mirror of
https://github.com/MODSetter/SurfSense.git
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f0e00bd3ee
Cut the docstrings and Field(description=...) text across the entire automations/ tree down to single-line intent statements, matching the multi_agent_chat conciseness style: - Module docstrings: one line stating what the file is. - Class docstrings: deleted when the class name + module docstring already cover intent; kept only where they add a constraint or rationale not visible in the signature. - Pydantic Field descriptions: short noun phrases / clauses, not full sentences. Reasoning that belonged in the design plan moved out of the code. - Enum values: per-value docstrings replaced with terse inline comments where the meaning isn't obvious from the name. Behaviour is unchanged. The same 33 files, same public surface, same imports — verified by re-running the 10-point registry smoke test and the 8-point schema round-trip / constraint suite from commits 9 and 10. LOC: 1180 → 691 (-42%).
27 lines
886 B
Python
27 lines
886 B
Python
"""``AgentTaskActionConfig`` — config for the ``agent_task`` action type."""
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from __future__ import annotations
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from typing import Any
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from pydantic import BaseModel, ConfigDict, Field
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class AgentTaskActionConfig(BaseModel):
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"""Run a LangGraph Deep Agent restricted to a scoped capability list."""
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model_config = ConfigDict(extra="forbid")
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prompt: str = Field(..., min_length=1, description="Task prompt; Jinja-rendered.")
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tools: list[str] = Field(
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default_factory=list,
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description="Capability IDs the agent may call. Empty = no tool access.",
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)
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model: str | None = Field(
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default=None,
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description="LiteLLM model id. Defaults to the search space's agent_llm_id.",
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)
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output_schema: dict[str, Any] | None = Field(
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default=None,
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description="JSON Schema the agent must return. Recommended.",
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)
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