different model or update the workspace's Anthropic access and retry."
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)
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if isinstance(exc, (openai.BadRequestError, openai.NotFoundError)):
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body = _error_body(exc)
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error_code = _nested_str(body, "error", "code")
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if error_code in {"model_not_found", "model_not_available"}:
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provider_message = _nested_str(body, "error", "message") or str(exc)
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return (
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"The selected OpenAI model is not available to this workspace. "
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f"OpenAI returned: {provider_message} "
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"Choose a different model or update the workspace's OpenAI access and retry."
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)
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# Bedrock access/lookup failures embed the caller's role ARN and account id in the
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# raw botocore message; surface only the error code so identifiers never reach logs
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# or the user-facing channel.
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if isinstance(exc, ClientError):
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code = exc.response.get("Error", {}).get("Code", "")
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if code in {"AccessDeniedException", "ResourceNotFoundException"}:
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return (
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"The selected Bedrock model is not available to this deployment "
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f"(Bedrock error: {code}). Verify the model's inference-profile access and "
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"IAM permissions, choose a different model, and retry."
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)
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return None
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class ModelFallbackMiddleware(AgentMiddleware):
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"""Retry the model call against a fallback provider on transient errors."""
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def __init__(self, fallback_model: BaseChatModel) -> None:
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super().__init__()
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self._fallback_model = fallback_model
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def wrap_model_call(
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self,
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request: ModelRequest,
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handler: Callable[[ModelRequest], ModelResponse],
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) -> ModelCallResult:
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try:
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return handler(request)
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except Exception as exc:
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access_error_message = _provider_access_error_message(exc)
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if access_error_message is not None:
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logger.warning("Model access error surfaced to user: %s", type(exc).__name__)
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return AIMessage(content=access_error_message)
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if not _should_fallback(exc):
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raise
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logger.warning(
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"Primary model failed (%s); falling back to %s",
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type(exc).__name__,
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getattr(self._fallback_model, "model_name", None)
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or getattr(self._fallback_model, "model", "fallback"),
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)
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return handler(request.override(model=self._fallback_model))
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async def awrap_model_call(
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self,
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request: ModelRequest,
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handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
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) -> Any:
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try:
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return await handler(request)
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except Exception as exc:
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access_error_message = _provider_access_error_message(exc)
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if access_error_message is not None:
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logger.warning("Model access error surfaced to user: %s", type(exc).__name__)
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return AIMessage(content=access_error_message)
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if not _should_fallback(exc):
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raise
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logger.warning(
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"Primary model failed (%s); falling back to %s",
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type(exc).__name__,
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getattr(self._fallback_model, "model_name", None)
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or getattr(self._fallback_model, "model", "fallback"),
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)
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return await handler(request.override(model=self._fallback_model))
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