| """Meta / self-control tools: abstain, confidence_estimate.""" |
| from __future__ import annotations |
|
|
| from typing import Any, Dict |
|
|
| from .base import Tool, ToolContext, ToolResult |
|
|
|
|
| class AbstainTool(Tool): |
| name = "abstain" |
| description = ( |
| "Declare that the visual evidence is insufficient to answer the " |
| "question. The agent loop will short-circuit and emit a fallback " |
| "answer chosen by the calling driver (typically '</Response> " |
| "Unable to answer', or a best-guess option when an explicit option " |
| "list was given). Use this when the question explicitly allows " |
| "'no answer' (e.g. hallucination-detection style prompts) and " |
| "MEMORY truly has no relevant evidence; on standard questions " |
| "with a fixed answer space, prefer answering with </Response> " |
| "directly so the model commits to a real choice." |
| ) |
| parameters = { |
| "type": "object", |
| "properties": { |
| "reason": { |
| "type": "string", |
| "description": "Short rationale for abstaining (logged, not shown to user).", |
| } |
| }, |
| "required": [], |
| } |
|
|
| def run(self, args: Dict[str, Any], ctx: ToolContext) -> ToolResult: |
| ctx.abstain_reason = str(args.get("reason", ""))[:256] |
| return ToolResult(self.name, True, data={"reason": ctx.abstain_reason}) |
|
|
