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https://github.com/Sea-Haven-Industries/open-swe.git
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25d4886b9a
Port five upstream PRs onto dev: - #1621 / #1658: durable dispatch with loopback webhook defense, create_durable_run helper, _config_with_prepare_run_id, degradation to None for relative/loopback completion webhook URLs - #1696: run-level completion webhook deduplication (replace claim-then-post with post-then-flag per run_id), DeferredErrorModel for graph-factory resilience, ToolRetryMiddleware for task subagents, TimeoutWrapupMiddleware for all three graphs - #1697: lazy-load __init__.py for agent.middleware, agent.tools, agent.dashboard (PEP 562); defer heavy imports (exa_py in web_search, agent.webapp in request_pr_review, deepagents in sandbox.py); add ttl_cache.py with stale-while-revalidate for tool loaders Refs: #137
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from __future__ import annotations
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import logging
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from typing import Any
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from langchain_core.language_models import BaseChatModel
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from .model import make_model
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logger = logging.getLogger(__name__)
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class DeferredErrorModel(BaseChatModel):
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"""Model placeholder that raises a stored setup error on first invocation."""
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error_message: str
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model_id: str | None = None
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@property
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def _llm_type(self) -> str:
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return "deferred-error"
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def _get_ls_params(self, stop: Any = None, **kwargs: Any) -> dict[str, Any]:
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params = super()._get_ls_params(stop=stop, **kwargs)
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if self.model_id:
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params["ls_model_name"] = self.model_id
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if ":" in self.model_id:
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params["ls_provider"] = self.model_id.split(":", 1)[0]
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return params
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def bind_tools(self, tools: Any, **kwargs: Any) -> DeferredErrorModel:
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return self
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def _generate(self, messages: Any, stop: Any = None, run_manager: Any = None, **kwargs: Any):
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raise ValueError(self.error_message)
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def make_deferred_error_model(
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error: BaseException, *, model_id: str | None = None
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) -> BaseChatModel:
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return DeferredErrorModel(error_message=f"{type(error).__name__}: {error}", model_id=model_id)
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def make_model_or_defer(model_id: str, **kwargs: Any) -> BaseChatModel:
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"""Call ``make_model`` and wrap any setup error in a ``DeferredErrorModel``.
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This lets graph factories pass a model placeholder into the agent graph on
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startup without crashing the process. The error is raised later inside the
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agent run when the model is first invoked, so the caller can recover
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gracefully (e.g. via fallback middleware or by surfacing the error to the
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user).
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"""
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try:
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return make_model(model_id, **kwargs)
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except Exception as e: # noqa: BLE001
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logger.warning("Deferring model setup failure for %s", model_id, exc_info=True)
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return make_deferred_error_model(e, model_id=model_id)
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