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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.)
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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