df8d09f2b6
When the LLM returns malformed tool arguments (e.g. a list or string instead of a dict), validate_params would crash with AttributeError in _validate() when calling val.items(). Now returns a clear validation error instead of crashing.
107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
"""Base class for agent tools."""
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from abc import ABC, abstractmethod
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from typing import Any
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class Tool(ABC):
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"""
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Abstract base class for agent tools.
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Tools are capabilities that the agent can use to interact with
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the environment, such as reading files, executing commands, etc.
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"""
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_TYPE_MAP = {
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"string": str,
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"integer": int,
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"number": (int, float),
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"boolean": bool,
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"array": list,
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"object": dict,
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}
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@property
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@abstractmethod
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def name(self) -> str:
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"""Tool name used in function calls."""
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pass
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@property
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@abstractmethod
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def description(self) -> str:
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"""Description of what the tool does."""
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pass
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@property
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@abstractmethod
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def parameters(self) -> dict[str, Any]:
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"""JSON Schema for tool parameters."""
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pass
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@abstractmethod
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async def execute(self, **kwargs: Any) -> str:
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"""
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Execute the tool with given parameters.
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Args:
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**kwargs: Tool-specific parameters.
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Returns:
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String result of the tool execution.
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"""
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pass
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def validate_params(self, params: dict[str, Any]) -> list[str]:
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"""Validate tool parameters against JSON schema. Returns error list (empty if valid)."""
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if not isinstance(params, dict):
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return [f"parameters must be an object, got {type(params).__name__}"]
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schema = self.parameters or {}
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if schema.get("type", "object") != "object":
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raise ValueError(f"Schema must be object type, got {schema.get('type')!r}")
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return self._validate(params, {**schema, "type": "object"}, "")
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def _validate(self, val: Any, schema: dict[str, Any], path: str) -> list[str]:
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t, label = schema.get("type"), path or "parameter"
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if t in self._TYPE_MAP and not isinstance(val, self._TYPE_MAP[t]):
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return [f"{label} should be {t}"]
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errors = []
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if "enum" in schema and val not in schema["enum"]:
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errors.append(f"{label} must be one of {schema['enum']}")
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if t in ("integer", "number"):
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if "minimum" in schema and val < schema["minimum"]:
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errors.append(f"{label} must be >= {schema['minimum']}")
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if "maximum" in schema and val > schema["maximum"]:
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errors.append(f"{label} must be <= {schema['maximum']}")
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if t == "string":
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if "minLength" in schema and len(val) < schema["minLength"]:
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errors.append(f"{label} must be at least {schema['minLength']} chars")
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if "maxLength" in schema and len(val) > schema["maxLength"]:
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errors.append(f"{label} must be at most {schema['maxLength']} chars")
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if t == "object":
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props = schema.get("properties", {})
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for k in schema.get("required", []):
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if k not in val:
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errors.append(f"missing required {path + '.' + k if path else k}")
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for k, v in val.items():
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if k in props:
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errors.extend(self._validate(v, props[k], path + "." + k if path else k))
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if t == "array" and "items" in schema:
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for i, item in enumerate(val):
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errors.extend(
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self._validate(item, schema["items"], f"{path}[{i}]" if path else f"[{i}]")
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)
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return errors
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def to_schema(self) -> dict[str, Any]:
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"""Convert tool to OpenAI function schema format."""
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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": self.parameters,
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},
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}
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