82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""BaseTool ABC — foundation for all agent-callable tools."""
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import logging
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import Any, ClassVar
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from pydantic import BaseModel, ValidationError
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from app.models.config import AppConfig
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from app.models.tool_call import ToolResult, ToolResultStatus
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logger = logging.getLogger(__name__)
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class BaseTool(ABC):
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"""Abstract base class for all agent tools.
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Subclasses must set the class-level ``name``, ``description``, and
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``params_model`` attributes and implement ``execute``.
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"""
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name: ClassVar[str]
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description: ClassVar[str]
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params_model: ClassVar[type[BaseModel]]
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def __init__(self, workspace_root: Path, config: AppConfig) -> None:
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self.workspace_root = workspace_root
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self.config = config
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self.logger = logging.getLogger(f"{__name__}.{self.name}")
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@abstractmethod
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def execute(self, *, tool_call_id: str, **kwargs: Any) -> ToolResult:
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"""Execute the tool with validated parameters.
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Subclasses implement the actual tool logic here.
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"""
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def run(self, tool_call_id: str, arguments: dict[str, Any]) -> ToolResult:
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"""Public entry point: validate arguments, execute, guarantee a ToolResult.
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Never raises — all exceptions are caught and returned as error results.
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"""
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try:
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validated = self.params_model(**arguments)
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except ValidationError as exc:
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self.logger.warning("Validation error for %s: %s", self.name, exc)
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return ToolResult(
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tool_call_id=tool_call_id,
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tool_name=self.name,
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status=ToolResultStatus.ERROR,
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error=f"Invalid arguments: {exc}",
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)
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try:
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return self.execute(tool_call_id=tool_call_id, **validated.model_dump())
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except Exception as exc:
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self.logger.exception("Unexpected error in tool %s", self.name)
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return ToolResult(
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tool_call_id=tool_call_id,
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tool_name=self.name,
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status=ToolResultStatus.ERROR,
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error=f"Tool execution failed: {exc}",
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)
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def get_openai_schema(self) -> dict[str, Any]:
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"""Return the OpenAI function-calling schema for this tool."""
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schema = self.params_model.model_json_schema()
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# Remove the top-level title/description that Pydantic adds —
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# those belong on the function object, not the parameters.
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schema.pop("title", None)
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schema.pop("description", None)
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return {
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"type": "function",
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"function": {
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"name": self.name,
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"description": self.description,
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"parameters": schema,
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},
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}
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