activity-core/tests/test_instruction_evaluation.py

from __future__ import annotations

import json

import pytest

from activity_core import activities


class FakeLLMClient:
    def __init__(self, response: str) -> None:
        self.response = response
        self.calls: list[tuple[str, str, dict | None]] = []

    def complete(
        self,
        prompt: str,
        model: str = "",
        config: dict | None = None,
    ) -> str:
        self.calls.append((prompt, model, config))
        return self.response


@pytest.mark.asyncio
async def test_evaluate_instructions_returns_task_specs_with_audit(monkeypatch) -> None:
    llm = FakeLLMClient(json.dumps([
        {
            "title": "Run daily triage",
            "description": "Review State Hub loose ends.",
            "priority": "high",
            "labels": ["triage"],
        }
    ]))
    monkeypatch.setattr(activities, "get_llm_client", lambda: llm)

    result = await activities.evaluate_instructions({
        "instructions": [
            {
                "id": "daily-triage",
                "trusted_fields": ["context.summary.open_tasks"],
                "model": "test-model",
                "prompt": "Open tasks: {context.summary.open_tasks}",
                "output_schema": "",
                "review_required": False,
            }
        ],
        "event": {},
        "context": {"summary": {"open_tasks": 3}},
    })

    task_specs = result["task_specs"]
    assert len(task_specs) == 1
    spec = task_specs[0]
    assert spec["title"] == "Run daily triage"
    assert spec["source_type"] == "instruction"
    assert spec["source_id"] == "daily-triage"
    assert spec["model"] == "test-model"
    assert spec["output_validated"] is True
    assert spec["review_required"] is False
    assert spec["prompt_hash"] is not None
    assert len(spec["prompt_hash"]) == 64
    assert result["reports"] == []
    assert llm.calls == [
        ("Open tasks: 3", "test-model", {"model_name": "test-model"})
    ]


@pytest.mark.asyncio
async def test_evaluate_instructions_returns_report_payload(monkeypatch) -> None:
    llm = FakeLLMClient(json.dumps({
        "summary": "State Hub has open loose ends.",
        "recommendations": [{"candidate": "CUST-WP-0045", "action": "work-next"}],
    }))
    monkeypatch.setattr(activities, "get_llm_client", lambda: llm)

    result = await activities.evaluate_instructions({
        "instructions": [
            {
                "id": "daily-triage-report",
                "trusted_fields": [],
                "model": "test-model",
                "prompt": "Run report.",
                "output_schema": "schemas/daily-triage-report.json",
                "review_required": False,
            }
        ],
        "event": {},
        "context": {},
    })

    assert result["task_specs"] == []
    assert len(result["reports"]) == 1
    report = result["reports"][0]
    assert report["instruction_id"] == "daily-triage-report"
    assert report["report"]["summary"] == "State Hub has open loose ends."
    assert report["output_validated"] is True
    assert report["prompt_hash"] is not None


@pytest.mark.asyncio
async def test_evaluate_instructions_without_llm_client_returns_no_tasks(monkeypatch) -> None:
    class RaisingClient:
        def complete(
            self,
            prompt: str,
            model: str = "",
            config: dict | None = None,
        ) -> str:  # noqa: ARG002
            raise RuntimeError("not configured")

    monkeypatch.setattr(activities, "get_llm_client", lambda: RaisingClient())

    result = await activities.evaluate_instructions({
        "instructions": [
            {
                "id": "daily-triage",
                "trusted_fields": [],
                "model": "test-model",
                "prompt": "Run triage.",
                "output_schema": "schemas/daily-triage-report.json",
            }
        ],
        "event": {},
        "context": {},
    })

    assert result == {"task_specs": [], "reports": []}


@pytest.mark.asyncio
async def test_evaluate_instructions_forwards_llm_connect_depth_config(monkeypatch) -> None:
    llm = FakeLLMClient(json.dumps({"summary": "ok", "recommendations": []}))
    monkeypatch.setattr(activities, "get_llm_client", lambda: llm)

    await activities.evaluate_instructions({
        "instructions": [
            {
                "id": "daily-triage-report",
                "trusted_fields": [],
                "model": "custodian-triage-balanced",
                "temperature": 0.2,
                "max_tokens": 1200,
                "max_depth": 2,
                "model_params": {"reasoning_effort": "medium"},
                "prompt": "Run report.",
                "output_schema": "schemas/daily-triage-report.json",
                "review_required": False,
            }
        ],
        "event": {},
        "context": {},
    })

    assert llm.calls[0][2] == {
        "model_name": "custodian-triage-balanced",
        "temperature": 0.2,
        "max_tokens": 1200,
        "max_depth": 2,
        "model_params": {"reasoning_effort": "medium"},
    }